DBMS > Apache Druid vs. JanusGraph vs. Linter vs. Manticore Search vs. Postgres-XL

System Properties Comparison Apache Druid vs. JanusGraph vs. Linter vs. Manticore Search vs. Postgres-XL

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

RDBMS for high security requirements

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

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	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

docs.janusgraph.org

manual.manticoresearch.com

www.postgres-xl.org/documentation

Developer

Apache Software Foundation and contributors

Linux Foundation; originally developed as Titan by Aurelius

relex.ru

Manticore Software

Initial release

2012

2017

1990

2017

2014

since 2012, originally named StormDB

Current release

29.0.1, April 2024

0.6.3, February 2023

6.0, February 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

commercial

Open Source

GPL version 2

Open Source

Mozilla public license

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 and C++

C++

Server operating systems

Linux
OS X
Unix

Linux
OS X
Unix
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

FreeBSD
Linux
macOS
Windows

Linux
macOS

Data scheme

yes

schema-less columns are supported

yes

Fixed schema

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

yes

XML type, but no XML query functionality

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL for querying

yes

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Clojure
Java
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

Sharding

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Source-replica replication

Synchronous replication based on Galera library

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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