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

System Properties Comparison Apache Phoenix vs. Bangdb 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

Converged and high performance database for device data, events, time series, document and graph

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

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

phoenix.apache.org

bangdb.com

www.ibm.com/products/cloudant

spark.apache.org/sql

Technical documentation

phoenix.apache.org

docs.bangdb.com

cloud.ibm.com/docs/Cloudant

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

Developer

Apache Software Foundation

Sachin Sinha, BangDB

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

BangDB 2.0, October 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

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

C, C++

Erlang

Scala

Server operating systems

Linux
Unix
Windows

Linux

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

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

SQL like support with command line tool

SQL-like DML and DDL statements

APIs and other access methods

JDBC

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

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

C
C#
C++
Java
Python

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, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor, Knob for CAP (enterprise version only)

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

Tunable consistency, set CAP knob accordingly

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

yes, optimistic concurrency control

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

User concepts

Access control

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

yes (enterprise version only)

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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