DBMS > Apache IoTDB vs. KeyDB vs. OpenEdge vs. Oracle

System Properties Comparison Apache IoTDB vs. KeyDB vs. OpenEdge vs. Oracle

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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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Application development environment with integrated database management system

Widely used RDBMS

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Website

iotdb.apache.org

github.com/Snapchat/KeyDB
keydb.dev

www.progress.com/openedge

www.oracle.com/database

Technical documentation

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

docs.keydb.dev

documentation.progress.com/output/ua/OpenEdge_latest

docs.oracle.com/en/database

Developer

Apache Software Foundation

EQ Alpha Technology Ltd.

Progress Software Corporation

Oracle

Initial release

2018

2019

1984

1980

Current release

1.1.0, April 2023

OpenEdge 12.2, March 2020

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD-3

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

AIX
HP-UX
Linux
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

SQL-like query language

yes

close to SQL 92

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Progress proprietary ABL (Advanced Business Language)

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

Lua

yes

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning

since Version 11.4

Sharding, horizontal partitioning

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
Source-replica replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

yes

simple password-based access control and ACL

Users and groups

fine grained access rights according to SQL-standard

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