DBMS > Apache Druid vs. Apache IoTDB vs. Geode vs. Manticore Search

System Properties Comparison Apache Druid vs. Apache IoTDB vs. Geode vs. Manticore Search

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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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Key-value store

Search engine

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.22
Rank	#312	Overall
	#21	Search engines

Website

Technical documentation

druid.apache.org/docs/latest/design

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

geode.apache.org/docs

manual.manticoresearch.com

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Manticore Software

Initial release

2012

2018

2002

2017

Current release

29.0.1, April 2024

1.1.0, April 2023

1.1, February 2017

6.0, February 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

GPL version 2

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)

All OS with a Java VM

the JDK (8 or later) is also required

FreeBSD
Linux
macOS
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

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

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL for querying

SQL-like query language

SQL-like query language (OQL)

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Native API

Java Client API
Memcached protocol
RESTful HTTP API

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

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Cache Event Listeners

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

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

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

Synchronous replication based on Galera library

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

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

In-memory capabilities

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

yes

User concepts

Access control

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

yes

Access rights per client and object definable

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