PostgreSQL is a database that has gained a lot of popularity and reputation because of its reliability, data integrity, and correctness. this database is mostly used in web and enterprise applications, and it handles a large amount of data as well as high levels of concurrency.
Sometimes we may have Large data stored in the PostgreSQL database and when accessing we might experience Slow Speeds, There are various techniques we can carry out on this database to optimize. Some of the tips we can utilize are listed below
PostgreSQL Database Optimization Tips
- we can often vacuum the PostgreSQL database to reclaim space and ensure good performance.
- We can use the Command EXPLAIN to show the execution of the query and understand how to optimize slow queries.
- Appropriate use of Indexes on the columns commonly searched will help a lot to speed up the data retrieval.
- Always ensure the Memory allocated to the database is enough to ensure it has enough to work with.
- Partition large tables into smaller and more manageable ones.
- The pg_dump and pg_restore help us to back up and restore the databases.
- Database performance, memory usage, disk I/O, and network traffic Monitoring can be done using the pgAdmin command
- to track the performance of the queries that usually run we use the command pg_stat_statements
- We have tools such as Munin and Nagios that assist us to Monitor PostgreSQL Server
- We can reduce the overhead of opening and closing connections by using a connection Pooler to manage the connection to the database. an example is PgBouncer.
- We can use pg_buffercache to monitor the shared buffer cache usage.
- pg_stat_bgwriter assists in monitoring the amount of data written to disk and the background writer process.
Before making changes in the PostgreSQL production database, we usually recommend implementing it in UAT Environments. Optimization of the PostgreSQL Database will also be dependent on the architecture of infrastructure as well as usage of the application, Always read through the PostgreSQL guides and documentation for detailed guidance
Remember that is important to test the performance of your database in a staging environment before implementing any changes in a production environment. Also, keep in mind that these are general tips, and the specific optimization steps for your database will depend on the architecture and usage of your application. It’s always a good idea to consult the official PostgreSQL documentation and Manual for more detailed guidance.
PostgreSQL commands that can help you optimize your database
VACUUM: helps to reclaim space and clean up deleted or outdated rows.
ANALYZE: Provides statistics about the contents of tables, and the planner will be able to generate efficient queries.
EXPLAIN: this command shows the execution plan for a query, which assists us to identify and optimize slow queries.
CLUSTER: this command re-orders the order of rows in a table based on the values of a given index.
REINDEX: in case the index is fragmented this command will rebuild.
pg_dump: it helps to create a backup of the database.
pg_restore: it restores a database from a backup created by pg_dump.
pg_stat_activity: This command shows us information regarding current connections and queries running on the server.
pg_stat_replication: The command shows us the information regarding the status of replication and replication slots.
pg_buffercache: The command shows us the shared buffer cache, including the state of each buffer and the usage of memory.
pg_repack: this command allows us to re-organize a table and its indexes to find space and improve the database performance.
pg_prewarm: Preloads the cache with data that is to be utilized hence enabling faster reading.
When running the above Commands note that some may require elevated user rights, either as the database owner or super user. Be careful when running these commands so that you do not find that you have tampered with the Data.
Optimized query in PostgreSQL Examples
EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = '123456' AND order_date >= '2023-01-01' ORDER BY order_date DESC;
The Above query retrieves all the rows from the “orders” table where the “customer_id” is “123456” and the “order_date” is on or after “2023-01-01”, and orders the result by “order_date” in descending order.
EXPLAIN ANALYZE the command will help us to get the execution plan and the actual time taken for the query to complete. the query can still be optimized by adding indexes on the column used in the WHERE clause, without the index the query will be much slower but by adding an index on both columns the performance will be highly improved, example is below
CREATE INDEX idx_orders_cust_id_order_date ON orders (customer_id, order_date);
We can optimize our query using Join based on the query needs, When we only need matching rows we use inner join but when we need all the rows regardless if they match or not we use left join
SELECT * FROM orders INNER JOIN customers ON orders.customer_id = customers.customer_id WHERE orders.customer_id = '12345' AND orders.order_date >= '2022-01-01' ORDER BY orders.order_date DESC
For us to Optimize the Query we can implement the use of a temporary table, this is in a situation you are performing a lot of operations on the same data .it will save resources and time by reducing the times the data is being read from the disk.
CREATE TEMP TABLE temp_orders AS SELECT * FROM orders WHERE customer_id = '12345' AND order_date >= '2022-01-01' ORDER BY order_date DESC;
Another way to optimize the database is to use the right data type for the columns, it will help save on space and better performance
ALTER TABLE orders ALTER COLUMN order_date SET DATA TYPE date;
The Choice of optimization usually depends on the complexity of the query and how the data has been structured. Database Monitoring is a continuous process that should be done to keep up to date on performance and be in a position to sort out an issue in case it arises and the right action is taken.
Kipkorir is the founder of advancelearnlinux.com with over 6 years in information technology(Microservices, Analytics, Power BI, and Database Development). He's also passionate about Data analytics and Data Science.