I’m running into some performance issues with my MySQL setup and could really use some advice. Here’s my situation:
I have a Python web application deployed across roughly 75 production servers that all connect to the same MySQL database. The database server itself has 256GB of RAM and is configured to use 85 threads.
Most of our stored data consists of JSON documents and we primarily perform SELECT operations. On a typical day we process around 220 million read queries.
I’ve noticed that certain business workflows are getting sluggish because of database bottlenecks. I want to start tuning things but I’m not sure where to begin.
I tried using some load testing tools and tweaked a few configuration parameters in my test environment but didn’t see much improvement. The challenge is that I can’t really simulate the full production load in my lab setup.
What are the most effective strategies for boosting MySQL read performance at this scale? Any specific configuration changes or indexing approaches you’d recommend?
Given your massive query volume, query caching becomes critical but often gets overlooked. With 220 million reads, even small inefficiencies compound rapidly. I’ve dealt with similar loads and found that partitioning your tables based on access patterns made a substantial difference - especially for JSON data where you’re likely querying specific document ranges or time periods. Your 85 thread configuration might actually be too conservative for read-heavy workloads. Consider increasing max_connections and thread_cache_size since you have 75 servers connecting simultaneously. Also examine your slow query log to identify the worst performers first rather than trying to optimize everything at once. Sometimes a single poorly optimized query can consume disproportionate resources and fixing that gives you the biggest bang for your buck.
At your scale, adding read replicas could significantly alleviate the bottleneck you’re facing with 220 million read queries daily. Consider implementing 3-5 read replicas behind a load balancer for your SELECT operations while keeping write operations on the master. Make sure you’re using MySQL 8.0 for its enhanced JSON functionality, allowing for functional indexes on frequently accessed JSON paths. It’s vital to adjust the innodb_buffer_pool_size to around 70-80% of your 256GB RAM, as many setups underestimate this allocation. Lastly, don’t overlook connection pooling in your application, as managing too many connections from 75 servers can lead to inefficiencies.
honestly sounds like your hitting the limits of single server architecture. with that many servers hammering one db, network latency becomes a huge factor too. have you considered sharding your json data? splitting by user_id or timestamp could distribute the load better than just read replicas.