DBMS > Bangdb vs. Linter vs. Microsoft Azure Synapse Analytics vs. Splice Machine vs. ToroDB

System Properties Comparison Bangdb vs. Linter vs. Microsoft Azure Synapse Analytics vs. Splice Machine vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

RDBMS for high security requirements

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

bangdb.com

linter.ru

azure.microsoft.com/services/synapse-analytics

splicemachine.com

github.com/torodb/server

Technical documentation

docs.bangdb.com

docs.microsoft.com/azure/synapse-analytics

splicemachine.com/how-it-works

Developer

Sachin Sinha, BangDB

relex.ru

Microsoft

Splice Machine

8Kdata

Initial release

2012

1990

2016

2014

2016

Current release

BangDB 2.0, October 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

C and C++

C++

Java

Server operating systems

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

hosted

Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

string, integer, double, boolean, date, object_id

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

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C#
C++
Java
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C#
Java
PHP

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

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

Transact SQL

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

none

Sharding, horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

yes

Multi-source replication
Source-replica 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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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)

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

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

yes

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

Access rights for users and roles

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