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)
![ilike redshift ilike redshift](https://www.redshift3d.com/cms/ce_image/made/cms/assets/user_gallery/Image3_1200_900.jpg)
(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.
![ilike redshift ilike redshift](https://i.imgur.com/QNhBa3h.jpg)
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. If a more general purpose approach is needed then sample data of the concerning corner-cases will be needed.Typically, the primary timestamp or date field of any given table will be the best candidate for your sortkey. Select replace(replace(replace(column_name, ': True', ': true'), So as an untested example of what this will look like: Your case statement is in error as it will only allow for one keyword to be replaced per row of text which isn’t true for you example data. If this is all true then the change should be straight forward to make. Also it is likely, but you will need to confirm, that a colon won’t appear in a data value followed by one of these keywords AND that these keywords will follow a colon by only one space character. Since this is json-like text a few assumptions can be made – that these keywords will always follow a colon (not in a list) and that colons won’t appear in a field name. Please let me know what would be the right way to use the regexp_replace ? When column_name ilike ('%False%')then regexp_replace(replace(column_name, '\'', '"'), 'False', 'false') When column_name ilike ('%True%') then regexp_replace(replace(column_name, '\'', '"'), 'True', 'true') (in this case for the lead key and not the name and path of the company).
![ilike redshift ilike redshift](https://static.skillshare.com/uploads/project/317864/cover_800_3233e9ef596ef436d7477152f1e6ca6a.png)
Now, I want to replace the False/True/None values occurrences across the entire JSON array wherever there is a True, False, None values. Bearing in my mind my JSON array has some keys whose values also contain the name True.
![ilike redshift ilike redshift](https://www.redshift3d.com/cms/ce_image/made/cms/assets/user_gallery/Image4_1200_900.jpg)
Now since JSON only accepts lower case values and don’t accept None, I want to convert those values into lower case true/false/null values. I have a JSON array in my redshift table which has True/False/None values.