DBMS > Apache Phoenix vs. Derby vs. HEAVY.AI vs. MariaDB vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Derby vs. HEAVY.AI vs. MariaDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

phoenix.apache.org

db.apache.org/derby

github.com/heavyai/heavydb
www.heavy.ai

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

spark.apache.org/sql

Technical documentation

phoenix.apache.org

db.apache.org/derby/manuals/index.html

docs.heavy.ai

mariadb.com/kb/en/library

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

Developer

Apache Software Foundation

HEAVY.AI, Inc.

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Apache Software Foundation

Initial release

2014

1997

2016

2009

Fork of MySQL, which was first released in 1995

2014

Current release

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

10.17.1.0, November 2023

5.10, January 2022

11.3.2, February 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL version 2, commercial enterprise subscription available

Open Source

Apache 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.

STACKIT MariaDB offers MariaDB in a fully managed version in enterprise grade, 100% GDPR-compliant.

Implementation language

Java

C++ and CUDA

C and C++

Scala

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

Linux

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Dynamic columns are supported

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

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

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

Java

All languages supporting JDBC/ODBC/Thrift
Python

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

yes

PL/SQL compatibility added with version 10.3

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

several options for horizontal partitioning and Sharding

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

Multi-source replication
Source-replica replication

none

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

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

In-memory capabilities

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

yes

with MEMORY storage engine

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow, Verizon and Walgreens Featured...
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Market metrics

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Licensing and pricing models

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