DBMS > Graph Engine vs. Hazelcast vs. HBase vs. Solr vs. VoltDB

System Properties Comparison Graph Engine vs. Hazelcast vs. HBase vs. Solr vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A widely adopted in-memory data grid

Wide-column store based on Apache Hadoop and on concepts of BigTable

A widely used distributed, scalable search engine based on Apache Lucene

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Graph DBMS
Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

Technical documentation

www.graphengine.io/docs/manual

hazelcast.org/imdg/docs

hbase.apache.org/book.html

solr.apache.org/resources.html

docs.voltdb.com

Developer

Microsoft

Hazelcast

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Apache Software Foundation

VoltDB Inc.

Initial release

2010

2008

2006

2010

Current release

5.3.6, November 2023

2.3.4, January 2021

9.6.0, April 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

.NET and C

Java

Java, C++

Server operating systems

.NET

All OS with a Java VM

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X

for development

Data scheme

yes

schema-free

schema-free, schema definition possible

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

supports customizable data types and automatic typing

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

Java API
RESTful HTTP/JSON API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C#
C++
F#
Visual Basic

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Coprocessors in Java

Java plugins

Java

Triggers

yes

Events

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

Single row ACID (across millions of columns)

optimistic locking

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

yes

Users and roles with access to stored procedures

More information provided by the system vendor

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More resources

DB-Engines blog posts

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Recent citations in the news

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13 February 2017, InfoWorld

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VoltDB Turns to Real-Time Analytics with NewSQL Database
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