DBMS > Apache IoTDB vs. EDB Postgres vs. H2 vs. Manticore Search vs. Oracle Coherence

System Properties Comparison Apache IoTDB vs. EDB Postgres vs. H2 vs. Manticore Search vs. Oracle Coherence

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.

Multi-storage database for search, including full-text search.

Oracles in-memory data grid solution

Primary database model

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

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	0.29
Rank	#302	Overall
	#21	Search engines

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Website

www.oracle.com/java/coherence

Technical documentation

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

www.enterprisedb.com/docs

www.h2database.com/html/main.html

manual.manticoresearch.com

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation

EnterpriseDB

Thomas Mueller

Manticore Software

Oracle

Initial release

2018

2005

2017

2007

Current release

1.1.0, April 2023

14, December 2021

2.2.220, July 2023

6.0, February 2023

14.1, August 2023

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

GPL version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

All OS with a Java VM

FreeBSD
Linux
macOS
Windows

All OS with a Java VM

Data scheme

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

yes

SQL-like query language

APIs and other access methods

JDBC
Native API

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

JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JCache
JPA
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Java

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C++
Java

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

Live Events

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

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

Synchronous replication based on Galera library

yes, with selectable consistency level

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

configurable

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

optionally

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

authentification to access the cache via certificates or http basic authentication

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