Compression Options in Hadoop - A Tale of Tradeoffs

Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo!`s Hadoop clusters. A key component that enables this efficient operation is data compression. With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This paper attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented. The paper also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on “Big Data” who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.

1. Compression Options In Hadoop – A Tale of Tradeoffs Govind Kamat, Sumeet Singh Hadoop Summit (San Jose), June 27, 2013

2. Introduction 2 Sumeet Singh Director of Products, Hadoop Cloud Engineering Group 701 First Avenue Sunnyvale, CA 94089 USA Govind Kamat Technical Yahoo!, Hadoop Cloud Engineering Group Member of Technical Staff in the Hadoop Services team at Yahoo ! Focuses on HBase and Hadoop performance Worked with the Performance Engineering Group on improving the performance and scalability of several Yahoo! applications Experience includes development of large-scale software systems, microprocessor architecture, instruction-set simulators, compiler technology and electronic design 701 First Avenue Sunnyvale, CA 94089 USA Leads Hadoop products team at Yahoo! Responsible for Product Management, Customer Engagements, Evangelism, and Program Management Prior to this role, led Strategy functions for the Cloud Platform Group at Yahoo!

3. Agenda 3 Data Compression in Hadoop 1 Available Compression Options 2 Understanding and Working with Compression Options 3 Problems Faced at Yahoo! with Large Data Sets 4 Performance Evaluations, Native Bzip2, and IPP Libraries 5 Wrap-up and Future Work 6

4. Compression Needs and Tradeoffs in Hadoop 4 Storage Disk I/O Network bandwidth CPU Time Hadoop jobs are data-intensive, compressing data can speed up the I/O operations MapReduce jobs are almost always I/O bound Compressed data can save storage space and speed up data transfers across the network Capital allocation for hardware can go further R educed I/O and network load can bring significant performance improvements MapReduce jobs can finish faster overall On the other hand, CPU utilization and processing time increases during compression and decompression Understanding the tradeoffs is important for MapReduce pipeline’s overall performance The Compression Tradeoff

5. Data Compression in Hadoop’s MR Pipeline 5 Input splits Map Source: Hadoop: The Definitive Guide, Tom White Output Reduce Buffer in memory Partition and Sort fetch Merge on disk Merge and sort Other maps Other reducers I/P compressed Mapper decompresses Mapper O/P compressed 1 Map Reduce Reduce I/P Map O/P Reducer I/P decompresses Reducer O/P compressed 2 3 Sort & Shuffle Compress De c ompress

6. Compression Options in Hadoop (1/2) 6 Format Algorithm Strategy Emphasis Comments zlib Uses DEFLATE (LZ77 and Huffman coding) Dictionary -based, API Compression ratio Default codec gzip Wrapper around zlib Dictionary -based, standard compression utility Same as zlib, codec operates on and produces standard gzip files For data interchange on and off Hadoop bzip2 Burrows-Wheeler transform Transform -based, block-oriented Higher compression ratios than zlib Common for Pig LZO Variant of LZ77 Dictionary-based, block-oriented, API High compression speeds Common for intermediate compression, HBase tables LZ4 Simplified variant of LZ77 Fast scan, API Very high compression speeds Available in newer Hadoop distributions Snappy LZ77 Block -oriented, API Very high compression speeds Came out of Google, previously known as Zippy

7. Compression Options in Hadoop (2/2) 7 Format Codec (Defined in io.compression.codecs ) File Extn. Splittable Java/ Native zlib/ DEFLATE (default) org.apache.hadoop.io.compress.DefaultCodec .deflate N Y/ Y gzip org.apache.hadoop.io.compress.GzipCodec .gz N Y/ Y bzip2 org.apache.hadoop.io.compress.BZip2Codec .bz2 Y Y/ Y LZO (download separately) com.hadoop.compression.lzo.LzoCodec .lzo N N/ Y LZ4 org.apache.hadoop.io.compress.Lz4Codec .lz4 N N/ Y Snappy org.apache.hadoop.io.compress.SnappyCodec .snappy N N/ Y NOTES: Splittability – Bzip2 is “splittable”, can be decompressed in parallel by multiple MapReduce tasks. Other algorithms require all blocks together for decompression with a single MapReduce task. LZO – Removed from Hadoop because the LZO libraries are licensed under the GNU GPL. LZO format is still supported and the codec can be downloaded separately and enabled manually. Native bzip2 codec – added by Yahoo! as part of this work in Hadoop 0.23

8. Space-Time Tradeoff of Compression Options 8 Bzip2 Zlib (Deflate, Gzip) LZO Snappy LZ4 Note : A 265 MB corpus from Wikipedia was used for the performance comparisons. Space savings is defined as [1 – (Compressed/ Uncompressed)] Codec Performance on the Wikipedia Text Corpus High Compression Ratio High Compression Speed

9. Using Data Compression in Hadoop 9 Phase in MR Pipeline Config Values Input data to Map File extension recognized automatically for decompression File extensions for supported formats Note : For SequenceFile, headers have the information [ compression (boolean), block compression (boolean), and compression codec] One of the supported c odecs one defined in io.compression.codecs Intermediate (Map) Output mapreduce.map.output.compress false (default), true mapreduce.map.output.compress.codec one defined in io.compression.codecs Final (Reduce) Output mapreduce.output.fileoutputformat. compress false (default), true mapreduce.output.fileoutputformat. compress.codec one defined in io.compression.codecs mapreduce.output.fileoutputformat. compress.type Type of compression to use for SequenceFile outputs: NONE, RECORD (default), BLOCK 1 2 3

10. Compress the input data, if large Always use compression, particularly if spillage or slow network transfers Compress for storage/ archival , better write speeds, or between MR jobs Use splittable algo such as bzip2, or use zlib wi th SequenceFile format Use faster codecs such as LZO, LZ4, or Snappy Use standard utility such as gzip or bzip2 for data interchange, and faster codecs for chained jobs When to Use Compression and Which Codec 10 Map Reduce Shuffle & Sort Input data to Map Intermediate (Map) Output I/P compressed Mapper decompresses Mapper O/P compressed 1 Reducer I/P decompresses Reducer O/P compressed 2 3 Compress De c ompress Final Reduce Output

11. Compression in the Hadoop Ecosystem 11 Component When to Use What to Use Pig Compressing data between MR job Typical in Pig scripts that include joins or other operators that expand your data size Enable compression and select the codec: pig.tmpfilecompression = true pig.tmpfilecompression.codec = gzip, lzo Hive Intermediate files produced by Hive between multiple map-reduce jobs Hive writes output to a table Enable intermediate or output compression: hive.exec.compress.intermediate = true hive.exec.compress.output = true HBase Compress data at the CF level (support for LZO, gzip, Snappy, and LZ4) List required JNI libraries: hbase.regionserver.codecs Enabling compression: create ’table', { NAME => 'colfam', COMPRESSION => ’LZO' } alter ’table', { NAME => 'colfam', COMPRESSION => ’LZO' }

12. 4.2M Jobs, Jun 10-16, 2013 Compression in Hadoop at Yahoo! 12 LZO 98.3% gzip 1.1% zlib / default 0.5% bzip2 0.1% Map Reduce Shuffle & Sort Input data to Map Intermediate (Map) Output 1 2 3 Final Reduce Output LZO 55% gzip 35% bzip2 5% zlib / default 5% 4.2M Jobs, Jun 10-16, 2013 zlib / default 73% gzip 22% bzip2 4% LZO 1% 380M Files on Jun 16, 2013 (/data, /projects) Includes intermediate Pig/ Hive compression Pig Intermediate Compressed

13. Compression for Data Storage Efficiency DSE considerations at Yahoo! RCFile instead of SequenceFile Faster implementation of bzip2 Native-code bzip2 codec HADOOP-8462 1 , available in 0.23.7 Substituting the IPP library 13 1 Native-code bzip2 implementation done in collaboration with Jason Lowe, Hadoop Core PMC member

14. IPP Libraries Integrated Performance Primitives from Intel Algorithmic and architectural optimizations Processor-specific variants of each function Applications remain processor-neutral Compression: LZ, RLE, BWT, LZO High level formats include: zlib , gzip , bzip2 and LZO 14

15. Measuring Standalone Performance Standard programs ( gzip , bzip2) used Driver program written for other cases 32-bit mode Single-threaded JVM load overhead discounted Default compression level Quad-core Xeon machine 15

16. Data Corpuses Used Binary files Generated text from randomtextwriter Wikipedia corpus Silesia corpus 16

17. Compression Ratio 17

18. Compression Performance 18

19. Compression Performance (Fast Algorithms) 19

20. Decompression Performance 20

21. Decompression Performance (Fast Algorithms) 21

22. Compression Performance within Hadoop Daytona performance framework GridMix v1 Loadgen and sort jobs Input data compressed with zlib / bzip2 LZO used for intermediate compression 35 datanodes , dual-quad-core machines 22

23. Map Performance 23

24. Reduce Performance 24

25. Job Performance 25

26. Future Work Splittability support for native-code bzip2 codec Enhancing Pig to use common bzip2 codec Optimizing the JNI interface and buffer copies Varying the compression effort parameter Performance evaluation for 64-bit mode Updating the zlib codec to specify alternative libraries Other codec combinations, such as zlib for transient data Other compression algorithms 26

27. Considerations in Selecting Compression Type Nature of the data set Chained jobs Data-storage efficiency requirements Frequency of compression vs. decompression Requirement for compatibility with a standard data format Splittability requirements Size of the intermediate and final data Alternative implementations of compression libraries 27