DBMS > Hive vs. Ignite vs. JanusGraph vs. Lovefield vs. XTDB

System Properties Comparison Hive vs. Ignite vs. JanusGraph vs. Lovefield vs. XTDB

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Document store

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

hive.apache.org

ignite.apache.org

janusgraph.org

google.github.io/lovefield

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

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

apacheignite.readme.io/docs

docs.janusgraph.org

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

www.xtdb.com/docs

Developer

Apache Software Foundation

initially developed by Facebook

Apache Software Foundation

Linux Foundation; originally developed as Titan by Aurelius

Google

Juxt Ltd.

Initial release

2012

2015

2017

2014

2019

Current release

3.1.3, April 2022

Apache Ignite 2.6

0.6.3, February 2023

2.1.12, February 2017

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

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

C++, Java, .Net

Java

JavaScript

Clojure

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

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

tested with Chrome, Firefox, IE, Safari

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

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

via JavaScript builder pattern

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
Thrift

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP REST
JDBC

Supported programming languages

C++
Java
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java
Python

JavaScript

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

yes (cache interceptors and events)

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes (replicated cache)

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual 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, 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.

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles

Security Hooks for custom implementations

User authentification and security via Rexster Graph Server

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