The article referenced below assumes a worst case scenario for
bulk-loading with hash partitioned tables. It assumes that the values
being inserted are in strict ascending or descending order with no gaps
(like a sequence number incrementing by 1), thereby ensuring every
partition is hit in order before repeating the process. If the values
being inserted are not strictly sequential with no gaps, then the
performance is much better. Obviously, what part of the tables and
indexes are in memory has a lot to do with it as well.
Regards,
Michael Vitale
Imre Samu wrote on 6/5/2020 7:48 AM:
> "Bulk loads ...",
As I see - There is an interesting bulkload benchmark:
"How Bulkload performance is affected by table partitioning in
PostgreSQL" by Beena Emerson (Enterprisedb, December 4, 2019 )
/SUMMARY: This article covers how benchmark tests can be used to
demonstrate the effect of table partitioning on performance. Tests
using range- and hash-partitioned tables are compared and the reasons
for their different results are explained:
1. Range partitions
2. Hash partitions
3. Combination graphs
4. Explaining the behavior
5. Conclusion/
/
/
/"For the hash-partitioned table, the first value is inserted in the
first partition, the second number in the second partition and so on
till all the partitions are reached before it loops back to the first
partition again until all the data is exhausted. Thus it exhibits the
worst-case scenario where the partition is repeatedly switched for
every value inserted. As a result, the number of times the partition
is switched in a range-partitioned table is equal to the number of
partitions, while in a hash-partitioned table, the number of times the
partition has switched is equal to the amount of data being inserted.
This causes the massive difference in timing for the two partition
types."/
https://www.enterprisedb.com/postgres-tutorials/how-bulkload-performance-affected-table-partitioning-postgresql
Regards,
Imre
