DBMS > GridDB vs. HBase vs. IBM Db2 vs. InfluxDB vs. Microsoft Azure Cosmos DB

System Properties Comparison GridDB vs. HBase vs. IBM Db2 vs. InfluxDB vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Common in IBM host environments, 2 different versions for host and Windows/Linux

DBMS for storing time series, events and metrics

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Time Series DBMS

Wide column store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Key-value store
Relational DBMS

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

with GEO package

Spatial DBMS

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	125.90
Rank	#9	Overall
	#6	Relational DBMS

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

griddb.net

hbase.apache.org

www.ibm.com/products/db2

www.influxdata.com/products/influxdb-overview

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.griddb.net

hbase.apache.org/book.html

www.ibm.com/docs/en/db2

docs.influxdata.com/influxdb

learn.microsoft.com/azure/cosmos-db

Developer

Toshiba Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM

Microsoft

Initial release

2013

2008

1983

host version

2013

2014

Current release

5.1, August 2022

2.3.4, January 2021

12.1, October 2016

2.7.6, April 2024

License

Commercial or Open Source

Open Source

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

Open Source

Apache version 2

commercial

free version is available

Open Source

MIT-License; commercial enterprise version 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++

Java

C and C++

Server operating systems

Linux

Linux
Unix
Windows

using Cygwin

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
OS X

through Homebrew

hosted

Data scheme

yes

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

options to bring your own types, AVRO

yes

Numeric data and Strings

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)

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

HTTP API
JSON over UDP

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

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

Sharding

in enterprise version only

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

with separate tools (MQ, InfoSphere)

selectable replication factor

in enterprise version only

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

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency or Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

Single row ACID (across millions of columns)

ACID

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Depending on used storage engine

User concepts

Access control

Access rights for users can be defined per database

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

simple rights management via user accounts

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

InfluxData is the creator of InfluxDB , the open source time series database. It...
» more

Competitive advantages

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

Time to Value InfluxDB is available in all the popular languages and frameworks,...
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Typical application scenarios

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

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
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Key customers

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

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more

Market metrics

GitHub trending repository
» more

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more

Licensing and pricing models

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

Open source core with closed source clustering available either on-premise or on...
» more

News

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