DBMS > openGemini vs. Splice Machine vs. Warp 10

System Properties Comparison openGemini vs. Splice Machine vs. Warp 10

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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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

www.opengemini.org
github.com/openGemini

splicemachine.com

www.warp10.io

Technical documentation

docs.opengemini.org/guide

splicemachine.com/how-it-works

www.warp10.io/content/02_Getting_started

Developer

Huawei and openGemini community

Splice Machine

SenX

Initial release

2022

2014

2015

Current release

1.1, July 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL 3.0, commercial license available

Open Source

Apache License 2.0

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

Server operating systems

Linux
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

Integer, Float, Boolean, String

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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HTTP REST

JDBC
Native Spark Datasource
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

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

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

Server-side scripts

Stored procedures

yes

Java

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

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

based on HBase

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

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources

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