DBMS > Bangdb vs. InfluxDB vs. Microsoft SQL Server vs. Splice Machine

System Properties Comparison Bangdb vs. InfluxDB vs. Microsoft SQL Server vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

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

DBMS for storing time series, events and metrics

Microsofts flagship relational DBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

Document store
Graph DBMS
Spatial DBMS

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

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	22.12
Rank	#28	Overall
	#1	Time Series DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

bangdb.com

www.influxdata.com/products/influxdb-overview

www.microsoft.com/en-us/sql-server

splicemachine.com

Technical documentation

docs.bangdb.com

docs.influxdata.com/influxdb

learn.microsoft.com/en-US/sql/sql-server

splicemachine.com/how-it-works

Developer

Sachin Sinha, BangDB

Microsoft

Splice Machine

Initial release

2012

2013

1989

2014

Current release

BangDB 2.0, October 2021

2.7.6, April 2024

SQL Server 2022, November 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

MIT-License; commercial enterprise version available

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

Java

Server operating systems

Linux

Linux
OS X

through Homebrew

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

Numeric data and Strings

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

SQL-like query language

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP API
JSON over UDP

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
Native Spark Datasource
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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Java

Triggers

yes, Notifications (with Streaming only)

yes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Shared Nothhing Auto-Sharding, Columnar Partitioning

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

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

yes

User concepts

Access control

yes (enterprise version only)

simple rights management via user accounts

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
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Licensing and pricing models

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