DBMS > Atos Standard Common Repository vs. Ignite vs. SpaceTime vs. Splice Machine vs. XTDB

System Properties Comparison Atos Standard Common Repository vs. Ignite vs. SpaceTime vs. Splice Machine vs. XTDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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.

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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

Document store
Key-value store

Key-value store
Relational DBMS

Spatial DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS

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

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

ignite.apache.org

www.mireo.com/spacetime

splicemachine.com

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

Technical documentation

apacheignite.readme.io/docs

splicemachine.com/how-it-works

www.xtdb.com/docs

Developer

Atos Convergence Creators

Apache Software Foundation

Mireo

Splice Machine

Juxt Ltd.

Initial release

2016

2015

2020

2014

2019

Current release

1703

Apache Ignite 2.6

3.1, March 2021

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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

Java

C++, Java, .Net

C++

Java

Clojure

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Solaris
Windows

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

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

optional

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

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

A subset of ANSI SQL is implemented

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

LDAP

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

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

HTTP REST
JDBC

Supported programming languages

All languages with LDAP bindings

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Python

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

Clojure
Java

Server-side scripts

Stored procedures

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

yes

Java

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Fixed-grid hypercubes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

Real-time block device replication (DRBD)

Multi-source replication
Source-replica replication

yes, each node contains all data

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

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

LDAP bind authentication

Security Hooks for custom implementations

yes

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

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