DBMS > CrateDB vs. Dgraph vs. Oracle Berkeley DB vs. PouchDB

System Properties Comparison CrateDB vs. Dgraph vs. Oracle Berkeley DB vs. PouchDB

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Distributed and scalable native Graph DBMS

Widely used in-process key-value store

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Graph DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Website

cratedb.com

dgraph.io

www.oracle.com/database/technologies/related/berkeleydb.html

pouchdb.com

Technical documentation

cratedb.com/docs

dgraph.io/docs

docs.oracle.com/cd/E17076_05/html/index.html

pouchdb.com/guides

Developer

Crate

Dgraph Labs, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Initial release

2013

2016

1994

2012

Current release

5.8.1, August 2024

18.1.40, May 2020

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial license available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, Java, C++ (depending on the Berkeley DB edition)

JavaScript

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

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

schema-free

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

via views

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL interfaced based on SQLite is available

APIs and other access methods

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

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
JavaScript (Node.js)
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

JavaScript

Server-side scripts

Stored procedures

user defined functions (Javascript)

View functions in JavaScript

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Synchronous replication via Raft

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

Immediate Consistency

Eventual 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Planned for future releases

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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