DBMS > BaseX vs. CrateDB vs. Derby vs. Firebase Realtime Database

System Properties Comparison BaseX vs. CrateDB vs. Derby vs. Firebase Realtime Database

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Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Distributed Database based on Lucene

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Primary database model

Native XML DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Website

basex.org

cratedb.com

db.apache.org/derby

firebase.google.com/products/realtime-database

Technical documentation

docs.basex.org

cratedb.com/docs

db.apache.org/derby/manuals/index.html

firebase.google.com/docs/database

Developer

BaseX GmbH

Crate

Apache Software Foundation

Google

acquired by Google 2014

Initial release

2007

2013

1997

2012

Current release

10.7, August 2023

10.17.1.0, November 2023

License

Commercial or Open Source

Open Source

BSD license

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

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

Server operating systems

Linux
OS X
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

hosted

Data scheme

schema-free

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

yes

schema-free

Typing

predefined data types such as float or date

XQuery supports types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

JDBC

Android
iOS
JavaScript API
RESTful HTTP API

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.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

Java

Java
JavaScript
Objective-C

Server-side scripts

Stored procedures

yes

user defined functions (Javascript)

Java Stored Procedures

limited functionality with using 'rules'

Triggers

yes

via events

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Users with fine-grained authorization concept on 4 levels

rights management via user accounts

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

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