DBMS > JaguarDB vs. Splice Machine

System Properties Comparison JaguarDB vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

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

Primary database model

Key-value store
Vector DBMS

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.jaguardb.com

splicemachine.com

Technical documentation

www.jaguardb.com/support.html

splicemachine.com/how-it-works

Developer

DataJaguar, Inc.

Splice Machine

Initial release

2015

2014

Current release

3.3 July 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

GPL V3.0

Open Source

AGPL 3.0, commercial license 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

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Data 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.

Secondary indexes

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

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

Eventual Consistency

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)

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

rights management via user accounts

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

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