DBMS > Heroic vs. Manticore Search vs. Microsoft SQL Server vs. NSDb vs. NuoDB

System Properties Comparison Heroic vs. Manticore Search vs. Microsoft SQL Server vs. NSDb vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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	0.51
Rank	#255	Overall
	#21	Time Series DBMS

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	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

github.com/spotify/heroic

manticoresearch.com

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

nsdb.io

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

spotify.github.io/heroic

manual.manticoresearch.com

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

nsdb.io/Architecture

doc.nuodb.com

Developer

Spotify

Manticore Software

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2014

2017

1989

2017

2013

Current release

6.0, February 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

limited edition free

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Java, Scala

C++

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux
Windows

Linux
macOS

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

schema-free

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

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

via Elasticsearch

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

HQL (Heroic Query Language, a JSON-based language)
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)

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
ODBC

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

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

Server-side scripts

Stored procedures

user defined functions

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

Java, SQL

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

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication based on Galera library

yes, but depending on the SQL-Server Edition

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

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

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

User concepts

Access control

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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