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Posted to issues@spark.apache.org by "Takeshi Yamamuro (JIRA)" <ji...@apache.org> on 2018/06/16 09:42:00 UTC
[jira] [Commented] (SPARK-24407) Spark + Parquet + Snappy: Overall
compression ratio loses after spark shuffles data
[ https://issues.apache.org/jira/browse/SPARK-24407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16514736#comment-16514736 ]
Takeshi Yamamuro commented on SPARK-24407:
------------------------------------------
You should ask in the spark-user mailing list.
> Spark + Parquet + Snappy: Overall compression ratio loses after spark shuffles data
> -----------------------------------------------------------------------------------
>
> Key: SPARK-24407
> URL: https://issues.apache.org/jira/browse/SPARK-24407
> Project: Spark
> Issue Type: Question
> Components: SQL
> Affects Versions: 2.1.2
> Reporter: shining
> Priority: Major
>
> Please help me understand how to get better compression ratio with Spark?
> Let me describe case:
> # I have dataset, let's call it *product* on HDFS which was imported using Sqoop ImportTool as-parquet-file using codec *snappy*. As result of import, I have 100 files with total _46 GB_ du, files with diffrrent size (min 11MB, max 1.5GB, avg ~ 500MB). Total count of records a little bit more than _8 billions_ with _84 columns_
> # I'm doing simple read/repartition/write with Spark using *snappy* as well and as result I'm getting:
> ~*100 GB* output size with the same files count, same codec, same count and same columns.
> Code snippet:
> {{val productDF = spark.read.parquet("/ingest/product/20180202/22-43/") productDF .repartition(100) .write.mode(org.apache.spark.sql.SaveMode.Overwrite) .option("compression", "snappy") .parquet("/processed/product/20180215/04-37/read_repartition_write/general") }}
> # Using parquet-tools I have looked into random files from both ingest and processed and they looks as below:
> ingest:
> {{creator: parquet-mr version 1.5.0-cdh5.11.1 (build ${buildNumber}) extra: parquet.avro.schema = {"type":"record","name":"AutoGeneratedSchema","doc":"Sqoop import of QueryResult","fields" and almost all columns looks like AVAILABLE: OPTIONAL INT64 R:0 D:1 row group 1: RC:3640100 TS:36454739 OFFSET:4 AVAILABLE: INT64 SNAPPY DO:0 FPO:172743 SZ:370515/466690/1.26 VC:3640100 ENC:RLE,PLAIN_DICTIONARY,BIT_PACKED ST:[min: 126518400000, max: 1577692800000, num_nulls: 2541633] }}
> processed:
> {{creator: parquet-mr version 1.5.0-cdh5.12.0 (build ${buildNumber}) extra: org.apache.spark.sql.parquet.row.metadata = {"type":"struct","fields" AVAILABLE: OPTIONAL INT64 R:0 D:1 ... row group 1: RC:6660100 TS:243047789 OFFSET:4 AVAILABLE: INT64 SNAPPY DO:0 FPO:4122795 SZ:4283114/4690840/1.10 VC:6660100 ENC:BIT_PACKED,PLAIN_DICTIONARY,RLE ST:[min: -2209136400000, max: 10413820800000, num_nulls: 4444993] }}
> In other hand, without repartition or using coalesce - size remains close to ingest data size.
> # Going forward, I did following:
> ** read dataset and write it back with
> {{productDF .write.mode(org.apache.spark.sql.SaveMode.Overwrite) .option("compression", "none") .parquet("/processed/product/20180215/04-37/read_repartition_write/nonewithoutshuffle") }}
> ** read dataset, repartition and write it back with
> {{productDF .repartition(500) .write.mode(org.apache.spark.sql.SaveMode.Overwrite) .option("compression", "none") .parquet("/processed/product/20180215/04-37/read_repartition_write/nonewithshuffle") }}
> As result: *80 GB* without and *283 GB* with repartition with same # of output files
> 80GB parquet meta example:
> {{AVAILABLE: INT64 UNCOMPRESSED DO:0 FPO:456753 SZ:1452623/1452623/1.00 VC:11000100 ENC:RLE,PLAIN_DICTIONARY,BIT_PACKED ST:[min: -1735747200000, max: 2524550400000, num_nulls: 7929352] }}
> 283 GB parquet meta example:
> {{AVAILABLE: INT64 UNCOMPRESSED DO:0 FPO:2800387 SZ:2593838/2593838/1.00 VC:3510100 ENC:RLE,PLAIN_DICTIONARY,BIT_PACKED ST:[min: -2209136400000, max: 10413820800000, num_nulls: 2244255] }}
> It seems, that parquet itself (with encoding?) much reduce size of data even without uncompressed data. How ? :)
> I tried to read uncompressed 80GB, repartition and write back - I've got my 283 GB
> * The first question for me is why I'm getting bigger size after spark repartitioning/shuffle?
> * The second is how to efficiently shuffle data in spark to benefit parquet encoding/compression if there any?
> In general, I don't want that my data size growing after spark processing, even if I didn't change anything.
> Also, I failed to find, is there any _configurable compression rate_ for snappy, e.g. -1 ... -9? As I know, gzip has this, but what is the way to control this rate in Spark/Parquet writer?
> Appreciate for any help!
> Thanks!
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