DBMS > Ingres vs. Microsoft Azure Synapse Analytics vs. PostgreSQL vs. Splice Machine vs. Transwarp Hippo

System Properties Comparison Ingres vs. Microsoft Azure Synapse Analytics vs. PostgreSQL vs. Splice Machine vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

Well established RDBMS

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Vector DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.05
Rank	#386	Overall
	#15	Vector DBMS

Website

www.actian.com/databases/ingres

azure.microsoft.com/services/synapse-analytics

www.postgresql.org

splicemachine.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

docs.actian.com/ingres

docs.microsoft.com/azure/synapse-analytics

www.postgresql.org/docs

splicemachine.com/how-it-works

Developer

Actian Corporation

Microsoft

PostgreSQL Global Development Group

www.postgresql.org/developer

Splice Machine

Initial release

1974

originally developed at University Berkely in early 1970s

2016

1989

1989: Postgres, 1996: PostgreSQL

2014

2023

Current release

11.2, May 2022

16.3, May 2024

3.1, March 2021

1.0, May 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

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Implementation language

C++

Java

C++

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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.

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

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

standard with numerous extensions

yes

APIs and other access methods

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

C#
Java
PHP

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java
Python

Server-side scripts

Stored procedures

yes

Transact SQL

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding, horizontal partitioning

partitioning by range, list and (since PostgreSQL 11) by hash

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

yes

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

Role based access control and fine grained access rights

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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