DBMS > CouchDB vs. Google BigQuery vs. Hawkular Metrics vs. IBM Db2 warehouse vs. Kdb

System Properties Comparison CouchDB vs. Google BigQuery vs. Hawkular Metrics vs. IBM Db2 warehouse vs. Kdb

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Cloud-based data warehousing service

High performance Time Series DBMS

Primary database model

Time Series DBMS
Vector DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Website

couchdb.apache.org

cloud.google.com/bigquery

www.hawkular.org

www.ibm.com/products/db2/warehouse

kx.com

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

code.kx.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Community supported by Red Hat

IBM

Kx Systems, a division of First Derivatives plc

Initial release

2005

2010

2014

2000

kdb was released 2000, kdb+ in 2003

Current release

3.3.3, December 2023

3.6, May 2018

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

commercial

free 32-bit version

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Import/export of XML data possible

yes

Secondary indexes

yes

via views

yes

table attribute 'grouped'

SQL

Support of SQL

yes

SQL-like query language (q)

APIs and other access methods

RESTful HTTP/JSON API

HTTP REST

.NET Client API
JDBC
ODBC
OLE DB

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

PL/SQL, SQL PL

user defined functions

Triggers

yes

via Hawkular Alerting

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Sharding

based on Cassandra

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

similar paradigm used for internal processing

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

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

Access rights for users can be defined per database

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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