DBMS > Apache Impala vs. CouchDB vs. Google BigQuery vs. Solr

System Properties Comparison Apache Impala vs. CouchDB vs. Google BigQuery vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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 widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Document store

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

impala.apache.org

couchdb.apache.org

cloud.google.com/bigquery

solr.apache.org

Technical documentation

impala.apache.org/impala-docs.html

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

solr.apache.org/resources.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

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

Google

Apache Software Foundation

Initial release

2013

2005

2010

2006

Current release

4.1.0, June 2022

3.3.3, December 2023

9.5.0, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

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

C++

Erlang

Java

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

via views

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

View functions in JavaScript

user defined functions

in JavaScript

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

optimistic locking

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, groups and roles

based on Apache Sentry and Kerberos

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)

yes

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