Soumettre la recherche
Mettre en ligne
April 2014 HUG : Apache Phoenix
•
Télécharger en tant que PPTX, PDF
•
14 j'aime
•
6,202 vues
Yahoo Developer Network
Suivre
April 2014 HUG : Apache Phoenix
Lire moins
Lire la suite
Données & analyses
Technologie
Signaler
Partager
Signaler
Partager
1 sur 21
Télécharger maintenant
Recommandé
Apache phoenix: Past, Present and Future of SQL over HBAse
Apache phoenix: Past, Present and Future of SQL over HBAse
enissoz
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
DataWorks Summit/Hadoop Summit
The Evolution of a Relational Database Layer over HBase
The Evolution of a Relational Database Layer over HBase
DataWorks Summit
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Josh Elser
Five major tips to maximize performance on a 200+ SQL HBase/Phoenix cluster
Five major tips to maximize performance on a 200+ SQL HBase/Phoenix cluster
mas4share
Local Secondary Indexes in Apache Phoenix
Local Secondary Indexes in Apache Phoenix
Rajeshbabu Chintaguntla
Apache phoenix
Apache phoenix
University of Moratuwa
Apache Phoenix Query Server
Apache Phoenix Query Server
Josh Elser
Recommandé
Apache phoenix: Past, Present and Future of SQL over HBAse
Apache phoenix: Past, Present and Future of SQL over HBAse
enissoz
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
DataWorks Summit/Hadoop Summit
The Evolution of a Relational Database Layer over HBase
The Evolution of a Relational Database Layer over HBase
DataWorks Summit
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Josh Elser
Five major tips to maximize performance on a 200+ SQL HBase/Phoenix cluster
Five major tips to maximize performance on a 200+ SQL HBase/Phoenix cluster
mas4share
Local Secondary Indexes in Apache Phoenix
Local Secondary Indexes in Apache Phoenix
Rajeshbabu Chintaguntla
Apache phoenix
Apache phoenix
University of Moratuwa
Apache Phoenix Query Server
Apache Phoenix Query Server
Josh Elser
Apache Phoenix + Apache HBase
Apache Phoenix + Apache HBase
DataWorks Summit/Hadoop Summit
Apache Phoenix: Transforming HBase into a SQL Database
Apache Phoenix: Transforming HBase into a SQL Database
DataWorks Summit
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
DataWorks Summit
HBaseConEast2016: HBase and Spark, State of the Art
HBaseConEast2016: HBase and Spark, State of the Art
Michael Stack
Apache phoenix
Apache phoenix
Osama Hussein
HBase state of the union
HBase state of the union
enissoz
Dancing with the elephant h base1_final
Dancing with the elephant h base1_final
asterix_smartplatf
Apache HBase Internals you hoped you Never Needed to Understand
Apache HBase Internals you hoped you Never Needed to Understand
Josh Elser
Apache Big Data EU 2015 - Phoenix
Apache Big Data EU 2015 - Phoenix
Nick Dimiduk
Apache Phoenix Query Server PhoenixCon2016
Apache Phoenix Query Server PhoenixCon2016
Josh Elser
Mapreduce over snapshots
Mapreduce over snapshots
enissoz
Major advancements in Apache Hive towards full support of SQL compliance
Major advancements in Apache Hive towards full support of SQL compliance
DataWorks Summit/Hadoop Summit
eHarmony @ Hbase Conference 2016 by vijay vangapandu.
eHarmony @ Hbase Conference 2016 by vijay vangapandu.
Vijaykumar Vangapandu
Practical Kerberos with Apache HBase
Practical Kerberos with Apache HBase
Josh Elser
De-Mystifying the Apache Phoenix QueryServer
De-Mystifying the Apache Phoenix QueryServer
Josh Elser
Apache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New Features
HBaseCon
HBaseCon 2013: Full-Text Indexing for Apache HBase
HBaseCon 2013: Full-Text Indexing for Apache HBase
Cloudera, Inc.
Apache Hive ACID Project
Apache Hive ACID Project
DataWorks Summit/Hadoop Summit
Apache Hive on ACID
Apache Hive on ACID
DataWorks Summit/Hadoop Summit
Taming HBase with Apache Phoenix and SQL
Taming HBase with Apache Phoenix and SQL
HBaseCon
HBase + Hue - LA HBase User Group
HBase + Hue - LA HBase User Group
gethue
Contenu connexe
Tendances
Apache Phoenix + Apache HBase
Apache Phoenix + Apache HBase
DataWorks Summit/Hadoop Summit
Apache Phoenix: Transforming HBase into a SQL Database
Apache Phoenix: Transforming HBase into a SQL Database
DataWorks Summit
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
DataWorks Summit
HBaseConEast2016: HBase and Spark, State of the Art
HBaseConEast2016: HBase and Spark, State of the Art
Michael Stack
Apache phoenix
Apache phoenix
Osama Hussein
HBase state of the union
HBase state of the union
enissoz
Dancing with the elephant h base1_final
Dancing with the elephant h base1_final
asterix_smartplatf
Apache HBase Internals you hoped you Never Needed to Understand
Apache HBase Internals you hoped you Never Needed to Understand
Josh Elser
Apache Big Data EU 2015 - Phoenix
Apache Big Data EU 2015 - Phoenix
Nick Dimiduk
Apache Phoenix Query Server PhoenixCon2016
Apache Phoenix Query Server PhoenixCon2016
Josh Elser
Mapreduce over snapshots
Mapreduce over snapshots
enissoz
Major advancements in Apache Hive towards full support of SQL compliance
Major advancements in Apache Hive towards full support of SQL compliance
DataWorks Summit/Hadoop Summit
eHarmony @ Hbase Conference 2016 by vijay vangapandu.
eHarmony @ Hbase Conference 2016 by vijay vangapandu.
Vijaykumar Vangapandu
Practical Kerberos with Apache HBase
Practical Kerberos with Apache HBase
Josh Elser
De-Mystifying the Apache Phoenix QueryServer
De-Mystifying the Apache Phoenix QueryServer
Josh Elser
Apache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New Features
HBaseCon
HBaseCon 2013: Full-Text Indexing for Apache HBase
HBaseCon 2013: Full-Text Indexing for Apache HBase
Cloudera, Inc.
Apache Hive ACID Project
Apache Hive ACID Project
DataWorks Summit/Hadoop Summit
Apache Hive on ACID
Apache Hive on ACID
DataWorks Summit/Hadoop Summit
Tendances
(20)
Apache Phoenix + Apache HBase
Apache Phoenix + Apache HBase
Apache Phoenix: Transforming HBase into a SQL Database
Apache Phoenix: Transforming HBase into a SQL Database
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks Technical Workshop: HBase and Apache Phoenix
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
HBaseConEast2016: HBase and Spark, State of the Art
HBaseConEast2016: HBase and Spark, State of the Art
Apache phoenix
Apache phoenix
HBase state of the union
HBase state of the union
Dancing with the elephant h base1_final
Dancing with the elephant h base1_final
Apache HBase Internals you hoped you Never Needed to Understand
Apache HBase Internals you hoped you Never Needed to Understand
Apache Big Data EU 2015 - Phoenix
Apache Big Data EU 2015 - Phoenix
Apache Phoenix Query Server PhoenixCon2016
Apache Phoenix Query Server PhoenixCon2016
Mapreduce over snapshots
Mapreduce over snapshots
Major advancements in Apache Hive towards full support of SQL compliance
Major advancements in Apache Hive towards full support of SQL compliance
eHarmony @ Hbase Conference 2016 by vijay vangapandu.
eHarmony @ Hbase Conference 2016 by vijay vangapandu.
Practical Kerberos with Apache HBase
Practical Kerberos with Apache HBase
De-Mystifying the Apache Phoenix QueryServer
De-Mystifying the Apache Phoenix QueryServer
Apache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New Features
HBaseCon 2013: Full-Text Indexing for Apache HBase
HBaseCon 2013: Full-Text Indexing for Apache HBase
Apache Hive ACID Project
Apache Hive ACID Project
Apache Hive on ACID
Apache Hive on ACID
En vedette
Taming HBase with Apache Phoenix and SQL
Taming HBase with Apache Phoenix and SQL
HBaseCon
HBase + Hue - LA HBase User Group
HBase + Hue - LA HBase User Group
gethue
Hue: The Hadoop UI - Hadoop Singapore
Hue: The Hadoop UI - Hadoop Singapore
gethue
August 2013 HUG: Hue: the UI for Apache Hadoop
August 2013 HUG: Hue: the UI for Apache Hadoop
Yahoo Developer Network
Using Apache Solr
Using Apache Solr
pittaya
Apache Solr crash course
Apache Solr crash course
Tommaso Teofili
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
Cloudera, Inc.
Battle of the giants: Apache Solr vs ElasticSearch
Battle of the giants: Apache Solr vs ElasticSearch
Rafał Kuć
Apache HBase Performance Tuning
Apache HBase Performance Tuning
Lars Hofhansl
Solr vs. Elasticsearch - Case by Case
Solr vs. Elasticsearch - Case by Case
Alexandre Rafalovitch
Building a real time, solr-powered recommendation engine
Building a real time, solr-powered recommendation engine
Trey Grainger
En vedette
(11)
Taming HBase with Apache Phoenix and SQL
Taming HBase with Apache Phoenix and SQL
HBase + Hue - LA HBase User Group
HBase + Hue - LA HBase User Group
Hue: The Hadoop UI - Hadoop Singapore
Hue: The Hadoop UI - Hadoop Singapore
August 2013 HUG: Hue: the UI for Apache Hadoop
August 2013 HUG: Hue: the UI for Apache Hadoop
Using Apache Solr
Using Apache Solr
Apache Solr crash course
Apache Solr crash course
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
Battle of the giants: Apache Solr vs ElasticSearch
Battle of the giants: Apache Solr vs ElasticSearch
Apache HBase Performance Tuning
Apache HBase Performance Tuning
Solr vs. Elasticsearch - Case by Case
Solr vs. Elasticsearch - Case by Case
Building a real time, solr-powered recommendation engine
Building a real time, solr-powered recommendation engine
Similaire à April 2014 HUG : Apache Phoenix
Transactional operations in Apache Hive: present and future
Transactional operations in Apache Hive: present and future
DataWorks Summit
HBaseConAsia2018 Track2-4: HTAP DB-System: AsparaDB HBase, Phoenix, and Spark
HBaseConAsia2018 Track2-4: HTAP DB-System: AsparaDB HBase, Phoenix, and Spark
Michael Stack
Whats newinhive090hadoopsummit2012bof
Whats newinhive090hadoopsummit2012bof
Gopi Krishna
Transactional SQL in Apache Hive
Transactional SQL in Apache Hive
DataWorks Summit
Impala 2.0 Update #impalajp
Impala 2.0 Update #impalajp
Cloudera Japan
Hive 3 a new horizon
Hive 3 a new horizon
Artem Ervits
Bay area Cassandra Meetup 2011
Bay area Cassandra Meetup 2011
mubarakss
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon
HiveACIDPublic
HiveACIDPublic
Inderaj (Raj) Bains
MySQL 8.0 New Features -- September 27th presentation for Open Source Summit
MySQL 8.0 New Features -- September 27th presentation for Open Source Summit
Dave Stokes
Adding Value to HBase with IBM InfoSphere BigInsights and BigSQL
Adding Value to HBase with IBM InfoSphere BigInsights and BigSQL
Piotr Pruski
Be A Hero: Transforming GoPro Analytics Data Pipeline
Be A Hero: Transforming GoPro Analytics Data Pipeline
Chester Chen
Hive
Hive
Srinath Reddy
HBaseCon2015-final
HBaseCon2015-final
Maryann Xue
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., Ltd
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., Ltd
Michael Stack
Introduction into MySQL Query Tuning for Dev[Op]s
Introduction into MySQL Query Tuning for Dev[Op]s
Sveta Smirnova
Hive in Practice
Hive in Practice
András Fehér
Apache Drill talk ApacheCon 2018
Apache Drill talk ApacheCon 2018
Aman Sinha
How Database Convergence Impacts the Coming Decades of Data Management
How Database Convergence Impacts the Coming Decades of Data Management
SingleStore
Write Faster SQL with Trino.pdf
Write Faster SQL with Trino.pdf
Eric Xiao
Similaire à April 2014 HUG : Apache Phoenix
(20)
Transactional operations in Apache Hive: present and future
Transactional operations in Apache Hive: present and future
HBaseConAsia2018 Track2-4: HTAP DB-System: AsparaDB HBase, Phoenix, and Spark
HBaseConAsia2018 Track2-4: HTAP DB-System: AsparaDB HBase, Phoenix, and Spark
Whats newinhive090hadoopsummit2012bof
Whats newinhive090hadoopsummit2012bof
Transactional SQL in Apache Hive
Transactional SQL in Apache Hive
Impala 2.0 Update #impalajp
Impala 2.0 Update #impalajp
Hive 3 a new horizon
Hive 3 a new horizon
Bay area Cassandra Meetup 2011
Bay area Cassandra Meetup 2011
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HiveACIDPublic
HiveACIDPublic
MySQL 8.0 New Features -- September 27th presentation for Open Source Summit
MySQL 8.0 New Features -- September 27th presentation for Open Source Summit
Adding Value to HBase with IBM InfoSphere BigInsights and BigSQL
Adding Value to HBase with IBM InfoSphere BigInsights and BigSQL
Be A Hero: Transforming GoPro Analytics Data Pipeline
Be A Hero: Transforming GoPro Analytics Data Pipeline
Hive
Hive
HBaseCon2015-final
HBaseCon2015-final
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., Ltd
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., Ltd
Introduction into MySQL Query Tuning for Dev[Op]s
Introduction into MySQL Query Tuning for Dev[Op]s
Hive in Practice
Hive in Practice
Apache Drill talk ApacheCon 2018
Apache Drill talk ApacheCon 2018
How Database Convergence Impacts the Coming Decades of Data Management
How Database Convergence Impacts the Coming Decades of Data Management
Write Faster SQL with Trino.pdf
Write Faster SQL with Trino.pdf
Plus de Yahoo Developer Network
Developing Mobile Apps for Performance - Swapnil Patel, Verizon Media
Developing Mobile Apps for Performance - Swapnil Patel, Verizon Media
Yahoo Developer Network
Athenz - The Open-Source Solution to Provide Access Control in Dynamic Infras...
Athenz - The Open-Source Solution to Provide Access Control in Dynamic Infras...
Yahoo Developer Network
Athenz & SPIFFE, Tatsuya Yano, Yahoo Japan
Athenz & SPIFFE, Tatsuya Yano, Yahoo Japan
Yahoo Developer Network
Athenz with Istio - Single Access Control Model in Cloud Infrastructures, Tat...
Athenz with Istio - Single Access Control Model in Cloud Infrastructures, Tat...
Yahoo Developer Network
CICD at Oath using Screwdriver
CICD at Oath using Screwdriver
Yahoo Developer Network
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, Oath
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, Oath
Yahoo Developer Network
How @TwitterHadoop Chose Google Cloud, Joep Rottinghuis, Lohit VijayaRenu
How @TwitterHadoop Chose Google Cloud, Joep Rottinghuis, Lohit VijayaRenu
Yahoo Developer Network
The Future of Hadoop in an AI World, Milind Bhandarkar, CEO, Ampool
The Future of Hadoop in an AI World, Milind Bhandarkar, CEO, Ampool
Yahoo Developer Network
Apache YARN Federation and Tez at Microsoft, Anupam Upadhyay, Adrian Nicoara,...
Apache YARN Federation and Tez at Microsoft, Anupam Upadhyay, Adrian Nicoara,...
Yahoo Developer Network
Containerized Services on Apache Hadoop YARN: Past, Present, and Future, Shan...
Containerized Services on Apache Hadoop YARN: Past, Present, and Future, Shan...
Yahoo Developer Network
HDFS Scalability and Security, Daryn Sharp, Senior Engineer, Oath
HDFS Scalability and Security, Daryn Sharp, Senior Engineer, Oath
Yahoo Developer Network
Hadoop {Submarine} Project: Running deep learning workloads on YARN, Wangda T...
Hadoop {Submarine} Project: Running deep learning workloads on YARN, Wangda T...
Yahoo Developer Network
Moving the Oath Grid to Docker, Eric Badger, Oath
Moving the Oath Grid to Docker, Eric Badger, Oath
Yahoo Developer Network
Architecting Petabyte Scale AI Applications
Architecting Petabyte Scale AI Applications
Yahoo Developer Network
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...
Yahoo Developer Network
Jun 2017 HUG: YARN Scheduling – A Step Beyond
Jun 2017 HUG: YARN Scheduling – A Step Beyond
Yahoo Developer Network
Jun 2017 HUG: Large-Scale Machine Learning: Use Cases and Technologies
Jun 2017 HUG: Large-Scale Machine Learning: Use Cases and Technologies
Yahoo Developer Network
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...
Yahoo Developer Network
February 2017 HUG: Exactly-once end-to-end processing with Apache Apex
February 2017 HUG: Exactly-once end-to-end processing with Apache Apex
Yahoo Developer Network
February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics
February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics
Yahoo Developer Network
Plus de Yahoo Developer Network
(20)
Developing Mobile Apps for Performance - Swapnil Patel, Verizon Media
Developing Mobile Apps for Performance - Swapnil Patel, Verizon Media
Athenz - The Open-Source Solution to Provide Access Control in Dynamic Infras...
Athenz - The Open-Source Solution to Provide Access Control in Dynamic Infras...
Athenz & SPIFFE, Tatsuya Yano, Yahoo Japan
Athenz & SPIFFE, Tatsuya Yano, Yahoo Japan
Athenz with Istio - Single Access Control Model in Cloud Infrastructures, Tat...
Athenz with Istio - Single Access Control Model in Cloud Infrastructures, Tat...
CICD at Oath using Screwdriver
CICD at Oath using Screwdriver
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, Oath
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, Oath
How @TwitterHadoop Chose Google Cloud, Joep Rottinghuis, Lohit VijayaRenu
How @TwitterHadoop Chose Google Cloud, Joep Rottinghuis, Lohit VijayaRenu
The Future of Hadoop in an AI World, Milind Bhandarkar, CEO, Ampool
The Future of Hadoop in an AI World, Milind Bhandarkar, CEO, Ampool
Apache YARN Federation and Tez at Microsoft, Anupam Upadhyay, Adrian Nicoara,...
Apache YARN Federation and Tez at Microsoft, Anupam Upadhyay, Adrian Nicoara,...
Containerized Services on Apache Hadoop YARN: Past, Present, and Future, Shan...
Containerized Services on Apache Hadoop YARN: Past, Present, and Future, Shan...
HDFS Scalability and Security, Daryn Sharp, Senior Engineer, Oath
HDFS Scalability and Security, Daryn Sharp, Senior Engineer, Oath
Hadoop {Submarine} Project: Running deep learning workloads on YARN, Wangda T...
Hadoop {Submarine} Project: Running deep learning workloads on YARN, Wangda T...
Moving the Oath Grid to Docker, Eric Badger, Oath
Moving the Oath Grid to Docker, Eric Badger, Oath
Architecting Petabyte Scale AI Applications
Architecting Petabyte Scale AI Applications
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...
Jun 2017 HUG: YARN Scheduling – A Step Beyond
Jun 2017 HUG: YARN Scheduling – A Step Beyond
Jun 2017 HUG: Large-Scale Machine Learning: Use Cases and Technologies
Jun 2017 HUG: Large-Scale Machine Learning: Use Cases and Technologies
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...
February 2017 HUG: Exactly-once end-to-end processing with Apache Apex
February 2017 HUG: Exactly-once end-to-end processing with Apache Apex
February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics
February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics
Dernier
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf
Human37
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
Boston Institute of Analytics
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
marianagonzalez07
IMA MSN - Medical Students Network (2).pptx
IMA MSN - Medical Students Network (2).pptx
dolaknnilon
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data Story
Jeremy Anderson
Customer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptx
Emmanuel Dauda
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
natarajan8993
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
soniya singh
Saket, (-DELHI )+91-9654467111-(=)CHEAP Call Girls in Escorts Service Saket C...
Saket, (-DELHI )+91-9654467111-(=)CHEAP Call Girls in Escorts Service Saket C...
Sapana Sha
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdf
John Sterrett
原版1:1定制南十字星大学毕业证(SCU毕业证)#文凭成绩单#真实留信学历认证永久存档
原版1:1定制南十字星大学毕业证(SCU毕业证)#文凭成绩单#真实留信学历认证永久存档
208367051
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
jennyeacort
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
yuu sss
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
VICTOR MAESTRE RAMIREZ
Generative AI for Social Good at Open Data Science East 2024
Generative AI for Social Good at Open Data Science East 2024
Colleen Farrelly
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
soniya singh
Beautiful Sapna Vip Call Girls Hauz Khas 9711199012 Call /Whatsapps
Beautiful Sapna Vip Call Girls Hauz Khas 9711199012 Call /Whatsapps
sapnasaifi408
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
17djon017
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
jennyeacort
Multiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdf
chwongval
Dernier
(20)
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
IMA MSN - Medical Students Network (2).pptx
IMA MSN - Medical Students Network (2).pptx
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data Story
Customer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptx
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Saket, (-DELHI )+91-9654467111-(=)CHEAP Call Girls in Escorts Service Saket C...
Saket, (-DELHI )+91-9654467111-(=)CHEAP Call Girls in Escorts Service Saket C...
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdf
原版1:1定制南十字星大学毕业证(SCU毕业证)#文凭成绩单#真实留信学历认证永久存档
原版1:1定制南十字星大学毕业证(SCU毕业证)#文凭成绩单#真实留信学历认证永久存档
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
Generative AI for Social Good at Open Data Science East 2024
Generative AI for Social Good at Open Data Science East 2024
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
Beautiful Sapna Vip Call Girls Hauz Khas 9711199012 Call /Whatsapps
Beautiful Sapna Vip Call Girls Hauz Khas 9711199012 Call /Whatsapps
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
Multiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdf
April 2014 HUG : Apache Phoenix
1.
© Hortonworks Inc.
2011 Apache Phoenix – SQL skin over HBase Jeffrey Zhong jzhong@hortonworks.com jeffreyz@apache.org
2.
© Hortonworks Inc.
2011 Overview •What is Phoenix? •Major Phoenix Features •Futures •Phoenix In Action •Summary Architecting the Future of Big Data
3.
© Hortonworks Inc.
2011 What is Phoenix? SQL skin for HBase originally developed by folks in Salesforce.com and now is an Apache Incubator Project Targets low latency queries over HBase data Query engine transforms SQL into native HBase APIs: put, delete, parallel scans instead of Map/Reduce Delivered as an fat JDBC driver(client) •Support features not provided by HBase: Secondary Indexing, Multi-tenancy, simple Hash Join and more Architecting the Future of Big Data
4.
© Hortonworks Inc.
2011 Phoenix Semantics Support Architecting the Future of Big Data Feature Supported? UPSERT / DELETE Yes SELECT Yes WHERE / HAVING Yes GROUP BY Yes ORDER BY Yes LIMIT Yes Views Yes JOIN Yes (Introduced in 4.0), limited to hash joins Transactions No
5.
© Hortonworks Inc.
2011 Why Phoenix? Leverage existing tooling SQL client •Free the burden to write huge amount code to do simple things SELECT COUNT(*) FROM WEB_STAT WHERE HOST='EU' and CORE > 35 GROUP BY DOMAIN; •Performance optimizations transparent to the user Phoenix breaks up queries into multiple scans and runs them in parallel. For aggregate queries, coprocessors complete partial aggregation on local region server and only returns relevant data to the client Architecting the Future of Big Data
6.
© Hortonworks Inc.
2011 Phoenix Query Optimization 0: jdbc:phoenix:localhost> explain SELECT count(*) FROM WEB_STAT WHERE HOST='EU' and CORE > 35 GROUP BY DOMAIN; +------------+ | PLAN | +------------+ | CLIENT PARALLEL 32-WAY RANGE SCAN OVER WEB_STAT ['EU'] | | SERVER FILTER BY USAGE.CORE > 35 | | SERVER AGGREGATE INTO DISTINCT ROWS BY [DOMAIN] | | CLIENT MERGE SORT | +------------+ Architecting the Future of Big Data CREATE TABLE IF NOT EXISTS WEB_STAT ( HOST CHAR(2) NOT NULL, DOMAIN VARCHAR NOT NULL, FEATURE VARCHAR NOT NULL, DATE DATE NOT NULL, USAGE.CORE BIGINT, USAGE.DB BIGINT, STATS.ACTIVE_VISITOR INTEGER CONSTRAINT PK PRIMARY KEY (HOST, DOMAIN, FEATURE, DATE) ); SELECT count(*) FROM WEB_STAT WHERE HOST='EU' and CORE > 35 GROUP BY DOMAIN; WEB_STAT Table Schema
7.
© Hortonworks Inc.
2011 Major Features In Phoenix DDL support: CREATE/DROP/ALTER TABLE for adding/removing columns Extend Schema at query time: Dynamic Column Salting Mapping to an existing HBase table DML support: UPSERT VALUES for row-by-row insertion, UPSERT SELECT for mass data transfer between the same or different tables and DELETE for deleting rows Secondary Indexes to improve performance for queries on non- row key columns(still maturing) Multi-Tenancy (Available in Phoenix 3.0/4.0) Limited Hash Join(Available in Phoenix 3.0/4.0) Architecting the Future of Big Data
8.
© Hortonworks Inc.
2011 Phoenix Futures •Improved Secondary Indexing. –Tolerant of region split/merge, RegionServer failures. •Improved JOIN support. •Transaction support. •Improved Phoenix / Hive interoperability. •More at http://phoenix.incubator.apache.org/roadmap.html Architecting the Future of Big Data
9.
© Hortonworks Inc.
2011 Mapping an existing HBase Table Architecting the Future of Big Data • create 't1', {NAME=>'f1', VERSIONS => 3} – put 't1', 'r1', 'f1.col1', 'val1’ – put 't1', ’r2', 'f1.col2', 'val2’ • Mapping t1 into Phoenix Table – Phoenix stores its own metadata in Table SYSTEM.CATALOG so you need recreate Phoenix Table or Views to mapping the existing HBase Table – By default, Phoenix uses capital characters, so it’s a better practice to use always “”. • create table "t1" (myPK VARCHAR PRIMARY KEY, "f1"."col1" VARCHAR); 0: jdbc:phoenix:localhost> select * from "t1"; +------------+------------+ | MYPK | col1 | +------------+------------+ | r1 | val1 | | r2 | null | +------------+------------+ 2 rows selected (0.049 seconds) 0: jdbc:phoenix:localhost> select * from t1; Error: ERROR 1012 (42M03): Table undefined. tableName=T1 (state=42M03,code=1012)
10.
© Hortonworks Inc.
2011 Changes Behind Scenes of Mapping Architecting the Future of Big Data • Metadata are inserted into SYSTEM.CATALOG table 0: jdbc:phoenix:localhost> select table_name, column_name, table_type from system.catalog where table_name='t1'; +------------+-------------+------------+ | TABLE_NAME | COLUMN_NAME | TABLE_TYPE | +------------+-------------+------------+ | t1 | null | u | | t1 | MYPK | null | | t1 | col1 | null | +------------+-------------+------------+ • Empty cell is created for each row. It’s used to enforce PRIMAY KEY constraints because HBase doesn’t store cells with NULL values. hbase(main):023:0> scan 't1' ROW COLUMN+CELL r1 column=f1:_0, timestamp=1397527184229, value= r1 column=f1:col1, timestamp=1397527184229, value=val1 r2 column=f1:_0, timestamp=1397527197205, value= r2 column=f1:col2, timestamp=1397527197205, value=val2
11.
© Hortonworks Inc.
2011 Mapping an existing HBase Table – Cont. •The bytes were serialized must match the way the bytes are serialized by Phoenix. You can refer to Phoenix data types. (http://phoenix.incubator.apache.org/language/datat ypes.html) Architecting the Future of Big Data
12.
© Hortonworks Inc.
2011 Dynamic Columns - Extend Schema During Query •HBase can create new columns(qualifier) after table created. In Phoenix, a subset of columns may be specified at table create time while the rest is possibly surfaced at query time through dynamic columns. – In the previous table mapping, we only mapped one column “f1”.”col1” create table "t1" (myPK VARCHAR PRIMARY KEY, "f1"."col1" VARCHAR); – In order to get data from col2, we can do 0: jdbc:phoenix:localhost> select * from "t1"("f1"."col2" VARCHAR); +------------+------------+------------+ | MYPK | col1 | col2 | +------------+------------+------------+ | r1 | val1 | null | | r2 | null | val2 | +------------+------------+------------+ 2 rows selected (0.065 seconds) Architecting the Future of Big Data
13.
© Hortonworks Inc.
2011 Secondary Index •Index data are stored in separate HBase table and located in different region servers other than data table. •Two types of Secondary Index Immutable Indexes – Targets tables where rows are immutable after written – When new rows are inserted, updates are sent to data table and then index table – Client handles failures Mutable Indexes Architecting the Future of Big Data
14.
© Hortonworks Inc.
2011 Phoenix Secondary Index – Cont. Mutable Indexes –Implemented through coprocessors –Aborts region server when index updates fails(could change with custom IndexFailurePolicy) Courtesy of Jesse Yates from SF Hbase User Group Slides
15.
© Hortonworks Inc.
2011 Phoenix Secondary Index – Cont. •Index Creation –Same statement to create both types of indexes. Immutable Indexes are created for tables created with “IMMUTABLE_ROWS=true” otherwise mutable indexes are created –DDL Statement: CREATE INDEX <index_name> ON <table_name>(<columns_to_index>…) INCLUDE (<columns_to_cover>…); –Examples – create index "t1_index" on "t1" ("f1"."col1") – Verify index will be used 0: jdbc:phoenix:localhost> explain select * from "t1" where "f1"."col1"='val1'; +------------+ | PLAN | +------------+ | CLIENT PARALLEL 1-WAY RANGE SCAN OVER t1_index ['val1'] | +------------+ 1 row selected (0.037 seconds) Architecting the Future of Big Data
16.
© Hortonworks Inc.
2011 Phoenix Secondary Index – Cont. •How Index Data are Stored hbase(main):008:0> scan 't1_index' ROW COLUMN+CELL x00r2 column=0:_0, timestamp=1397611429248, value= val1x00r1 column=0:_0, timestamp=1397611429248, value= Row key are concatenated with index column values delimited by a zero byte character end with data table primary key. If you define covered columns, you’ll see cells with their values as well in the index table. Architecting the Future of Big Data
17.
© Hortonworks Inc.
2011 Salted Table •HBase uses salting to prevent region server hot spotting if row key is monotonically increasing. Phoenix provides a way to salt the row key with salting bytes during table creation time. For optimal performance, number of salt buckets should match number of region servers Architecting the Future of Big Data CREATE TABLE table (a_key VARCHAR PRIMARY KEY, a_col VARCHAR) SALT_BUCKETS = 20;
18.
© Hortonworks Inc.
2011 Resources •Apache Phoenix Home Page –http://phoenix.incubator.apache.org/index.html •Mailing Lists –http://phoenix.incubator.apache.org/mailing_list.html •Latest Release –Phoenix 3.0 for HBase0.94.*, Phoenix 4.0 for HBase0.98.1+(http://phoenix.incubator.apache.org/do wnload.html) – HDP(Hortonworks Data Platform)2.1 will ship Phoenix 4.0 Architecting the Future of Big Data
19.
© Hortonworks Inc.
2011 Try by yourself •Load Sample Data ./psql.py localhost ../examples/WEB_STAT.sql ../examples/WEB_STAT.csv •Start Sql Client ./sqlline.py localhost •Run Performance Test ./performance.py localhost 10000 Architecting the Future of Big Data Assuming HBase Zookeeper Quorum String = “localhost” and you are under bin folder of the installation.
20.
© Hortonworks Inc.
2011 Summary •Phoenix vs HBase Native APIs As a rule of thumb, you should leverage Phoenix as your Hbase client whenever is possible because Phoenix provides easy to use APIs and performance optimizations. Architecting the Future of Big Data
21.
© Hortonworks Inc.
2011 Questions? Comments?
Notes de l'éditeur
About Me:HBase & Phoenix committerMember of HBase Team at Hortonworks
Example of CF delete processing
From the query plan, we can see we push the predicates down to the data. This + parallel scan achieves huge performance gain.
In the above example, we create a phoenix table which only maps one column “f1”,”col1”
In the above example, we create a phoenix table which only maps one column “f1”,”col1”
Télécharger maintenant