Hi, Flink works a bit differently than Spark. By default, Flink uses pipelined shuffles which push results of the sender immediately to the receivers (btw. this is one of the building blocks for stream processing). However, pipelined shuffles require that all receivers are online. Hence, there number of partitions determines the number of running tasks. There is also a batch shuffle mode, but it needs to be explicitly enabled and AFAIK does not resolve the dependency of number of partitions and task parallelism.
However, the community is currently working on many improvements for batch processing, including scheduling and fault-tolerance. Batched shuffles are an important building block for this and there might be better support for your use case in the future. Best, Fabian Am Fr., 15. März 2019 um 03:56 Uhr schrieb qi luo <luoqi...@gmail.com>: > Hi Ken, > > That looks awesome! I’ve implemented something similar to your bucketing > sink, but using multiple internal writers rather than multiple internal > output. > > Besides this, I’m also curious whether Flink can achieve this like Spark: > allow user to specify partition number in partitionBy() method (so no > multiple output formats are needed). But this seems to need non-trivial > changes in Flink core. > > Thanks, > Qi > > On Mar 15, 2019, at 2:36 AM, Ken Krugler <kkrugler_li...@transpac.com> > wrote: > > Hi Qi, > > See https://github.com/ScaleUnlimited/flink-utils/, for a rough but > working version of a bucketing sink. > > — Ken > > > On Mar 13, 2019, at 7:46 PM, qi luo <luoqi...@gmail.com> wrote: > > Hi Ken, > > Agree. I will try partitonBy() to reducer the number of parallel sinks, > and may also try sortPartition() so each sink could write files one by one. > Looking forward to your solution. :) > > Thanks, > Qi > > On Mar 14, 2019, at 2:54 AM, Ken Krugler <kkrugler_li...@transpac.com> > wrote: > > Hi Qi, > > On Mar 13, 2019, at 1:26 AM, qi luo <luoqi...@gmail.com> wrote: > > Hi Ken, > > Do you mean that I can create a batch sink which writes to N files? > > > Correct. > > That sounds viable, but since our data size is huge (billions of records & > thousands of files), the performance may be unacceptable. > > > The main issue with performance (actually memory usage) is how many > OutputFormats do you need to have open at the same time. > > If you partition by the same key that’s used to define buckets, then the > max number is less, as each parallel instance of the sink only gets a > unique subset of all possible bucket values. > > I’m actually dealing with something similar now, so I might have a > solution to share soon. > > — Ken > > > I will check Blink and give it a try anyway. > > Thank you, > Qi > > On Mar 12, 2019, at 11:58 PM, Ken Krugler <kkrugler_li...@transpac.com> > wrote: > > Hi Qi, > > If I understand what you’re trying to do, then this sounds like a > variation of a bucketing sink. > > That typically uses a field value to create a directory path or a file > name (though the filename case is only viable when the field is also what’s > used to partition the data) > > But I don’t believe Flink has built-in support for that, in batch mode > (see BucketingSink > <https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/streaming/connectors/fs/bucketing/BucketingSink.html> > for > streaming). > > Maybe Blink has added that? Hoping someone who knows that codebase can > chime in here. > > Otherwise you’ll need to create a custom sink to implement the desired > behavior - though abusing a MapPartitionFunction > <https://ci.apache.org/projects/flink/flink-docs-release-1.7/api/java/org/apache/flink/api/common/functions/MapPartitionFunction.html> > would > be easiest, I think. > > — Ken > > > > On Mar 12, 2019, at 2:28 AM, qi luo <luoqi...@gmail.com> wrote: > > Hi Ken, > > Thanks for your reply. I may not make myself clear: our problem is not > about reading but rather writing. > > We need to write to N files based on key partitioning. We have to use > *setParallelism() *to set the output partition/file number, but when the > partition number is too large (~100K), the parallelism would be too high. > Is there any other way to achieve this? > > Thanks, > Qi > > On Mar 11, 2019, at 11:22 PM, Ken Krugler <kkrugler_li...@transpac.com> > wrote: > > Hi Qi, > > I’m guessing you’re calling createInput() for each input file. > > If so, then instead you want to do something like: > > Job job = Job.getInstance(); > > for each file… > FileInputFormat.addInputPath(job, new org.apache.hadoop.fs.Path(file > path)); > > env.createInput(HadoopInputs.createHadoopInput(…, job) > > Flink/Hadoop will take care of parallelizing the reads from the files, > given the parallelism that you’re specifying. > > — Ken > > > On Mar 11, 2019, at 5:42 AM, qi luo <luoqi...@gmail.com> wrote: > > Hi, > > We’re trying to distribute batch input data to (N) HDFS files partitioning > by hash using DataSet API. What I’m doing is like: > > *env.createInput(…)* > * .partitionByHash(0)* > * .setParallelism(N)* > * .output(…)* > > This works well for small number of files. But when we need to distribute > to* large number of files (say 100K)*, the parallelism becomes too large > and we could not afford that many TMs. > > In spark we can write something like ‘rdd.partitionBy(N)’ and control the > parallelism separately (using dynamic allocation). Is there anything > similar in Flink or other way we can achieve similar result? Thank you! > > Qi > > > -------------------------- > Ken Krugler > +1 530-210-6378 > http://www.scaleunlimited.com > Custom big data solutions & training > Flink, Solr, Hadoop, Cascading & Cassandra > > > > -------------------------- > Ken Krugler > +1 530-210-6378 > http://www.scaleunlimited.com > Custom big data solutions & training > Flink, Solr, Hadoop, Cascading & Cassandra > > > > -------------------------- > Ken Krugler > +1 530-210-6378 > http://www.scaleunlimited.com > Custom big data solutions & training > Flink, Solr, Hadoop, Cascading & Cassandra > > > > -------------------------- > Ken Krugler > +1 530-210-6378 > http://www.scaleunlimited.com > Custom big data solutions & training > Flink, Solr, Hadoop, Cascading & Cassandra > > >
