DBMS > IBM Db2 Event Store vs. PouchDB vs. Spark SQL vs. SQLite vs. Warp 10

System Properties Comparison IBM Db2 Event Store vs. PouchDB vs. Spark SQL vs. SQLite vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

JavaScript DBMS with an API inspired by CouchDB

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Event Store
Time Series DBMS

Document store

Relational DBMS

Time Series 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	2.34
Rank	#112	Overall
	#21	Document stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

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

Technical documentation

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

pouchdb.com/guides

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

www.warp10.io/content/02_Getting_started

Developer

IBM

Apache Software Foundation

Dwayne Richard Hipp

SenX

Initial release

2017

2012

2014

2000

2015

Current release

2.0

7.1.1, June 2019

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache License 2.0

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++

JavaScript

Scala

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

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

Linux
OS X
Windows

server-less

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP API
Jupyter
WebSocket

Supported programming languages

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

JavaScript

Java
Python
R
Scala

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

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

yes, utilizing Spark Core

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

based on HBase

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

via file-system locks

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 IndexedDB, WebSQL or LevelDB as backend

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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.