DBMS > FatDB vs. GBase vs. HarperDB vs. Ignite vs. Splunk

System Properties Comparison FatDB vs. GBase vs. HarperDB vs. Ignite vs. Splunk

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Analytics Platform for Big Data

Primary database model

Document store
Key-value store

Relational DBMS

Document store

Key-value store
Relational DBMS

Search engine

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

Technical documentation

docs.harperdb.io/docs

apacheignite.readme.io/docs

docs.splunk.com/Documentation/Splunk

Developer

FatCloud

General Data Technology Co., Ltd.

HarperDB

Apache Software Foundation

Splunk Inc.

Initial release

2012

2004

2017

2015

2003

Current release

GBase 8a, GBase 8s, GBase 8c

3.1, August 2021

Apache Ignite 2.6

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

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

C, Java, Python

Node.js

C++, Java, .Net

Server operating systems

Windows

Linux

Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

Standard with numerous extensions

SQL-like data manipulation statements

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

Splunk Search Processing Language for search commands

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

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

HTTP REST

Supported programming languages

.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

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

via applications

user defined functions

Custom Functions

since release 3.1

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

yes

Triggers

yes

via applications

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

the nodes on which a table resides can be defined

yes (replicated cache)

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

Can implement custom security layer via applications

yes

Access rights for users and roles

Security Hooks for custom implementations

Access rights for users and roles

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More resources

DB-Engines blog posts

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

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Recent citations in the news

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17 January 2023, Markets Insider

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