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Performance numbers of other vendors demonstrate that we could expect 2x-4x decrease in required disk space and >1.5x increase in throughput (ops/sec) on typical workloads.

Competitive

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Analysis

This section describes general compression approaches and their pros and cons. The following compression mechanisms are implemented in practice:

1. Data Format 
2. Index Prefix Compression
3. Page Compression
4. WAL compression
5. Column Compression
6. Columnar Store
7. Row Compression with Dictionary
8. Misc approaches

Data size could be reduced with efficient data format. Common row metadata could be skipped. Numeric values could be encoded to occupy less space. Fixed-length strings could be trimmed. NULL and zero values could be skipped with help of small row bitmaps.

Next compression could be applied to specific columns, either manually or transparently.

Data pages could be compressed on per-page basis, what gives 2x-4x compression rate on typical workloads. Depending on concrete implementation pages could be stored in memory in compressed or uncompressed forms. File system specific features, such as hole punching, could be applied to reduce IO.

Indexes are compressed differently because it is necessary to keep index page structure for fast lookups. Prefix compression is a common mechanism.

Extreme compression rates up to 10x-15x are possible with column store formats. But it is only applicable to historical data with minimal update rate and is not suitable for lookups.

WAL could be compressed to decrease log size. In some cases it may improve overall system throughput.

Last, compression could be applied to special cases. Examples are compression of large values (LOBs, long strings) and per-page dictionary compression during data load.

Data Format

Efficient disk usage starts with proper data layout. Vendors strive to place data in pages in such a way that total overhead is kept as low as possible while still maintaining high read speed. Typically this is achieved as follows:

• Common metadata

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• is stored outside of data page
• Numeric types are written using varlen encoding (e.g. int data type may take 1-5 bytes instead of 4)
• Fixed-length string data types (CHAR, NCHAR) are trimmed
• NULL and zero values are optimized to consume no space, typically with special bitmap (e.g. if there are 

Examples:

1. SQL Server row format [1] - varlen, CHAR trimming, NULL/zero optimization
2. MySQL row format [2] - no varlen, no CHAR trimming, NULL/zero optimization

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Prefix compression could be applied to the following index types:

• Non-unique single column secondary index
• Non-unique and unique multi-column secondary index

Prefix compression could be implemented as follows:

• Static - compression is applied to all index rows irrespective of whether it is beneficial or not. Attributes with low cardinality are compressed well. Contrary, attributes with high cardinality may have negative compression rates. Decision whether to compress or not is delegated to user (typically DBA)
• Dynamic - compression is either applied or not applied to indexed values on page-by-page basis based on some internal heuristics. Negative compression rates are avoided automatically, but implementation is more complex.

Examples:

1. Oracle index compression (static) [1]
2. Oracle advanced index compression (dynamic) [2]
3. MongoDB index compression [3]

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[1] https://blogs.oracle.com/dbstorage/updates-in-row-compressed-tables
[2] https://blogs.oracle.com/dbstorage/advanced-row-compression-improvements-with-oracle-database-12c-release-2
[3] https://docs.microsoft.com/en-us/sql/relational-databases/data-compression/page-compression-implementation?view=sql-server-2017

WAL Compression

Write-ahead log writes all changes to data to journal. Data is read back only during crash recovery. Data chunks being written to journal may be compressed. It reduces number of disk writes and saves WAL space increasing likelihood of delta-update in case of temporal node shutdown. Compression could be applied to specific records - the larger the record, the more savings are expected. Compression typically takes more time than decompression, so operation latency may increase. However less number of IO calls typically increases overall system throughput because disk resources are typically more deficient than CPU.

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[1] https://www.pgcon.org/2016/schedule/attachments/432_WAL-Reduction.pdf
[2] https://mariadb.com/kb/en/library/compressing-events-to-reduce-size-of-the-binary-log/
[3] https://docs.mongodb.com/manual/core/wiredtiger/#storage-wiredtiger-journal

Column Compression

It is possible to compress specific columns only. This can be done either manually using built-in functions, or transparently if column compression hint is defined in CREATE TABLE or ALTER TABLE commands. 

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[1] https://docs.microsoft.com/en-us/sql/t-sql/functions/compress-transact-sql?view=sql-server-2017
[2] https://dev.mysql.com/doc/refman/8.0/en/encryption-functions.html#function_compress
[3] https://www.percona.com/doc/percona-server/LATEST/flexibility/compressed_columns.html
[4] https://mariadb.com/kb/en/library/storage-engine-independent-column-compression/

Draft materials

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Columnar Store

Usually data is stored in row format, when all attributes are stored together. Alternative approach is to store data column-wise. In this case all values of a specific column for a set of rows are stored near each other. This allows to improve compression rates dramatically, up to 10x. This approach saves a lot of spaces and improves scan speed, especially in OLAP cases. However, it suffers from read amplification for row lookups and write amplification for updates. Hence, it is usually used for cold data. 

Advantages:

• Dramatic disk savings - up to 10x-15x on typical historical data sets
• Improved scan speed

Disadvantages:

• Very slow lookups
• Very slow updates

Examples:

1. Oracle Exadata Hybrid Columnar Compression - oragnize rows into groups, then transform them to columnar format and applies compression [1]
2. SQL Server Columnsotre Indexes - very similar to Oracle's idea [2] [3] [4]

[1] http://www.

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oracle.com/

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technetwork/

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database/

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features/

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availability/

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311358-132337.pdf
[2] https://msdn.microsoft.com/en-us/library/gg492088(v=sql.120).aspx
[3] https://

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docs.microsoft.com/

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en-us/sql/relational-databases/indexes/columnstore-indexes-overview?view=sql-server-2017
[4] https://docs.microsoft.com/en-us/sql/relational-databases/data-compression/data-compression?view=sql-server-2017#using-columnstore-and-columnstore-archive-compression

Row Compression with Dictionary

Usually data is stored in row format, and there's a lot of overlap between values in different rows. Flags, enum values, dates or strings can have same byte sequences repeating from row to row. It is possible to harvest a set of typical rows for a table, create an external dictionary based on them, and then reuse this dictionary when writing each next row. This only offers limited benefits for classical RDBMS since their row format is low-overhead and with fixed field offset lookups, which are defeated by compression. However, BinaryObjects used in Ignite are high-overhead, with field/type information repeating in every record, and offset lookups are not used. Row compression can provide high yield with low overhead. In theory it is possible to share dictionary between nodes, but having separate dictionaries look more practical.

Advantages:

• Easy to implement - no architectural changes
• Reasonably fast in both writing and reading
• 2.5x compression on mock data with naive prototype
• More data per page - more data fits in RAM - less latency even if throughput is lower

Disadvantages:

• Need to keep dictionary alongside the data
• Occassionally need to update the dictionary as data evolves
• Keep track of multiple dictionaries per node
• Pages from different nodes use different dictionaries, might interfere with checkpointing

Examples:

1. IBM DB2 supports this approach on per-table basis [1]
2. A prototype of dictionary-based row compression for Apache Ignite [2]

[1] https://www.

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ibm.com/support/knowledgecenter/en/SSEPGG_10.5.0/com.ibm.db2.luw.admin.dbobj.doc/doc/c0052331.html

[2] https://github.com/apache/ignite/pull/4295

Misc Approaches

Compressing Large Values

Large values, such as LOBs and long varchars, cannot be stored in original data block. Some vendors compress these values and then split into pieces. 

Examples:

1. PostgreSQL TOAST [1]
2. Oracle Advanced LOB Compression [2] [3]

[1] https://wiki.postgresql.org/wiki/TOAST
[2] https://docs.oracle.com/database/121/ADLOB/adlob_smart.htm#ADLOB45944
[3] https://docs.microfocus.com/itom/Network_Automation:10.50/Administer/DB_Compression/ConfiguringLOB_Oracle

Compression During Data Load

Oracle attempts to compress values during data load (Direct Path, CTAS, INSERT ... APPEND) [1]. Compression is applied on per-block basis using dictionary approach. Oracle may decide to skip compression if there are no benefits. Alternatively, it may reorder attrbutes in rows to get longer common prefixes and improve compression ratio.

[1] https://www.red-gate.com/simple-talk/sql/oracle/compression-oracle-basic-table-compression/

Proposed Changes

Some approaches adds more value than others. Some approaches are hard to implement, some are easy. For this reason compression should be implemented in phases, with the most efficient and simple techniques being developed first. Proposed plan:

Phase 1: Low Hanging Fruits

• Index Prefix Compression - efficient and relatively easy to implement
• WAL Compression - could increase throughput and easy to evaluate

Phase 2: The Battle

• Page Compression - efficient, but implementation would be complex. with lots of changes to storage engine
• or, Row Compression with Dictionary - no changes to storage engine, but adds management of dictionaries

Phase 3: Excellence

• Data Format improvements - moderate value for the system, complex to implement, benefit may be cancelled out by actual compression
• Column Compression - depends on new data format 

Out of Scope

The following changes are not likely to be implemented in the nearest time due to their complexity and/or limited impact on general use cases:

• Columnar store - require large changes in storage engine

Best practices

Granularity

Motivation

TBD

Proposed changes

TBD

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Tickets