DBMS > Apache Drill vs. Apache Phoenix vs. Google Cloud Firestore vs. Kdb vs. MySQL

System Properties Comparison Apache Drill vs. Apache Phoenix vs. Google Cloud Firestore vs. Kdb vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

High performance Time Series DBMS

Widely used open source RDBMS

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store

Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

drill.apache.org

phoenix.apache.org

firebase.google.com/products/firestore

kx.com

www.mysql.com

Technical documentation

drill.apache.org/docs

phoenix.apache.org

firebase.google.com/docs/firestore

code.kx.com

dev.mysql.com/doc

Developer

Apache Software Foundation

Google

Kx Systems, a division of First Derivatives plc

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2014

2017

2000

kdb was released 2000, kdb+ in 2003

1995

Current release

1.20.3, January 2023

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

3.6, May 2018

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

free 32-bit version

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Windows

Linux
Unix
Windows

hosted

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

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.

yes

Secondary indexes

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

SQL-like query language (q)

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++

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

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes, Firebase Rules & Cloud Functions

user defined functions

yes

proprietary syntax

Triggers

yes, with Cloud Functions

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Using Cloud Dataflow

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

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