50 points by golfer123 1 year ago flag hide 11 comments
postgres_pro 1 year ago next
Use advanced features like partitioning, indexing, and query optimization. Create appropriate indexes, especially for the columns used in the WHERE and JOIN conditions. Check the query planner for your specific queries to see how PostgreSQL is executing them and optimize accordingly.
dba_newbie 1 year ago next
Great! Some background info: my application displays real-time analytics and needs to run complex queries with a high frequency. Could you please elaborate on partitioning?
postgres_pro 1 year ago next
Partitioning allows you to split a large table into smaller pieces to improve performance and manageability. Range, List, and Hash are common partitioning methods. Depending on your data, you could use time range partitioning, e.g., by months, to store and query data more efficiently.
dba_newbie 1 year ago next
Thanks! That sounds useful. Should I partition existing tables or add partitioning at table creation?
postgres_pro 1 year ago next
Partitioning on an existing table requires an exclusive table lock, so it's recommended to create tables with partitions. This can be done by defining a partitioned table using 'CREATE TABLE' and adding partitions with 'CREATE TABLE AND INHERIT'. Follow tutorials on PostgreSQL's official site for detailed steps.
datawrangler 1 year ago prev next
For query optimization, vacuum frequently, use the auto-explain extension for EXPLAIN ANALYZE output, and check for missing indexes or outdated statistics.
optimizedb_consultant 1 year ago prev next
Another point is using connection poolers and load balancers to distribute read queries across multiple replicas and allocate separate connections to your analytics application. Follow PostgreSQL best practices for this.
bestpracticesbob 1 year ago next
Could you share some open-source tools for PostgreSQL connection pooling and load balancing?
optimizedb_consultant 1 year ago next
PgBouncer for lightweight connection pooling, Pgpool-II for more advanced features like load balancing and parallel query processing, and HAProxy for more generic TCP/HTTP load balancing.
rookie_dev 1 year ago next
We're using a single hard drive in one server. Could that be a performance bottleneck for real-time analytics and what can we do to mitigate it?
storage_guru 1 year ago next
Yes, a single hard drive can be a bottleneck. You'll likely encounter I/O issues under heavy load. To mitigate this, consider SSDs instead of HDDs. Implement RAID for data redundancy and striping for increasing throughput. For even better performance, consider using multiple SSDs and implementing a caching strategy with tools like PostgreSQL's pg_buffercache or the Linux-based tools like 'lrucache', 'diskcache', or 'mdcache'.