Flink ParquetAvroWriters Sink

[anuj.aj07]

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

[Arvid Heise-3]

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

[anuj.aj07]

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

[Arvid Heise-3]

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

[Arvid Heise-3]

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

[anuj.aj07]

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

[Arvid Heise-3]

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

[anuj.aj07]

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

[anuj.aj07]

Hi Arvid,

I have implemented the code with envelope schema as you suggested but now I am facing issues with the consumer . I have written code like this:

FlinkKafkaConsumer010 kafkaConsumer010 = new FlinkKafkaConsumer010(KAFKA_TOPICS,
                new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                properties);

And the Deserialization class looks like this :

pblic class KafkaGenericAvroDeserializationSchema implements KeyedDeserializationSchema<GenericRecord> {

    private final String registryUrl;
    private transient KafkaAvroDeserializer inner;

    public KafkaGenericAvroDeserializationSchema(String registryUrl) {
        this.registryUrl = registryUrl;
    }

    @Override
    public GenericRecord deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) {
        checkInitialized();
        return (GenericRecord) inner.deserialize(topic, message);
    }

    @Override
    public boolean isEndOfStream(GenericRecord nextElement) {
        return false;
    }

    @Override
    public TypeInformation<GenericRecord> getProducedType() {
        return TypeExtractor.getForClass(GenericRecord.class);
    }

    private void checkInitialized() {
        if (inner == null) {
            Map<String, Object> props = new HashMap<>();
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
            SchemaRegistryClient client =
                    new CachedSchemaRegistryClient(
                            registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
            inner = new KafkaAvroDeserializer(client, props);
        }
    }
}

It's working locally on my machine but when I deployed it on yarn cluster I am getting below exception:

java.lang.Exception: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.checkThrowSourceExecutionException(SourceStreamTask.java:212)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.performDefaultAction(SourceStreamTask.java:132)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)

    at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705)

    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)

    at java.lang.Thread.run(Thread.java:748)

Caused by: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)

    at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)

    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202)

Caused by: com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList

Serialization trace:

types (org.apache.avro.Schema$UnionSchema)

schema (org.apache.avro.Schema$Field)

fieldMap (org.apache.avro.Schema$RecordSchema)

schema (org.apache.avro.generic.GenericData$Record)

    at com.twitter.chill.Instantiators$$anon$1.newInstance(KryoBase.scala:136)

    at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1061)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.create(CollectionSerializer.java:89)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:93)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:22)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:761)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:143)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:21)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:657)

    at org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer.copy(KryoSerializer.java:262)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:635)

    ... 13 more

Caused by: java.lang.IllegalAccessException: Class com.twitter.chill.Instantiators$$anonfun$normalJava$1 can not access a member of class org.apache.avro.Schema$LockableArrayList with modifiers "public"

Please help me to resolve this issue.

Thanks,

Anuj

On Mon, Jan 20, 2020 at 9:42 PM aj <[hidden email]> wrote:

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

[Arvid Heise-3]

Hi Anuj,

I recommend using the ConfluentRegistryAvroDeserializationSchema [1] with a specific record that has been generated with the Avro Maven Plugin [2] or Avro Gradle Plugin [3]. That should result into almost no code and maximal maintainability.

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html#the-deserializationschema

[2] https://avro.apache.org/docs/1.8.2/gettingstartedjava.html

[3] https://github.com/davidmc24/gradle-avro-plugin

On Wed, Jan 22, 2020 at 6:43 PM aj <[hidden email]> wrote:

Hi Arvid,

I have implemented the code with envelope schema as you suggested but now I am facing issues with the consumer . I have written code like this:

FlinkKafkaConsumer010 kafkaConsumer010 = new FlinkKafkaConsumer010(KAFKA_TOPICS,
                new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                properties);

And the Deserialization class looks like this :

pblic class KafkaGenericAvroDeserializationSchema implements KeyedDeserializationSchema<GenericRecord> {

    private final String registryUrl;
    private transient KafkaAvroDeserializer inner;

    public KafkaGenericAvroDeserializationSchema(String registryUrl) {
        this.registryUrl = registryUrl;
    }

    @Override
    public GenericRecord deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) {
        checkInitialized();
        return (GenericRecord) inner.deserialize(topic, message);
    }

    @Override
    public boolean isEndOfStream(GenericRecord nextElement) {
        return false;
    }

    @Override
    public TypeInformation<GenericRecord> getProducedType() {
        return TypeExtractor.getForClass(GenericRecord.class);
    }

    private void checkInitialized() {
        if (inner == null) {
            Map<String, Object> props = new HashMap<>();
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
            SchemaRegistryClient client =
                    new CachedSchemaRegistryClient(
                            registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
            inner = new KafkaAvroDeserializer(client, props);
        }
    }
}

It's working locally on my machine but when I deployed it on yarn cluster I am getting below exception:

java.lang.Exception: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.checkThrowSourceExecutionException(SourceStreamTask.java:212)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.performDefaultAction(SourceStreamTask.java:132)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)

    at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705)

    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)

    at java.lang.Thread.run(Thread.java:748)

Caused by: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)

    at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)

    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202)

Caused by: com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList

Serialization trace:

types (org.apache.avro.Schema$UnionSchema)

schema (org.apache.avro.Schema$Field)

fieldMap (org.apache.avro.Schema$RecordSchema)

schema (org.apache.avro.generic.GenericData$Record)

    at com.twitter.chill.Instantiators$$anon$1.newInstance(KryoBase.scala:136)

    at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1061)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.create(CollectionSerializer.java:89)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:93)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:22)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:761)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:143)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:21)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:657)

    at org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer.copy(KryoSerializer.java:262)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:635)

    ... 13 more

Caused by: java.lang.IllegalAccessException: Class com.twitter.chill.Instantiators$$anonfun$normalJava$1 can not access a member of class org.apache.avro.Schema$LockableArrayList with modifiers "public"

Please help me to resolve this issue.

Thanks,

Anuj

On Mon, Jan 20, 2020 at 9:42 PM aj <[hidden email]> wrote:

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

[anuj.aj07]

 Hi Arvid,

I want to keep generic records only and I do not want to keep the schema definition on the consumer side and should be resolve from the schema registry only. I am following the below post

https://stackoverflow.com/questions/58849635/is-it-possible-to-deserialize-avro-messageconsuming-message-from-kafka-without/59865360#59865360

so please help me what is wrong with my code.

On Thu, Jan 23, 2020, 00:38 Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I recommend using the ConfluentRegistryAvroDeserializationSchema [1] with a specific record that has been generated with the Avro Maven Plugin [2] or Avro Gradle Plugin [3]. That should result into almost no code and maximal maintainability.

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html#the-deserializationschema

[2] https://avro.apache.org/docs/1.8.2/gettingstartedjava.html

[3] https://github.com/davidmc24/gradle-avro-plugin

On Wed, Jan 22, 2020 at 6:43 PM aj <[hidden email]> wrote:

Hi Arvid,

I have implemented the code with envelope schema as you suggested but now I am facing issues with the consumer . I have written code like this:

FlinkKafkaConsumer010 kafkaConsumer010 = new FlinkKafkaConsumer010(KAFKA_TOPICS,
                new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                properties);

And the Deserialization class looks like this :

pblic class KafkaGenericAvroDeserializationSchema implements KeyedDeserializationSchema<GenericRecord> {

    private final String registryUrl;
    private transient KafkaAvroDeserializer inner;

    public KafkaGenericAvroDeserializationSchema(String registryUrl) {
        this.registryUrl = registryUrl;
    }

    @Override
    public GenericRecord deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) {
        checkInitialized();
        return (GenericRecord) inner.deserialize(topic, message);
    }

    @Override
    public boolean isEndOfStream(GenericRecord nextElement) {
        return false;
    }

    @Override
    public TypeInformation<GenericRecord> getProducedType() {
        return TypeExtractor.getForClass(GenericRecord.class);
    }

    private void checkInitialized() {
        if (inner == null) {
            Map<String, Object> props = new HashMap<>();
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
            SchemaRegistryClient client =
                    new CachedSchemaRegistryClient(
                            registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
            inner = new KafkaAvroDeserializer(client, props);
        }
    }
}

It's working locally on my machine but when I deployed it on yarn cluster I am getting below exception:

java.lang.Exception: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.checkThrowSourceExecutionException(SourceStreamTask.java:212)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.performDefaultAction(SourceStreamTask.java:132)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)

    at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705)

    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)

    at java.lang.Thread.run(Thread.java:748)

Caused by: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)

    at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)

    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202)

Caused by: com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList

Serialization trace:

types (org.apache.avro.Schema$UnionSchema)

schema (org.apache.avro.Schema$Field)

fieldMap (org.apache.avro.Schema$RecordSchema)

schema (org.apache.avro.generic.GenericData$Record)

    at com.twitter.chill.Instantiators$$anon$1.newInstance(KryoBase.scala:136)

    at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1061)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.create(CollectionSerializer.java:89)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:93)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:22)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:761)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:143)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:21)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:657)

    at org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer.copy(KryoSerializer.java:262)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:635)

    ... 13 more

Caused by: java.lang.IllegalAccessException: Class com.twitter.chill.Instantiators$$anonfun$normalJava$1 can not access a member of class org.apache.avro.Schema$LockableArrayList with modifiers "public"

Please help me to resolve this issue.

Thanks,

Anuj

On Mon, Jan 20, 2020 at 9:42 PM aj <[hidden email]> wrote:

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

[Arvid Heise-3]

The issue is that your are not providing any meaningful type information, so that Flink has to resort to Kryo. You need to extract the schema during query compilation (in your main) and pass it to your deserialization schema.

public TypeInformation<T> getProducedType() {
      return (TypeInformation<T>) new GenericRecordAvroTypeInfo(this.schema);
}

If you don't want to extract it statically you need to tell Flink how to handle arbitrary GenericRecords. You could implement your own serializer [1], which would write GenericRecords to byte[] and vice versa.

Note that I still recommend to just bundle the schema with your Flink application and not reinvent the wheel.

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/custom_serializers.html

On Thu, Jan 23, 2020 at 2:22 AM aj <[hidden email]> wrote:

 Hi Arvid,

I want to keep generic records only and I do not want to keep the schema definition on the consumer side and should be resolve from the schema registry only. I am following the below post

https://stackoverflow.com/questions/58849635/is-it-possible-to-deserialize-avro-messageconsuming-message-from-kafka-without/59865360#59865360

so please help me what is wrong with my code.

On Thu, Jan 23, 2020, 00:38 Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I recommend using the ConfluentRegistryAvroDeserializationSchema [1] with a specific record that has been generated with the Avro Maven Plugin [2] or Avro Gradle Plugin [3]. That should result into almost no code and maximal maintainability.

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html#the-deserializationschema

[2] https://avro.apache.org/docs/1.8.2/gettingstartedjava.html

[3] https://github.com/davidmc24/gradle-avro-plugin

On Wed, Jan 22, 2020 at 6:43 PM aj <[hidden email]> wrote:

Hi Arvid,

I have implemented the code with envelope schema as you suggested but now I am facing issues with the consumer . I have written code like this:

FlinkKafkaConsumer010 kafkaConsumer010 = new FlinkKafkaConsumer010(KAFKA_TOPICS,
                new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                properties);

And the Deserialization class looks like this :

pblic class KafkaGenericAvroDeserializationSchema implements KeyedDeserializationSchema<GenericRecord> {

    private final String registryUrl;
    private transient KafkaAvroDeserializer inner;

    public KafkaGenericAvroDeserializationSchema(String registryUrl) {
        this.registryUrl = registryUrl;
    }

    @Override
    public GenericRecord deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) {
        checkInitialized();
        return (GenericRecord) inner.deserialize(topic, message);
    }

    @Override
    public boolean isEndOfStream(GenericRecord nextElement) {
        return false;
    }

    @Override
    public TypeInformation<GenericRecord> getProducedType() {
        return TypeExtractor.getForClass(GenericRecord.class);
    }

    private void checkInitialized() {
        if (inner == null) {
            Map<String, Object> props = new HashMap<>();
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
            SchemaRegistryClient client =
                    new CachedSchemaRegistryClient(
                            registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
            inner = new KafkaAvroDeserializer(client, props);
        }
    }
}

It's working locally on my machine but when I deployed it on yarn cluster I am getting below exception:

java.lang.Exception: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.checkThrowSourceExecutionException(SourceStreamTask.java:212)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.performDefaultAction(SourceStreamTask.java:132)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)

    at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705)

    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)

    at java.lang.Thread.run(Thread.java:748)

Caused by: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)

    at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)

    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202)

Caused by: com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList

Serialization trace:

types (org.apache.avro.Schema$UnionSchema)

schema (org.apache.avro.Schema$Field)

fieldMap (org.apache.avro.Schema$RecordSchema)

schema (org.apache.avro.generic.GenericData$Record)

    at com.twitter.chill.Instantiators$$anon$1.newInstance(KryoBase.scala:136)

    at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1061)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.create(CollectionSerializer.java:89)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:93)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:22)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:761)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:143)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:21)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:657)

    at org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer.copy(KryoSerializer.java:262)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:635)

    ... 13 more

Caused by: java.lang.IllegalAccessException: Class com.twitter.chill.Instantiators$$anonfun$normalJava$1 can not access a member of class org.apache.avro.Schema$LockableArrayList with modifiers "public"

Please help me to resolve this issue.

Thanks,

Anuj

On Mon, Jan 20, 2020 at 9:42 PM aj <[hidden email]> wrote:

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

[anuj.aj07]

Hi Arvid, 

I am not clear with this " Note that I still recommend to just bundle the schema with your Flink application and not reinvent the wheel."

Can you please help with some sample code on how it should be written. Or can we connect some way so that I can understand with you .

On Thu, Jan 23, 2020 at 2:09 PM Arvid Heise <[hidden email]> wrote:

The issue is that your are not providing any meaningful type information, so that Flink has to resort to Kryo. You need to extract the schema during query compilation (in your main) and pass it to your deserialization schema.

public TypeInformation<T> getProducedType() {
      return (TypeInformation<T>) new GenericRecordAvroTypeInfo(this.schema);
}

If you don't want to extract it statically you need to tell Flink how to handle arbitrary GenericRecords. You could implement your own serializer [1], which would write GenericRecords to byte[] and vice versa.

Note that I still recommend to just bundle the schema with your Flink application and not reinvent the wheel.

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/custom_serializers.html

On Thu, Jan 23, 2020 at 2:22 AM aj <[hidden email]> wrote:

 Hi Arvid,

I want to keep generic records only and I do not want to keep the schema definition on the consumer side and should be resolve from the schema registry only. I am following the below post

https://stackoverflow.com/questions/58849635/is-it-possible-to-deserialize-avro-messageconsuming-message-from-kafka-without/59865360#59865360

so please help me what is wrong with my code.

On Thu, Jan 23, 2020, 00:38 Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I recommend using the ConfluentRegistryAvroDeserializationSchema [1] with a specific record that has been generated with the Avro Maven Plugin [2] or Avro Gradle Plugin [3]. That should result into almost no code and maximal maintainability.

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html#the-deserializationschema

[2] https://avro.apache.org/docs/1.8.2/gettingstartedjava.html

[3] https://github.com/davidmc24/gradle-avro-plugin

On Wed, Jan 22, 2020 at 6:43 PM aj <[hidden email]> wrote:

Hi Arvid,

I have implemented the code with envelope schema as you suggested but now I am facing issues with the consumer . I have written code like this:

FlinkKafkaConsumer010 kafkaConsumer010 = new FlinkKafkaConsumer010(KAFKA_TOPICS,
                new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                properties);

And the Deserialization class looks like this :

pblic class KafkaGenericAvroDeserializationSchema implements KeyedDeserializationSchema<GenericRecord> {

    private final String registryUrl;
    private transient KafkaAvroDeserializer inner;

    public KafkaGenericAvroDeserializationSchema(String registryUrl) {
        this.registryUrl = registryUrl;
    }

    @Override
    public GenericRecord deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) {
        checkInitialized();
        return (GenericRecord) inner.deserialize(topic, message);
    }

    @Override
    public boolean isEndOfStream(GenericRecord nextElement) {
        return false;
    }

    @Override
    public TypeInformation<GenericRecord> getProducedType() {
        return TypeExtractor.getForClass(GenericRecord.class);
    }

    private void checkInitialized() {
        if (inner == null) {
            Map<String, Object> props = new HashMap<>();
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
            SchemaRegistryClient client =
                    new CachedSchemaRegistryClient(
                            registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
            inner = new KafkaAvroDeserializer(client, props);
        }
    }
}

It's working locally on my machine but when I deployed it on yarn cluster I am getting below exception:

java.lang.Exception: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.checkThrowSourceExecutionException(SourceStreamTask.java:212)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.performDefaultAction(SourceStreamTask.java:132)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)

    at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705)

    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)

    at java.lang.Thread.run(Thread.java:748)

Caused by: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)

    at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)

    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202)

Caused by: com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList

Serialization trace:

types (org.apache.avro.Schema$UnionSchema)

schema (org.apache.avro.Schema$Field)

fieldMap (org.apache.avro.Schema$RecordSchema)

schema (org.apache.avro.generic.GenericData$Record)

    at com.twitter.chill.Instantiators$$anon$1.newInstance(KryoBase.scala:136)

    at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1061)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.create(CollectionSerializer.java:89)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:93)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:22)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:761)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:143)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:21)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:657)

    at org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer.copy(KryoSerializer.java:262)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:635)

    ... 13 more

Caused by: java.lang.IllegalAccessException: Class com.twitter.chill.Instantiators$$anonfun$normalJava$1 can not access a member of class org.apache.avro.Schema$LockableArrayList with modifiers "public"

Please help me to resolve this issue.

Thanks,

Anuj

On Mon, Jan 20, 2020 at 9:42 PM aj <[hidden email]> wrote:

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

[anuj.aj07]

I am able to resolve this issue by setting classloader.resolve-order as  parent-first. 

On Wed, Jan 22, 2020, 23:13 aj <[hidden email]> wrote:

Hi Arvid,

I have implemented the code with envelope schema as you suggested but now I am facing issues with the consumer . I have written code like this:

FlinkKafkaConsumer010 kafkaConsumer010 = new FlinkKafkaConsumer010(KAFKA_TOPICS,
                new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                properties);

And the Deserialization class looks like this :

pblic class KafkaGenericAvroDeserializationSchema implements KeyedDeserializationSchema<GenericRecord> {

    private final String registryUrl;
    private transient KafkaAvroDeserializer inner;

    public KafkaGenericAvroDeserializationSchema(String registryUrl) {
        this.registryUrl = registryUrl;
    }

    @Override
    public GenericRecord deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) {
        checkInitialized();
        return (GenericRecord) inner.deserialize(topic, message);
    }

    @Override
    public boolean isEndOfStream(GenericRecord nextElement) {
        return false;
    }

    @Override
    public TypeInformation<GenericRecord> getProducedType() {
        return TypeExtractor.getForClass(GenericRecord.class);
    }

    private void checkInitialized() {
        if (inner == null) {
            Map<String, Object> props = new HashMap<>();
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
            SchemaRegistryClient client =
                    new CachedSchemaRegistryClient(
                            registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
            inner = new KafkaAvroDeserializer(client, props);
        }
    }
}

It's working locally on my machine but when I deployed it on yarn cluster I am getting below exception:

java.lang.Exception: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.checkThrowSourceExecutionException(SourceStreamTask.java:212)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.performDefaultAction(SourceStreamTask.java:132)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)

    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)

    at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705)

    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)

    at java.lang.Thread.run(Thread.java:748)

Caused by: org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: Could not forward element to next operator

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727)

    at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)

    at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)

    at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)

    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)

    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100)

    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63)

    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202)

Caused by: com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList

Serialization trace:

types (org.apache.avro.Schema$UnionSchema)

schema (org.apache.avro.Schema$Field)

fieldMap (org.apache.avro.Schema$RecordSchema)

schema (org.apache.avro.generic.GenericData$Record)

    at com.twitter.chill.Instantiators$$anon$1.newInstance(KryoBase.scala:136)

    at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1061)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.create(CollectionSerializer.java:89)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:93)

    at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:22)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:761)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:143)

    at com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:21)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:679)

    at com.esotericsoftware.kryo.serializers.ObjectField.read(ObjectField.java:106)

    at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:528)

    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:657)

    at org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer.copy(KryoSerializer.java:262)

    at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:635)

    ... 13 more

Caused by: java.lang.IllegalAccessException: Class com.twitter.chill.Instantiators$$anonfun$normalJava$1 can not access a member of class org.apache.avro.Schema$LockableArrayList with modifiers "public"

Please help me to resolve this issue.

Thanks,

Anuj

On Mon, Jan 20, 2020 at 9:42 PM aj <[hidden email]> wrote:

Thanks, Arvid for all the clarification. I will work on the approach you suggested. 

Thanks,

Anuj

On Sat, Jan 18, 2020 at 10:17 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I think that there may be a fundamental misunderstanding about the role of a schema registry in Kafka. So let me first clarify that.

In each Avro/Parquet file, all records have the same schema. The schema is stored within the file, such that we can always retrieve the writer schema for the records.

When Avro was first applied to Kafka, there was the basic question on how the writer schema for any record is known to the consumer. Storing the complete schema on each record would mean that each record would be much larger than needed. Hence, they added the schema registry that assigns a unique id to schema, which is then embedded into the records.

Now, whenever I update a schema in my producer, I would have old records with the old schema id and new records with the new schema id.

In my consumer, I'd use a fixed reader schema, such that my application would not need to worry if the record is written with old or new schema; my consumer would only see records with the reader schema.

Given that background information, you see that in general, it's impossible with a generic approach to write the parquet with the same schema as it has been written in Kafka: the parquet schema needs to be supplied statically during query compilation while the actual used Avro schema in Kafka is only known when actually consuming data.

But looking further down the road:

* since you need one schema to write the parquet files, you'd need to decide: do you want to write with the new or the old schema in case of a schema update? That should also be the reader schema of your application for a given event type.

* this decision has further implications: your application need to extract exactly one specific version of the schema from the schema registry at query compilation. That could be either a specific schema id or the latest schema for the event type.

* that means that the output schema is locked until you restart your application and fetch a new latest schema in case of an update.

* at that point, it might just be easier to use the approach that I outlined previously by bundling a specific schema with your application.

If you want to extract the latest schema for a subject:

var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));

On Sat, Jan 18, 2020 at 5:22 PM aj <[hidden email]> wrote:

Thanks, Arvid. 

I do not fully understand the above approach, 

so currently, I am thinking to go with the envelope approach that you suggested. 

One more question I have if I do not want to keep schema in my consumer project even its a single envelope schema. I want it to be fetched from the schema registry and pass to my parquet-sink so that I always use the same schema that is used by the producer.  Can you provide a sample code how can i infer the schema from the generic record or get it from schema registry?

Regards,

Anuj

On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <[hidden email]> wrote:

(Readded user mailing list)

Hi Anuj,

since I'd still recommend going with distinct sources/sinks, let me try to solve your issues in this mail. If that doesn't work out, I'd address your concerns about the envelope format later.

In Flink, you can have several subtopologies in the same application.

Thus, for each event type, you can add

AvroSource(eventType) -> generic transformation/validation -> AvroSink(eventType)

for each event.

I'd put all avro schema in one project and use an avro plugin to generate the respective Java Classes. Then I'd simply create a map of Avro Schema (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name (event-a, event-b, ...).

Next, I'd iterate over the list to add the respective subtopologies to env.

Finally, execute everything.

You have one project where all validations reside. But you'd have almost no overhead to process a given source of eventType. The downside of that approach is of course, that each new event type would require a redeployment, but that seems like what you'd want to do anyhow.

Best,

Arvid

On Sat, Jan 18, 2020 at 2:08 PM aj <[hidden email]> wrote:

Thanks, Arvid.

1. I like your approach as I can write a single consumer and put the data in S3 in parquet format. The only challenge is there are extra columns that always going to be null as at a time I will get one type of event.

2. if I go with a separate schema I am not sure how I can solve it using a single generalize consumer. Till now what my understanding is I have to write a consumer for each type of event. Each consumer will read the whole data then filter the respective events from this and then I can pass this stream to sink. But this does not look scalable solution as the new events keep growing then I have to write a consumer for each new type.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Example :

 1st Consumer:

                  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> aInput = input.filter("event_name"= "a")

 2nd Consumer:

  DataStreamSource<GenericRecord> input = env.addSource(

                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

                 DataStream<GenericRecord> bInput = input.filter("event_name"= "b")

Can you help me How I solve this using a single consumer as I do not want to write a separate consumer for each type of schema? 

For example, this is my consumer that contains different types of records.

DataStreamSource<GenericRecord> input = env

        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I can not write this stream directly as there is no common schema of records in this stream. So possible way I am thinking is 

1. Can I create multiple streams from this stream using the key by on "event_name"  and then write each stream separately. 

   

Just wanna know is this possible ?? 

Thanks,

Anuj

On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

I originally understood that you would like to store data in the same Kafka topic and also want to store it in the same parquet file. In the past, I mostly used schema registry with Kafka Streams, where you could only store a schema for a key and value respectively. To use different record types in the same kafka topic, you had to disable schema compatibility checks and just stored the schemas as different versions under the same subject.

Your approach is much better. You can ensure full schema compatibility. Nevertheless, it still shares the same drawback that consumption is much harder (using GenericRecord is proof of that) if you want to read/write everything into the same place. Also you will never be able to write one consistent file, as they can only have one schema (both on Avro and Parquet).

So you only have two options:

* keep schemas separated, but then you also need to write separate files per record type.

* have a common schema (either my outlined approach or any other wrapper schema).

The approach with a common schema makes only sense if you want to write it into one table/kafka topic.

However, in the last mail you pointed out that you actually want to store the record types separately. Then, you should keep everything separated. Then you should have a sink for each type each getting the respective schema. Note that you'd need to fetch the schema manually from the schema registry when creating the query as you would need to pass it to the sink.

Btw, do you actually have a need to write all events into one Kafka topic? The only real use case is to preserve the time order per key. Everything else is much more complicated then storing events individually.

Best,

Arvid

On Thu, Jan 16, 2020 at 3:39 PM aj <[hidden email]> wrote:

Hi Arvid, 

Thanks for the quick response. I am new to this Avro design so can you please help me understand and design for my use case.

I have use case like this :

1. we have an app where a lot of action happened from the user side.

2. for each action we collect some set of information that defined using some key-value pairs. This information we want to define as proper schemas so that we maintain the proper format and not push random data.

3. So we are defining for each action a schema and register in the schema registry using  topic+record.name as the subject .

4. So I do not think the producer side has any issue as whenever we push the event to Kafka we register a new schema with the above subject. 

Example :

{

event_name : "a"

"timestamp": 

"properties"  :[

  "key-1 : "val-1"

   "key-2 : "val-2"

]

}

{

event_name : "b"

"timestamp": 

"properties"  :[

  "key-3 : "val-3"

   "key-4 : "val-4"

]

}

Now I  have a consumer that will parse the data by fetching the schema from schema registry and deserialize in the generic record streams. 

Why you think it will break as I am always deserializing with writer schema only.

As you suggested to keep an envelope Avro schema and not separate schema for each type of event that I am generating. I have some doubts about that:

1. How I enforce a schema on each event as it subtypes in the main schema. so when I am getting a JSON event of type "a" how I enforce and convert it to subschema type of "a" and push to Kafka.

2. I want to create a separate hive table for each of the events so when I write this data and lets says I have 20 events than for 19 columns I am getting null values always in data.

Please help me in doing this right way. It will be a great help and learning for me.

Thanks,

Anuj

On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <[hidden email]> wrote:

Hi Anuj,

you should always avoid having records with different schemas in the same topic/dataset. You will break the compatibility features of the schema registry and your consumer/producer code is always hard to maintain.

A common and scalable way to avoid it is to use some kind of envelope format.

{
  "namespace": "example",
  "name": "Envelope",
  "type": "record",
  "fields": [
    {
      "name": "type1",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    },
    {
      "name": "type2",
      "type": ["null", {
        "type": "record",
        "fields": [ ... ]
      }],
      "default": null
    }
  ]
}

This envelope is evolvable (arbitrary addition/removal of wrapped types, which by themselves can be evolved), and adds only a little overhead (1 byte per subtype). The downside is that you cannot enforce that exactly one of the subtypes is set.

This schema is fully compatible with the schema registry, so no need to parse anything manually.

This schema can easily be used with Parquet. If you can't change the input format anymore, you can at least use that approach on your output.

Best,

Arvid

On Thu, Jan 16, 2020 at 2:53 PM aj <[hidden email]> wrote:

Hi All,

I have a use case where I am getting a different set of Avro records in Kafka. I am using the schema registry to store Avro schema. One topic can also have different types of records.

Now I have created a GenericRecord Stream using kafkaAvroDeseralizer by defining custom

Deserializer class like this  

@Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}

private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}

And this is my consumer code :

DataStreamSource<GenericRecord> input = env
        .addSource(
                new FlinkKafkaConsumer010<GenericRecord>(topics,
                        new KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
                        config).setStartFromEarliest());

Now I want to write this stream partition on event_name="a"/year=/month=/day= in parquet format so that I can expose hive tables directly on top of this data.

event_name is common field for all types of records that I am getting in Kafka.

I am stuck as parquet writer needs a schema to write but my different records have different schemas  So how do I write this stream in s3 in above partition format.

Thanks & Regards,
Anuj Jain

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07

--

Thanks & Regards,
Anuj Jain
Mob. : [phone]
Skype : anuj.jain07