![]() ![]() Orders from the United States will be stored in Partition 1 Suppose we want to partition the table into two partitions based on the region column: In this partitioning, the table is partitioned based on a list of values in a specified column. There are three types of partitioning supported in PostgreSQL. Type of partitioning supported in PostgreSQL When queries or updates access a large percentage of a single partition, performance can be improved by using a sequential scan of that partition instead of using an index, which would require random-access reads scattered across the whole table. Moreover, these commands completely bypass the overhead associated with the "VACUUM" the operation, which can be a major performance bottleneck in the case of a bulk "DELETE" operation.Īrchival: Infrequently accessed data or older data can be migrated to different storage Improved query performance: As PGSQL query planner knows which all partitions to targetĭropping an individual partition using the "DROP TABLE" command or detaching a partition using "ALTER TABLE DETACH PARTITION" is much faster compared to performing a bulk delete operation. Now that we understand the use cases and scenarios where partitioning can be helpful, let's list the benefits of partitioning: ![]() By partitioning your data by date as discussed above, you could easily delete or archive old partitions that are no longer needed, without affecting the rest of the data. If your order data table goes back several years, you might not need to keep all of that data readily available in your active database. And when you query the table, the system knows which partition to target based on the date range you specify in the query thus limiting the search scans. ![]() For example, you could create a new partition for orders placed in January, February, March, and so on. ![]() To make things more efficient, you could partition the table by date, creating separate partitions for each month of orders. This can lead to slower query response times and decreased database performance. In such a scenario, the queries will take longer to execute as they have to scan through the entire order table to retrieve/update the relevant data. In the use case mentioned above, suppose you have a query pattern where you need to fetch or update all the orders from a specific month or the current month. As your user base grows and you start processing more orders, the table becomes very large and it takes longer and longer to query update, or delete the data.īased on this scenario let's understand the use case in retrieving, updating, and deleting the data. The table has columns for the customer name, order date, order ID, product ID, and product quantity etc. Let's say you have a large database table that stores customer orders for an e-commerce website. This similar book organization concept can be applied to real-world data stored in our PostgreSQL table. For example, you might keep all of your maths books in one section, and all of your history books in another section. Instead of dumping all of your books together in one big pile, you might sort them by category or author and store them in different sections(Partitions) of a bookshelf(Table). Partitioning can be thought of as, organizing a book collection in a book cabinet. It is a technique used to organize large tables into smaller, more manageable pieces. "Partitioning refers to splitting what is logically one large table into smaller physical pieces" - PostgreSQL. ![]()
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