DBMS > Faircom DB vs. Ignite vs. Splice Machine vs. TimesTen

System Properties Comparison Faircom DB vs. Ignite vs. Splice Machine vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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.

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

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store
Relational DBMS

Relational DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

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

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.faircom.com/products/faircom-db

ignite.apache.org

splicemachine.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

apacheignite.readme.io/docs

splicemachine.com/how-it-works

docs.oracle.com/database/timesten-18.1

Developer

FairCom Corporation

Apache Software Foundation

Splice Machine

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1979

2015

2014

1998

Current release

V12, November 2020

Apache Ignite 2.6

3.1, March 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

commercial

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

ANSI C, C++

C++, Java, .Net

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, ANSI SQL with proprietary extensions

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

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

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

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

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

yes

Java

PL/SQL

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes (replicated cache)

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

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

Security Hooks for custom implementations

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

fine grained access rights according to SQL-standard

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