DBMS > Apache Druid vs. Apache IoTDB vs. Couchbase vs. Prometheus

System Properties Comparison Apache Druid vs. Apache IoTDB vs. Couchbase vs. Prometheus

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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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Open-source Time Series DBMS and monitoring system

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Document store

Time Series DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series 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	16.59
Rank	#36	Overall
	#5	Document stores

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

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

docs.couchbase.com

prometheus.io/docs

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Couchbase, Inc.

Initial release

2012

2018

2011

2015

Current release

29.0.1, April 2024

1.1.0, April 2023

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache 2.0

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, C++, Go and Erlang

Server operating systems

Linux
OS X
Unix

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like query language

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Native API

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Triggers

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

Automatic Sharding

Sharding

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

Multi-source replication

including cross data center replication
Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

none

Foreign keys

Referential integrity

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

In-memory capabilities

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

yes

Ephemeral buckets

User concepts

Access control

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

yes

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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