DBMS > Splice Machine vs. YottaDB

System Properties Comparison Splice Machine vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

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

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	0.54
Rank	#238	Overall
	#112	Relational DBMS

Score	0.18
Rank	#312	Overall
	#45	Key-value stores

Website

splicemachine.com

yottadb.com

Technical documentation

splicemachine.com/how-it-works

yottadb.com/resources/documentation

Developer

Splice Machine

YottaDB, LLC

Initial release

2014

2001

Current release

3.1, March 2021

License

Commercial or Open Source

Open Source

AGPL 3.0, commercial license available

Open Source

AGPL 3.0

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
OS X
Solaris
Windows

Docker
Linux

Data scheme

yes

schema-free

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

yes

by using the Octo plugin

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

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

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

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

Users and groups based on OS-security mechanisms

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More resources

Recent citations in the news

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine SQL Database Scales On Hadoop
22 September 2023, InformationWeek

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

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16 April 2021, Electrek.co

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provided by Google News

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