DBMS > Apache Pinot vs. Google Cloud Firestore vs. Google Cloud Spanner vs. jBASE vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Pinot vs. Google Cloud Firestore vs. Google Cloud Spanner vs. jBASE vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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 horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A robust multi-value DBMS comprising development tools and middleware

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

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

Website

pinot.apache.org

firebase.google.com/products/firestore

cloud.google.com/spanner

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.pinot.apache.org

firebase.google.com/docs/firestore

cloud.google.com/spanner/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation and contributors

Google

Rocket Software (formerly Zumasys)

Microsoft

Initial release

2015

2017

1991

2014

Current release

1.0.0, September 2023

5.7

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java JDK11 or higher

hosted

AIX
Linux
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

SQL-like query language

yes

Query statements complying to ANSI 2011

Embedded SQL for jBASE in BASIC

SQL-like query language

APIs and other access methods

JDBC

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

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

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

Supported programming languages

Go
Java
Python

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

Go
Java
JavaScript (Node.js)
Python

.Net
Basic
Jabbascript
Java

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

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

JavaScript

Triggers

yes, with Cloud Functions

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication with 3 replicas for regional instances.

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

using Google Cloud Dataflow

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

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

yes

ACID

Strict serializable isolation

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

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 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 based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

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