DBMS > IBM Db2 Event Store vs. InterSystems Caché vs. Newts vs. Postgres-XL vs. Sequoiadb

System Properties Comparison IBM Db2 Event Store vs. InterSystems Caché vs. Newts vs. Postgres-XL vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Distributed Event Store optimized for Internet of Things use cases

A multi-model DBMS and application server

Time Series DBMS based on Cassandra

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

NewSQL database with distributed OLTP and SQL

Primary database model

Event Store
Time Series DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

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

www.intersystems.com/products/cache

opennms.github.io/newts

www.postgres-xl.org

www.sequoiadb.com

Technical documentation

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

docs.intersystems.com

github.com/OpenNMS/newts/wiki

www.postgres-xl.org/documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

IBM

InterSystems

OpenNMS Group

Sequoiadb Ltd.

Initial release

2017

1997

2014

since 2012, originally named StormDB

2013

Current release

2.0

2018.1.4, May 2020

10 R1, October 2018

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Apache 2.0

Open Source

Mozilla public license

Open Source

Server: AGPL; Client: Apache V2

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++

Java

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
macOS

Linux

Data scheme

yes

depending on used data model

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

distributed, parallel query execution

SQL-like query language

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
RESTful HTTP API

HTTP REST
Java API

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

proprietary protocol using JSON

Supported programming languages

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

C#
C++
Java

Java

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

selectable replication factor

based on Cassandra

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

Document is locked during a transaction

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

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, groups and roles

fine grained access rights according to SQL-standard

simple password-based access control

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