DBMS > Badger vs. Blazegraph vs. EJDB vs. XTDB

System Properties Comparison Badger vs. Blazegraph vs. EJDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Primary database model

Key-value store

Graph DBMS
RDF store

Document store

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/dgraph-io/badger

blazegraph.com

github.com/Softmotions/ejdb

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

Technical documentation

godoc.org/github.com/dgraph-io/badger

wiki.blazegraph.com

github.com/Softmotions/ejdb/blob/master/README.md

www.xtdb.com/docs

Developer

DGraph Labs

Blazegraph

Softmotions

Juxt Ltd.

Initial release

2017

2006

2012

2019

Current release

2.1.5, March 2019

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

extended commercial license available

Open Source

GPLv2

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.

Implementation language

Java

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

server-less

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

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

string, integer, double, bool, date, object_id

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

SQL

Support of SQL

SPARQL is used as query language

limited SQL, making use of Apache Calcite

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

in-process shared library

HTTP REST
JDBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in Graphs

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

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More resources

Recent citations in the news

Harnessing GPUs Delivers a Big Speedup for Graph Analytics
15 December 2015, Datanami

Back to the future: Does graph database success hang on query language?
5 March 2018, ZDNet

This AI Paper Introduces A Comprehensive RDF Dataset With Over 26 Billion Triples Covering Scholarly Data Across All Scientific Disciplines
19 August 2023, MarkTechPost

Representation Learning on RDF* and LPG Knowledge Graphs
24 September 2020, Towards Data Science

Faster with GPUs: 5 turbocharged databases
26 September 2016, InfoWorld

provided by Google News

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