DBMS > CrateDB vs. RocksDB vs. Sphinx vs. TypeDB

System Properties Comparison CrateDB vs. RocksDB vs. Sphinx vs. TypeDB

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Open source search engine for searching in data from different sources, e.g. relational databases

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

Search engine

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Secondary database models

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.73
Rank	#229	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Score	0.81
Rank	#217	Overall
	#19	Graph DBMS
	#99	Relational DBMS

Website

Technical documentation

cratedb.com/docs

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

typedb.com/docs

Developer

Crate

Facebook, Inc.

Sphinx Technologies Inc.

Vaticle

Initial release

2013

2001

2016

Current release

8.11.4, April 2024

3.5.1, February 2023

2.26.3, January 2024

License

Commercial or Open Source

Open Source

BSD

Open Source

GPL version 2, commercial licence available

Open Source

GPL Version 3, commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

C++

Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

C++ API
Java API

Proprietary protocol

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

user defined functions (Javascript)

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

none

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

substituted by the relationship feature

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

unique row identifiers can be used for implementing an optimistic concurrency control strategy

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

rights management via user accounts

yes

at REST API level; other APIs in progress

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

TypeDB is a polymorphic database with a conceptual data model, a strong subtyping...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

TypeDB provides a new level of expressivity, extensibility, interoperability, and...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Life sciences : TypeDB makes working with biological data much easier and accelerates...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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Licensing and pricing models

See CrateDB pricing >
» more

Apache f or language drivers, and AGPL and Commercial for the database server. The...
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