DBMS > BoltDB vs. CouchDB vs. Google BigQuery vs. Impala

System Properties Comparison BoltDB vs. CouchDB vs. Google BigQuery vs. Impala

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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

Analytic DBMS for Hadoop

Primary database model

Key-value store

Document store

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store

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

Score	0.88
Rank	#220	Overall
	#35	Key-value stores

Score	14.88
Rank	#41	Overall
	#7	Document stores

Score	54.43
Rank	#21	Overall
	#14	Relational DBMS

Score	17.99
Rank	#39	Overall
	#24	Relational DBMS

Website

github.com/boltdb/bolt

couchdb.apache.org

cloud.google.com/bigquery

www.cloudera.com/products/open-source/apache-hadoop/impala.html

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

docs.cloudera.com/documentation/enterprise/latest/topics/impala.html

Developer

Apache Software Foundation

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

Google

Cloudera

Initial release

2013

2005

2010

2013

Current release

3.3.0, January 2023

4.1.0, June 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache version 2

commercial

Open Source

Apache Version 2

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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

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

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

improved architecture with release 2.0

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

atomic operations within a single document possible

Since BigQuery is designed for querying data

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.

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)

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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