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File Format Benchmark - Avro, JSON, ORC & Parquet
1.
File Format Benchmark
- Avro, JSON, ORC, & Parquet Owen O’Malley owen@hortonworks.com @owen_omalley August 2016
2.
2 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Who Am I? Worked on Hadoop since Jan 2006 MapReduce, Security, Hive, and ORC Worked on different file formats –Sequence File, RCFile, ORC File, T-File, and Avro requirements
3.
3 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Goal Seeking to discover unknowns –How do the different formats perform? –What could they do better? –Best part of open source is looking inside! Use real & diverse data sets –Over-reliance on similar datasets leads to weakness Open & reviewed benchmarks
4.
4 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved The File Formats
5.
5 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Avro Cross-language file format for Hadoop Schema evolution was primary goal Schema segregated from data –Unlike Protobuf and Thrift Row major format
6.
6 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved JSON Serialization format for HTTP & Javascript Text-format with MANY parsers Schema completely integrated with data Row major format Compression applied on top
7.
7 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved ORC Originally part of Hive to replace RCFile –Now top-level project Schema segregated into footer Column major format with stripes Rich type model, stored top-down Integrated compression, indexes, & stats
8.
8 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Parquet Design based on Google’s Dremel paper Schema segregated into footer Column major format with stripes Simpler type-model with logical types All data pushed to leaves of the tree Integrated compression and indexes
9.
9 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Data Sets
10.
10 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved NYC Taxi Data Every taxi cab ride in NYC from 2009 –Publically available –http://tinyurl.com/nyc-taxi-analysis 18 columns with no null values –Doubles, integers, decimals, & strings 2 months of data – 22.7 million rows
11.
11 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
12.
12 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Github Logs All actions on Github public repositories –Publically available –https://www.githubarchive.org/ 704 columns with a lot of structure & nulls –Pretty much the kitchen sink 1/2 month of data – 10.5 million rows
13.
13 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Finding the Github Schema The data is all in JSON. No schema for the data is published. We wrote a JSON schema discoverer. –Scans the document and figures out the type Available at https://github.com/hortonworks/hive-json Schema is huge (12k)
14.
14 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Sales Generated data –Real schema from a production Hive deployment –Random data based on the data statistics 55 columns with lots of nulls –A little structure –Timestamps, strings, longs, booleans, list, & struct 25 million rows
15.
15 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Storage costs
16.
16 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Compression Data size matters! –Hadoop stores all your data, but requires hardware –Is one factor in read speed ORC and Parquet use RLE & Dictionaries All the formats have general compression –ZLIB (GZip) – tight compression, slower –Snappy – some compression, faster
17.
17 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
18.
18 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Taxi Size Analysis Don’t use JSON Use either Snappy or Zlib compression Avro’s small compression window hurts Parquet Zlib is smaller than ORC –Group the column sizes by type
19.
19 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
20.
20 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
21.
21 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Taxi Size Analysis ORC did better than expected –String columns have small cardinality –Lots of timestamp columns –No doubles Need to revalidate results with original –Improve random data generator –Add non-smooth distributions
22.
22 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
23.
23 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Github Size Analysis Surprising win for JSON and Avro –Worst when uncompressed –Best with zlib Many partially shared strings –ORC and Parquet don’t compress across columns Need to investigate shared dictionaries.
24.
24 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Use Cases
25.
25 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Full Table Scans Read all columns & rows All formats except JSON are splitable –Different workers do different parts of file
26.
26 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
27.
27 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Taxi Read Performance Analysis JSON is very slow to read –Large storage size for this data set –Needs to do a LOT of string parsing Tradeoff between space & time –Less compression is sometimes faster
28.
28 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
29.
29 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Sales Read Performance Analysis Read performance is dominated by format –Compression matters less for this data set –Straight ordering: ORC, Avro, Parquet, & JSON Garbage collection is important –ORC 0.3 to 1.4% of time –Avro < 0.1% of time –Parquet 4 to 8% of time
30.
30 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved
31.
31 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Github Read Performance Analysis Garbage collection is critical –ORC 2.1 to 3.4% of time –Avro 0.1% of time –Parquet 11.4 to 12.8% of time A lot of columns needs more space –Suspect that we need bigger stripes –Rows/stripe - ORC: 18.6k, Parquet: 88.1k
32.
32 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Column Projection Often just need a few columns –Only ORC & Parquet are columnar –Only read, decompress, & deserialize some columns Dataset format compression us/row projection Percent time github orc zlib 21.319 0.185 0.87% github parquet zlib 72.494 0.585 0.81% sales orc zlib 1.866 0.056 3.00% sales parquet zlib 12.893 0.329 2.55% taxi orc zlib 2.766 0.063 2.28% taxi parquet zlib 3.496 0.718 20.54%
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33 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Projection & Predicate Pushdown Sometimes have a filter predicate on table –Select a superset of rows that match –Selective filters have a huge impact Improves data layout options –Better than partition pruning with sorting ORC has added optional bloom filters
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34 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Metadata Access ORC & Parquet store metadata –Stored in file footer –File schema –Number of records –Min, max, count of each column Provides O(1) Access
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35 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Conclusions
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36 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Recomendations Disclaimer – Everything changes! –Both these benchmarks and the formats will change. For complex tables with common strings –Avro with Snappy is a good fit (w/o projection) For other tables –ORC with Zlib is a good fit Experiment with the benchmarks
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37 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Fun Stuff Built open benchmark suite for files Built pieces of a tool to convert files –Avro, CSV, JSON, ORC, & Parquet Built a random parameterized generator –Easy to model arbitrary tables –Can write to Avro, ORC, or Parquet
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38 © Hortonworks
Inc. 2011 – 2016. All Rights Reserved Thank you! Twitter: @owen_omalley Email: owen@hortonworks.com
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