DBMS > openGemini vs. Splice Machine vs. SQLite vs. TerarkDB

System Properties Comparison openGemini vs. Splice Machine vs. SQLite vs. TerarkDB

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Editorial information provided by DB-Engines

Name

Description

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

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

Widely used embeddable, in-process RDBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

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

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

www.opengemini.org
github.com/openGemini

splicemachine.com

www.sqlite.org

github.com/bytedance/terarkdb

Technical documentation

docs.opengemini.org/guide

splicemachine.com/how-it-works

www.sqlite.org/docs.html

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Huawei and openGemini community

Splice Machine

Dwayne Richard Hipp

ByteDance, originally Terark

Initial release

2022

2014

2000

2016

Current release

1.1, July 2023

3.1, March 2021

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL 3.0, commercial license available

Open Source

Public Domain

commercial

restricted open source version available

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++

Server operating systems

Linux
Windows

Linux
OS X
Solaris
Windows

server-less

Data scheme

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

Integer, Float, Boolean, String

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

HTTP REST

JDBC
Native Spark Datasource
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

C++ API
Java API

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++
Java

Server-side scripts

Stored procedures

yes

Java

Triggers

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

yes

Multi-source replication
Source-replica replication

none

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

Administrators and common users accounts

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

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