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Spark + Parquet + Snappy: Overall compression ratio loses after spark shuffles data

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Please help me understand how to get better compression ratio with Spark?


Let me describe case:


1. 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 46.4 G 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


2. 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 ="/ingest/product/20180202/22-43/")

.option("compression", "snappy")



3. Using parquet-tools I have looked into random files from both ingest and processed and they looks as below:




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

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]





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.


4. Going forward, I did following:


 - read dataset and write it back with 

.option("compression", "none")


- read dataset, repartition and write it back with 

.option("compression", "none")

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!




New Contributor
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