DBMS > Apache IoTDB vs. Manticore Search vs. Qdrant vs. Riak KV

System Properties Comparison Apache IoTDB vs. Manticore Search vs. Qdrant vs. Riak KV

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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

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

A high-performance vector database with neural network or semantic-based matching

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

iotdb.apache.org

manticoresearch.com

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

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

manual.manticoresearch.com

qdrant.tech/documentation

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation

Manticore Software

Qdrant

OpenSource, formerly Basho Technologies

Initial release

2018

2017

2021

2009

Current release

1.1.0, April 2023

6.0, February 2023

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

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++

Rust

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
macOS
Windows

Docker
Linux
macOS
Windows

Linux
OS X

Data scheme

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

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

Numbers, Strings, Geo, 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

yes

Keywords, numberic ranges, geo, full-text

restricted

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JDBC
Native API

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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

Sharding

no "single point of failure"

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

Synchronous replication based on Galera library

Collection-level replication

selectable replication factor

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

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency, tunable consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

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.

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

Key-based authentication

yes, using Riak Security

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