DATABASE COMPRESSION. Pooja Nilangekar [ ] Rohit Agrawal [ ] : Advanced Database Systems
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![edu ] Rohit Agrawal [](/docs-images/79/79296396/images/1-1.jpg "rohit10@cmu.")
1 DATABASE COMPRESSION Pooja Nilangekar [ poojan@cmu.edu ] Rohit Agrawal [ rohit10@cmu.edu ] : Advanced Database Systems
2 PROJECT OBJECTIVE Compressing the DBMS :- Use less space to store cold data Process less data per query Minimize speed overhead Use Delta & Dictionary encoding
3 GOALS (Revised) 75 % 100 % 125 % > Compressed Tile Class > Metadata per tile > Delta Encoding of Integers > Insertion of Data, followed by Compression > Delta Encoding of Decimals > SELECT on compressed and uncompressed data > Checkpointing the Compressed Tile > Deduplication of variable length field. > Order Preserving Dictionary Encoding > Compressing DataTable Offline. > TileGroup Compaction
4 QUICK OVERVIEW Storage in Peloton Name ID Year Age Class Messi A TileGroup #1 Ronaldo B Rooney C Tile #1 Tile #2 Tile #3 Name ID Year Age Class TileGroup #2 Bob A Sam B Carl C Data Table
5 METHODOLOGY TileGroup #1 (FULL) TileGroup #2 (FULL) TileGroup #3 (FULL) COMPRESS TABLE Compressed TileGroup #1 Compressed TileGroup #2 Compressed TileGroup #3 Uncompressed TileGroup #4 TileGroup #4 (EMPTY SLOTS) Uncompressed Data Table Compressed Data Table
6 COMPRESSION : INTEGERS Using Delta Encoding 1. Sort the Data 2. Base Value for Encoding MEDIAN 3. Find Compressed Type a. Min Offset Min - Median & Max Offset Max - Median b. Biggest DataType that can store these offsets Compressed Type 4. Calculate and store offsets from base
7 OVERVIEW OF DELTA ENCODING ID (BIGINT) Year (SMALLINT) ZipCode (INTEGER) ID (BIGINT) Year (SMALLINT) ZipCode (INTEGER) SORT
8 OVERVIEW OF DELTA ENCODING ID Year ZipCode Median : Minimum Offset : -600 Maximum Offset : 143 Median : 1994 Minimum Offset : -4 Maximum Offset : 6 Median : Minimum Offset : -15 Maximum Offset : 4 SMALLINT TINYINT TINYINT
9 OVERVIEW OF DELTA ENCODING ID (BIGINT) Year (SMALLINT) ZipCode (INTEGER) ID (SMALLINT) Year (TINYINT) ZipCode (TINYINT) METADATA : SIZE : 70 bytes SIZE : 34 bytes
10 COMPRESSION : DECIMALS 1. Get Max Exponent for column 2. Multiply each value with this exponent Integer Column 3. Compress like Integer Column [Delta Encoding] 4. In uncompression, instead of just adding offset to base, add [offset/exponent] to base
11 COMPRESSION : DECIMALS TEMPERATURE (DECIMAL) TEMPERATURE (DECIMAL) METADATA : SIZE : 40 bytes SIZE : 21 bytes
12 COMPRESSION : VARCHAR Using Deduplication 1. Cuckoo Hash Dictionary 2. Store dictionary key instead of actual VARCHAR. 3. Compression Type : TINYINT / SMALLINT
13 COMPRESSION : VARCHAR CITY (VARCHAR) Pittsburgh New York New York Pittsburgh New York Pittsburgh CITY (TINYINT) METADATA : Pittsburgh 0 New York 1 SIZE : 99 bytes SIZE : 41 bytes
14 EVALUATION Workload: Temperature Dataset Metrics Evaluated Compression Ratio Sequential Scan Timing.
15 EVALUATION : Temperature Dataset Temperature Dataset Characteristics : Size : MB Number of Tuples ~ 2.5 million Tuple : SITE (VARCHAR) MONTH (INTEGER) DAY (INTEGER) YEAR (INTEGER) TEMPERATURE (DECIMAL) Source :
16 EVALUATION : COMPRESSION RATIO Data Uncompressed Storage Compressed Storage Metadata Storage Size MB MB MB 78.9 %
17 EVALUATION : TIMING Time taken to compress the tuples : seconds Sequential Scan Time Comparison SELECT * FROM TABLE Sequential Scan Time Comparison SELECT COUNT(*) FROM TABLE
18 EVALUATION : TIMING BREAKDOWN Slowdown Analysis per column
19 ANALYSIS Compression Ratio Analysis: Compression Ratios match Hyper, SAP HANA Timing Analysis : Significant Performance slowdown while accessing compressed data Reasons : Cuckoo Hash Lack of support for Vectorized Instructions. Range scan and point queries on Compressed Data Integration with LLVM
20 Source Code Contributions - PELOTON SOURCE TEST Files Added : src/include/storage/compressed_tile.h src/storage/compressed_tile.cpp Files Modified : src/ include/storage/data_table.h src/storage/data_table.cpp src/include/storage/tile_group.h src/storage/tile_group.cpp src/include/storage/tile.h src/storage/tile.cpp Correctness Test [INTEGERS, DECIMALS & VARCHAR] >> INSERT -> COMPRESS -> INSERT (UNCOMPRESSED) -> SELECT. Compression Size Test [INTEGER, DECIMALS & VARCHAR]
21 FUTURE WORK Vectorization using SIMD Run Query on Compressed Data Dictionary Encoding Compress Tile Groups Parallely SPEED, SPEED and MORE SPEED!
22 Thank You
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