DBMS > Apache Phoenix vs. HBase vs. Heroic vs. Snowflake

System Properties Comparison Apache Phoenix vs. HBase vs. Heroic vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

phoenix.apache.org

hbase.apache.org

github.com/spotify/heroic

www.snowflake.com

Technical documentation

phoenix.apache.org

hbase.apache.org/book.html

spotify.github.io/heroic

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Spotify

Snowflake Computing Inc.

Initial release

2014

2008

2014

Current release

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

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
Unix
Windows

using Cygwin

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free, schema definition possible

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

via Elasticsearch

SQL

Support of SQL

yes

APIs and other access methods

JDBC

Java API
RESTful HTTP API
Thrift

HQL (Heroic Query Language, a JSON-based language)
HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes

Coprocessors in Java

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users with fine-grained authorization concept, user roles and pluggable authentication

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