DBMS > Apache Druid vs. EsgynDB vs. Manticore Search vs. Riak TS vs. Spark SQL

System Properties Comparison Apache Druid vs. EsgynDB vs. Manticore Search vs. Riak TS vs. Spark SQL

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS
Time Series DBMS

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

manual.manticoresearch.com

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation and contributors

Esgyn

Manticore Software

Open Source, formerly Basho Technologies

Apache Software Foundation

Initial release

2012

2015

2017

2015

2014

Current release

29.0.1, April 2024

6.0, February 2023

3.0.0, September 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2

Open Source

Apache 2.0

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++, Java

C++

Erlang

Scala

Server operating systems

Linux
OS X
Unix

Linux

FreeBSD
Linux
macOS
Windows

Linux
OS X

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

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

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.

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

yes, limited

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

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

HTTP API
Native Erlang Interface

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

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

Sharding

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

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication between multi datacenters

Synchronous replication based on Galera library

selectable replication factor

none

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 datasets 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

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.

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

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