DBMS > EJDB vs. Hawkular Metrics vs. JanusGraph vs. Quasardb vs. TimesTen

System Properties Comparison EJDB vs. Hawkular Metrics vs. JanusGraph vs. Quasardb vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Distributed, high-performance timeseries database

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

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

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

github.com/Softmotions/ejdb

www.hawkular.org

janusgraph.org

quasar.ai

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

Technical documentation

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

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.janusgraph.org

doc.quasar.ai/master

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Softmotions

Community supported by Red Hat

Linux Foundation; originally developed as Titan by Aurelius

quasardb

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

2014

2017

2009

1998

Current release

1.0.0, October 2023

3.14.1, January 2024

Release 22.1

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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

C++

Server operating systems

server-less

Linux
OS X
Windows

Linux
OS X
Unix
Windows

BSD
Linux
OS X
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

in-process shared library

HTTP REST

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP API

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

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

Go
Java
Python
Ruby

Clojure
Java
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

yes

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Relationships in graphs

yes

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

by using LevelDB

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

User authentification and security via Rexster Graph Server

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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