Compare CrateDB

How is CrateDB different from other databases in the market?

Overview MongoDB TimescaleDB InfluxDB Azure TSI

There is no one-size-fits-all when it comes to databases: the ideal database will be determined by your use case and dataset. The characteristics of CrateDB make it optimal for real-time use-cases with high data volumes, data variety, and heavy load.

Analytics/AI/ML use-case

CrateDB

	High data volume			Unlimited Scalability
	Different types of data			Versatile data model
	Concurrent users			High concurrency performance
	Real-time response			Dynamic architecture
	Frequent changes			Easy integration
	Efficiency as priority			Cost efficiency
	Need for versatility			Run in any cloud, EDGE and on premises

High data volume		Unlimited Scalability
Different types of data		Versatile data model
Concurrent users		High concurrency performance
Real-time response		Dynamic architecture
Frequent changes		Easy integration
Efficiency as priority		Cost efficiency
Need for versatility		Run in any cloud, EDGE and on premises

CrateDB is neither a relational database, nor a NoSQL database, nor a time-series database; instead, it offers the best of these worlds. CrateDB is a distributed database built on a NoSQL foundation, but fully accessible through SQL.

CrateDB is able to work with large datasets without losing accuracy, keeping things simple and efficient. And due to its real-time features, indexing, and schemas, CrateDB is ideal for handling huge amounts of time-series data.

	Traditional RDBMS	NoSQL	Time-series databases	CrateDB
High data ingestion
High data versatility
Real-time performance
Simple scalability
SQL access

CrateDB VS
Traditional RDBMS

Traditional relational databases can be an excellent choice for a use case without huge data volumes or high scalability needs. But they are insufficient for large data volume applications demanding horizontal scalability and the real-time processing of massive datasets.

	Traditional RDBMS	CrateDB
Accessibility	ANSI SQL	ANSI SQL
Core architecture	Monolithic or master/slave	Shared-nothing nodes
Data model	Rigid schemas of tabular data	Dynamic schema, structured & unstructured data
Consistency model	ACID	Eventually consistent
Scalability model	Vertical scaling	Horizontal scalability
Partitioning	Manual	Built-in
Full-text search	Not built-in	Built-in (Lucene indexing)
Microservices architecture	Compatible	Ideal

WHITE PAPER

CrateDB vs PostgreSQL

In a benchmark querying 312 million rows of sensor readings, CrateDB provided 33x better price-performance than PostgreSQL.

Download the benchmark

CrateDB vs NoSQL

NoSQL databases like MongoDB are not the best choice if you are dealing with huge volumes of time-series data that need to be queried in real-time. NoSQL databases can get very expensive for large data volume use cases at scale, consuming unnecessarily large memory and storage capacity. At the same time, you must abandon the power and convenience of SQL.

WHITE PAPER

Comparing databases for an Industrial IoT use-case

In this blogpost we talk about our experience as developers working with MongoDB, TimescaleDB and InfluxDB, discussing the pros and cons of every database for large-scale IoT.

Read our blog post

CrateDB vs time-series databases

Time series databases like InfluxDB or TimescaleDB are unable to handle many real-time applications by themselves—if they require metadata operations, for example. They often need to work in combination with an RDBMS, duplicating maintenance efforts and costs. Besides, their performance is usually optimized for single-node processes.