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From Robert Schmidtke <ro.schmid...@gmail.com>
Subject Re: Terminology: Split, Group and Partition
Date Fri, 13 Jan 2017 15:34:27 GMT
Just a side note, I'm guessing there's a bug here:

It should say createProgramPlan("unnamed job", false);

Otherwise I'm getting an exception complaining that no new sinks have been
added after the last execution. So currently it is not possible for me to
first get the execution plan and then run execute the program.


On Fri, Jan 13, 2017 at 3:14 PM, Robert Schmidtke <ro.schmidtke@gmail.com>

> Hi Fabian,
> thanks for the quick and comprehensive reply. I'll have a look at the
> ExecutionPlan using your suggestion to check what actually gets computed,
> and I'll use the properties as well. If I stumble across something else
> I'll let you know.
> Many thanks again!
> Robert
> On Fri, Jan 13, 2017 at 2:40 PM, Fabian Hueske <fhueske@gmail.com> wrote:
>> Hi Robert,
>> let me first describe what splits, groups, and partitions are.
>> * Partition: This is basically all data that goes through the same task
>> instance. If you have an operator with a parallelism of 80, you have 80
>> partitions. When you call sortPartition() you'll have 80 sorted streams, if
>> you call mapPartition you iterate over all records in one partition.
>> * Split: Splits are a concept of InputFormats. An InputFormat can process
>> several splits. All splits that are processed by the same data source task
>> make up the partition of that task. So a split is a subset of a partition.
>> In your case where each task reads exactly one split, the split is
>> equivalent to the partition.
>> * Group: A group is based on the groupBy attribute and hence data-driven
>> and does not depend on the parallelism. A groupReduce requires a
>> partitioning such that all records with the same grouping attribute are
>> sent to the same operator, i.e., all are part of the same partition.
>> Depending on the number of distinct grouping keys (and the hash-function) a
>> partition can have zero, one, or more groups.
>> Now coming to your use case. You have 80 sources running on 5 machines.
>> All source on the same machine produce records with the same grouping key
>> (hostname). You can actually give a hint to Flink, that the data returned
>> by a split is partitioned, grouped, or sorted in a specific way. This works
>> as follows:
>> // String is hostname, Integer is parallel id of the source task
>> DataSet<Tuple3<String, Integer, Long>> = env.createInput(YourFormat);
>> SplitDataProperties<Tuple3<String, Integer, Long>> splitProps =
>> ((DataSource)text).getSplitDataProperties();
>> splitProps.splitsGroupedBy(0,1)
>> splitProps.splitsPartitionedBy(0,1)
>> With this info, Flink knows that the data returned by our source is
>> partitioned and grouped. Now you can do groupBy(0,1).groupReduce(XXX) to
>> run a local groupReduce operation on each of the 80 tasks (hostname and
>> parallel index result in 80 keys) and locally reduce the data.
>> Next step would be another .groupBy(0).groupReduce() which gives 16
>> groups which are distributed across your tasks.
>> However, you have to be careful with the SplitDataProperties. If you get
>> them wrong, the optimizer makes false assumption and the resulting plan
>> might not compute what you are looking for.
>> I'd recommend to read the JavaDocs and play a bit with this feature to
>> see how it behaves. ExecutionEnvironment.getExecutionPlan() can help to
>> figure out what is happening.
>> Best,
>> Fabian
>> 2017-01-13 12:14 GMT+01:00 Robert Schmidtke <ro.schmidtke@gmail.com>:
>>> Hi all,
>>> I'm having some trouble grasping what the meaning of/difference between
>>> the following concepts is:
>>> - Split
>>> - Group
>>> - Partition
>>> Let me elaborate a bit on the problem I'm trying to solve here. In my
>>> tests I'm using a 5-node cluster, on which I'm running Flink 1.1.3 in
>>> standalone mode. Each node has 64G of memory and 32 cores. I'm starting the
>>> JobManager on one node, and a TaskManager on each node. I'm assigning 16
>>> slots to each TaskManager, so the overall parallelism is 80 (= 5 TMs x 16
>>> Slots).
>>> The data I want to process resides in a local folder on each worker with
>>> the same path (say /tmp/input). There can be arbitrarily many input files
>>> in each worker's folder. I have written a custom input format that
>>> round-robin assigns the files to each of the 16 local input splits (
>>> https://github.com/robert-schmidtke/hdfs-statistics-adapter
>>> /blob/master/sfs-analysis/src/main/java/de/zib/sfs/analysis/
>>> io/SfsInputFormat.java) to obtain a total of 80 input splits that need
>>> processing. Each split reads zero or more files, parsing the contents into
>>> records that are emitted correctly. This works as expected.
>>> Now we're getting to the questions. How do these 80 input splits relate
>>> to groups and partitions? My understanding of a partition is a subset of my
>>> DataSet<X> that is local to each node. I.e. if I were to repartition the
>>> data according to some scheme, a shuffling over workers would occur. After
>>> reading all the data, I have 80 partitions, correct?
>>> What is less clear to me is the concept of a group, i.e. the result of a
>>> groupBy operation. The input files I have are produced on each worker by
>>> some other process. I first want to do pre-aggregation (I hope that's the
>>> term) on each node before sending data over the network. The records I'm
>>> processing contain a 'hostname' attribute, which is set to the worker's
>>> hostname that processes the data, because the DataSources are local. That
>>> means the records produced by the worker on host1 always contain the
>>> attribute hostname=host1. Similar for the other 4 workers.
>>> Now what happens if I do a groupBy("hostname")? How do the workers
>>> realize that no network transfer is necessary? Is a group a logical
>>> abstraction, or a physical one (in my understanding a partition is physical
>>> because it's local to exactly one worker).
>>> What I'd like to do next is a reduceGroup to merge multiple records into
>>> one (some custom, yet straightforward, aggregation) and emit another record
>>> for every couple of input records. Am I correct in assuming that the
>>> Iterable<X> values passed to the reduce function all have the same hostname
>>> value? That is, will the operation have a parallelism of 80, where 5x16
>>> operations will have the same hostname value? Because I have 16 splits per
>>> host, the 16 reduces on host1 should all receive values with
>>> hostname=host1, correct? And after the operation has finished, will the
>>> reduced groups (now actual DataSets again) still be local to the workers?
>>> This is quite a lot to work on I have to admit. I'm happy for any hints,
>>> advice and feedback on this. If there's need for clarification I'd be happy
>>> to provide more information.
>>> Thanks a lot in advance!
>>> Robert
>>> --
>>> My GPG Key ID: 336E2680
> --
> My GPG Key ID: 336E2680

My GPG Key ID: 336E2680

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