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Page1 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC 2015: Faster, Better, Smaller
Prasanth Jayachandran
Apache H...
Page2 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Apache ORC – Optimized Row-Columnar File
Apache TLP – orc.apache...
Page 3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: Format Specification
How ORC stores data?
Page 4 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC File Layout
 File Footer and Postscript
 Stripes
 Indexe...
Page 5 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC Writer
Schema: <i:int,m:map<k:string,v:struct<s:string,d:do...
Page 6 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC Data Streams
Schema: <i:int,m:map<k:string,v:struct<s:strin...
Page 7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: Features Timeline
How ORC improved over time?
Page 8 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
February 2013
 Stinger Initiative Announcement*
 Roa...
Page 9 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
March 2013
Optimized Row Columnar (ORC)
file format co...
Page 10 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
March 2013
| 2013
| 2014
| 2015
Predicate Pushdown
 ...
Page 11 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Run Length Encoding Improvements
RLE (hive 0.11) RLE (hive >= ...
Page 12 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
April 2013
| 2013
| 2014
| 2015
Vectorized ORC reader...
Page 13 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
October 2013
| 2013
| 2014
| 2015
Statistics Interfac...
Page 14 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
February 2014
| 2013
| 2014
| 2015
Zero copy read pat...
Page 15 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Serialization Improvements
0
200
400
600
800
1000
1200
1400
16...
Page 16 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Serialization Improvements
241.679
171.045
174.163
0
50
100
15...
Page 17 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
June 2014
| 2013
| 2014
| 2015
Adaptive compression b...
Page 18 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Fast File Merging
1091
651
245
816
0
200
400
600
800
1000
1200...
Page 19 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
July 2014
| 2013
| 2014
| 2015
ORC Padding Improvemen...
Page 20 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
September 2014
| 2013
| 2014
| 2015
String Dictionary...
Page 21 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
String Dictionary Improvements
767
540
0
100
200
300
400
500
6...
Page 22 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
September 2014
| 2013
| 2014
| 2015
Improved ZLIB com...
Page 23 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ZLIB Improvements
178.5
172.2
225.1
0
50
100
150
200
250
ORC +...
Page 24 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ZLIB Improvements
674
433
389
0
100
200
300
400
500
600
700
80...
Page 25 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
September 2014
| 2013
| 2014
| 2015
ACID transactions...
Page 26 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
January 2015
| 2013
| 2014
| 2015
hasNull flag in ORC...
Page 27 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
hasNull in Index Improvement
Bytes Read: 208.77 GB vs 539 MB
6...
Page 28 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
February 2015
| 2013
| 2014
| 2015
Bloom Filter Index...
Page 29 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Bloom Filter Indexes Improvements
5999989709
540,000
10,000
No...
Page 30 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Bloom Filter Indexes Improvements
74
4.5 1.34
No Indexes Min-M...
Page 31 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
April 2015
| 2013
| 2014
| 2015
Split Strategies
 BI...
Page 32 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Timeline
April 2015
| 2013
| 2014
| 2015
ORC became Apache Top...
Page 33 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: In Production
Page34 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC at Facebook
Saved more than 1,400
servers worth of storage....
Page35 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC at Spotify
16x less HDFS read when
using ORC versus Avro.(3...
Page36 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC at Yahoo!
6-50x speedup when using
ORC versus Text File.(4)...
Page 37 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: LLAP and Sub-second
ORC – Pushing for Sub-second
Page38 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: LLAP
- JIT Performance for short queries+
Row-group level ...
Page39 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: Vectorization + SIMD
0x00007f13d2e6afb0: vmovdqu 0x10(%rsi...
Page40 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ORC: LLAP (+ SIMD + Split Strategies + Row Indexes)
select * fr...
Page41 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Questions
?
Interested? Stop by the Hortonworks booth to learn ...
Page42 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Endnotes
(1) https://cwiki.apache.org/confluence/display/Hive/L...
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ORC 2015: Faster, Better, Smaller Slide 1 ORC 2015: Faster, Better, Smaller Slide 2 ORC 2015: Faster, Better, Smaller Slide 3 ORC 2015: Faster, Better, Smaller Slide 4 ORC 2015: Faster, Better, Smaller Slide 5 ORC 2015: Faster, Better, Smaller Slide 6 ORC 2015: Faster, Better, Smaller Slide 7 ORC 2015: Faster, Better, Smaller Slide 8 ORC 2015: Faster, Better, Smaller Slide 9 ORC 2015: Faster, Better, Smaller Slide 10 ORC 2015: Faster, Better, Smaller Slide 11 ORC 2015: Faster, Better, Smaller Slide 12 ORC 2015: Faster, Better, Smaller Slide 13 ORC 2015: Faster, Better, Smaller Slide 14 ORC 2015: Faster, Better, Smaller Slide 15 ORC 2015: Faster, Better, Smaller Slide 16 ORC 2015: Faster, Better, Smaller Slide 17 ORC 2015: Faster, Better, Smaller Slide 18 ORC 2015: Faster, Better, Smaller Slide 19 ORC 2015: Faster, Better, Smaller Slide 20 ORC 2015: Faster, Better, Smaller Slide 21 ORC 2015: Faster, Better, Smaller Slide 22 ORC 2015: Faster, Better, Smaller Slide 23 ORC 2015: Faster, Better, Smaller Slide 24 ORC 2015: Faster, Better, Smaller Slide 25 ORC 2015: Faster, Better, Smaller Slide 26 ORC 2015: Faster, Better, Smaller Slide 27 ORC 2015: Faster, Better, Smaller Slide 28 ORC 2015: Faster, Better, Smaller Slide 29 ORC 2015: Faster, Better, Smaller Slide 30 ORC 2015: Faster, Better, Smaller Slide 31 ORC 2015: Faster, Better, Smaller Slide 32 ORC 2015: Faster, Better, Smaller Slide 33 ORC 2015: Faster, Better, Smaller Slide 34 ORC 2015: Faster, Better, Smaller Slide 35 ORC 2015: Faster, Better, Smaller Slide 36 ORC 2015: Faster, Better, Smaller Slide 37 ORC 2015: Faster, Better, Smaller Slide 38 ORC 2015: Faster, Better, Smaller Slide 39 ORC 2015: Faster, Better, Smaller Slide 40 ORC 2015: Faster, Better, Smaller Slide 41 ORC 2015: Faster, Better, Smaller Slide 42
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ORC 2015: Faster, Better, Smaller

  1. 1. Page1 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC 2015: Faster, Better, Smaller Prasanth Jayachandran Apache Hive Team, Hortonworks @prasanth_j
  2. 2. Page2 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Apache ORC – Optimized Row-Columnar File Apache TLP – orc.apache.org+ Type Specific Encodings+ Came out of Apache Hive+ Vectorized Readers (Java, C++)+ Projection and Predicate Pushdown+ Columnar Storage+ Block Compression+ Hive ACID transactions+ Single SerDe Format+ Protobuf Metadata Storage+
  3. 3. Page 3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: Format Specification How ORC stores data?
  4. 4. Page 4 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC File Layout  File Footer and Postscript  Stripes  Indexes (Row group indexes and Bloom Filter interleaved)  Min/Max stats, Positions for every 10K rows  Data  Multiple streams per column encoded and compressed independently  Stripe Footer  Locations to streams, type of encoding  Full specification at [1]
  5. 5. Page 5 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC Writer Schema: <i:int,m:map<k:string,v:struct<s:string,d:double>,t:time>  One tree writer per flattened column  Multiple streams per column  PRESENT  DATA  LENGTH  DICTIONARY_DATA  SECONDARY  ROW_INDEX  BLOOM_FILTER
  6. 6. Page 6 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC Data Streams Schema: <i:int,m:map<k:string,v:struct<s:string,d:double>,t:time>  Streams can be suppressed.  Example: PRESENT stream is suppressed when all values in a stripe are non-null. IS_PRESENT DATA DICTIONARY LENGTH SECONDARY Compression Buffers
  7. 7. Page 7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: Features Timeline How ORC improved over time?
  8. 8. Page 8 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline February 2013  Stinger Initiative Announcement*  Roadmap to improve Apache Hive’s performance by 100x  Delivered in 100% Apache Open Source * http://hortonworks.com/blog/100x-faster-hive/ | 2013 | 2014 | 2015 SQL Engine Vectorized SQL Engine Columnar Storage ORC + + Distributed Execution Apache Tez = 100x
  9. 9. Page 9 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline March 2013 Optimized Row Columnar (ORC) file format committed to Hive  Hive version: 0.11  Native data format in Hive | 2013 | 2014 | 2015
  10. 10. Page 10 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline March 2013 | 2013 | 2014 | 2015 Predicate Pushdown  SARG interface  Prune stripes and row groups based on min/max statistics Improved Run Length Encoding  Tighter bit packing  Longer runs  DELTA, SHORT_REPEATS, DIRECT, PATCHED_BASE
  11. 11. Page 11 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Run Length Encoding Improvements RLE (hive 0.11) RLE (hive >= 0.12) Compression Ratio Encoding Time (in ms) Decoding Time (in ms) Compression Ratio Encoding Time (in ms) Decoding Time (in ms) Twitter Census API ID (24,556,361 records) 2.32 1770 1263 6.97 1558 864 HTTP Archive (bytes.json) 79.4 198 191 200.82 263 125 Github Archive (root.payload.name.txt.dict-len) 114.05 21 15 260.73 23 15 AOL Querylog Epoch (36,389,577 records) 2.51 553 364 3.7 652 246 Reference: https://issues.apache.org/jira/secure/attachment/12596722/ORC-Compression-Ratio-Comparison.xlsx
  12. 12. Page 12 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline April 2013 | 2013 | 2014 | 2015 Vectorized ORC readers  Read and process columns in batches of size 1024 Null stream suppression  Suppress PRESENT stream if no nulls in a stripe  Enables fast path in vectorization June 2013
  13. 13. Page 13 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline October 2013 | 2013 | 2014 | 2015 Statistics Interface  Writer – Update statistics during load time  Reader – ANALYZE TABLE .. NOSCAN Split Elimination  Stripe level column statistics  Eliminate stripes that do not satisfy predicate conditions November 2013
  14. 14. Page 14 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline February 2014 | 2013 | 2014 | 2015 Zero copy read path  HDFS caching APIs to read directly into memory without extra data copies Serialization Improvements  Bit width alignment (trade-off space for speed)  Unrolled bit packing and unpacking  Buffered double reader and writer June 2014
  15. 15. Page 15 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Serialization Improvements 0 200 400 600 800 1000 1200 1400 1600 1800 1 2 4 8 16 24 32 40 48 56 64 MeanTime(ms) Bit Width ORC Read Integer Performance (smaller is better) hive 0.13 unpacking hive-1.0 unpacking (new)
  16. 16. Page 16 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Serialization Improvements 241.679 171.045 174.163 0 50 100 150 200 250 300 hive <= 0.13 buffered + BE buffered + LE MeanTime(ms) Double Read Modes ORC Read Double Performance (smaller is better) ~1.4x improvement
  17. 17. Page 17 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline June 2014 | 2013 | 2014 | 2015 Adaptive compression buffer size  >1000 columns adjust compression buffer size based on available memory  Avoids wide table OOMs Fast stripe level file merging  Many small files to few large files  No Decompression, No Decoding  ALTER TABLE … CONCATENATE July 2014
  18. 18. Page 18 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Fast File Merging 1091 651 245 816 0 200 400 600 800 1000 1200 1400 1600 ORC RCFile TotalTimeinseconds CONCAT Supporting File Formats ETL With File Merging – TPC-H 1000 Scale Lineitem (smaller is better) Merge Time Load Time 1336 1467 ~3.33x improvement in merge time
  19. 19. Page 19 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline July 2014 | 2013 | 2014 | 2015 ORC Padding Improvements  Pad bytes to avoid remote HDFS reads  Last stripe is adjusted to fit within HDFS block boundary (worst case: 5% wastage) Decouple stripe size vs block size  Smaller stripes (64MB)  More stripes per block (4 per block)  Better parallelism & split elimination
  20. 20. Page 20 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline September 2014 | 2013 | 2014 | 2015 String Dictionary Improvements  Row group level checking  Remember decision across stripes  Avoids expensive RBTree insertions
  21. 21. Page 21 © Hortonworks Inc. 2011 – 2015. All Rights Reserved String Dictionary Improvements 767 540 0 100 200 300 400 500 600 700 800 900 hive <= 0.13 hive > 0.13 Timeinseconds Hive Version String Dictionary Improvements - TPC-H 1000 Scale Lineitem (smaller is better) Load Time ~1.4x improvement
  22. 22. Page 22 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline September 2014 | 2013 | 2014 | 2015 Improved ZLIB compression  Different streams compressed with different zlib strategies/levels  Compress integers and doubles differently  Data and Dictionary stream - Looks for smaller byte patterns  All other streams - Less LZ77, More Huffman
  23. 23. Page 23 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ZLIB Improvements 178.5 172.2 225.1 0 50 100 150 200 250 ORC + (old ZLIB) ORC + (new ZLIB) ORC + SNAPPY DataSizeinGBs File Format + Compression Codec Data Size Improvements - TPC-H 1000 Scale Lineitem (smaller is better) ~4% improvement ~1.3x smaller
  24. 24. Page 24 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ZLIB Improvements 674 433 389 0 100 200 300 400 500 600 700 800 ORC + (old ZLIB) ORC + (new ZLIB) ORC + SNAPPY DataSizeinGBs File Format + Compression Codec Load Time Improvements - TPC-H 1000 Scale Lineitem (smaller is better) ~1.6x improvement Only ~10% slower than SNAPPY
  25. 25. Page 25 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline September 2014 | 2013 | 2014 | 2015 ACID transactions  Order of millions of rows  Not designed for OLTP requirements  Streaming Ingest via Flume or Storm  Atomically add base and delta directories  Minor compaction – Merge many delta files  Major compaction – Re-write base files to incorporate delta file changes Broken pattern: Add Partitions for Atomicity-
  26. 26. Page 26 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline January 2015 | 2013 | 2014 | 2015 hasNull flag in ORC internal index  Better pruning of row groups  Improves the performance of SELECT .. WHERE column IS NULL;
  27. 27. Page 27 © Hortonworks Inc. 2011 – 2015. All Rights Reserved hasNull in Index Improvement Bytes Read: 208.77 GB vs 539 MB 66.73 7.87 0 10 20 30 40 50 60 70 80 hive < 1.1.0 hive >= 1.1.0 ExecutionTimeinseconds Hive Version select * from lineitem where l_shipdate is null (smaller is better) Execution Time~8.5x improvement
  28. 28. Page 28 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline February 2015 | 2013 | 2014 | 2015 Bloom Filter Index  Much better row group pruning when compared to min/max  Bloom filter evaluated after the fast Min/Max based elimination
  29. 29. Page 29 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Bloom Filter Indexes Improvements 5999989709 540,000 10,000 No Indexes Min-Max Indexes Bloomfilter Indexes select * from tpch_1000.lineitem where l_orderkey = 1212000001; (log scale – smaller is better) Rows Read
  30. 30. Page 30 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Bloom Filter Indexes Improvements 74 4.5 1.34 No Indexes Min-Max Indexes Bloomfilter Indexes select * from tpch_1000.lineitem where l_orderkey=1212000001; (smaller is better) Time Taken (seconds) ~16x improvement ~3.3x improvement
  31. 31. Page 31 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline April 2015 | 2013 | 2014 | 2015 Split Strategies  BI – Skip reading file footer  ETL – Read and cache file footer  HYBRID – Default. Chooses BI/ETL based on number of files and average file size  Group splits based on columnar projection size instead of file size
  32. 32. Page 32 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Timeline April 2015 | 2013 | 2014 | 2015 ORC became Apache Top Level Project  C++ reader with contributions from Hortonworks, HP and Microsoft  Column encryption to encrypt sensitive columns http://orc.apache.org/
  33. 33. Page 33 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: In Production
  34. 34. Page34 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC at Facebook Saved more than 1,400 servers worth of storage.(2) Compressioni Compression ratio increased from 5x to 8x globally.(2) Compressioni
  35. 35. Page35 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC at Spotify 16x less HDFS read when using ORC versus Avro.(3) IOi 32x less CPU when using ORC versus Avro.(3) CPUi
  36. 36. Page36 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC at Yahoo! 6-50x speedup when using ORC versus Text File.(4) Speedupi 1.6-30x speedup when using ORC versus RCFile.(4) Speedupi
  37. 37. Page 37 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: LLAP and Sub-second ORC – Pushing for Sub-second
  38. 38. Page38 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: LLAP - JIT Performance for short queries+ Row-group level caching+ Asynchronous IO Elevator+ + Multi-threaded Column Vector processing+
  39. 39. Page39 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: Vectorization + SIMD 0x00007f13d2e6afb0: vmovdqu 0x10(%rsi,%rax,8),%ymm2 0x00007f13d2e6afb6: vaddpd %ymm1,%ymm2,%ymm2 0x00007f13d2e6afba: movslq %eax,%r10 0x00007f13d2e6afbd: vmovdqu 0x30(%rsi,%r10,8),%ymm3 ;*daload vector.expressions.gen.DoubleColAddDoubleColumn::evaluate (line 94) Example: Query: select ss_ext_tax + 1.0 from store_sales_orc; JVM Options: HADOOP_OPTS=“ -XX:+PrintCompilation -XX:+UnlockDiagnosticVMOptions -XX:+PrintAssembly” Note: Make sure to have hotspot disassembler in $JAVA_HOME/jre/lib Generated Assembly:  Allocation free tight inner loops enables JDK’s auto-vectorization  Vectors can be filtered early in ORC  String dictionary can be used to binary-search  Vectorized SIMD Join  Improves performance for single key joins AVX - Vector Addition Packed Double 4 doubles loaded to 256 bit registers
  40. 40. Page40 © Hortonworks Inc. 2011 – 2015. All Rights Reserved ORC: LLAP (+ SIMD + Split Strategies + Row Indexes) select * from tpch_1000.lineitem where l_orderkey=1212000001;
  41. 41. Page41 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Questions ? Interested? Stop by the Hortonworks booth to learn more
  42. 42. Page42 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Endnotes (1) https://cwiki.apache.org/confluence/display/Hive/LanguageManual+ORC#LanguageManualORC-orc- specORCFormatSpecification (2) https://code.facebook.com/posts/229861827208629/scaling-the-facebook-data-warehouse-to-300-pb/ (3) http://www.slideshare.net/AdamKawa/a-perfect-hive-query-for-a-perfect-meeting-hadoop-summit-2014 (4) http://www.slideshare.net/Hadoop_Summit/w-1205p230-aradhakrishnan-v3
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