DBMS > Apache Druid vs. eXtremeDB vs. Manticore Search vs. RocksDB vs. Rockset

System Properties Comparison Apache Druid vs. eXtremeDB vs. Manticore Search vs. RocksDB vs. Rockset

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

Natively in-memory DBMS with options for persistency, high-availability and clustering

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Relational DBMS
Time Series DBMS

Search engine

Key-value store

Document store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Relational DBMS
Search engine

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	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

Technical documentation

druid.apache.org/docs/latest/design

www.mcobject.com/docs/extremedb.htm

manual.manticoresearch.com

github.com/facebook/rocksdb/wiki

docs.rockset.com

Developer

Apache Software Foundation and contributors

McObject

Manticore Software

Facebook, Inc.

Rockset

Initial release

2012

2001

2017

2013

2019

Current release

29.0.1, April 2024

8.2, 2021

6.0, February 2023

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

C++

Server operating systems

Linux
OS X
Unix

AIX
HP-UX
Linux
macOS
Solaris
Windows

FreeBSD
Linux
macOS
Windows

Linux

hosted

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

dynamic typing

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.

support of XML interfaces available

Can index from XML

ingestion from XML files supported

Secondary indexes

yes

full-text index on all search fields

all fields are automatically indexed

SQL

Support of SQL

SQL for querying

yes

with the option: eXtremeSQL

SQL-like query language

Read-only SQL queries, including JOINs

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

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

C++ API
Java API

HTTP REST

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
Java
Lua
Python
Scala

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

C
C++
Go
Java
Perl
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

horizontal partitioning / sharding

Sharding

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

horizontal partitioning

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Synchronous replication based on Galera library

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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

Access rights for users and organizations can be defined via Rockset console

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

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