DBMS > Apache Phoenix vs. Postgres-XL vs. Vertica

System Properties Comparison Apache Phoenix vs. Postgres-XL vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Cloud or on-premises analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Column oriented

Secondary database models

Document store
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	2.81
Rank	#108	Overall
	#56	Relational DBMS

Score	0.71
Rank	#208	Overall
	#99	Relational DBMS

Score	19.04
Rank	#38	Overall
	#24	Relational DBMS

Website

phoenix.apache.org

www.postgres-xl.org

www.vertica.com

Technical documentation

phoenix.apache.org

www.postgres-xl.org/documentation

vertica.com/documentation

Developer

Apache Software Foundation

Micro Focus

prior to that Hewlett Packard

Initial release

2014

since 2012, originally named StormDB

2005

Current release

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

10 R1, October 2018

11.1, February 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Mozilla public license

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Vertica Accelerator’s high-performance analytics and machine learning with SQL or Python is available as a managed service.

Implementation language

Java

C++

Server operating systems

Linux
Unix
Windows

Linux
macOS

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

yes

yes, but unstructured data can be stored in specific Flex-Tables

Typing

predefined data types such as float or date

yes

yes: BINARY, BOOLEAN, CHAR, VARCHAR, LONG VARCHAR, DATE, TIME, TIMESTAMP, INTERVAL, INTERVAL DAY TO SECOND, INTERVAL YEAR TO MONTH, DOUBLE PRECISION, FLOAT, INTEGER, BIGINT, SMALLINT,NUMERIC, DECIMAL, NUMBER, MONEY,GEOMETRY

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

XML type, but no XML query functionality

Secondary indexes

yes

No Indexes Required

SQL

Support of SQL

yes

distributed, parallel query execution

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC

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

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
C++
Java
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

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

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy on-premises,...
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Competitive advantages

Fast, scalable and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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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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