DBMS > Machbase Neo vs. Microsoft SQL Server vs. Sphinx vs. Vertica vs. XTDB

System Properties Comparison Machbase Neo vs. Microsoft SQL Server vs. Sphinx vs. Vertica vs. XTDB

Editorial information provided by DB-Engines

Name

Description

TimeSeries DBMS for AIoT and BigData

Microsofts flagship relational DBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

machbase.com

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

sphinxsearch.com

www.vertica.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

machbase.com/dbms

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

sphinxsearch.com/docs

vertica.com/documentation

www.xtdb.com/docs

Developer

Machbase

Microsoft

Sphinx Technologies Inc.

OpenText

previously Micro Focus and Hewlett Packard

Juxt Ltd.

Initial release

2013

1989

2001

2005

2019

Current release

V8.0, August 2023

SQL Server 2022, November 2022

3.5.1, February 2023

12.0.3, January 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free test version available

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

commercial

Limited community edition free

Open Source

MIT License

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Clojure

Server operating systems

Linux
macOS
Windows

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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.

yes

Secondary indexes

yes

full-text index on all search fields

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language (SphinxQL)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

limited SQL, making use of Apache Calcite

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

Proprietary protocol

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Clojure
Java

Server-side scripts

Stored procedures

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

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

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

horizontal partitioning, hierarchical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, but depending on the SQL-Server Edition

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

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

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 Sphinx index.

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

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Licensing and pricing models

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