DBMS > Apache Phoenix vs. Brytlyt vs. PouchDB vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Brytlyt vs. PouchDB vs. Spark SQL

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Name

Description

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Relational DBMS

Document store

Relational DBMS

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Trend Chart

Score	3.01
Rank	#103	Overall
	#52	Relational DBMS

Score	0.33
Rank	#269	Overall
	#125	Relational DBMS

Score	3.09
Rank	#98	Overall
	#18	Document stores

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

Technical documentation

phoenix.apache.org

pouchdb.com/guides

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

Developer

Apache Software Foundation

Brytlyt

Apache Software Foundation

Initial release

2014

2016

2012

2014

Current release

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

0.9, October 2016

7.1.1, June 2019

3.2.0, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++ and CUDA

JavaScript

Scala

Server operating systems

Linux
Unix
Windows

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

via views

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

JavaScript

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

fine grained access rights according to SQL-standard

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