DBMS > Apache IoTDB vs. JanusGraph vs. Microsoft SQL Server vs. SAP Adaptive Server vs. Snowflake

System Properties Comparison Apache IoTDB vs. JanusGraph vs. Microsoft SQL Server vs. SAP Adaptive Server vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A Graph DBMS optimized for distributed clusters

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

Microsofts flagship relational DBMS

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

Primary database model

Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	32.67
Rank	#25	Overall
	#17	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

iotdb.apache.org

janusgraph.org

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

www.sap.com/products/technology-platform/sybase-ase.html

www.snowflake.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.janusgraph.org

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

help.sap.com/docs/SAP_ASE

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

SAP, Sybase

Snowflake Computing Inc.

Initial release

2018

2017

1989

1987

2014

Current release

1.1.0, April 2023

1.0.0, October 2023

SQL Server 2022, November 2022

16.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Unix
Windows

Linux
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

hosted

Data scheme

yes

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

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

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

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Clojure
Java
Python

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

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

Java and Transact-SQL

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

yes

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

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

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

Durability

Support for making data persistent

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

User concepts

Access control

yes

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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

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