DBMS > Google Cloud Firestore vs. HBase vs. MarkLogic vs. Spark SQL

System Properties Comparison Google Cloud Firestore vs. HBase vs. MarkLogic vs. Spark SQL

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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.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Operational and transactional Enterprise NoSQL database

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store

Wide column store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

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

Score	10.03
Rank	#49	Overall
	#8	Document stores

Score	42.45
Rank	#26	Overall
	#2	Wide column stores

Score	9.62
Rank	#52	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#4	Search engines

Score	23.72
Rank	#35	Overall
	#21	Relational DBMS

Website

firebase.google.com/products/firestore

hbase.apache.org

www.marklogic.com

spark.apache.org/sql

Technical documentation

firebase.google.com/docs/firestore

hbase.apache.org

docs.marklogic.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

MarkLogic Corp.

Apache Software Foundation

Initial release

2017

2008

2001

2014

Current release

2.3.4, January 2021

9.0, 2017

3.3.0, June 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

Apache 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

Java

C++

Scala

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
OS X
Windows

Data scheme

schema-free

schema-free, schema definition possible

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92

SQL-like DML and DDL statements

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Coprocessors in Java

yes

via XQuery or JavaScript

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Single row ACID (across millions of columns)

ACID

can act as a resource manager in an XA/JTA transaction

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

yes, with Range Indexes

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Role-based access control at the document and subdocument levels

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