DBMS > IBM Db2 Event Store vs. Postgres-XL vs. Quasardb vs. YottaDB

System Properties Comparison IBM Db2 Event Store vs. Postgres-XL vs. Quasardb vs. YottaDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Distributed, high-performance timeseries database

A fast and solid embedded Key-value store

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Time Series DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

Relational DBMS

using the Octo plugin

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

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

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

www.postgres-xl.org

quasar.ai

yottadb.com

Technical documentation

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

www.postgres-xl.org/documentation

doc.quasar.ai/master

yottadb.com/resources/documentation

Developer

IBM

quasardb

YottaDB, LLC

Initial release

2017

2014

since 2012, originally named StormDB

2009

2001

Current release

2.0

10 R1, October 2018

3.14.1, January 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Mozilla public license

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

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

Linux
macOS

BSD
Linux
OS X
Windows

Docker
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

XML type, but no XML query functionality

Secondary indexes

yes

with tags

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

distributed, parallel query execution

SQL-like query language

by using the Octo plugin

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

HTTP API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop 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

MVCC

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

Users and groups based on OS-security mechanisms

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Advancements in streaming data storage, real-time analysis and machine learning
25 July 2019, IBM

How IBM Is Turning Db2 into an 'AI Database'
3 June 2019, Datanami

Best cloud databases of 2022
4 October 2022, ITPro

Why a robust data management strategy is essential today | IBM HDM
19 September 2019, Express Computer

provided by Google News

Record quasar is most luminous object in the universe
20 February 2024, EarthSky

Quasar Partners with PTC to Empower IoT Customers with High-Performance Data Solutions
11 September 2023, Datanami

QUASAR yacht (Bilgin, 46.8m, 2016)
3 July 2023, Boat International

Quasar Selected by National Renewable Energy Laboratory to Help with Energy System De-risking and Optimization
6 June 2023, PR Newswire

ESO - Data Release 1: The UVES Spectral Quasar Absorption Database (SQUAD)
8 March 2019, ESO.org

provided by Google News

Share this page