DBMS > Bangdb vs. FatDB vs. GridGain vs. SingleStore

System Properties Comparison Bangdb vs. FatDB vs. GridGain vs. SingleStore

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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

Converged and high performance database for device data, events, time series, document and graph

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

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

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Key-value store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

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

Score	5.91
Rank	#61	Overall
	#34	Relational DBMS

Website

bangdb.com

www.gridgain.com

www.singlestore.com

Technical documentation

docs.bangdb.com

www.gridgain.com/docs/index.html

docs.singlestore.com

Developer

Sachin Sinha, BangDB

FatCloud

GridGain Systems, Inc.

SingleStore Inc.

Initial release

2012

2007

2013

Current release

BangDB 2.0, October 2021

GridGain 8.5.1

8.5, January 2024

License

Commercial or Open Source

Open Source

BSD 3

commercial

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C, C++

Java, C++, .Net

C++, Go

Server operating systems

Linux

Windows

Linux
OS X
Solaris
Windows

Linux

64 bit version required

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

Via inetgration in SQL Server

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

yes

but no triggers and foreign keys

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

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

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

C
C#
C++
Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

via applications

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

yes

Triggers

yes, Notifications (with Streaming only)

yes

via applications

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

selectable replication factor

yes (replicated cache)

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Can implement custom security layer via applications

Security Hooks for custom implementations

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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More resources

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