DBMS > IBM Db2 Event Store vs. InfinityDB vs. PouchDB vs. SingleStore vs. SQLite

System Properties Comparison IBM Db2 Event Store vs. InfinityDB vs. PouchDB vs. SingleStore vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

A Java embedded Key-Value Store which extends the Java Map interface

JavaScript DBMS with an API inspired by CouchDB

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Widely used embeddable, in-process RDBMS

Primary database model

Event Store
Time Series DBMS

Key-value store

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

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

Technical documentation

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

boilerbay.com/infinitydb/manual

pouchdb.com/guides

docs.singlestore.com

www.sqlite.org/docs.html

Developer

IBM

Boiler Bay Inc.

Apache Software Foundation

SingleStore Inc.

Dwayne Richard Hipp

Initial release

2017

2002

2012

2013

2000

Current release

2.0

4.0

7.1.1, June 2019

8.5, January 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

commercial

free developer edition available

Open Source

Public Domain

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.

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Implementation language

C and C++

Java

JavaScript

C++, Go

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

Linux

64 bit version required

server-less

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

manual creation possible, using inversions based on multi-value capability

yes

via views

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

but no triggers and foreign keys

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP REST

only for PouchDB Server
JavaScript API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

Java

JavaScript

Bash
C
C#
Java
JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

hash partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication

stores two copies of each physical data partition on two separate nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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 IndexedDB, WebSQL or LevelDB as backend

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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 control via users, groups and roles

More information provided by the system vendor

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Key customers

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Market metrics

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Licensing and pricing models

F ree Tier and Enterprise Edition
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