DBMS > Apache Druid vs. Apache IoTDB vs. Citus vs. Elasticsearch vs. RocksDB

System Properties Comparison Apache Druid vs. Apache IoTDB vs. Citus vs. Elasticsearch vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Document store

Document store
Spatial DBMS
Vector DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

druid.apache.org

iotdb.apache.org

www.citusdata.com

www.elastic.co/elasticsearch

rocksdb.org

Technical documentation

druid.apache.org/docs/latest/design

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

docs.citusdata.com

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Elastic

Facebook, Inc.

Initial release

2012

2018

2010

2013

Current release

29.0.1, April 2024

1.1.0, April 2023

8.1, December 2018

8.6, January 2023

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

AGPL, commercial license also available

Open Source

Elastic License

Open Source

BSD

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++

Server operating systems

Linux
OS X
Unix

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL for querying

SQL-like query language

yes

standard, with numerous extensions

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Native API

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

Java API
RESTful HTTP/JSON API

C++ API
Java API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

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

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

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

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

other methods possible by using 3rd party extensions

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

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

Memcached and Redis integration

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes

fine grained access rights according to SQL-standard

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