Commmunity!   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 = 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")     3. 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.   4. 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! ... View more