DBMS > Alibaba Cloud Table Store vs. CouchDB vs. Google Cloud Firestore vs. Hive vs. Redis

System Properties Comparison Alibaba Cloud Table Store vs. CouchDB vs. Google Cloud Firestore vs. Hive vs. Redis

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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.

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

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Primary database model

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	157.80
Rank	#6	Overall
	#1	Key-value stores

Website

www.alibabacloud.com/product/table-store

couchdb.apache.org

firebase.google.com/products/firestore

hive.apache.org

redis.com
redis.io

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.couchdb.org/en/stable

firebase.google.com/docs/firestore

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

docs.redis.com/latest/index.html
redis.io/docs

Developer

Alibaba

Apache Software Foundation

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

Google

Apache Software Foundation

initially developed by Facebook

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Initial release

2016

2005

2017

2012

2009

Current release

3.3.3, December 2023

3.1.3, April 2022

7.2.4, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

Apache Version 2

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

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.

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

Erlang

Java

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

via views

yes

with RediSearch module

SQL

Support of SQL

SQL-like DML and DDL statements

with RediSQL module

APIs and other access methods

HTTP API

RESTful HTTP/JSON API

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

JDBC
ODBC
Thrift

proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

Java
Python

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

C++
Java
PHP
Python

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Server-side scripts

Stored procedures

View functions in JavaScript

yes, Firebase Rules & Cloud Functions

yes

user defined functions and integration of map-reduce

Lua; Redis Functions coming in Redis 7 (slides and Github)

Triggers

yes

yes, with Cloud Functions

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

improved architecture with release 2.0

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Multi-source replication

selectable replication factor

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

yes

query execution via MapReduce

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

atomic operations within a single document possible

yes

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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 based on subaccounts and tokens

Access rights for users can be defined per database

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

Access rights for users, groups and roles

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

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