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Bigpetstore - Flink integration

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rmetzger0
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Bigpetstore - Flink integration

rmetzger0
I'm starting a new discussion thread for the bigpetstore-flink integration ...


I took a closer look into the code you've posted. 
It seems to me that you are generating a lot of data locally on the client, before you actually submit a job to Flink. (Both "customers" and "stores" are generated locally)
Is that only some "seed" data?

I would actually try to generate as much data as possible in the cluster, making the generator very scalable.

I don't think that you need to register a Kryo serializer for the Product and Transaction type.
I was able to run the code without the serializer registration.


---------- Forwarded message ----------
From: jay vyas <[hidden email]>
Date: Wed, Sep 2, 2015 at 2:56 PM
Subject: Re: Hardware requirements and learning resources
To: [hidden email]


We're also working on a bigpetstore implementation of flink which will help onboard spark/mapreduce folks.

I have prototypical code here that runs a simple job in memory, contributions welcome,

right now there is a serialization error https://github.com/bigpetstore/bigpetstore-flink .

On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <[hidden email]> wrote:
Hi Juan,

I think the recommendations in the Spark guide are quite good, and are similar to what I would recommend for Flink as well. 
Depending on the workloads you are interested to run, you can certainly use Flink with less than 8 GB per machine. I think you can start Flink TaskManagers with 500 MB of heap space and they'll still be able to process some GB of data.

Everything above 2 GB is probably good enough for some initial experimentation (again depending on your workloads, network, disk speed etc.)




On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <[hidden email]> wrote:
Hi Juan,

Flink is quite nimble with hardware requirements; people have run it in old-ish laptops and also the largest instances available in cloud providers. I will let others chime in with more details.

I am not aware of something along the lines of a cheatsheet that you mention. If you actually try to do this, I would love to see it, and it might be useful to others as well. Both use similar abstractions at the API level (i.e., parallel collections), so if you stay true to the functional paradigm and not try to "abuse" the system by exploiting knowledge of its internals things should be straightforward. These apply to the batch APIs; the streaming API in Flink follows a true streaming paradigm, where you get an unbounded stream of records and operators on these streams.

Funny that you ask about a video for the DataStream slides. There is a Flink training happening as we speak, and a video is being recorded right now :-) Hopefully it will be made available soon.

Best,
Kostas


On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá <[hidden email]> wrote:
Answering to myself, I have found some nice training material at http://dataartisans.github.io/flink-training. There are even videos at youtube for some of the slides

Greetings, 

Juan

2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá <[hidden email]>:
Hi list, 

I'm new to Flink, and I find this project very interesting. I have experience with Apache Spark, and for I've seen so far I find that Flink provides an API at a similar abstraction level but based on single record processing instead of batch processing. I've read in Quora that Flink extends stream processing to batch processing, while Spark extends batch processing to streaming. Therefore I find Flink specially attractive for low latency stream processing. Anyway, I would appreciate if someone could give some indication about where I could find a list of hardware requirements for the slave nodes in a Flink cluster. Something along the lines of https://spark.apache.org/docs/latest/hardware-provisioning.html. Spark is known for having quite high minimal memory requirements (8GB RAM and 8 cores minimum), and I was wondering if it is also the case for Flink. Lower memory requirements would be very interesting for building small Flink clusters for educational purposes, or for small projects. 

Apart from that, I wonder if there is some blog post by the comunity about transitioning from Spark to Flink. I think it could be interesting, as there are some similarities in the APIs, but also deep differences in the underlying approaches. I was thinking in something like Breeze's cheatsheet comparing its matrix operatations with those available in Matlab and Numpy https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. Also, any pointer to some online course, book or training for Flink besides the official programming guides would be much appreciated

Thanks in advance for help

Greetings, 

Juan







--
jay vyas
jay vyas
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Re: Bigpetstore - Flink integration

jay vyas
Hey, thanks! 

Those are just seeds, the files aren't large.

The scale out data is the transactions.

The seed data needs to be the same, shipped to ALL nodes, and then

the nodes generate transactions. 


On Wed, Sep 2, 2015 at 9:21 AM, Robert Metzger <[hidden email]> wrote:
I'm starting a new discussion thread for the bigpetstore-flink integration ...


I took a closer look into the code you've posted. 
It seems to me that you are generating a lot of data locally on the client, before you actually submit a job to Flink. (Both "customers" and "stores" are generated locally)
Is that only some "seed" data?

I would actually try to generate as much data as possible in the cluster, making the generator very scalable.

I don't think that you need to register a Kryo serializer for the Product and Transaction type.
I was able to run the code without the serializer registration.


---------- Forwarded message ----------
From: jay vyas <[hidden email]>
Date: Wed, Sep 2, 2015 at 2:56 PM
Subject: Re: Hardware requirements and learning resources
To: [hidden email]


We're also working on a bigpetstore implementation of flink which will help onboard spark/mapreduce folks.

I have prototypical code here that runs a simple job in memory, contributions welcome,

right now there is a serialization error https://github.com/bigpetstore/bigpetstore-flink .

On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <[hidden email]> wrote:
Hi Juan,

I think the recommendations in the Spark guide are quite good, and are similar to what I would recommend for Flink as well. 
Depending on the workloads you are interested to run, you can certainly use Flink with less than 8 GB per machine. I think you can start Flink TaskManagers with 500 MB of heap space and they'll still be able to process some GB of data.

Everything above 2 GB is probably good enough for some initial experimentation (again depending on your workloads, network, disk speed etc.)




On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <[hidden email]> wrote:
Hi Juan,

Flink is quite nimble with hardware requirements; people have run it in old-ish laptops and also the largest instances available in cloud providers. I will let others chime in with more details.

I am not aware of something along the lines of a cheatsheet that you mention. If you actually try to do this, I would love to see it, and it might be useful to others as well. Both use similar abstractions at the API level (i.e., parallel collections), so if you stay true to the functional paradigm and not try to "abuse" the system by exploiting knowledge of its internals things should be straightforward. These apply to the batch APIs; the streaming API in Flink follows a true streaming paradigm, where you get an unbounded stream of records and operators on these streams.

Funny that you ask about a video for the DataStream slides. There is a Flink training happening as we speak, and a video is being recorded right now :-) Hopefully it will be made available soon.

Best,
Kostas


On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá <[hidden email]> wrote:
Answering to myself, I have found some nice training material at http://dataartisans.github.io/flink-training. There are even videos at youtube for some of the slides

Greetings, 

Juan

2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá <[hidden email]>:
Hi list, 

I'm new to Flink, and I find this project very interesting. I have experience with Apache Spark, and for I've seen so far I find that Flink provides an API at a similar abstraction level but based on single record processing instead of batch processing. I've read in Quora that Flink extends stream processing to batch processing, while Spark extends batch processing to streaming. Therefore I find Flink specially attractive for low latency stream processing. Anyway, I would appreciate if someone could give some indication about where I could find a list of hardware requirements for the slave nodes in a Flink cluster. Something along the lines of https://spark.apache.org/docs/latest/hardware-provisioning.html. Spark is known for having quite high minimal memory requirements (8GB RAM and 8 cores minimum), and I was wondering if it is also the case for Flink. Lower memory requirements would be very interesting for building small Flink clusters for educational purposes, or for small projects. 

Apart from that, I wonder if there is some blog post by the comunity about transitioning from Spark to Flink. I think it could be interesting, as there are some similarities in the APIs, but also deep differences in the underlying approaches. I was thinking in something like Breeze's cheatsheet comparing its matrix operatations with those available in Matlab and Numpy https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. Also, any pointer to some online course, book or training for Flink besides the official programming guides would be much appreciated

Thanks in advance for help

Greetings, 

Juan







--
jay vyas




--
jay vyas
rmetzger0
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Re: Bigpetstore - Flink integration

rmetzger0
Okay, I see.

As I said before, I was not able to reproduce the serialization issue you've reported.
Can you maybe post the exception you are seeing?

On Wed, Sep 2, 2015 at 3:32 PM, jay vyas <[hidden email]> wrote:
Hey, thanks! 

Those are just seeds, the files aren't large.

The scale out data is the transactions.

The seed data needs to be the same, shipped to ALL nodes, and then

the nodes generate transactions. 


On Wed, Sep 2, 2015 at 9:21 AM, Robert Metzger <[hidden email]> wrote:
I'm starting a new discussion thread for the bigpetstore-flink integration ...


I took a closer look into the code you've posted. 
It seems to me that you are generating a lot of data locally on the client, before you actually submit a job to Flink. (Both "customers" and "stores" are generated locally)
Is that only some "seed" data?

I would actually try to generate as much data as possible in the cluster, making the generator very scalable.

I don't think that you need to register a Kryo serializer for the Product and Transaction type.
I was able to run the code without the serializer registration.


---------- Forwarded message ----------
From: jay vyas <[hidden email]>
Date: Wed, Sep 2, 2015 at 2:56 PM
Subject: Re: Hardware requirements and learning resources
To: [hidden email]


We're also working on a bigpetstore implementation of flink which will help onboard spark/mapreduce folks.

I have prototypical code here that runs a simple job in memory, contributions welcome,

right now there is a serialization error https://github.com/bigpetstore/bigpetstore-flink .

On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <[hidden email]> wrote:
Hi Juan,

I think the recommendations in the Spark guide are quite good, and are similar to what I would recommend for Flink as well. 
Depending on the workloads you are interested to run, you can certainly use Flink with less than 8 GB per machine. I think you can start Flink TaskManagers with 500 MB of heap space and they'll still be able to process some GB of data.

Everything above 2 GB is probably good enough for some initial experimentation (again depending on your workloads, network, disk speed etc.)




On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <[hidden email]> wrote:
Hi Juan,

Flink is quite nimble with hardware requirements; people have run it in old-ish laptops and also the largest instances available in cloud providers. I will let others chime in with more details.

I am not aware of something along the lines of a cheatsheet that you mention. If you actually try to do this, I would love to see it, and it might be useful to others as well. Both use similar abstractions at the API level (i.e., parallel collections), so if you stay true to the functional paradigm and not try to "abuse" the system by exploiting knowledge of its internals things should be straightforward. These apply to the batch APIs; the streaming API in Flink follows a true streaming paradigm, where you get an unbounded stream of records and operators on these streams.

Funny that you ask about a video for the DataStream slides. There is a Flink training happening as we speak, and a video is being recorded right now :-) Hopefully it will be made available soon.

Best,
Kostas


On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá <[hidden email]> wrote:
Answering to myself, I have found some nice training material at http://dataartisans.github.io/flink-training. There are even videos at youtube for some of the slides

Greetings, 

Juan

2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá <[hidden email]>:
Hi list, 

I'm new to Flink, and I find this project very interesting. I have experience with Apache Spark, and for I've seen so far I find that Flink provides an API at a similar abstraction level but based on single record processing instead of batch processing. I've read in Quora that Flink extends stream processing to batch processing, while Spark extends batch processing to streaming. Therefore I find Flink specially attractive for low latency stream processing. Anyway, I would appreciate if someone could give some indication about where I could find a list of hardware requirements for the slave nodes in a Flink cluster. Something along the lines of https://spark.apache.org/docs/latest/hardware-provisioning.html. Spark is known for having quite high minimal memory requirements (8GB RAM and 8 cores minimum), and I was wondering if it is also the case for Flink. Lower memory requirements would be very interesting for building small Flink clusters for educational purposes, or for small projects. 

Apart from that, I wonder if there is some blog post by the comunity about transitioning from Spark to Flink. I think it could be interesting, as there are some similarities in the APIs, but also deep differences in the underlying approaches. I was thinking in something like Breeze's cheatsheet comparing its matrix operatations with those available in Matlab and Numpy https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. Also, any pointer to some online course, book or training for Flink besides the official programming guides would be much appreciated

Thanks in advance for help

Greetings, 

Juan







--
jay vyas




--
jay vyas
Stephan Ewen
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Re: Bigpetstore - Flink integration

Stephan Ewen
In reply to this post by rmetzger0
If a lot of the data is generated locally, this may face the same issue as Greg did with oversized payloads (dropped by Akka).

On Wed, Sep 2, 2015 at 3:21 PM, Robert Metzger <[hidden email]> wrote:
I'm starting a new discussion thread for the bigpetstore-flink integration ...


I took a closer look into the code you've posted. 
It seems to me that you are generating a lot of data locally on the client, before you actually submit a job to Flink. (Both "customers" and "stores" are generated locally)
Is that only some "seed" data?

I would actually try to generate as much data as possible in the cluster, making the generator very scalable.

I don't think that you need to register a Kryo serializer for the Product and Transaction type.
I was able to run the code without the serializer registration.


---------- Forwarded message ----------
From: jay vyas <[hidden email]>
Date: Wed, Sep 2, 2015 at 2:56 PM
Subject: Re: Hardware requirements and learning resources
To: [hidden email]


We're also working on a bigpetstore implementation of flink which will help onboard spark/mapreduce folks.

I have prototypical code here that runs a simple job in memory, contributions welcome,

right now there is a serialization error https://github.com/bigpetstore/bigpetstore-flink .

On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <[hidden email]> wrote:
Hi Juan,

I think the recommendations in the Spark guide are quite good, and are similar to what I would recommend for Flink as well. 
Depending on the workloads you are interested to run, you can certainly use Flink with less than 8 GB per machine. I think you can start Flink TaskManagers with 500 MB of heap space and they'll still be able to process some GB of data.

Everything above 2 GB is probably good enough for some initial experimentation (again depending on your workloads, network, disk speed etc.)




On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <[hidden email]> wrote:
Hi Juan,

Flink is quite nimble with hardware requirements; people have run it in old-ish laptops and also the largest instances available in cloud providers. I will let others chime in with more details.

I am not aware of something along the lines of a cheatsheet that you mention. If you actually try to do this, I would love to see it, and it might be useful to others as well. Both use similar abstractions at the API level (i.e., parallel collections), so if you stay true to the functional paradigm and not try to "abuse" the system by exploiting knowledge of its internals things should be straightforward. These apply to the batch APIs; the streaming API in Flink follows a true streaming paradigm, where you get an unbounded stream of records and operators on these streams.

Funny that you ask about a video for the DataStream slides. There is a Flink training happening as we speak, and a video is being recorded right now :-) Hopefully it will be made available soon.

Best,
Kostas


On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá <[hidden email]> wrote:
Answering to myself, I have found some nice training material at http://dataartisans.github.io/flink-training. There are even videos at youtube for some of the slides

Greetings, 

Juan

2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá <[hidden email]>:
Hi list, 

I'm new to Flink, and I find this project very interesting. I have experience with Apache Spark, and for I've seen so far I find that Flink provides an API at a similar abstraction level but based on single record processing instead of batch processing. I've read in Quora that Flink extends stream processing to batch processing, while Spark extends batch processing to streaming. Therefore I find Flink specially attractive for low latency stream processing. Anyway, I would appreciate if someone could give some indication about where I could find a list of hardware requirements for the slave nodes in a Flink cluster. Something along the lines of https://spark.apache.org/docs/latest/hardware-provisioning.html. Spark is known for having quite high minimal memory requirements (8GB RAM and 8 cores minimum), and I was wondering if it is also the case for Flink. Lower memory requirements would be very interesting for building small Flink clusters for educational purposes, or for small projects. 

Apart from that, I wonder if there is some blog post by the comunity about transitioning from Spark to Flink. I think it could be interesting, as there are some similarities in the APIs, but also deep differences in the underlying approaches. I was thinking in something like Breeze's cheatsheet comparing its matrix operatations with those available in Matlab and Numpy https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. Also, any pointer to some online course, book or training for Flink besides the official programming guides would be much appreciated

Thanks in advance for help

Greetings, 

Juan







--
jay vyas
jay vyas
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Re: Bigpetstore - Flink integration

jay vyas
hmmm interesting looks to be working magically now... :)  I must have wrote some code late at night that magically fixed it and forgot.  The original errors I was getting were kayo related. 

The objects aren't being serialized on write to anything useful, but thats I'm sure an easy fix.

Onward and upward !

On Wed, Sep 2, 2015 at 9:31 AM, Stephan Ewen <[hidden email]> wrote:
If a lot of the data is generated locally, this may face the same issue as Greg did with oversized payloads (dropped by Akka).

On Wed, Sep 2, 2015 at 3:21 PM, Robert Metzger <[hidden email]> wrote:
I'm starting a new discussion thread for the bigpetstore-flink integration ...


I took a closer look into the code you've posted. 
It seems to me that you are generating a lot of data locally on the client, before you actually submit a job to Flink. (Both "customers" and "stores" are generated locally)
Is that only some "seed" data?

I would actually try to generate as much data as possible in the cluster, making the generator very scalable.

I don't think that you need to register a Kryo serializer for the Product and Transaction type.
I was able to run the code without the serializer registration.


---------- Forwarded message ----------
From: jay vyas <[hidden email]>
Date: Wed, Sep 2, 2015 at 2:56 PM
Subject: Re: Hardware requirements and learning resources
To: [hidden email]


We're also working on a bigpetstore implementation of flink which will help onboard spark/mapreduce folks.

I have prototypical code here that runs a simple job in memory, contributions welcome,

right now there is a serialization error https://github.com/bigpetstore/bigpetstore-flink .

On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <[hidden email]> wrote:
Hi Juan,

I think the recommendations in the Spark guide are quite good, and are similar to what I would recommend for Flink as well. 
Depending on the workloads you are interested to run, you can certainly use Flink with less than 8 GB per machine. I think you can start Flink TaskManagers with 500 MB of heap space and they'll still be able to process some GB of data.

Everything above 2 GB is probably good enough for some initial experimentation (again depending on your workloads, network, disk speed etc.)




On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <[hidden email]> wrote:
Hi Juan,

Flink is quite nimble with hardware requirements; people have run it in old-ish laptops and also the largest instances available in cloud providers. I will let others chime in with more details.

I am not aware of something along the lines of a cheatsheet that you mention. If you actually try to do this, I would love to see it, and it might be useful to others as well. Both use similar abstractions at the API level (i.e., parallel collections), so if you stay true to the functional paradigm and not try to "abuse" the system by exploiting knowledge of its internals things should be straightforward. These apply to the batch APIs; the streaming API in Flink follows a true streaming paradigm, where you get an unbounded stream of records and operators on these streams.

Funny that you ask about a video for the DataStream slides. There is a Flink training happening as we speak, and a video is being recorded right now :-) Hopefully it will be made available soon.

Best,
Kostas


On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá <[hidden email]> wrote:
Answering to myself, I have found some nice training material at http://dataartisans.github.io/flink-training. There are even videos at youtube for some of the slides

Greetings, 

Juan

2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá <[hidden email]>:
Hi list, 

I'm new to Flink, and I find this project very interesting. I have experience with Apache Spark, and for I've seen so far I find that Flink provides an API at a similar abstraction level but based on single record processing instead of batch processing. I've read in Quora that Flink extends stream processing to batch processing, while Spark extends batch processing to streaming. Therefore I find Flink specially attractive for low latency stream processing. Anyway, I would appreciate if someone could give some indication about where I could find a list of hardware requirements for the slave nodes in a Flink cluster. Something along the lines of https://spark.apache.org/docs/latest/hardware-provisioning.html. Spark is known for having quite high minimal memory requirements (8GB RAM and 8 cores minimum), and I was wondering if it is also the case for Flink. Lower memory requirements would be very interesting for building small Flink clusters for educational purposes, or for small projects. 

Apart from that, I wonder if there is some blog post by the comunity about transitioning from Spark to Flink. I think it could be interesting, as there are some similarities in the APIs, but also deep differences in the underlying approaches. I was thinking in something like Breeze's cheatsheet comparing its matrix operatations with those available in Matlab and Numpy https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. Also, any pointer to some online course, book or training for Flink besides the official programming guides would be much appreciated

Thanks in advance for help

Greetings, 

Juan







--
jay vyas





--
jay vyas
jay vyas
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Re: Bigpetstore - Flink integration

jay vyas
In reply to this post by rmetzger0
hmmm interesting looks to be working magically now... :)  I must have wrote some code late at night that magically fixed it and forgot.  The original errors I was getting were kryo related. 

The objects aren't being serialized on write to anything useful, but thats I'm sure an easy fix.

Onward and upward !

On Wed, Sep 2, 2015 at 9:33 AM, Robert Metzger <[hidden email]> wrote:
Okay, I see.

As I said before, I was not able to reproduce the serialization issue you've reported.
Can you maybe post the exception you are seeing?

On Wed, Sep 2, 2015 at 3:32 PM, jay vyas <[hidden email]> wrote:
Hey, thanks! 

Those are just seeds, the files aren't large.

The scale out data is the transactions.

The seed data needs to be the same, shipped to ALL nodes, and then

the nodes generate transactions. 


On Wed, Sep 2, 2015 at 9:21 AM, Robert Metzger <[hidden email]> wrote:
I'm starting a new discussion thread for the bigpetstore-flink integration ...


I took a closer look into the code you've posted. 
It seems to me that you are generating a lot of data locally on the client, before you actually submit a job to Flink. (Both "customers" and "stores" are generated locally)
Is that only some "seed" data?

I would actually try to generate as much data as possible in the cluster, making the generator very scalable.

I don't think that you need to register a Kryo serializer for the Product and Transaction type.
I was able to run the code without the serializer registration.


---------- Forwarded message ----------
From: jay vyas <[hidden email]>
Date: Wed, Sep 2, 2015 at 2:56 PM
Subject: Re: Hardware requirements and learning resources
To: [hidden email]


We're also working on a bigpetstore implementation of flink which will help onboard spark/mapreduce folks.

I have prototypical code here that runs a simple job in memory, contributions welcome,

right now there is a serialization error https://github.com/bigpetstore/bigpetstore-flink .

On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <[hidden email]> wrote:
Hi Juan,

I think the recommendations in the Spark guide are quite good, and are similar to what I would recommend for Flink as well. 
Depending on the workloads you are interested to run, you can certainly use Flink with less than 8 GB per machine. I think you can start Flink TaskManagers with 500 MB of heap space and they'll still be able to process some GB of data.

Everything above 2 GB is probably good enough for some initial experimentation (again depending on your workloads, network, disk speed etc.)




On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <[hidden email]> wrote:
Hi Juan,

Flink is quite nimble with hardware requirements; people have run it in old-ish laptops and also the largest instances available in cloud providers. I will let others chime in with more details.

I am not aware of something along the lines of a cheatsheet that you mention. If you actually try to do this, I would love to see it, and it might be useful to others as well. Both use similar abstractions at the API level (i.e., parallel collections), so if you stay true to the functional paradigm and not try to "abuse" the system by exploiting knowledge of its internals things should be straightforward. These apply to the batch APIs; the streaming API in Flink follows a true streaming paradigm, where you get an unbounded stream of records and operators on these streams.

Funny that you ask about a video for the DataStream slides. There is a Flink training happening as we speak, and a video is being recorded right now :-) Hopefully it will be made available soon.

Best,
Kostas


On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá <[hidden email]> wrote:
Answering to myself, I have found some nice training material at http://dataartisans.github.io/flink-training. There are even videos at youtube for some of the slides

Greetings, 

Juan

2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá <[hidden email]>:
Hi list, 

I'm new to Flink, and I find this project very interesting. I have experience with Apache Spark, and for I've seen so far I find that Flink provides an API at a similar abstraction level but based on single record processing instead of batch processing. I've read in Quora that Flink extends stream processing to batch processing, while Spark extends batch processing to streaming. Therefore I find Flink specially attractive for low latency stream processing. Anyway, I would appreciate if someone could give some indication about where I could find a list of hardware requirements for the slave nodes in a Flink cluster. Something along the lines of https://spark.apache.org/docs/latest/hardware-provisioning.html. Spark is known for having quite high minimal memory requirements (8GB RAM and 8 cores minimum), and I was wondering if it is also the case for Flink. Lower memory requirements would be very interesting for building small Flink clusters for educational purposes, or for small projects. 

Apart from that, I wonder if there is some blog post by the comunity about transitioning from Spark to Flink. I think it could be interesting, as there are some similarities in the APIs, but also deep differences in the underlying approaches. I was thinking in something like Breeze's cheatsheet comparing its matrix operatations with those available in Matlab and Numpy https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. Also, any pointer to some online course, book or training for Flink besides the official programming guides would be much appreciated

Thanks in advance for help

Greetings, 

Juan







--
jay vyas




--
jay vyas




--
jay vyas
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