My team is migrating a massive relational database to Cloud Spanner for its global consistency and horizontal scaling. However, we are seeing unexpected latency in our write operations. I suspect our primary key design might be causing "hotspotting." Can someone explain the best way to choose keys in Spanner to ensure even distribution across nodes while maintaining query performance?
3 answers
Hotspotting is the number one performance killer in Cloud Spanner. If you use a monotonically increasing value like a timestamp or a sequential integer as your primary key, all writes hit a single split. To fix this, you should use a UUID v4 or a bit-reversed sequence. In our global retail app, we switched from a simple 'order_id' to a hashed version, and our write throughput increased by 500% instantly. Also, make sure you aren't using too many secondary indexes, as each index adds overhead to every write operation across all your regional replicas.
Have you monitored your CPU utilization in the Spanner Console to see if the load is actually imbalanced across your nodes, or if it's a lock contention issue?
Don't forget about interleaved tables. If you have a parent-child relationship, interleaving the child table into the parent can significantly reduce latency for related joins.
Great point, Deborah. Interleaving is a unique Spanner feature that really helps with data locality and making those massive joins much faster in a distributed environment.
Mark, the Introspection tools in the GCP console showed exactly what Barbara mentioned—one node was at 90% while others were at 10%. It was a classic hotspot. We also realized we had a "long-running transaction" that was holding locks for too long during our batch uploads. Fixing the key design was the first step, but optimizing our transaction logic was the second piece of the puzzle.