Right-sizing PostgreSQL cloud instances

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I'll help you evaluate your Cloud SQL Postgres sizing. To make a good recommendation, I need to understand your workload better.

I can help you analyze this. To give you the best recommendation, I need a bit more context about your workload patterns: **Key questions to consider:** 1. **Spike frequency & duration**: How often do the 85% spikes occur, and how long do they last? (daily/hourly, seconds/minutes) 2. **Performance impact**: During those spikes, are you seeing: - Query latency increases? - Connection pool saturation? - Timeouts or slow responses? 3. **Spike patterns**: Are the spikes: - Predictable (e.g., daily batch jobs, peak business hours)? - Random/unpredictable? - Growing in frequency over time? 4. **Growth trajectory**: Is your workload growing, stable, or declining? **Initial assessment based on what you've shared:** - **45% average CPU** → Generally healthy, not over-provisioned - **85% spikes** → Concerning if frequent or prolonged **General recommendations:** - **Keep current size** if spikes are brief (<5 min), infrequent (few times/day), and cause no user-facing issues - **Scale up** if: - Spikes cause performance degradation - Spikes are increasing in frequency - You're approaching 85%+ regularly (less than 15% headroom is risky) - You have critical workloads requiring consistent low latency - **Scale down** only if average drops below 30% AND spikes stay below 70% **Better metrics to check:** Could you share more details about your workload patterns? Also, it would help to look at: - Memory utilization - Connection count - Disk I/O metrics - Query performance during spike periods Would you like me to help you write a script to analyze Cloud SQL monitoring data or set up better alerting?

Look at `postgresql-active-queries` and `postgresql-backends` during those 85% CPU spike windows — if query counts aren't spiking and there's no queuing, your current provisioning is fine and those bursts are normal. CPU utilization alone doesn't tell you if you're under-provisioned; what matters is whether queries are slowing down or backing up during spikes. In Cloud SQL, brief spikes to 85% with good query performance indicate healthy burst behavior, not a need to scale up.

Examine `buffer-hit` ratio — it should be above 99% for typical OLTP workloads and above 95% for mixed workloads. A low hit ratio suggests either insufficient shared_buffers (see `insufficient-shared-buffers-cache`) or a working set larger than available memory. However, check your shared_buffers configuration first: Cloud SQL defaults are often conservative, and increasing shared_buffers to 25% of RAM can dramatically improve cache efficiency without scaling up the instance.

Check `postgresql-temp-files` and `postgresql-temp-bytes` — any non-zero values mean queries with sorts, aggregations, or hash joins are spilling to disk because work_mem is too low (typically defaults to just 4MB). This causes massive slowdowns and makes your instance appear CPU/IO-bound when the real issue is memory configuration. As noted in `work-mem-disk-spill` and `insufficient-work-mem`, increasing work_mem to 16-64MB for OLTP or 128MB+ for analytics can eliminate this bottleneck without resizing the instance.

Review `postgresql-blk-read-time` and `postgresql-blk-write-time` during your CPU spike windows — high block read times (>5-10ms average) indicate I/O bottleneck rather than CPU constraint. If I/O wait is low during CPU spikes, you're truly CPU-bound and might benefit from more vCPUs. If I/O wait is high, the problem is disk throughput or inefficient queries causing excessive reads, not insufficient CPU, and scaling up CPU cores won't help much.

Look at `postgresql-sequential-scans` on tables over 1M rows (correlate with `postgresql-database-size` to identify large tables) — frequent sequential scans generate unnecessary CPU and I/O load that makes your instance appear under-provisioned when the real issue is missing indexes. High sequential scan counts during your CPU spikes suggest that adding indexes could eliminate the need to scale up. This is especially common after schema changes or with poorly optimized application queries.

If shared_buffers is set above 40% of total RAM, you're likely experiencing double buffering (see `double-buffering-shared-buffers-oversized`) where the same data pages exist in both PostgreSQL's cache and the OS page cache, wasting memory while other hot pages get evicted. This makes the instance appear memory-constrained when it's actually misconfigured. Check your Cloud SQL shared_buffers parameter — if it's over 40%, reduce it to 25% and monitor whether cache performance improves without scaling up.