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 >> <mailto: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 >>> <mailto: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 >>>> <mailto: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 >>>>> <mailto: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 <http://www.scaleunlimited.com/> >>>> Custom big data solutions & training >>>> Flink, Solr, Hadoop, Cascading & Cassandra >>>> >>> >> >> -------------------------- >> Ken Krugler >> +1 530-210-6378 >> http://www.scaleunlimited.com <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
