The Guide for Time Series Data Projects is out.

Download now
Skip to content

Compare CrateDB with InfluxDB

How is CrateDB different from other databases in the market?

InfluxDB is a database specialized in time series, with excellent performance for single-node operations. But InfluxDB was not built to handle the scalability, adaptability, and high-concurrency performance required by many large data volume use cases. InfluxDB was born as a single-server database; its distributed functionality is not mature, and its response to high-cardinality queries is significantly slower than CrateDB.

CrateDB vs InfluxDB: Benchmark

Unlike InfluxDB, CrateDB was designed to optimize the use of resources under multiple clients, providing millisecond responses even while performing writes. It features full horizontal scaling, a shared-nothing architecture, and automatic data rebalancing. To scale, just add more nodes to the cluster. CrateDB does the rest automatically.

Cluster type Shared-nothing, two cluster architecture ( meta nodes & data nodes) Shared-nothing
Scalability Limited Full horizontal scalability
Data replication At table level At table level
On-disk Compression Type dependent LZF
Open-source MIT License Apache 2.0
Access language Flux ANSI SQL
Schemas None Dynamic
Columnar indexing No Yes
Aggregation queries Yes Yes
JOINs No Full
Full-text search No Yes (Lucene powered)

Besides, InfluxDB uses its own language, Flux. This implies that the only source available for support is the InfluxDB documentation. InfluxDB’s data model is non-relational, and it doesn’t support text search, log search, or analytics. On the contrary, CrateDB is fully accessible by SQL. And CrateDB schemas are flexible, allowing the user to add columns at any point without slowing performance or downtime.

InfluxDB can be an excellent option for single-node time-series workloads—but for real-time applications with high volumes, data variety, and heavy load, CrateDB is a better choice, due to its distributed nature, its SQL access, and its unparalleled cost-efficiency. Its indexing structure is perfectly suited to handle huge volumes of time-series data together with metadata. With CrateDB, you will only need one database to handle your workload.