DBMS > Alibaba Cloud Table Store vs. Google Cloud Firestore vs. Riak TS vs. RocksDB vs. Solr

System Properties Comparison Alibaba Cloud Table Store vs. Google Cloud Firestore vs. Riak TS vs. RocksDB vs. Solr

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

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.

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.alibabacloud.com/product/table-store

firebase.google.com/products/firestore

rocksdb.org

solr.apache.org

Technical documentation

www.alibabacloud.com/help/en/tablestore

firebase.google.com/docs/firestore

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

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Alibaba

Google

Open Source, formerly Basho Technologies

Facebook, Inc.

Apache Software Foundation

Initial release

2016

2017

2015

2013

2006

Current release

3.0.0, September 2022

9.2.1, May 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2

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

C++

Java

Server operating systems

hosted

Linux
OS X

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

yes

All search fields are automatically indexed

SQL

Support of SQL

yes, limited

Solr Parallel SQL Interface

APIs and other access methods

HTTP API

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

HTTP API
Native Erlang Interface

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

Java
Python

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

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Erlang

Java plugins

Triggers

yes, with Cloud Functions

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

Atomic single-row operations

yes

optimistic locking

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

User concepts

Access control

Access rights based on subaccounts and tokens

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

yes

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