What is an incremental checkpoint?
An incremental checkpoint is a durable point-in-time capture of index state that lets recovery or startup apply only the changes recorded since that capture — the delta — instead of replaying an entire write-ahead history or rebuilding the structure from raw vectors every time.
How does an incremental checkpoint differ from a full rebuild?
A full rebuild reconstructs the index from source objects or from every commit-log entry ever written. Cost grows with corpus age and size: tens of millions of HNSW mutations can mean minutes of replay on every restart. An incremental checkpoint freezes the compacted result of that history at time T. Later startups load the checkpoint, then replay only logs (or WAL records) with timestamps or sequence numbers after T. Each new checkpoint advances T, so steady-state recovery tracks recent churn, not lifetime churn. The checkpoint is “incremental” in the operational sense — you keep taking newer baselines — even when each file is a full image of the current graph rather than a binary diff format.
Write-ahead logs make checkpoints necessary; checkpoints make logs practical at scale.
How do checkpoints and WALs work together?
The WAL or commit log is the continuous stream of mutations; the checkpoint is a periodic fold of that stream into a loadable baseline. Without checkpoints, safe crash recovery still works but mean-time-to-recovery explodes. Without logs, a checkpoint alone freezes the past and loses acknowledged writes that happened afterward. The healthy loop is: append mutations to the log, occasionally compact logs into a new checkpoint (atomically published), delete or truncate log regions the checkpoint fully covers, repeat. Readers of recovery always compose “latest good checkpoint + remaining delta.” That composition is why destroying a checkpoint after deleting its covered logs is catastrophic — there is nothing left to replay.
Vector indexes and LSM stores both use the idea, with different artifacts.
Where do incremental checkpoints appear in vector databases?
For HNSW, the checkpoint is typically a snapshot file: compacted neighbor state, often checksummed and versioned. Startup loads the newest snapshot, then replays post-snapshot commit-log files — even a fresh snapshot usually leaves at least one newer log to apply. For LSM object and inverted stores, flushing a memtable to an immutable segment plays a related role: once the flush succeeds, the corresponding WAL can be marked complete so future boots skip replaying those records. Segment compaction merges small segments into larger ones, another form of advancing a durable baseline. None of these require rewriting embeddings from scratch; they advance on-disk representations of work already done in memory.
Weaviate’s HNSW snapshots are the clearest incremental-checkpoint story on the vector path.
How does Weaviate implement incremental checkpoints for HNSW?
Weaviate can reconstruct an HNSW graph entirely from commit-log replay, but large indexes made that too slow. Snapshots checkpoint the graph so restarts load the snapshot and only the delta logs afterward — reported speedups on multi-million-object indexes are often an order of magnitude. A commit-log compactor creates new snapshots when worthwhile, atomically renames them into place, and removes covered logs so disk stays proportional to the live index. Compaction keeps final connection state rather than every historical edge edit, so a snapshot is denser than the logs it replaces. Automatic snapshot management in recent versions makes checkpoints part of normal storage, not an optional tuning hobby. Inactive tenants may lag until activation triggers compaction into a current checkpoint. If a snapshot fails checksum validation, restore from backup — unlike a WAL tail, the checkpoint is not truncated back to health.
Operators should treat checkpoint freshness as a recovery-time knob.
What should you monitor about checkpoint lag?
Watch how large the post-snapshot commit-log delta grows between compactions; long gaps mean the next restart pays a bigger replay. Size disk for transient peaks while old checkpoint, merging logs, and new temp snapshot coexist. After upgrades, allow compactors to rewrite checkpoints before judging startup time. Pair checkpoints with tested backups: checkpoints optimize crash restart on intact disks; backups cover lost volumes. Do not manually delete snapshot files while leaving log state inconsistent. Prefer letting the engine advance checkpoints over scheduled “rebuild the whole HNSW from objects” jobs unless a migration guide explicitly recommends reimport.
An incremental checkpoint turns endless log replay into “load baseline, apply recent delta” — the reason Weaviate HNSW snapshots cut recovery from minutes toward seconds. Next, read “What is tombstone deletion?” and “What is deleted-slot reuse?” for how deletes show up in those logs and graphs, or revisit atomic snapshots and WALs if you want the publish and append mechanics behind each checkpoint cycle.