How can I improve my INSERT query?
1 Answer
- Also if there are 83 millions rows then the insert generate a lot of the REDO information.
- Also you can use asynchronous writing into your online redo log with commit_wait , commit_logging tips.
- You can set up a job queue to schedule a long-running operation in the background.
- You can use parallel DML.
How can I improve my database?
The Path to Improving Database Performance
- Improve your indexing strategies.
- Steer clear of correlated subqueries and coding loops.
- Keep certain files on separate disks.
- Upgrade your hardware.
- Know who has access.
- Check your connection capacity.
- Get smart about caching.
- More quick tips for boosting database performance.
Which is more efficient load data INSERT?
LOAD DATA (all forms) is more efficient than INSERT because it loads rows in bulk. The server must parse and interpret only one statement, not several. Also, the index needs flushing only after all rows have been processed, rather than after each row. LOAD DATA is more efficient without LOCAL than with it.
How to improve insert performance in PostgreSQL database?
Don’t insert your data row-by-row – instead, try at least hundreds (or thousands) of rows per INSERT. This allows the database to spend less time on connection management, transaction overhead, SQL parsing, etc., and more time on data processing.
When to use foreign keys in PostgreSQL insert?
Sometimes it’s necessary to build foreign keys (FK) from one table to other relational tables. When you have an FK constraint, every INSERT will typically then need to read from your referenced table, which can degrade performance.
Is it possible to speed up queries in PostgreSQL?
Having the right indexes can speed up your queries, but they’re not a silver bullet. Incrementally maintaining indexes with each new row requires additional work.
How to insert data into a PostgreSQL database?
Instead, arrange your bulk load so that data from all servers are inserted in loose timestamp order (e.g., day 1 across all servers in parallel, then day 2 across all servers in parallel, etc.) 11.