DBMS > Apache Druid vs. Ingres vs. Manticore Search vs. Riak KV vs. YottaDB

System Properties Comparison Apache Druid vs. Ingres vs. Manticore Search vs. Riak KV vs. YottaDB

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

Well established RDBMS

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

Distributed, fault tolerant key-value store

A fast and solid embedded Key-value store

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Search engine

Key-value store

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

Key-value store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Relational DBMS

using the Octo plugin

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	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

druid.apache.org

www.actian.com/databases/ingres

manticoresearch.com

yottadb.com

Technical documentation

druid.apache.org/docs/latest/design

docs.actian.com/ingres

manual.manticoresearch.com

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

yottadb.com/resources/documentation

Developer

Apache Software Foundation and contributors

Actian Corporation

Manticore Software

OpenSource, formerly Basho Technologies

YottaDB, LLC

Initial release

2012

1974

originally developed at University Berkely in early 1970s

2017

2009

2001

Current release

29.0.1, April 2024

11.2, May 2022

6.0, February 2023

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2

Open Source

Apache version 2, commercial enterprise edition

Open Source

AGPL 3.0

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++

Erlang

Server operating systems

Linux
OS X
Unix

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
macOS
Windows

Linux
OS X

Docker
Linux

Data scheme

yes

schema-less columns are supported

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

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.

but tools for importing/exporting data from/to XML-files available

Can index from XML

Secondary indexes

yes

full-text index on all search fields

restricted

SQL

Support of SQL

SQL for querying

yes

SQL-like query language

by using the Octo plugin

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

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

HTTP API
Native Erlang Interface

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

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

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

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

Sharding

manual/auto, time-based

horizontal partitioning

Ingres Star to access multiple databases simultaneously

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

yes, via HDFS, S3 or other storage engines

Ingres Replicator

Synchronous replication based on Galera library

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

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

fine grained access rights according to SQL-standard

yes, using Riak Security

Users and groups based on OS-security mechanisms

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