DBMS > Dragonfly vs. Graph Engine vs. GridDB vs. H2 vs. Heroic

System Properties Comparison Dragonfly vs. Graph Engine vs. GridDB vs. H2 vs. Heroic

Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Scalable in-memory time series database optimized for IoT and Big Data

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Key-value store

Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Key-value store
Relational DBMS

Spatial DBMS

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.graphengine.io

griddb.net

www.h2database.com

github.com/spotify/heroic

Technical documentation

www.dragonflydb.io/docs

www.graphengine.io/docs/manual

docs.griddb.net

www.h2database.com/html/main.html

spotify.github.io/heroic

Developer

DragonflyDB team and community contributors

Microsoft

Toshiba Corporation

Thomas Mueller

Spotify

Initial release

2023

2010

2013

2005

2014

Current release

1.0, March 2023

5.1, August 2022

2.2.220, July 2023

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

MIT License

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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

C++

.NET and C

C++

Java

Server operating systems

Linux

.NET

Linux

All OS with a Java VM

Data scheme

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes

numerical, string, blob, geometry, boolean, timestamp

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

via Elasticsearch

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C#
C++
F#
Visual Basic

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java

Server-side scripts

Stored procedures

Lua

yes

Java Stored Procedures and User-Defined Functions

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic execution of command blocks and scripts

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Password-based authentication

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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