DBMS > Dgraph vs. GridDB vs. HarperDB vs. Microsoft Azure Cosmos DB

System Properties Comparison Dgraph vs. GridDB vs. HarperDB vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Graph DBMS

Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Key-value store
Relational DBMS

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

dgraph.io

griddb.net

www.harperdb.io

azure.microsoft.com/services/cosmos-db

Technical documentation

dgraph.io/docs

docs.griddb.net

docs.harperdb.io/docs

learn.microsoft.com/azure/cosmos-db

Developer

Dgraph Labs, Inc.

Toshiba Corporation

HarperDB

Microsoft

Initial release

2016

2013

2017

2014

Current release

5.1, August 2022

3.1, August 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

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

commercial

free community edition available

commercial

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

C++

Node.js

Server operating systems

Linux
OS X
Windows

Linux

Linux
OS X

hosted

Data scheme

schema-free

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

JSON data types

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

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

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Source-replica replication

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

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

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID at container level

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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 can be defined per database

Access rights for users and roles

Access rights can be defined down to the item level

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