dataset sort

[david westwood]

Hi:

I would like to sort historical data using the dataset api.

env.setParallelism(10)

val dataset = [(Long, String)] ..

.paritionByRange(_._1)

.sortPartition(_._1, Order.ASCEDING)

.writeAsCsv("mydata.csv").setParallelism(1)

the data is out of order (in local order)

but
.print()

prints the data in to correct order. I have run a small toy sample multiple times.

Is there a way to sort the entire dataset with parallelism > 1 and write it to a single file in ascending order?

[Till Rohrmann]

Hi David,

Flink only supports sorting within partitions. Thus, if you want to write out a globally sorted dataset you should set the parallelism to 1 which effectively results in a single partition. Decreasing the parallelism of an operator will cause the individual partitions to lose its sort order because the individual partitions are read in a non deterministic order.

Cheers,

Till

On Thu, Feb 8, 2018 at 8:07 PM, david westwood <[hidden email]> wrote:

Hi:

I would like to sort historical data using the dataset api.

env.setParallelism(10)

val dataset = [(Long, String)] ..

.paritionByRange(_._1)

.sortPartition(_._1, Order.ASCEDING)

.writeAsCsv("mydata.csv").setParallelism(1)

the data is out of order (in local order)

but
.print()

prints the data in to correct order. I have run a small toy sample multiple times.

Is there a way to sort the entire dataset with parallelism > 1 and write it to a single file in ascending order?

[Fabian Hueske-2]

You can also partition by range and sort and write each partition. Once all partitions have been written to files, you can concatenate the files.

As Till said it is not possible to sort in parallel and write in order to a single file.

Best, Fabian

2018-02-09 10:35 GMT+01:00 Till Rohrmann <[hidden email]>:

Hi David,

Flink only supports sorting within partitions. Thus, if you want to write out a globally sorted dataset you should set the parallelism to 1 which effectively results in a single partition. Decreasing the parallelism of an operator will cause the individual partitions to lose its sort order because the individual partitions are read in a non deterministic order.

Cheers,

Till

On Thu, Feb 8, 2018 at 8:07 PM, david westwood <[hidden email]> wrote:

Hi:

I would like to sort historical data using the dataset api.

env.setParallelism(10)

val dataset = [(Long, String)] ..

.paritionByRange(_._1)

.sortPartition(_._1, Order.ASCEDING)

.writeAsCsv("mydata.csv").setParallelism(1)

the data is out of order (in local order)

but
.print()

prints the data in to correct order. I have run a small toy sample multiple times.

Is there a way to sort the entire dataset with parallelism > 1 and write it to a single file in ascending order?

[david westwood]

Thanks.

I have to stream in the historical data and its out-of-boundedness >> real-time data. I thought there was some elegant way using mapPartition that I wasn't seeing.

On Fri, Feb 9, 2018 at 5:10 AM, Fabian Hueske <[hidden email]> wrote:

You can also partition by range and sort and write each partition. Once all partitions have been written to files, you can concatenate the files.

As Till said it is not possible to sort in parallel and write in order to a single file.

Best, Fabian

2018-02-09 10:35 GMT+01:00 Till Rohrmann <[hidden email]>:

Hi David,

Flink only supports sorting within partitions. Thus, if you want to write out a globally sorted dataset you should set the parallelism to 1 which effectively results in a single partition. Decreasing the parallelism of an operator will cause the individual partitions to lose its sort order because the individual partitions are read in a non deterministic order.

Cheers,

Till

On Thu, Feb 8, 2018 at 8:07 PM, david westwood <[hidden email]> wrote:

Hi:

I would like to sort historical data using the dataset api.

env.setParallelism(10)

val dataset = [(Long, String)] ..

.paritionByRange(_._1)

.sortPartition(_._1, Order.ASCEDING)

.writeAsCsv("mydata.csv").setParallelism(1)

the data is out of order (in local order)

but
.print()

prints the data in to correct order. I have run a small toy sample multiple times.

Is there a way to sort the entire dataset with parallelism > 1 and write it to a single file in ascending order?

[Fabian Hueske-2]

The reason why this isn't working in Flink are that

* a file can only be written by a single process

* Flink does not support merging of sorted network partitions but reads round-robin from incoming network channels.

I think if you sort the historic data in parallel (without range partitioning, i.e., randomly partitioned) and write it out in multiple files, you could implement a source function that reads all files in parallel and generates ascending watermarks.

It would be important that you have as many parallel source tasks as you have files to ensure that watermarks are properly generated. Apart from that, this should result in a nicely sorted stream.

The watermark handling of the DataStream API will take care to "merge" the sorted files.

Best, Fabian

2018-02-09 16:23 GMT+01:00 david westwood <[hidden email]>:

Thanks.

I have to stream in the historical data and its out-of-boundedness >> real-time data. I thought there was some elegant way using mapPartition that I wasn't seeing.

On Fri, Feb 9, 2018 at 5:10 AM, Fabian Hueske <[hidden email]> wrote:

You can also partition by range and sort and write each partition. Once all partitions have been written to files, you can concatenate the files.

As Till said it is not possible to sort in parallel and write in order to a single file.

Best, Fabian

2018-02-09 10:35 GMT+01:00 Till Rohrmann <[hidden email]>:

Hi David,

Flink only supports sorting within partitions. Thus, if you want to write out a globally sorted dataset you should set the parallelism to 1 which effectively results in a single partition. Decreasing the parallelism of an operator will cause the individual partitions to lose its sort order because the individual partitions are read in a non deterministic order.

Cheers,

Till

On Thu, Feb 8, 2018 at 8:07 PM, david westwood <[hidden email]> wrote:

Hi:

I would like to sort historical data using the dataset api.

env.setParallelism(10)

val dataset = [(Long, String)] ..

.paritionByRange(_._1)

.sortPartition(_._1, Order.ASCEDING)

.writeAsCsv("mydata.csv").setParallelism(1)

the data is out of order (in local order)

but
.print()

prints the data in to correct order. I have run a small toy sample multiple times.

Is there a way to sort the entire dataset with parallelism > 1 and write it to a single file in ascending order?