DBMS > JanusGraph vs. Lovefield vs. ObjectBox vs. TimescaleDB vs. Yaacomo

System Properties Comparison JanusGraph vs. Lovefield vs. ObjectBox vs. TimescaleDB vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

A Graph DBMS optimized for distributed clusters

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

Embeddable relational database for web apps written in pure JavaScript

Extremely fast embedded database for small devices, IoT and Mobile

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	1.91
Rank	#135	Overall
	#12	Graph DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	1.22
Rank	#172	Overall
	#5	Object oriented DBMS

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Website

janusgraph.org

google.github.io/lovefield

objectbox.io

www.timescale.com

yaacomo.com

Technical documentation

docs.janusgraph.org

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.objectbox.io

docs.timescale.com

Developer

Linux Foundation; originally developed as Titan by Aurelius

Google

ObjectBox Limited

Timescale

Q2WEB GmbH

Initial release

2017

2014

2017

2009

Current release

0.6.3, February 2023

2.1.12, February 2017

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

Apache 2.0

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

JavaScript

C and C++

Server operating systems

Linux
OS X
Unix
Windows

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

tested with Chrome, Firefox, IE, Safari

Android
iOS
Linux
macOS
Windows

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

SQL-like query language

via JavaScript builder pattern

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Proprietary native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

Clojure
Java
Python

JavaScript

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

none

yes, across time and space (hash partitioning) attributes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

online/offline synchronization between client and server

Source-replica replication with hot standby and reads on replicas

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Durability

Support for making data persistent

yes

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

User authentification and security via Rexster Graph Server

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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More resources

Recent citations in the news

Simple Deployment of a Graph Database: JanusGraph | by Edward Elson Kosasih
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Database Deep Dives: JanusGraph
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Compose for JanusGraph arrives on Bluemix
15 September 2017, IBM

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3 October 2019, The New Stack

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17 May 2023, PR Newswire

Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
22 February 2022, Business Wire

provided by Google News

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