#Ilike redshift manual#
Tip 1: Don’t Run Manual VACUUM or ANALYZE Without Reason In this article, we will share a few best practices for VACUUM and ANALYZE. However, they are often confused about running these processes manually or setting the optimal values for the configuration parameters. Fortunately, DBAs don’t have to worry much about their internals. ANALYZE – either run manually by the DBA or automatically by PostgreSQL after an autovacuum – ensures the statistics are up-to-date.Īlthough they sound relatively straightforward, behind-the-scenes, vacuuming, and analyzing are two complex processes. As rows are inserted, deleted, and updated in a database, the column statistics also change. PostgreSQL query engine uses these statistics to find the best query plan. When a vacuum process runs, the space occupied by these dead tuples is marked reusable by other tuples.Īn “analyze” operation does what its name says – it analyzes the contents of a database’s tables and collects statistics about the distribution of values in each column of every table. PostgreSQL doesn’t physically remove the old row from the table but puts a “marker” on it so that queries don’t return that row. A dead tuple is created when a record is either deleted or updated (a delete followed by an insert). Left join temp_staging_tables_2 t2 on t1.tableid = t2.VACUUM and ANALYZE are the two most important PostgreSQL database maintenance operations.Ī vacuum is used for recovering space occupied by “dead tuples” in a table. THEN 1 ELSE t2.min_blocks_per_slice END as pct_skew_across_slices,ĬAST(100 * t2.slice_count AS FLOAT) / (SELECT COUNT(*) FROM STV_SLICES) as pct_slices_populated CASE WHEN (t2.min_blocks_per_slice = 0) Temp_staging_tables_2 as (SELECT tableid, MIN(c) as min_blocks_per_slice, MAX(c) as max_blocks_per_slice, COUNT(DISTINCT slice) as slice_countįROM (SELECT t.tableid, slice, COUNT(*) AS cįROM temp_staging_tables_1 t, STV_BLOCKLIST bġ00 * CAST(t2.max_blocks_per_slice - t2.min_blocks_per_slice AS FLOAT) (SELECT COUNT(*) FROM STV_BLOCKLIST b WHERE b.tbl = c.oid) as size_in_megabytesĪND nspname NOT IN ('pg_catalog', 'pg_toast', 'information_schema') With temp_staging_tables_1 as (SELECT n.nspname as schemaname, c.relname as tablename, c.oid as tableid, Here is a view of that script for analyzing table design in case anyone wants it. To learn more about optimizing performance in Redshift, check out this blog post by one of our analysts. (You may be able to specify a SORT ONLY VACUUM in order to save time)
This is because newly added rows will reside, at least temporarily, in a separate region on the disk.
#Ilike redshift update#
The script checks if you’ve got sort keys, distribution keys, and column compression dialed in.Īs you update tables, it’s good practice to vacuum. Redshift has a nice page with a script that you can run to analyze your table design.
#Ilike redshift how to#
Redshift has a page on how to best choose sort and distribution setups depending on data configuration. For tables that join into other tables (such as dimension tables in the star schema world), it is best to set the primary key to be the distribution key.
This helps Redshift speed up queries that are sorted or limited by that column.įor tables that join in other tables with a great deal of frequency (such as fact tables in the star schema world), it’s best to set your distribution key to be the foreign key with the greatest cardinality. Typically, the primary timestamp or date field of any given table will be the best candidate for your sortkey.
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