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From "Newport, Billy" <Billy.Newp...@gs.com>
Subject RE: Flink parquet read.write performance
Date Wed, 23 Aug 2017 15:12:01 GMT
Thanks Aljoscha for the prompt response.

Can you explain the technical reason for the single predecessor rule? This makes what we are
trying to do much more expensive. Really what we’re doing is reading a parquet file, doing
several maps/filters on the records and writing to the parquet. There is no serialization
besides the parquet operations needed at all. The current flink implementation adds an expensive
serialize/deserialize for no apparent purpose in the code.


From: Aljoscha Krettek [mailto:aljoscha@apache.org]
Sent: Saturday, August 19, 2017 1:45 AM
To: Chan, Regina [Tech]
Cc: Newport, Billy [Tech]; user@flink.apache.org
Subject: Re: Flink parquet read.write performance


The Sink cannot be chained to the previous two operations because there are two operations.
Chaining only works if there is one predecessor operation. Data transfer should still be pipelined
but you will see serialisation overhead. What kind of TypeSerializer is used at that boundary?

On 18. Aug 2017, at 21:15, Chan, Regina <Regina.Chan@gs.com<mailto:Regina.Chan@gs.com>>

We profiled it and it looks like its sending the output of the datastoure->filter->map->map
to the an intermediate result partition instead of writing directly to the data sink. Because
of this we think it’s slow because it’s spending its time serializing it for no reason.
Why does it do the forward rather than chain to the datasink?



From: Aljoscha Krettek [mailto:aljoscha@apache.org]
Sent: Friday, August 18, 2017 12:14 PM
To: Newport, Billy [Tech]
Cc: user@flink.apache.org<mailto:user@flink.apache.org>
Subject: Re: Flink parquet read.write performance

Hi Billy,

Do you also have the data (picture) from the "Timeline" tab of the completed job? This would
give some hints about how long that other DataSource (with chain) was active. It might be
that the sink is waiting for the other input to become online.


On 18. Aug 2017, at 14:45, Newport, Billy <Billy.Newport@gs.com<mailto:Billy.Newport@gs.com>>


I’m trying to figure out why reading and writing ~5GB worth of parquet files seems to take
3-4 minutes with 10 TaskManagers, 2 slots, 20GB memory, 20 Parallelism. I’ve copied in the
execution plan the taskmanager times below. Other details include that we’re reading 20
snappy compresed parquet files each ~240MB each. (see below)

I’m trying to use this for a milestoning logic where we take new avro files from staging
and join with the existing milestoned parquet data. I have a small staging file with only
about 1500 records inside so I reduce the number of records sent to the cogroup in order to
make this faster. To do this, I’m basically reading in GenericRecords from parquet files
twice, once to filter out for “live” records where we then further filter the records
for ones with keys matching what we found in a separate avro file. This is so reduction of
records makes that part of the plan total to 1 minute 58 secs.

The concern is the other records with non-live/not-matching-keys. In theory, I expect this
to be fast since it’s just chaining the operations across all the way through to the sink.
However, this part takes about 4 minutes. We’re not doing anything different from the other
Datasource aside from mapping a DataSet<GenericRecord> to a Tuple2<Short,GenericRecord>
where the short is a bitmap value mapping to where the record needs to be written.

Other notes:
I checked the backpressure on the datasource->filter->map->map and it was OK. I’m
not sure what else could be holding it up.
I also profiled it when I ran it on a single task manager single slot and it seems to spend
most of the time waiting.

Any ideas? Instead of truly chaining is it writing to disk and serializing multiple times
inside each operation?

Data Source :
hdfs dfs -du -h <folder_name>
240.2 M  <folder_name>/0_partMapper-m-00013.snappy.parquet
237.2 M  <folder_name>/10_partMapper-m-00019.snappy.parquet
241.9 M  <folder_name>/11_partMapper-m-00002.snappy.parquet
243.3 M  <folder_name>/12_partMapper-m-00000.snappy.parquet
238.2 M  <folder_name>/13_partMapper-m-00016.snappy.parquet
241.7 M  <folder_name>/14_partMapper-m-00003.snappy.parquet
241.0 M  <folder_name>/15_partMapper-m-00006.snappy.parquet
240.3 M  <folder_name>/16_partMapper-m-00012.snappy.parquet
240.3 M  <folder_name>/17_partMapper-m-00011.snappy.parquet
239.5 M  <folder_name>/18_partMapper-m-00014.snappy.parquet
237.6 M  <folder_name>/19_partMapper-m-00018.snappy.parquet
240.7 M  <folder_name>/1_partMapper-m-00009.snappy.parquet
240.7 M  <folder_name>/20_partMapper-m-00008.snappy.parquet
236.5 M  <folder_name>/2_partMapper-m-00020.snappy.parquet
242.1 M  <folder_name>/3_partMapper-m-00001.snappy.parquet
241.7 M  <folder_name>/4_partMapper-m-00004.snappy.parquet
240.5 M  <folder_name>/5_partMapper-m-00010.snappy.parquet
241.7 M  <folder_name>/6_partMapper-m-00005.snappy.parquet
239.1 M  <folder_name>/7_partMapper-m-00015.snappy.parquet
237.9 M  <folder_name>/8_partMapper-m-00017.snappy.parquet
240.8 M  <folder_name>/9_partMapper-m-00007.snappy.parquet

yarn-session.sh -nm "delp_uat-IMD_Trading_v1_PROD_PerfTest-REFINER_INGEST"  -jm 4096 -tm 20480
-s 2 -n 10  -d]





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