DBMS > Google BigQuery vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Riak KV vs. Typesense

System Properties Comparison Google BigQuery vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Riak KV vs. Typesense

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Globally distributed, horizontally scalable, multi-model database service

Oracles in-memory data grid solution

Distributed, fault tolerant key-value store

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

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

Search engine

Secondary database models

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.76
Rank	#219	Overall
	#14	Search engines

Website

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

www.oracle.com/java/coherence

typesense.org

Technical documentation

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/middleware/standalone/coherence

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

typesense.org/docs

Developer

Google

Microsoft

Oracle

OpenSource, formerly Basho Technologies

Initial release

2010

2014

2007

2009

2015

Current release

14.1, August 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

GPL V3

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

Erlang

C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X

Linux

Data scheme

yes

schema-free

pre-defined schema optional

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

restricted

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JCache
JPA
RESTful HTTP API

HTTP API
Native Erlang Interface

RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C++
Java

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

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

Erlang

Triggers

JavaScript

yes

Live Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, with selectable consistency level

selectable replication factor

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

Multi-item ACID transactions with snapshot isolation within a partition

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

authentification to access the cache via certificates or http basic authentication

yes, using Riak Security

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