DBMS > Badger vs. Datomic vs. GridGain vs. Hawkular Metrics

System Properties Comparison Badger vs. Datomic vs. GridGain vs. Hawkular Metrics

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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.

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

GridGain is an in-memory computing platform, built on Apache Ignite

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Key-value store

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Website

github.com/dgraph-io/badger

www.datomic.com

www.gridgain.com

www.hawkular.org

Technical documentation

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

docs.datomic.com

www.gridgain.com/docs/index.html

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

DGraph Labs

Cognitect

GridGain Systems, Inc.

Community supported by Red Hat

Initial release

2017

2012

2007

2014

Current release

1.0.6735, June 2023

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

commercial

Open Source

Apache 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, Clojure

Java, C++, .Net

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

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

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP REST

Supported programming languages

Clojure
Java

C#
C++
Java
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes (compute grid and cache interceptors can be used instead)

Triggers

By using transaction functions

yes (cache interceptors and events)

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes (replicated cache)

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

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

Security Hooks for custom implementations

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