DBMS > Coveo vs. etcd vs. Google Cloud Firestore vs. MonetDB vs. Riak TS

System Properties Comparison Coveo vs. etcd vs. Google Cloud Firestore vs. MonetDB vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

A distributed reliable key-value store

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A relational database management system that stores data in columns

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	2.14
Rank	#116	Overall
	#11	Search engines

Score	7.05
Rank	#53	Overall
	#5	Key-value stores

Score	6.63
Rank	#54	Overall
	#9	Document stores

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

www.coveo.com

etcd.io
github.com/etcd-io/etcd

firebase.google.com/products/firestore

www.monetdb.org

Technical documentation

docs.coveo.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

firebase.google.com/docs/firestore

www.monetdb.org/Documentation

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

Developer

Coveo

Google

MonetDB BV

Open Source, formerly Basho Technologies

Initial release

2012

2017

2004

2015

Current release

3.4, August 2019

Dec2023 (11.49), December 2023

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

Open Source

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

Erlang

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

schema-free

yes

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes, limited

APIs and other access methods

RESTful HTTP API

gRPC
JSON over HTTP

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

C#
Java
JavaScript
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

yes, Firebase Rules & Cloud Functions

yes, in SQL, C, R

Erlang

Triggers

yes

yes, watching key changes

yes, with Cloud Functions

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

none

Source-replica replication available in experimental status

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

yes

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.

User concepts

Access control

granular access controls, API key management, content filters

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

More information provided by the system vendor

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Related products and services

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More resources

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