- Accelerate queries at runtime—efficiently prunes ranges of data for reads from Firebolt File Format (F3).
- Defined by you in
CREATE TABLEclause. Only one per table because of physical sort in F3. Updated automatically with incremental ingestion.
- Specify columns frequently used in
WHEREpredicates that drastically filter, in order of filtering effect (highest degree of filtering first). Also specify columns used as join keys.
- Sparse indexes behind the scenes—one entry per data block vs. mapping every search key value like dense index, less I/O and maintenance.
- Work with partitions, pruning data after partitioning.
For more information and examples, see Using primary indexes.
- Accelerate queries with aggregate functions—used by query analyzer instead of scanning table to calculate results, like a materialized view but integrated with F3 format.
- Defined by you on fact tables with
CREATE AGGREGATING INDEX, as many as you want. Built with columns first, like a primary index for pruning, followed by aggregations exactly as they are used in analytics queries.
- Best created on an empty fact table, before first
INSERT. You can use
CREATEon populated fact table (memory-intensive).
- To be used at query runtime, all columns, measures (and aggregations), and join key columns in the query aggregations must in your index definition.
For more information and examples, see Using aggregating indexes.
- Accelerate joins—stored in RAM and used by query optimizer instead of performing the actual join at runtime.
- Defined by you on dimension tables with
CREATE JOIN INDEX, as many as you want. Specify join key column first, and then other dimension columns used in analytics queries.
- Most effective in schema with many-fact-to-one-dimension table relationship, large dimension tables, and small subset of available dimension columns used in join.
- Must have dimension join key column defined with
UNIQUEattribute. Join key column must have unique values for accurate results.