DBMS > Badger vs. FileMaker vs. Splice Machine vs. Tkrzw

System Properties Comparison Badger vs. FileMaker vs. Splice Machine vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

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

Primary database model

Key-value store

Relational DBMS

Key-value store

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

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	45.20
Rank	#20	Overall
	#14	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Website

github.com/dgraph-io/badger

www.claris.com/filemaker

splicemachine.com

dbmx.net/tkrzw

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.claris.com/resources/documentation

splicemachine.com/how-it-works

Developer

DGraph Labs

Claris

a subsidiary of Apple

Splice Machine

Mikio Hirabayashi

Initial release

2017

1983

2014

2020

Current release

19.4.1, November 2021

3.1, March 2021

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

iOS

client part only
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

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.

yes

A http query request can return the data in XML format

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

yes

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

PHP

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication, warm standby

since Version 14

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

none

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

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

simple rights management via user accounts and connection to external directory services

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

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More resources

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