DBMS > Google BigQuery vs. Hive vs. Percona Server for MongoDB vs. Sphinx

System Properties Comparison Google BigQuery vs. Hive vs. Percona Server for MongoDB vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Document store

Search engine

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	0.55
Rank	#253	Overall
	#39	Document stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

cloud.google.com/bigquery

hive.apache.org

www.percona.com/mongodb/software/percona-server-for-mongodb

sphinxsearch.com

Technical documentation

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.percona.com/percona-distribution-for-mongodb

sphinxsearch.com/docs

Developer

Google

Apache Software Foundation

initially developed by Facebook

Percona

Sphinx Technologies Inc.

Initial release

2010

2012

2015

2001

Current release

3.1.3, April 2022

3.4.10-2.10, November 2017

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL Version 2

Open Source

GPL version 2, commercial licence 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

Java

C++

Server operating systems

hosted

All OS with a Java VM

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

proprietary protocol using JSON

Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

via In-Memory Engine

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

Access rights for users and roles

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