DBMS > Apache Phoenix vs. EventStoreDB vs. IBM Cloudant vs. Spark SQL

System Properties Comparison Apache Phoenix vs. EventStoreDB vs. IBM Cloudant vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Database as a Service offering based on Apache CouchDB

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

Primary database model

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

phoenix.apache.org

www.eventstore.com

www.ibm.com/products/cloudant

spark.apache.org/sql

Technical documentation

phoenix.apache.org

developers.eventstore.com

cloud.ibm.com/docs/Cloudant

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

Developer

Apache Software Foundation

Event Store Limited

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Apache Software Foundation

Initial release

2014

2012

2010

2014

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

21.2, February 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

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

Erlang

Scala

Server operating systems

Linux
Unix
Windows

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

View functions (Map-Reduce) in JavaScript

Triggers

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
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights for users can be defined per database

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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