They knew they had created an index, and were curious as to why the index was not being used. Our next data point to gather was information about the index itself, and it turned out that they had created their index like so: CREATE INDEX idx_org_dept ON org ((info -> 'dept'::text) -> 'name'::text)) Postgres=# explain SELECT * FROM org where 'aa'::text IN (SELECT jsonb_array_elements(info -> 'dept') -> 'name') We got right to work to help them out, and our first stone to turn over was to have them send us their EXPLAIN ANALYZE output for the query, which yielded:

They were seeing a slow performance in their development environments and were understandably worried about the impact that they’d see if they went to production with poor query performance.

We recently received a request from one of our customers, concerned about a slow query on one of their JSON columns. When it comes to dealing with poor database and query performance, it’s a daunting task to venture into the dark cavern of query planning and optimization, but fear not! EXPLAIN is our friend in those dark and lonely places. Sure, there’s Slack and all manner of collaboration tools, but it’s not quite the same as walking up to someone’s cubicle and getting a second pair of eyes to look at a problem, not to mention that our co-workers might be busy trying to juggle deadlines and unruly kids in the home. There are no conditions for this type of join,Īnd it just returns all the possible pairs.With many people working from home these days because of the coronavirus pandemic, it can be a little challenging to get help from a colleague remotely. The simplest type of join is a CROSS JOIN. When an unpaired row is returned, the attributes from the other table are set to NULL. FULL (All rows from both relations appear at least once)Ī join that is not OUTER is INNER (only rows that get paired are returned).RIGHT (All rows from the right relation appear at least once).LEFT (All rows from the left relation appear at least once).Rows that do not have any matches can still be returned if an OUTER join is specified. Often this is a predicate, but there are other implied rules that may be specified. The relations are referred to as the left and right sides of the joinīased on how they are written in the join clause.Įach result row has the columns from both relations.Ī join uses a rule to match pairs of rows from each relation. Joins are a fundamental relational operation used to connect two tables or relations horizontally. key ) - select a 10% sample from a table SELECT * FROM table_name TABLESAMPLE 10 % - select a sample of 10 rows from a table SELECT * FROM table_name TABLESAMPLE 10 ROWS - use the FROM-first syntax with WHERE clause and aggregation FROM range ( 100 ) AS t ( i ) SELECT sum ( t. i FROM range ( 100 ) AS t ( i ) - select all columns from the CSV file called 'test.csv' SELECT * FROM 'test.csv' - select all columns from a subquery SELECT * FROM ( SELECT * FROM table_name ) - select the entire row of the table as a struct SELECT t FROM t - select the entire row of the subquery as a struct (i.e., a single column) SELECT t FROM ( SELECT unnest ( generate_series ( 41, 43 )) AS x, 'hello' AS y ) t - join two tables together SELECT * FROM table_name JOIN other_table ON ( table_name. table_name - select the column 'i' from the table function 'range', where the first column of the range function is renamed to 'i' SELECT t. select all columns from the table called 'table_name' SELECT * FROM table_name - select all columns from the table called 'table_name' using the FROM-first syntax FROM table_name SELECT * - select all columns using the FROM-first syntax and omitting the SELECT clause FROM table_name - select all columns from the table called 'table_name' in the schema 'schema_name SELECT * FROM schema_name.

Postgres join types