DBMS > GridDB vs. HBase vs. Oracle Berkeley DB vs. Redis

System Properties Comparison GridDB vs. HBase vs. Oracle Berkeley DB vs. Redis

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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

Widely used in-process key-value store

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Primary database model

Time Series DBMS

Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Secondary database models

Key-value store
Relational DBMS

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	2.52
Rank	#114	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	156.44
Rank	#6	Overall
	#1	Key-value stores

Website

griddb.net

hbase.apache.org

www.oracle.com/database/technologies/related/berkeleydb.html

redis.com
redis.io

Technical documentation

docs.griddb.net

hbase.apache.org/book.html

docs.oracle.com/cd/E17076_05/html/index.html

docs.redis.com/latest/index.html
redis.io/docs

Developer

Toshiba Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Initial release

2013

2008

1994

2009

Current release

5.1, August 2022

2.3.4, January 2021

18.1.40, May 2020

7.2.4, January 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

Open Source

commercial license available

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

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.

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

C++

Java

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

Linux
Unix
Windows

using Cygwin

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Data scheme

yes

schema-free, schema definition possible

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

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

with RediSearch module

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL interfaced based on SQLite is available

with RediSQL module

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Lua; Redis Functions coming in Redis 7 (slides and Github)

Triggers

yes

only for the SQL API

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

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

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency or Eventual Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Foreign keys

Referential integrity

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

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

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

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

More information provided by the system vendor

Specific characteristics

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

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

GitHub trending repository
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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.