DBMS > Manticore Search vs. Microsoft Azure Table Storage vs. Oracle Rdb vs. Snowflake vs. TimescaleDB

System Properties Comparison Manticore Search vs. Microsoft Azure Table Storage vs. Oracle Rdb vs. Snowflake vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

A Wide Column Store for rapid development using massive semi-structured datasets

Cloud-based data warehousing service for structured and semi-structured data

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

manticoresearch.com

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/rdb.html

www.snowflake.com

www.timescale.com

Technical documentation

manual.manticoresearch.com

www.oracle.com/database/technologies/related/rdb-doc.html

docs.snowflake.net/manuals/index.html

docs.timescale.com

Developer

Manticore Software

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Snowflake Computing Inc.

Timescale

Initial release

2017

2012

1984

2014

2017

Current release

6.0, February 2023

7.4.1.1, 2021

2.15.0, May 2024

License

Commercial or Open Source

Open Source

GPL version 2

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

FreeBSD
Linux
macOS
Windows

hosted

HP Open VMS

hosted

Linux
OS X
Windows

Data scheme

Fixed schema

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

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

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

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

RESTful HTTP API

CLI Client
JDBC
ODBC

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

Supported programming languages

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Implicit feature of the cloud service

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

optimistic locking

yes, on a single node

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 capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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