Aggregate queries which touch large swathes of time-series data can take a long time to compute because the system needs to scan large amounts of data on every query execution. Our continuous aggregates continuously calculate the results of a query in the background and materialize the results. Queries to the continuous aggregate view are then significantly faster as they do not need to touch the raw data in the hypertable, instead using the pre-computed aggregates in the view.

Continuous aggregates are somewhat similar to PostgreSQL materialized views, but unlike a materialized view, continuous aggregates do not need to be refreshed manually; the view will be refreshed automatically in the background as new data is added, or old data is modified. Additionally, it does not need to re-calculate all of the data on every refresh. Only new and/or invalidated data will be calculated. Since this re-aggregation is automatic, it doesn’t add any maintenance burden to your database.

Our continuous aggregate approach supports high-ingest rates by avoiding the high-write amplification associated with trigger-based approaches. Instead, we use invalidation techniques to track what data has changed, and then correct the materialized aggregate the next time that the automated process executes.

More information can be found on our docs overview or in this tutorial.

Major Features * #1184 Add continuous aggregate functionality

Minor Features * #1005 Enable creating indexes with one transaction per chunk * #1007 Remove hypertable parent from query plans * #1038 Infer time_bucket_gapfill arguments from WHERE clause * #1062 Make constraint aware append parallel safe * #1067 Add treat_null_as_missing option to locf * #1112 Add support for window functions to gapfill * #1130 Add support for cross datatype chunk exclusion for time types * #1134 Add support for partitionwise aggregation * #1153 Add time_bucket support to chunk exclusion * #1170 Add functions for turning restoring on/off and setting license key * #1177 Add transformed time_bucket comparison to quals * #1182 Enable optimizing SELECTs within INSERTs * #1201 Add telemetry for policies: drop_chunk & reorder

Bugfixes * #1010 Add session locks to CLUSTER * #1115 Fix ordered append optimization for join queries * #1123 Fix gapfill with prepared statements * #1125 Fix column handling for columns derived from GROUP BY columns * #1132 Adjust ordered append path cost * #1155 Limit initial max_open_chunks_per_insert to PG_INT16_MAX * #1167 Fix postgres.conf ApacheOnly license * #1183 Handle NULL in a check constraint name * #1195 Fix cascade in scheduled drop chunks * #1196 Fix UPSERT with prepared statements

Thanks * @spickman for reporting a segfault with ordered append and JOINs * @comicfans for reporting a performance regression with ordered append * @Specter-Y for reporting a segfault with UPSERT and prepared statements * @erthalion submitting a bugfix for a segfault with validating check constraints

Distribution notes This will be the last release to be published for Ubuntu 14.04, since its support window ended in April. To continue receiving upgrades for TimescaleDB, please upgrade to a more recent version of Ubuntu.

The music for this release was The Band's The Last Waltz (40th anniversary edition).