DBMS > Apache Druid vs. Sphinx vs. Splice Machine vs. SwayDB vs. VoltDB

System Properties Comparison Apache Druid vs. Sphinx vs. Splice Machine vs. SwayDB vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Open source search engine for searching in data from different sources, e.g. relational databases

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

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

Relational DBMS
Time Series DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

sphinxsearch.com/docs

splicemachine.com/how-it-works

docs.voltdb.com

Developer

Apache Software Foundation and contributors

Sphinx Technologies Inc.

Splice Machine

Simer Plaha

VoltDB Inc.

Initial release

2012

2001

2014

2018

2010

Current release

30.0.0, June 2024

3.5.1, February 2023

3.1, March 2021

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

Open Source

GNU Affero GPL V3.0

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

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Implementation language

Java

C++

Java

Scala

Java, C++

Server operating systems

Linux
OS X
Unix

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
OS X

for development

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL for querying

SQL-like query language (SphinxQL)

yes

only a subset of SQL 99

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Java
Kotlin
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Partitioning is done manually, search queries against distributed index is supported

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Atomic execution of operations

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Users and roles with access to stored procedures

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
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