DBMS > Ignite vs. searchxml vs. SiteWhere vs. Splice Machine vs. TDSQL for MySQL

System Properties Comparison Ignite vs. searchxml vs. SiteWhere vs. Splice Machine vs. TDSQL for MySQL

Editorial information provided by DB-Engines

Name

Description

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.

DBMS for structured and unstructured content wrapped with an application server

M2M integration platform for persisting/querying time series data

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

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Key-value store
Relational DBMS

Native XML DBMS
Search engine

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Website

ignite.apache.org

www.searchxml.net/category/products

github.com/sitewhere/sitewhere

splicemachine.com

www.tencentcloud.com/products/dcdb

Technical documentation

apacheignite.readme.io/docs

www.searchxml.net/support/handouts

sitewhere1.sitewhere.io/index.html

splicemachine.com/how-it-works

www.tencentcloud.com/document/product/1042

Developer

Apache Software Foundation

informationpartners gmbh

SiteWhere

Splice Machine

Tencent

Initial release

2015

2010

2014

2013

Current release

Apache Ignite 2.6

1.0

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Common Public Attribution License Version 1.0

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java, .Net

C++

Java

Server operating systems

Linux
OS X
Solaris
Windows

Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

hosted

Data scheme

yes

schema-free

predefined scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

yes

APIs and other access methods

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

RESTful HTTP API
WebDAV
XQuery
XSLT

HTTP REST

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++

most other programming languages supported via APIs

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

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

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

yes

on the application server

yes

Java

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

Shared Nothhing Auto-Sharding, Columnar Partitioning

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

sychronisation to multiple collections

selectable replication factor

based on HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Security Hooks for custom implementations

Domain, group and role-based access control at the document level and for application services

Users with fine-grained authorization concept

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

Users with fine-grained authorization concept

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