DBMS > Google Cloud Firestore vs. Ignite vs. Microsoft Azure Cosmos DB vs. Sphinx vs. Tkrzw

System Properties Comparison Google Cloud Firestore vs. Ignite vs. Microsoft Azure Cosmos DB vs. Sphinx vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

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.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Globally distributed, horizontally scalable, multi-model database service

Open source search engine for searching in data from different sources, e.g. relational databases

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Document store

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Key-value store

Secondary database models

Spatial DBMS

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

firebase.google.com/products/firestore

ignite.apache.org

azure.microsoft.com/services/cosmos-db

sphinxsearch.com

dbmx.net/tkrzw

Technical documentation

firebase.google.com/docs/firestore

apacheignite.readme.io/docs

learn.microsoft.com/azure/cosmos-db

sphinxsearch.com/docs

Developer

Google

Apache Software Foundation

Microsoft

Sphinx Technologies Inc.

Mikio Hirabayashi

Initial release

2017

2015

2014

2001

2020

Current release

Apache Ignite 2.6

3.5.1, February 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Apache Version 2.0

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

C++, Java, .Net

C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

full-text index on all search fields

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

Proprietary protocol

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes (compute grid and cache interceptors can be used instead)

JavaScript

Triggers

yes, with Cloud Functions

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes (replicated cache)

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using specific database classes

User concepts

Access control

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

Security Hooks for custom implementations

Access rights can be defined down to the item level

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