So here we are at the last of my series of four blogs. If you’ve picked up the other three: Blog 1 of 4: Aggregation, Blog 2 of 4: Checking text within a larger string and Blog 3 of 4: Primary key considerations, thanks for reading! And watch this space, I’m preparing some more, so please check back soon to read those too.
As I said before, I’ve always admired the desire of the PostgreSQL community to share knowledge and expertise. Following in that tradition I’d like to share a few pearls of PostgreSQL wisdom that I’ve picked up over the years as a PostgreSQL developer. Now let’s get started with blog 4.
4 of 4: Moving data around
In my career, I’ve seen different ways of moving data from one table to another. For example, the table where the original data is stored may be a temporary storage that is used later in a process driven by external events. So you need to store the data on event 1, use it on event 2 and remove it from the table where it was stored by event 1. But to determine which data was used in event 2, you need to store it again somewhere else. Typically it might look like this:
FUNCTION event2 (bunch of parameters) 1) select records from data stored in event1 (table1) 2) do some analysis/processing on this data in combination with the parameters 3) insert as 'evidence' the data and outcome for later analysis into table2 4) delete the records from table1 5) return for example a status of the analysis/processing
For me, this raises the following questions:
- Can we improve the subsequent insert and delete?
- What if event2 never comes, what happens to the data of table1?
To answer to question 1, yes we definitely can improve it! As I hinted in my previous blog “Primary Key Considerations” we can use a Common Table Expression “CTE” for both. For the first question we could write a job to move data from table1 to table2 but give an indication that event2 did not occur. When event2 is called, we do a similar move. For the function: “event2” lines 3) and 4) we will have the following structure:
WITH deletions AS ( DELETE FROM table1 t1 WHERE t1.external_identification = parameter_session_id RETURNING * , 'DONE' as event2_status -- how did the data got into table2 , (now() - entry_time) as duration -- indcating the time between events ) INSERT INTO table2 SELECT * FROM deletions
WHERE-clause should be changed to e.g. a date/time based condition. Also the hardcoded
'DONE' as event2_statusand
duration(e.g. leave null) should be changed accordingly.
The reason why you would want to remove this from
table1 is that
event2 needs to be fast and the more records
table1 contains, the slower
event2 will eventually become. Another advantage is that a data analyst only needs to focus on the data in
And what if you needed to retreive only a few records for maintenance? Here again we could put the “CTE” into action. If we wrote it as a select of those few records you are looking for in a big table and use this prepared selection as a base to do another query (DML e.g.), it’s likely the optimiser might make a decision you don’t want it to. It could end up in a join (hash/merge/nested loop) that’s far from ideal. I solved such a situation by using the MATERIALIZED keyword.
WITH lost_in_space AS MATERIALIZED ( SELECT * -- star in heaven ;-) FROM galaxy -- possibly partitioned WHERE ... –- conditions where partitioning is not based upon .. -- .. but result in a small number of records ) SELECT ... FROM table1 JOIN lost_in_space ON (...) WHERE ...
As the size of the “galaxy” increased over time (years), the batch became very slow. By only adding the keyword “MATERIALIZED”, the processing was reduced from hours to minutes. Ta dah!
And there you have it. That’s the end of my run of four short blogs on pearls of PostgreSQL coding wisdom. I hope you enjoyed them. Thanks for reading!
What are other people reading?
Introducing the new release of Cortex and future developments. It took some time and we had some delay but finally the new release of Cortex is there. The delay was mainly caused by use cases given by our clients, which gave us a lot of inspiration to extend and improve the capabilities of Cortex. …
For me, migrating Oracle databases to PostgreSQL is a combination of science and art. Science is designed to be objective and guided by data and art is subjective and heavily influenced by feelings and opinions. Traditionally, art and science are treated as two separate disciplines, but when they are studied together, it becomes clear how one influences the other.
February 9, 2023, the PostgreSQL community has made available the new minor releases of PostgreSQL 11 through 15. For us, this means that based on these new minor releases, we have to start working on compiling the new releases of PostgresPURE. Meanwhile, we have done...