DBMS > Badger vs. Datomic vs. Drizzle vs. Yaacomo

System Properties Comparison Badger vs. Datomic vs. Drizzle vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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

Description

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Primary database model

Key-value store

Relational DBMS

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

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Website

github.com/dgraph-io/badger

www.datomic.com

yaacomo.com

Technical documentation

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

docs.datomic.com

Developer

DGraph Labs

Cognitect

Drizzle project, originally started by Brian Aker

Q2WEB GmbH

Initial release

2017

2012

2008

2009

Current release

1.0.7180, July 2024

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

Open Source

GNU GPL

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
OS X

Android
Linux
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

RESTful HTTP API

JDBC

JDBC
ODBC

Supported programming languages

Clojure
Java

C
C++
Java
PHP

Server-side scripts

Stored procedures

yes

Transaction Functions

Triggers

By using transaction functions

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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More resources

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