DBMS > Dgraph vs. Google Cloud Datastore vs. H2 vs. KairosDB vs. Transbase

System Properties Comparison Dgraph vs. Google Cloud Datastore vs. H2 vs. KairosDB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Distributed Time Series DBMS based on Cassandra or H2

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

dgraph.io

cloud.google.com/datastore

www.h2database.com

github.com/kairosdb/kairosdb

www.transaction.de/en/products/transbase.html

Technical documentation

dgraph.io/docs

cloud.google.com/datastore/docs

www.h2database.com/html/main.html

kairosdb.github.io

www.transaction.de/en/products/transbase/features.html

Developer

Dgraph Labs, Inc.

Google

Thomas Mueller

Transaction Software GmbH

Initial release

2016

2008

2005

2013

1987

Current release

2.2.220, July 2023

1.2.2, November 2018

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

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

Open Source

Apache 2.0

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL-like query language (GQL)

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

Java
JavaScript

Node.js
PHP
Python

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

Java Stored Procedures and User-Defined Functions

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication using Paxos

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

selectable replication factor

based on Cassandra

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

yes

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

User concepts

Access control

Planned for future releases

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

simple password-based access control

fine grained access rights according to SQL-standard

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