DBMS > Apache IoTDB vs. LMDB vs. TimesTen vs. WakandaDB

System Properties Comparison Apache IoTDB vs. LMDB vs. TimesTen vs. WakandaDB

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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

A high performant, light-weight, embedded key-value database library

In-Memory RDBMS compatible to Oracle

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

iotdb.apache.org

www.symas.com/symas-embedded-database-lmdb

www.oracle.com/database/technologies/related/timesten.html

wakanda.github.io

Technical documentation

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

www.lmdb.tech/doc

docs.oracle.com/database/timesten-18.1

wakanda.github.io/doc

Developer

Apache Software Foundation

Symas

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Wakanda SAS

Initial release

2018

2011

1998

2012

Current release

1.1.0, April 2023

0.9.32, January 2024

11 Release 2 (11.2.2.8.0)

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

AGPLv3, extended commercial license available

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++, JavaScript

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C++
Java
PL/SQL

JavaScript

Server-side scripts

Stored procedures

yes

PL/SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

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

none

Multi-source replication
Source-replica replication

none

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

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

by means of logfiles and checkpoints

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

yes

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