DBMS > CouchDB vs. Google BigQuery vs. HEAVY.AI vs. Ingres vs. Splunk

System Properties Comparison CouchDB vs. Google BigQuery vs. HEAVY.AI vs. Ingres vs. Splunk

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Well established RDBMS

Analytics Platform for Big Data

Primary database model

Document store

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial 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	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

couchdb.apache.org

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

www.actian.com/databases/ingres

www.splunk.com

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

docs.heavy.ai

docs.actian.com/ingres

docs.splunk.com/Documentation/Splunk

Developer

Apache Software Foundation

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

Google

HEAVY.AI, Inc.

Actian Corporation

Splunk Inc.

Initial release

2005

2010

2016

1974

originally developed at University Berkely in early 1970s

2003

Current release

3.3.3, December 2023

5.10, January 2022

11.2, May 2022

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Limited free edition and free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

C++ and CUDA

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

Splunk Search Processing Language for search commands

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

HTTP REST

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

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Sharding

Round robin

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Ingres Replicator

Multi-source replication

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

Eventual 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

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

MVCC

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

Access rights for users and roles

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