DBMS > Apache Phoenix vs. Spark SQL vs. STSdb vs. Warp 10

System Properties Comparison Apache Phoenix vs. Spark SQL vs. STSdb vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

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

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Key-value store

Time Series DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

phoenix.apache.org

spark.apache.org/sql

github.com/STSSoft/STSdb4

www.warp10.io

Technical documentation

phoenix.apache.org

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

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation

STS Soft SC

SenX

Initial release

2014

2011

2015

Current release

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

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

Java

Server operating systems

Linux
Unix
Windows

Linux
OS X
Windows

Windows

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

JDBC
ODBC

.NET Client API

HTTP API
Jupyter
WebSocket

Supported programming languages

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

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

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

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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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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