Algorithmic Compression - Teradata Vantage - Analytics Database

Database Design

Deployment
VantageCloud
VantageCore
Edition
VMware
Enterprise
IntelliFlex
Product
Analytics Database
Teradata Vantage
Release Number
17.20
Published
June 2022
ft:locale
en-US
ft:lastEdition
2025-11-21
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lifecycle
lifecycle
Product Category
Teradata Vantageā„¢

This section summarizes the benefits, best uses, and restrictions of algorithmic compression in Vantage.

Benefits of Algorithmic Compression

  • Easy to define
  • Can be specified for permanent, global temporary, and volatile table columns
  • Compresses compressible columns in all table rows regardless of their value

    When combined with multivalue compression for the same column, algorithmic compression only compresses the values that are not compressed by multivalue compression

  • Has no effect on table header size
  • Spools retain the algorithmic compression of their base tables
  • No limit to the number of table columns that can be compressed using algorithmic compression
  • No limit on the size of algorithmically compressed values
  • Useful for columns with many unique or nearly unique values
  • Easily and complementarily combined with multivalue compression
  • Easily combined with autocompression for column-partitioned tables and join indexes
  • Can specify algorithmic compression on a secondary index column, though the column data in the secondary index subtable is not algorithmically compressed
  • Can specify algorithmic compression on permanent and global temporary table columns
  • Automatically compresses nulls

Best Use of Algorithmic Compression

  • Very wide character columns, particularly Unicode columns
  • Columns that contain click stream data

Restrictions and Limitations of Algorithmic Compression

  • Both compression and decompression consume CPU resources, causing negative performance issues

    Performance issues are restricted to when an algorithmically compressed column is referenced directly

  • If you use your own compression algorithms, you must write and test your own compression UDFs to implement them
  • Can be used for only a few data types
  • Compressed values are stored within the row
  • Best not used in combination with block-level compression because of the combined cost of their decompression
  • Cannot specify for primary index or primary AMP index columns
  • Cannot specify for the partitioning columns of a partitioning expression