DBMS > IBM Db2 Event Store vs. Ingres vs. STSdb vs. Vertica

System Properties Comparison IBM Db2 Event Store vs. Ingres vs. STSdb vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Well established RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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

Event Store
Time Series DBMS

Relational DBMS

Key-value store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	4.67
Rank	#77	Overall
	#42	Relational DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

www.ibm.com/products/db2-event-store

www.actian.com/databases/ingres

github.com/STSSoft/STSdb4

www.vertica.com

Technical documentation

www.ibm.com/docs/en/db2-event-store

docs.actian.com/ingres

vertica.com/documentation

Developer

IBM

Actian Corporation

STS Soft SC

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2017

1974

originally developed at University Berkely in early 1970s

2011

2005

Current release

2.0

11.2, May 2022

4.0.8, September 2015

12.0.3, January 2023

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

GPLv2, commercial license available

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

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

Implementation language

C and C++

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Windows

Linux

Data scheme

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

primitive types and user defined types (classes)

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.

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

Secondary indexes

yes

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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

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

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

.NET Client API

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

character-based, interactive, front-end utility

Supported programming languages

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

C#
Java

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

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Ingres Replicator

none

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

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

No - written data is immutable

yes

MVCC

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

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

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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