Can a BRIN index be used instead of Table Partitioning in Postgres?

Question

Given an append only table with a GUID and a timestamp (and a bunch of other columns) which can grow by ~50Mio entries / year. I want to keep the number of indices low and just use a normal (B-Tree) index on data which is queried by id. Additionally, data will be queried by the timestamp for analysis. And for this I wanted to partition the table on a monthly or yearly base.

But since my table is append only and the timestamp will be quite continuous (maybe not to a 100% but generally I won't deliberately post items from the past) shouldn't a BRIN index basically give me the same functionality as table partitioning without the trouble of manually creating sub tables and instead of triggers? (at least that's how partitioning was described on the documentation: https://www.postgresql.org/docs/current/static/ddl-partitioning.html )

Update

Maybe some additional context - I thought about partitioning it by year or month since that is the only predicable thing. In both cases I'll have to perform queries on this table which will not include any timestamp information so for those queries I'd still be using the btree indices and in case of a pratition postgresql wouldn't be able to infer from the query which smaller table (and index) could be used.

Answer 1

In this particular scenario, the key distinction between employing BRIN (Block Range INdexes) and table partitioning lies in their impact on table size. Table partitioning effectively reduces the overall size of a table by dividing it into smaller, more manageable segments. This contrasts with BRIN, which does not alter the table size. The significance of table size reduction becomes particularly evident when considering the growth of indexes, especially btree indexes. In cases where indexes swell beyond the capacity of memory, performance issues may arise, not only for btrees but for other types of indexes as well.

Moreover, the management and operational aspects of databases also differ markedly between the two approaches. Large tables can significantly slow down maintenance tasks such as VACUUM and ANALYZE. These tasks, along with schema modification operations, might not only run slower but also require longer locks on the tables, which can impede the performance of concurrent operations and affect the overall efficiency of the database system.

On the other side of the coin, while table partitioning does alleviate some of these challenges by reducing table size and potentially improving performance, BRIN indexes would still be necessary even with partitioning. This is because the granularity of filtering provided by BRIN indexes is much finer than the coarse filtering achieved through partition-based constraint exclusion. In essence, BRIN indexes complement table partitioning by offering an efficient way to navigate the partitioned data, ensuring that queries are executed with optimal precision and speed.

Thus, while both BRIN and table partitioning offer significant benefits in managing large datasets, they serve different purposes and are often used in conjunction to balance the database's performance, manageability, and operational efficiency.