DBMS > Manticore Search vs. Microsoft SQL Server vs. NSDb vs. SAP SQL Anywhere vs. TerarkDB

System Properties Comparison Manticore Search vs. Microsoft SQL Server vs. NSDb vs. SAP SQL Anywhere vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

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

Microsofts flagship relational DBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS

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

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

manticoresearch.com

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

nsdb.io

www.sap.com/products/technology-platform/sql-anywhere.html

github.com/bytedance/terarkdb

Technical documentation

manual.manticoresearch.com

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

nsdb.io/Architecture

help.sap.com/docs/SAP_SQL_Anywhere

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Manticore Software

Microsoft

SAP

formerly Sybase

ByteDance, originally Terark

Initial release

2017

1989

2017

1992

2016

Current release

6.0, February 2023

SQL Server 2022, November 2022

17, July 2015

License

Commercial or Open Source

Open Source

GPL version 2

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Scala

C++

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux
Windows

Linux
macOS

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

Fixed schema

yes

schema-free

Typing

predefined data types such as float or date

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

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

yes

APIs and other access methods

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

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

gRPC
HTTP REST
WebSocket

ADO.NET
HTTP API
JDBC
ODBC

C++ API
Java API

Supported programming languages

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

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

Java
Scala

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

C++
Java

Server-side scripts

Stored procedures

user defined functions

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

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

yes, but depending on the SQL-Server Edition

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Using Apache Lucene

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

fine grained access rights according to SQL-standard

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