PostgreSQL 12 enables generated columns. Is there an overhead of using a generated column? If yes what is it?
Is table locked for read/write when such column is added?
Related
I tried to load the redshift table but failed on one column- The length of the data column 'column_description'is longer than the length defined in the table. Table: 65535, Data: 86555.
I tried to increase the length of column in RS table, looks like 65535 is the max length RS supports.
Do we have any alternatives to store value in Redshift?
The answer is that Redshift doesn't support anything larger and that one shouldn't store large artifacts in an analytic database. If you are using Redshift for its analytic powers to find specific artifacts (images, files, etc) then these should be stored in S3 and the object key (pointer) should be stored in redshift.
I have a table with more than 1 million records and table is growing everyday.I need to update two columns of that table everyday. What is the best way either to truncate the table and insert or update row wise?
example :-
today
userid activitycount
1 18
tomorrow
userid activitycount
1 19
Make sure that the fillfactor of the table is less than 50 and that the updated columns are not indexed.
Then the updates will become HOT updates that don't need to modify any index, and autovacuum will make sure that tomorrow's update will find enough free space.
The disadvantage is the bloat you have with this method, but you don't need to create new tables and rename them, which may be problematic with concurrent transactions.
Is faster to truncate table and copy it again. On Postgres docs you can learn how to do to populate tables with big datasets:
This section contains some suggestions on how to make this process as efficient as possible.
Use Copy: Use COPY to load all the rows in one command, instead of using a series of INSERT commands.
Remove Indexes: if you need indexes, just create indexes when data is already inserted.
Remove Foreign Key Constraints: Create constraints when data is already inserted.
Tuning Postgres installation: maintenance_work_mem, max_wal_size, Disable WAL Archival and Streaming Replication, ...
We have a table with nearly 2 billion events recorded. As per our data model, each event is uniquely identified with 4 columns combined primary key. Excluding the primary key, there are 5 B-tree indexes each on single different columns. So totally 6 B-tree indexes.
The events recorded span for years and now we need to remove the data older than 1 year.
We have a time column with long values recorded for each event. And we use the following query,
delete from events where ctid = any ( array (select ctid from events where time < 1517423400000 limit 10000) )
Does the indices gets updated?
During testing, it didn't.
After insertion,
total_table_size - 27893760
table_size - 7659520
index_size - 20209664
After deletion,
total_table_size - 20226048
table_size - 0
index_size - 20209664
Reindex can be done
Command: REINDEX
Description: rebuild indexes
Syntax:
REINDEX { INDEX | TABLE | DATABASE | SYSTEM } name [ FORCE ]
Considering #a_horse_with_no_name method is the good solution.
What we had:
Postgres version 9.4.
1 table with 2 billion rows with 21 columns (all bigint) and 5 columns combined primary key and 5 individual column indices with date spanning 2 years.
It looks similar to time-series data with a time column containing UNIX timestamp except that its analytics project, so time is not at an ordered increase. The table was insert and select only (most select queries use aggregate functions).
What we need: Our data span is 6 months and need to remove the old data.
What we did (with less knowledge on Postgres internals):
Delete rows at 10000 batch rate.
At inital, the delete was so fast taking ms, as the bloat increased each batch delete increased to nearly 10s. Then autovacuum got triggered and it ran for almost 3 months. The insert rate was high and each batch delete has increased the WAL size too. Poor stats in the table made the current queries so slow that they ran for minutes and hours.
So we decided to go for Partitioning. Using Table Inheritance in 9.4, we implemented it.
Note: Postgres has Declarative Partitioning from version 10, which handles most manual work needed in partitioning using Table Inheritance.
Please go through the official docs as they have clear explanation.
Simplified and how we implemented it:
Create parent table
Create child table inheriting it with check constraints. (We had monthly partitions and created using schedular)
Indexes are need to be created separately for each child table
To drop old data, just drop the table, so vacuum is not needed and will be instant.
Make sure to have the postgres property constraint_exclusion to partition.
VACUUM ANALYZE the old partition after started inserting in the new partition. (In our case, it helped the query planner to use Index-Only scan instead of Seq. scan)
Using Triggers as mentioned in the docs may make the inserts slower, so we deviated from it, as we partitioned based on time column, we calculated the table name at application level based on time value before every insert and it didn't affect the insert rate for us.
Also read other caveats mentioned there.
There are set of values to update. Example: table t1 has column c1 which has to be updated from 1 to x. There are around 300 such sets available in a file and around 15 such tables with over 100k of records.
What is the optimal way of doing this?
Approaches I can think of are:
individual update statement for old with new value in all tables
programmatically read the file and create dynamic update statement
using merge into table syntax
In one of the tables the column is primary key with tables referencing them as foreign key
OS: RHEL 7.2
PostgreSQL Version 9.6
I want toast to compress data. The average record length is around 500 Bytes in my tables. Although the columns show storage as extended, yet no compression is happening. Hence I want to modify toast_tuple_threshold to 500 bytes. Which file holds this value? And do we need to modify any other parameter?
I tried
ALTER TABLE tablename SET (TOAST_TUPLE_TARGET = 128);