DBMS > Derby vs. Lovefield vs. Splice Machine vs. Tkrzw vs. Yaacomo

System Properties Comparison Derby vs. Lovefield vs. Splice Machine vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Embeddable relational database for web apps written in pure JavaScript

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	5.30
Rank	#66	Overall
	#36	Relational DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Score	0.09
Rank	#354	Overall
	#51	Key-value stores

Website

db.apache.org/derby

google.github.io/lovefield

splicemachine.com

dbmx.net/tkrzw

yaacomo.com

Technical documentation

db.apache.org/derby/manuals/index.html

github.com/google/lovefield/blob/master/docs/spec_index.md

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Google

Splice Machine

Mikio Hirabayashi

Q2WEB GmbH

Initial release

1997

2014

2020

2009

Current release

10.17.1.0, November 2023

2.1.12, February 2017

3.1, March 2021

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

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

Java

JavaScript

Java

C++

Server operating systems

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Solaris
Windows

Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

JDBC

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC

Supported programming languages

Java

JavaScript

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Java

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

none

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

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

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

using specific database classes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

JDBC tutorial: Easy installation and setup with Apache Derby
20 December 2019, TheServerSide.com

Top Database Tools for Java Developers in 2023
17 November 2023, TechRepublic

Installing Apache Hive 3.1.2 on Windows 10 | by Hadi Fadlallah
3 May 2020, Towards Data Science

The Arrival of Java 20
21 March 2023, Oracle

No, Citrix did not kill CloudStack
15 September 2014, InfoWorld

provided by Google News

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

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

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

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

Unified MLOps: Feature Stores and Model Deployment
13 May 2022, InfoQ.com

provided by Google News

Share this page