Blog
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What to monitor on a vector database
The handful of signals that predict an incident, the ones that only confirm one, and why recall needs a synthetic probe rather than a metric.
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When your vector database gets slow
A diagnosis order for rising query latency: read the percentile shape first, then memory, filters, segments, tombstones, and search parameters.
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Backing up a vector database
Snapshots, what they miss, and why a vector-store backup is only real once you've restored it and run a recall check against a known query set.
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Reindexing without downtime
Embedding model changed, so the whole index has to be rebuilt. Four strategies, what each costs in memory and complexity, and how to roll back.
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Sizing a vector index in memory
Why your index needs several times the RAM of the raw vectors, which terms drive the total, and how to estimate before you provision.
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Capacity headroom for a growing index
Vector indexes don't degrade gradually as they fill — they hit a cliff. How to set a headroom policy and a projection that pages you weeks early.
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Bulk loading a collection that's already serving
A backfill through the live write path will degrade queries and blow out the segment count. How to stage a large load and cut over without downtime.
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When ingest outruns indexing
Writes are accepted, the queue never drains, and fresh records are searched by brute force. How to spot indexing lag and how to apply backpressure.
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Why deleting vectors doesn't free memory
You deleted a third of the collection and resident memory didn't move. Where tombstones live, what reclaims them, and how to measure the dead fraction.
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Compaction, and when to force it
Segment count is a direct multiplier on query work. How to tell if background compaction is keeping up, when to trigger it by hand, and what it costs.
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Running a vector index from disk
The index outgrew RAM and buying more isn't an option. What changes when the structure lives on disk, and how to tell if your storage can take it.
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When an index build fails partway
The build OOMs near the end, the pod restarts, and it begins the same build again. How to break the loop and what state the collection is actually in.
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Partitioning by the filter you always use
One collection, one filter on every query, and the filter is doing the work an index boundary should. How to pick a partition key and migrate onto it.
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Enabling vector compression on a live collection
Compression needs a rebuild and costs recall you have to measure, not assume. A rollout that gives you the number before you commit, and a way back.
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Archiving vectors nobody searches
Most collections carry records that have never been returned to anyone. How to find them, move them out, and keep the ability to bring them back.
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Splitting a collection across shards
Sharding is the mitigation with the longest lead time, so it has to be started before you need it. How to choose a shard key and cut over safely.
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Upgrading a vector database engine
The upgrade that goes wrong changes result quality, not availability. What to check before, how to stage it, and why your old snapshots may stop working.
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Failing over to a vector store replica
A replica that has never served traffic is not a replica. What replication does and doesn't cover for a vector index, and how to rehearse the failover.
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Warming a vector index after a restart
The first minutes after every restart are the slowest your service ever gets. What is actually cold, and how to warm it before traffic arrives.
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Changing a collection schema after it has data
Some schema changes are a config edit, some are a full rebuild, and the engine will not always tell you which. How to find out before you promise a date.
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Isolating tenants in one vector store
One large customer's traffic is degrading everyone else's latency, and one tenant's growth is your capacity plan. Isolation options and what each costs.
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Query timeouts and retry storms
A timeout that fires plus a client that retries turns a slow vector index into an unavailable one. How to set both so load is shed, not amplified.
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Auditing a collection against your system of record
The index and the source of truth drift apart quietly: orphaned vectors, missing chunks, stale copies. How to run a reconciliation and what to do with it.
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Load testing a vector database before it matters
A load test with random query vectors measures nothing useful. How to build a realistic query mix, and which failure points a test should actually find.
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Catching an embedding model change nobody announced
Query vectors from a new model version searched against an index built with the old one. Nothing errors, recall falls, and only a probe will tell you.