DBMS > GridGain vs. HarperDB vs. Oracle Berkeley DB vs. Riak KV

System Properties Comparison GridGain vs. HarperDB vs. Oracle Berkeley DB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

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.

Widely used in-process key-value store

Distributed, fault tolerant key-value store

Primary database model

Key-value store
Relational DBMS

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	0.55
Rank	#252	Overall
	#38	Document stores

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

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Website

www.gridgain.com

www.harperdb.io

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

Technical documentation

www.gridgain.com/docs/index.html

docs.harperdb.io/docs

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

www.tiot.jp/riak-docs/riak/kv/latest

Developer

GridGain Systems, Inc.

HarperDB

Oracle

originally developed by Sleepycat, which was acquired by Oracle

OpenSource, formerly Basho Technologies

Initial release

2007

2017

1994

2009

Current release

GridGain 8.5.1

3.1, August 2021

18.1.40, May 2020

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

commercial license available

Open Source

Apache version 2, commercial enterprise edition

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

Java, C++, .Net

Node.js

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

Erlang

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X

Data scheme

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data 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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

restricted

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like data manipulation statements

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API
Native Erlang Interface

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.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

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Custom Functions

since release 3.1

Erlang

Triggers

yes (cache interceptors and events)

yes

only for the SQL API

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

the nodes on which a table resides can be defined

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

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

Security Hooks for custom implementations

Access rights for users and roles

yes, using Riak Security

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