DBMS > Greenplum vs. HarperDB vs. Ingres vs. Microsoft SQL Server vs. TigerGraph

System Properties Comparison Greenplum vs. HarperDB vs. Ingres vs. Microsoft SQL Server vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

Well established RDBMS

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	7.71
Rank	#46	Overall
	#29	Relational DBMS

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Website

greenplum.org

www.harperdb.io

www.actian.com/databases/ingres

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

www.tigergraph.com

Technical documentation

docs.greenplum.org

docs.harperdb.io/docs

docs.actian.com/ingres

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

docs.tigergraph.com

Developer

Pivotal Software Inc.

HarperDB

Actian Corporation

Microsoft

Initial release

2005

2017

1974

originally developed at University Berkely in early 1970s

1989

2017

Current release

7.0.0, September 2023

3.1, August 2021

12.0, July 2024

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Node.js

C++

Server operating systems

Linux

Linux
OS X

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
Windows

Linux

Data scheme

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

since Version 4.2

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

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

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

Supported programming languages

C
Java
Perl
Python
R

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

C++
Java

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

yes

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

horizontal partitioning

Ingres Star to access multiple databases simultaneously

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

the nodes on which a table resides can be defined

Ingres Replicator

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

Access rights for users and roles

fine grained access rights according to SQL-standard

Role-based access control

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