DBMS > Apache IoTDB vs. Atos Standard Common Repository vs. EDB Postgres vs. Microsoft Azure Cosmos DB vs. VictoriaMetrics

System Properties Comparison Apache IoTDB vs. Atos Standard Common Repository vs. EDB Postgres vs. Microsoft Azure Cosmos DB vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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.

Globally distributed, horizontally scalable, multi-model database service

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.90
Rank	#135	Overall
	#63	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

iotdb.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.enterprisedb.com

azure.microsoft.com/services/cosmos-db

victoriametrics.com

Technical documentation

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

www.enterprisedb.com/docs

learn.microsoft.com/azure/cosmos-db

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Apache Software Foundation

Atos Convergence Creators

EnterpriseDB

Microsoft

VictoriaMetrics

Initial release

2018

2016

2005

2014

2018

Current release

1.1.0, April 2023

1703

14, December 2021

v1.91, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

BSD for PostgreSQL-components

commercial

Open Source

Apache Version 2.0

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

Linux
Windows

hosted

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

SQL-like query language

APIs and other access methods

JDBC
Native API

LDAP

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages with LDAP bindings

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
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.

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

cell division

horizontal partitioning

by hash, list or range

Sharding

Implicit feature of the cloud service

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

Multi-source replication

yes

Implicit feature of the cloud service

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

LDAP bind authentication

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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