DBMS > Apache IoTDB vs. EDB Postgres vs. H2 vs. Postgres-XL vs. Snowflake

System Properties Comparison Apache IoTDB vs. EDB Postgres vs. H2 vs. Postgres-XL 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

The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.91
Rank	#130	Overall
	#60	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

Technical documentation

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

www.enterprisedb.com/docs

www.h2database.com/html/main.html

www.postgres-xl.org/documentation

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

EnterpriseDB

Thomas Mueller

Snowflake Computing Inc.

Initial release

2018

2005

2014

since 2012, originally named StormDB

2014

Current release

1.1.0, April 2023

14, December 2021

2.2.220, July 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

BSD for PostgreSQL-components

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Mozilla public license

commercial

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

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

All OS with a Java VM

Linux
macOS

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

specific XML-type available, but no XML query functionality.

yes

XML type, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

yes

distributed, parallel query execution

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl

Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Java Stored Procedures and User-Defined Functions

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)

horizontal partitioning

by hash, list or range

none

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

Multi-source replication

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

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

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