DBMS > Bangdb vs. JanusGraph vs. Microsoft SQL Server vs. RisingWave vs. Snowflake

System Properties Comparison Bangdb vs. JanusGraph vs. Microsoft SQL Server vs. RisingWave vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Microsofts flagship relational DBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Graph DBMS
Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.58
Rank	#242	Overall
	#111	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

bangdb.com

janusgraph.org

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

www.risingwave.com/database

www.snowflake.com

Technical documentation

docs.bangdb.com

docs.janusgraph.org

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

docs.risingwave.com/docs/current/intro

docs.snowflake.net/manuals/index.html

Developer

Sachin Sinha, BangDB

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

RisingWave Labs

Snowflake Computing Inc.

Initial release

2012

2017

1989

2022

2014

Current release

BangDB 2.0, October 2021

0.6.3, February 2023

SQL Server 2022, November 2022

1.2, September 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C, C++

Java

C++

Rust

Server operating systems

Linux

Linux
OS X
Unix
Windows

Linux
Windows

Docker
Linux
macOS

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

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

yes

Standard SQL-types and JSON

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

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

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

JDBC
PostgreSQL wire protocol

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Clojure
Java
Python

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

Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

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

UDFs in Python or Java

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

similar concept for controling cloud resources

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

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

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

yes

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

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

yes

Relationships in graphs

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

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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)

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Users and Roles

Users with fine-grained authorization concept, user roles and pluggable authentication

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