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QuasarDB “Seneca” 3.14.1 Released

1 month ago

We are thrilled to announce the immediate availability of QuasarDB 3.14.1, an update from 3.14.0.

This update brings major new features while being 100% compatible with 3.14.0. Features such as streaming aggregation (named aggregated tables) and automatic data expiration are now available to all users.

Streaming aggregations enable you to aggregate the data as it comes and store the aggregate, unlocking ultra-low latency analytics while crushing disk usage.

In addition, we’ve added numerous performance enhancements, query functionalities, and bug fixes, that make the upgrade to 3.14.1 highly recommended!

Change log

(w.r.t. 3.14.0)

  • Protocol version 48
  • [arm64] Optimized several functions to leverage ARM Neon SIMD
  • [compression] Slight improvement in (de)compression speed
  • [daemon] Upsert now properly replaces all duplicates
  • [kernel] Add support for TTL in tables
  • [kernel] General availability of AGGREGATED tables (streaming analytics)
  • [kernel] Overall improvement of insertion performance across the board
  • [linux] Fixed stack trace symbol dump
  • [orderbook] Can now generate order books on a list of timestamps and offsets
  • [orderbook] Significant performance improvement of the Currenex order book engine
  • [orderbook] The account ID column is now properly displayed
  • [query] Add round, ceil, and floor functions
  • [query] Add support for complex ASOF joins across multiple tables
  • [query] Add support for count($timestamp)
  • [query] Add support for interpolation in GROUP BY queries
  • [query] Add support for JSON operators
  • [query] Add support for LEAD() and LAG() for sliding window queries
  • [query] ASOF now has a consistent behavior when there are NULL values
  • [query] Can now drop a column that has a name made of a single character
  • [query] Columns with different symbol tables can now be compared
  • [query] Fix invalid first() and last() result for certain complex queries
  • [query] Significant improvement for GROUP BY spanning thousands of tables
  • [query] Support composition for correlation
  • [query] Support for HAVING with PIVOT

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