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

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

Editorial information provided by DB-Engines

Name

Description

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Secondary database models

Key-value store
Relational DBMS

Spatial DBMS

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

Score	0.61
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

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

www.graphengine.io

griddb.net

www.h2database.com

github.com/spotify/heroic

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

www.graphengine.io/docs/manual

docs.griddb.net

www.h2database.com/html/main.html

spotify.github.io/heroic

ydb.tech/en/docs

Developer

Microsoft

Toshiba Corporation

Thomas Mueller

Spotify

Yandex

Initial release

2010

2013

2005

2014

2019

Current release

5.1, August 2022

2.2.220, July 2023

License

Commercial or Open Source

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

Open Source

Apache 2.0; commercial license available

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

.NET and C

C++

Java

Server operating systems

.NET

Linux

All OS with a Java VM

Linux

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL-like query language (YQL)

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

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

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

C#
C++
F#
Visual Basic

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

Java

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

Triggers

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

Active-passive shard replication

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

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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.

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