DBMS > Apache Druid vs. IRONdb vs. Manticore Search vs. Microsoft SQL Server vs. TerminusDB

System Properties Comparison Apache Druid vs. IRONdb vs. Manticore Search vs. Microsoft SQL Server vs. TerminusDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Microsofts flagship relational DBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS

Document store
RDF store

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	0.22
Rank	#312	Overall
	#21	Search engines

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

druid.apache.org

www.circonus.com/solutions/time-series-database/

manticoresearch.com

www.microsoft.com/en-us/sql-server

terminusdb.com

Technical documentation

druid.apache.org/docs/latest/design

docs.circonus.com/irondb/category/getting-started

manual.manticoresearch.com

learn.microsoft.com/en-US/sql/sql-server

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation and contributors

Circonus LLC.

Manticore Software

Microsoft

DataChemist Ltd.

Initial release

2012

2017

1989

2018

Current release

29.0.1, April 2024

V0.10.20, January 2018

6.0, February 2023

SQL Server 2022, November 2022

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2

commercial

restricted free version is available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Prolog, Rust

Server operating systems

Linux
OS X
Unix

Linux

FreeBSD
Linux
macOS
Windows

Linux
Windows

Linux

Data scheme

yes

schema-less columns are supported

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL for querying

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript
Python

Server-side scripts

Stored procedures

yes, in Lua

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Automatic, metric affinity per node

Sharding

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

configurable replication factor, datacenter aware

Synchronous replication based on Galera library

yes, but depending on the SQL-Server Edition

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

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

ACID

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 journaling

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

Role-based access control

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