DBMS > CrateDB vs. Dragonfly vs. Hypertable vs. XTDB

System Properties Comparison CrateDB vs. Dragonfly vs. Hypertable vs. XTDB

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Distributed Database based on Lucene

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

An open source BigTable implementation based on distributed file systems such as Hadoop

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

Wide column store

Document store

Secondary database models

Relational DBMS

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

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

cratedb.com

github.com/dragonflydb/dragonfly
www.dragonflydb.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cratedb.com/docs

www.dragonflydb.io/docs

www.xtdb.com/docs

Developer

Crate

DragonflyDB team and community contributors

Hypertable Inc.

Juxt Ltd.

Initial release

2013

2023

2009

2019

Current release

1.0, March 2023

0.9.8.11, March 2016

1.19, September 2021

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

GNU version 3. Commercial license available

Open Source

MIT License

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++

Clojure

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Linux
OS X
Windows

an inofficial Windows port is available

All OS with a Java 8 (and higher) VM
Linux

Data scheme

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

scheme-free

schema-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

yes, extensible-data-notation format

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

limited SQL, making use of Apache Calcite

APIs and other access methods

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

Proprietary protocol

RESP - REdis Serialization Protocol

C++ API
Thrift

HTTP REST
JDBC

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#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C++
Java
Perl
PHP
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

user defined functions (Javascript)

Lua

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Source-replica replication

selectable replication factor on file system level

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Password-based authentication

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

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

Typical application scenarios

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

Licensing and pricing models

See CrateDB pricing >
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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