DBMS > Google BigQuery vs. InterSystems Caché vs. Percona Server for MongoDB vs. Riak KV

System Properties Comparison Google BigQuery vs. InterSystems Caché vs. Percona Server for MongoDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

A multi-model DBMS and application server

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

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Document store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

cloud.google.com/bigquery

www.intersystems.com/products/cache

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

Technical documentation

cloud.google.com/bigquery/docs

docs.intersystems.com

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

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

InterSystems

Percona

OpenSource, formerly Basho Technologies

Initial release

2010

1997

2015

2009

Current release

2018.1.4, May 2020

3.4.10-2.10, November 2017

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

GPL Version 2

Open Source

Apache version 2, commercial enterprise edition

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

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux

Linux
OS X

Data scheme

yes

depending on used data model

schema-free

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.

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

HTTP API
Native Erlang Interface

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

JavaScript

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

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

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

yes, using Riak Security

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