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 talk attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented. The talk 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. Speaker: Govind Kamat, Member of Technical Staff, Yahoo!

1. Compression Options In Hadoop –
A Tale of Tradeoffs
Govind Kamat
39th Bay Area Hadoop Users Group (HUG) Meetup
Yahoo! URL’s Café
Sunnyvale, CA
August 21, 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 Hadoop1
Available Compression Options2
Understanding and Working with Compression Options3
Problems Faced at Yahoo! with Large Data Sets4
Performance Evaluations, Native Bzip2, and IPP Libraries5
Wrap-up and Future Work6

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
§ Reduced 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
ReduceBuffer 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 Decompress

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
64%, 32.3
71%, 60.0
47%, 4.842%, 4.0
44%, 2.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
40% 45% 50% 55% 60% 65% 70% 75%
CPUTimeinSec.
(Compress+Decompress)
Space Savings
Bzip2
Zlib
(Deflate, Gzip)
LZOSnappy
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 codecs 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 with
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 ReduceShuffle & 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 Decompress
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
99.8%
0.2%
LZO 98.3%
gzip 1.1%
zlib / default 0.5%
bzip2 0.1%
Map ReduceShuffle & Sort
Input data to Map Intermediate (Map) Output
1 2 3
Final Reduce Output
39.0%
61.0%
LZO 55%
gzip 35%
bzip2 5%
zlib / default 5%
4.2M Jobs, Jun 10-16, 2013
98%
2%
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-84621, 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
0
50
100
150
200
250
300
uncomp zlib bzip2 LZO Snappy LZ4
FileSize(MB)
exe rtext wiki silesia
17

18. Compression Performance
29
23
63
44
26
0
10
20
30
40
50
60
70
80
90
zlib IPP-zlib Java-bzip2 bzip2 IPP-bzip2
CPUTime(sec)
exe rtext wiki silesia
18

19. Compression Performance (Fast Algorithms)
3.2
2.9
1.7
0
0.5
1
1.5
2
2.5
3
3.5
LZO Snappy LZ4
CPUTime(sec)
exe rtext wiki silesia
19

20. Decompression Performance
3
2
21
17
12
0
5
10
15
20
25
zlib IPP-zlib Java-bzip2 bzip2 IPP-bzip2
CPUTime(sec)
exe rtext wiki silesia
20

21. Decompression Performance (Fast Algorithms)
1.6
1.1
0.7
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
LZO Snappy LZ4
CPUTime(sec)
exe rtext wiki silesia
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
47 46 46
33
0
5
10
15
20
25
30
35
40
45
50
Java-bzip2 bzip2 IPP-bzip2 zlib
MapTime(sec)
23

24. Reduce Performance
31
28
18
14
0
5
10
15
20
25
30
35
Java-bzip2 bzip2 IPP-bzip2 zlib
ReduceTime(min)
24

25. Job Performance
38
34
23
19
38
34
25
18
0
5
10
15
20
25
30
35
40
Java-bzip2 bzip2 IPP-bzip2 zlib
JobTime(min)
sort loadgen
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