DBMS > Drizzle vs. Hazelcast vs. Splice Machine vs. XTDB

System Properties Comparison Drizzle vs. Hazelcast vs. Splice Machine vs. XTDB

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A widely adopted in-memory data grid

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

hazelcast.com

splicemachine.com

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

Technical documentation

hazelcast.org/imdg/docs

splicemachine.com/how-it-works

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Hazelcast

Splice Machine

Juxt Ltd.

Initial release

2008

2014

2019

Current release

7.2.4, September 2012

5.3.6, November 2023

3.1, March 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2; commercial licenses available

Open Source

AGPL 3.0, commercial license available

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

C++

Java

Clojure

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

Linux
OS X
Solaris
Windows

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

Data scheme

yes

schema-free

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

HTTP REST
JDBC

Supported programming languages

C
C++
Java
PHP

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Java

Triggers

hooks for callbacks inside the server can be used.

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Replicated Map

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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.

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Role-based access control

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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