DBMS > Bangdb vs. FatDB vs. InfluxDB vs. Sequoiadb vs. Spark SQL

System Properties Comparison Bangdb vs. FatDB vs. InfluxDB vs. Sequoiadb vs. Spark SQL

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.

DBMS for storing time series, events and metrics

NewSQL database with distributed OLTP and SQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Key-value store

Time Series DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

bangdb.com

www.influxdata.com/products/influxdb-overview

www.sequoiadb.com

spark.apache.org/sql

Technical documentation

docs.bangdb.com

docs.influxdata.com/influxdb

www.sequoiadb.com/en/index.php?m=Files&a=index

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Sachin Sinha, BangDB

FatCloud

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2012

2013

2014

Current release

BangDB 2.0, October 2021

2.7.6, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache 2.0

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, C++

C++

Scala

Server operating systems

Linux

Windows

Linux
OS X

through Homebrew

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

yes

Numeric data and Strings

yes

oid, date, timestamp, binary, regex

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.

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

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

HTTP API
JSON over UDP

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

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

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

JavaScript

Triggers

yes, Notifications (with Streaming only)

yes

via applications

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

in enterprise version only

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

in enterprise version only

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

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

Depending on used storage engine

User concepts

Access control

yes (enterprise version only)

Can implement custom security layer via applications

simple rights management via user accounts

simple password-based access control

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
» more

Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more

Key customers

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

Market metrics

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

Licensing and pricing models

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

News

Converting Timestamp to Date in Java
7 May 2024

A Detailed Guide to C# TimeSpan
2 May 2024

The Final Frontier: Using InfluxDB on the International Space Station
30 April 2024

Getting the Current Time in C#: A Guide
26 April 2024

Sync Data from InfluxDB v2 to v3 With the Quix Template
8 April 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

show all

Recent citations in the news

Run and manage open source InfluxDB databases with Amazon Timestream | Amazon Web Services
14 March 2024, AWS Blog

Amazon Timestream: Managed InfluxDB for Time Series Data
14 March 2024, The New Stack

InfluxData Collaborating with AWS to Bring InfluxDB and Time Series Analytics to Developers Around the World
14 March 2024, businesswire.com

How the FDAP Stack Gives InfluxDB 3.0 Real-Time Speed, Efficiency
15 March 2024, Datanami

Time-series database startup InfluxData debuts self-managed version of InfluxDB
6 September 2023, SiliconANGLE News

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Performant IPv4 Range Spark Joins | by Jean-Claude Cote
24 January 2024, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Share this page