DBMS > Microsoft SQL Server vs. Percona Server for MongoDB vs. TigerGraph vs. Vertica

System Properties Comparison Microsoft SQL Server vs. Percona Server for MongoDB vs. TigerGraph vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

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.

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	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	0.50
Rank	#250	Overall
	#39	Document stores

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

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

www.percona.com/mongodb/software/percona-server-for-mongodb

www.tigergraph.com

www.vertica.com

Technical documentation

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

docs.percona.com/percona-distribution-for-mongodb

docs.tigergraph.com

vertica.com/documentation

Developer

Microsoft

Percona

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1989

2015

2017

2005

Current release

SQL Server 2022, November 2022

3.4.10-2.10, November 2017

12.0.3, January 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL Version 2

commercial

Limited community edition free

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

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Implementation language

C++

Server operating systems

Linux
Windows

Linux

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

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

SQL

Support of SQL

yes

SQL-like query language (GSQL)

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

APIs and other access methods

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

proprietary protocol using JSON

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

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

character-based, interactive, front-end utility

Supported programming languages

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

C++
Java

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

Server-side scripts

Stored procedures

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

JavaScript

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Source-replica replication

Multi-source replication

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

In-memory capabilities

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

yes

via In-Memory Engine

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Role-based access control

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

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