HelixDB Docs

Concept

For the complete documentation index optimized for AI agents, see llms.txt.

Every system makes design choices. Helix Cloud optimizes for durable, cost-effective graph, vector, and text workloads with strong transactional guarantees. These choices have implications.

Excels At

Area	Details
Durable storage	All data persists in object storage. No risk of data loss from local disk failure. Storage capacity scales independently of compute.
Read scalability	Readers auto-scale horizontally. Doubling readers doubles read throughput with no coordination overhead.
Serializable transactions	Every query runs against a stable snapshot with ACID semantics by default.
Mixed graph, vector, and text workloads	Graph traversals, vector search, and full-text search execute in the same transaction, against the same snapshot. No need to stitch together separate systems for those workloads.
Cost efficiency at scale	Object storage is significantly cheaper per GB than local SSDs or in-memory stores. Large datasets remain affordable.
Operational simplicity	Single writer eliminates distributed consensus. No leader election, no split-brain, no quorum management.

Not Optimal For

Area	Details
Sub-millisecond reads	Cache hits are fast, but cold reads require an object storage round trip. Workloads that require guaranteed sub-millisecond latency on every read are better served by in-memory databases.
Ultra-low write latency	Writes incur object storage latency for durability. Write throughput is high, but individual write latency has a floor set by object storage round-trip time.
Exhaustive vector recall	Vector search is approximate (ANN). Applications that require 100% exact nearest neighbor results should use brute-force search on smaller datasets.

Design Choices

Object storage as the system of record. Caches accelerate reads but are not required for correctness. This means cold starts are slower than systems that keep all data on local disk, but durability and cost characteristics are superior. Single writer. Serializing all writes through one process avoids distributed coordination at the cost of write throughput being bounded by a single node. In practice, batching and the high throughput of the writer process make this sufficient for most workloads. Specialized cache paths. Helix maintains separate cache paths for graph data, vector indexes, and text search artifacts. The tradeoff is less flexibility in cache allocation when only one workload dominates. Dynamic query model. Queries travel inline with each request, so there is no deploy step and ad-hoc queries are fully supported. The tradeoff is the small per-request cost of deserializing the query AST on every call.

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Operate

Tradeoffs

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Not Optimal For

Design Choices

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​Not Optimal For

​Design Choices

Excels At

Not Optimal For

Design Choices