1. The document discusses techniques for improving Apache Spark performance through mechanical sympathy, which means optimizing for hardware performance by considering factors like CPU cache usage and minimizing random memory access.
2. It provides examples of how to improve sorting, matrix multiplication, and thread synchronization by making them more cache-friendly and reducing cache misses and context switches.
3. The speaker demonstrates performance improvements from these techniques using Linux perf and flame graph profiling tools. Optimizations like Project Tungsten that customize Spark for the hardware are also discussed.
Advanced Analytics and Recommendations with Apache Spark - Spark Maryland/DC ...Chris Fregly
Title
Real-time, Advanced Analytics and Recommendations using Machine Learning, Graph Processing, Natural Language Processing, and Approximations with Apache Spark, Stanford CoreNLP, and Twitter Algebird
BONUS: Netflix Recommendations: Then and Now
Agenda
Intro
Live, Interactive Recommendations Demo
Spark ML, GraphX, Streaming, Kafka, Cassandra, Docker
Types of Similarity
Euclidean vs. Non-Euclidean Similarity
User-to-User Similarity
Content-based, Item-to-Item Similarity (Amazon)
Collaborative-based, User-to-Item Similarity (Netflix)
Graph-based, Item-to-Item Similarity Pathway (Spotify)
Similarity Approximations at Scale
Twitter Algebird
MinHash and Bucketing
Locality Sensitive Hashing (LSH)
BONUS: Netflix Recommendations: From Ratings to Real-Time
DVD-Ratings-based $1M Netflix Prize (2009)
Streaming-based "Trending Now" (2016)
Wrap Up
Q & A
Bio
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, and a Netflix Open Source Committer.
Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
Related Links
https://github.com/fluxcapacitor/pipeline/wiki
http://cdn.oreillystatic.com/en/assets/1/event/105/Algebra%20for%20Scalable%20Analytics%20Presentation.pdf
http://static.echonest.com/BoilTheFrog/
http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
http://www.cc.gatech.edu/~zha/CSE8801/CF/kdd-fp074-koren.pdf
Title:
Real-time, Advanced Analytics and Recommendations using Machine Learning, Natural Language Processing, Graph Processing, and Approximations with Apache Spark, Stanford CoreNLP, and Twitter Algebird
Agenda
Intro
Live, Interactive Recommendations Demo
Spark ML, GraphX, Streaming, Kafka, Cassandra, Docker
Types of Similarity
Euclidean vs. Non-Euclidean Similarity
User-to-User Similarity
Content-based, Item-to-Item Similarity (Amazon)
Collaborative-based, User-to-Item Similarity (Netflix)
Graph-based, Item-to-Item Similarity Pathway (Spotify)
Similarity Approximations at Scale
Twitter Algebird
MinHash and Bucketing
Locality Sensitive Hashing (LSH)
Netflix Recommendations: From Ratings to Real-Time
DVD-Ratings-based $1M Netflix Prize (2009)
Streaming-based "Trending Now" (2016)
Wrap Up
Q & A
*Bio*
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, and a Netflix Open Source Committer. Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com. Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
*Related Links*
https://github.com/fluxcapacitor/pipeline/wiki
http://cdn.oreillystatic.com/en/assets/1/event/105/Algebra%20for%20Scalable%20Analytics%20Presentation.pdf
http://static.echonest.com/BoilTheFrog/
http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
http://www.cc.gatech.edu/~zha/CSE8801/CF/kdd-fp074-koren.pdf
Zurich, Berlin, Vienna Spark and Big Data Meetup Nov 02 2015Chris Fregly
Zurich, Berlin, Vienna Spark Meetup Nov 02 2015
* Title *
Spark After Dark 1.5: Real-time, Advanced Analytics with Spark 1.5, Kafka, Cassandra, ElasticSearch, Zeppelin, and Docker
* Abstract *
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
5th Athens Big Data Meetup - PipelineIO Workshop - Real-Time Training and Dep...Athens Big Data
Title: Real-Time Training and Deploying Spark ML Recommendations With Kafka and NetflixOSS
Speaker: Chris Fregly (https://linkedin.com/in/cfregly/)
Date: Monday, October 17, 2016
Event: https://meetup.com/Athens-Big-Data/events/234546355/
Helsinki Spark Meetup Nov 20, 2015: Spark After Dark 1.5: Real-time, Advanced Analytics with Spark 1.5, Kafka, Cassandra, ElasticSearch, Zeppelin, and Docker
Feature Talk: Real-time Aggregations, Approximations, Similarities, and Recommendations at Scale using Spark Streaming, ML, GraphX, Kafka, Cassandra, Docker, CoreNLP, Word2Vec, LDA, and Twitter Algebird
Talk Abstract: Starting with a live, interactive demo generating audience-specific recommendations, we'll dive deep into each of the key components including NiFi, Kafka, Stanford CoreNLP, Docker, Word2Vec, LDA, Twitter Algebird, Spark Streaming, SQL, ML, GraphX. As a bonus, we'll discuss the latest Netflix Recommendations Pipeline and related open source projects.
Talk Agenda:
• Intro
• Live, Interactive Recommendations Demo
• Spark Streaming, ML, GraphX, Kafka, Cassandra, Docker, CoreNLP, Word2Vec, LDA, and Twitter Algebird (advancedspark.com)
• Types of Similarity
• Euclidean vs. Non-Euclidean Similarity
• Jaccard Similarity
• Cosine Similarity
• LogLikelihood Similarity
• Edit Distance
• Text-based Similarities and Analytics
• Word2Vec
• LDA Topic Extraction
• TextRank
• Similarity-based Recommendations
• User-to-User
• Content-based, Item-to-Item (Amazon)
• Collaborative-based, User-to-Item (Netflix)
• Graph-based, Item-to-Item "Pathways" (Spotify)
• Aggregations, Approximations, and Similarities at Scale
• Twitter Algebird
• MinHash and Bucketing
• Locality Sensitive Hashing (LSH)
• BloomFilters
• CountMin Sketch
• HyperLogLog
• Q & A
Speaker Bio: Chris Fregly is a Research Engineer @ Flux Capacitor AI in SF, an Apache Spark Contributor, and a Netflix Open Source Committer.
Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
Brussels Spark Meetup Oct 30, 2015: Spark After Dark 1.5: Real-time, Advanc...Chris Fregly
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, ...Chris Fregly
This talk highlights the Data Sources API which participates in the Spark SQL DataFrame Catalyst Optimizer. We dive deep into the super-advanced Cassandra's open source implementation @ github.com/datastax/spark-cassandra-connector. We discuss data locality, cluster deployment - as well as the pros and cons of mixing OLAP and OLTP workloads.
We also implement a SimpleDataSource which is a basic implementation of the DataSources API.
All analysis is done with Apache Zeppelin.
Paris Spark Meetup Oct 26, 2015 - Spark After Dark v1.5 - Best of Advanced Ap...Chris Fregly
* Title *
Spark After Dark 1.5: Deep Dive Into Latest Perf and Scale Improvements in Spark Ecosystem
* Abstract *
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
Advanced Analytics and Recommendations with Apache Spark - Spark Maryland/DC ...Chris Fregly
Title
Real-time, Advanced Analytics and Recommendations using Machine Learning, Graph Processing, Natural Language Processing, and Approximations with Apache Spark, Stanford CoreNLP, and Twitter Algebird
BONUS: Netflix Recommendations: Then and Now
Agenda
Intro
Live, Interactive Recommendations Demo
Spark ML, GraphX, Streaming, Kafka, Cassandra, Docker
Types of Similarity
Euclidean vs. Non-Euclidean Similarity
User-to-User Similarity
Content-based, Item-to-Item Similarity (Amazon)
Collaborative-based, User-to-Item Similarity (Netflix)
Graph-based, Item-to-Item Similarity Pathway (Spotify)
Similarity Approximations at Scale
Twitter Algebird
MinHash and Bucketing
Locality Sensitive Hashing (LSH)
BONUS: Netflix Recommendations: From Ratings to Real-Time
DVD-Ratings-based $1M Netflix Prize (2009)
Streaming-based "Trending Now" (2016)
Wrap Up
Q & A
Bio
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, and a Netflix Open Source Committer.
Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
Related Links
https://github.com/fluxcapacitor/pipeline/wiki
http://cdn.oreillystatic.com/en/assets/1/event/105/Algebra%20for%20Scalable%20Analytics%20Presentation.pdf
http://static.echonest.com/BoilTheFrog/
http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
http://www.cc.gatech.edu/~zha/CSE8801/CF/kdd-fp074-koren.pdf
Title:
Real-time, Advanced Analytics and Recommendations using Machine Learning, Natural Language Processing, Graph Processing, and Approximations with Apache Spark, Stanford CoreNLP, and Twitter Algebird
Agenda
Intro
Live, Interactive Recommendations Demo
Spark ML, GraphX, Streaming, Kafka, Cassandra, Docker
Types of Similarity
Euclidean vs. Non-Euclidean Similarity
User-to-User Similarity
Content-based, Item-to-Item Similarity (Amazon)
Collaborative-based, User-to-Item Similarity (Netflix)
Graph-based, Item-to-Item Similarity Pathway (Spotify)
Similarity Approximations at Scale
Twitter Algebird
MinHash and Bucketing
Locality Sensitive Hashing (LSH)
Netflix Recommendations: From Ratings to Real-Time
DVD-Ratings-based $1M Netflix Prize (2009)
Streaming-based "Trending Now" (2016)
Wrap Up
Q & A
*Bio*
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, and a Netflix Open Source Committer. Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com. Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
*Related Links*
https://github.com/fluxcapacitor/pipeline/wiki
http://cdn.oreillystatic.com/en/assets/1/event/105/Algebra%20for%20Scalable%20Analytics%20Presentation.pdf
http://static.echonest.com/BoilTheFrog/
http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
http://www.cc.gatech.edu/~zha/CSE8801/CF/kdd-fp074-koren.pdf
Zurich, Berlin, Vienna Spark and Big Data Meetup Nov 02 2015Chris Fregly
Zurich, Berlin, Vienna Spark Meetup Nov 02 2015
* Title *
Spark After Dark 1.5: Real-time, Advanced Analytics with Spark 1.5, Kafka, Cassandra, ElasticSearch, Zeppelin, and Docker
* Abstract *
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
5th Athens Big Data Meetup - PipelineIO Workshop - Real-Time Training and Dep...Athens Big Data
Title: Real-Time Training and Deploying Spark ML Recommendations With Kafka and NetflixOSS
Speaker: Chris Fregly (https://linkedin.com/in/cfregly/)
Date: Monday, October 17, 2016
Event: https://meetup.com/Athens-Big-Data/events/234546355/
Helsinki Spark Meetup Nov 20, 2015: Spark After Dark 1.5: Real-time, Advanced Analytics with Spark 1.5, Kafka, Cassandra, ElasticSearch, Zeppelin, and Docker
Feature Talk: Real-time Aggregations, Approximations, Similarities, and Recommendations at Scale using Spark Streaming, ML, GraphX, Kafka, Cassandra, Docker, CoreNLP, Word2Vec, LDA, and Twitter Algebird
Talk Abstract: Starting with a live, interactive demo generating audience-specific recommendations, we'll dive deep into each of the key components including NiFi, Kafka, Stanford CoreNLP, Docker, Word2Vec, LDA, Twitter Algebird, Spark Streaming, SQL, ML, GraphX. As a bonus, we'll discuss the latest Netflix Recommendations Pipeline and related open source projects.
Talk Agenda:
• Intro
• Live, Interactive Recommendations Demo
• Spark Streaming, ML, GraphX, Kafka, Cassandra, Docker, CoreNLP, Word2Vec, LDA, and Twitter Algebird (advancedspark.com)
• Types of Similarity
• Euclidean vs. Non-Euclidean Similarity
• Jaccard Similarity
• Cosine Similarity
• LogLikelihood Similarity
• Edit Distance
• Text-based Similarities and Analytics
• Word2Vec
• LDA Topic Extraction
• TextRank
• Similarity-based Recommendations
• User-to-User
• Content-based, Item-to-Item (Amazon)
• Collaborative-based, User-to-Item (Netflix)
• Graph-based, Item-to-Item "Pathways" (Spotify)
• Aggregations, Approximations, and Similarities at Scale
• Twitter Algebird
• MinHash and Bucketing
• Locality Sensitive Hashing (LSH)
• BloomFilters
• CountMin Sketch
• HyperLogLog
• Q & A
Speaker Bio: Chris Fregly is a Research Engineer @ Flux Capacitor AI in SF, an Apache Spark Contributor, and a Netflix Open Source Committer.
Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
Brussels Spark Meetup Oct 30, 2015: Spark After Dark 1.5: Real-time, Advanc...Chris Fregly
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, ...Chris Fregly
This talk highlights the Data Sources API which participates in the Spark SQL DataFrame Catalyst Optimizer. We dive deep into the super-advanced Cassandra's open source implementation @ github.com/datastax/spark-cassandra-connector. We discuss data locality, cluster deployment - as well as the pros and cons of mixing OLAP and OLTP workloads.
We also implement a SimpleDataSource which is a basic implementation of the DataSources API.
All analysis is done with Apache Zeppelin.
Paris Spark Meetup Oct 26, 2015 - Spark After Dark v1.5 - Best of Advanced Ap...Chris Fregly
* Title *
Spark After Dark 1.5: Deep Dive Into Latest Perf and Scale Improvements in Spark Ecosystem
* Abstract *
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
Apache Spark 2.0: Faster, Easier, and SmarterDatabricks
In this webcast, Reynold Xin from Databricks will be speaking about Apache Spark's new 2.0 major release.
The major themes for Spark 2.0 are:
- Unified APIs: Emphasis on building up higher level APIs including the merging of DataFrame and Dataset APIs
- Structured Streaming: Simplify streaming by building continuous applications on top of DataFrames allow us to unify streaming, interactive, and batch queries.
- Tungsten Phase 2: Speed up Apache Spark by 10X
A new look on Spark 2 features and Under the hood. We try to look at Apache spark latest release with an examining look, while still loving it, but also criticising it.
Jump Start with Apache Spark 2.0 on DatabricksAnyscale
Apache Spark 2.x has laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Apache Spark Fundamentals & Concepts
What’s new in Spark 2.x
SparkSessions vs SparkContexts
Datasets/Dataframes and Spark SQL
Introduction to Structured Streaming concepts and APIs
London Spark Meetup Project Tungsten Oct 12 2015Chris Fregly
Building on a previous talk about how Spark beat Hadoop @ 100TB Daytona GraySort, we present low-level details of Project Tungsten which includes many CPU and Memory optimizations.
Advertising Fraud Detection at Scale at T-MobileDatabricks
The development of big data products and solutions – at scale – brings many challenges to the teams of platform architects, data scientists, and data engineers. While it is easy to find ourselves working in silos, successful organizations intensively collaborate across disciplines such that problems can be understood, a proposed model and solution can be scaled and optimized on multi-terabytes of data.
Just enough DevOps for Data Scientists (Part II)Databricks
Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully.
Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018
https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Just Enough DevOps for Data Scientists Part II: Handling Infra Failures When ...Anya Bida
Abstract: Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully.
Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018
https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Bio: Anya Bida (https://www.linkedin.com/in/anyabida/)
Pandas on AWS - Let me count the ways.pdfChris Fregly
Chris Fregly (Principal Solution Architect, AI and machine learning at AWS) will give a brief presentation on the various ways to perform scalable Pandas, Modin, and Ray on AWS. He will then answer questions from the audience and moderator, Alejandro Herrera (whatever he is) at Ponder.
Chris Fregly is a Principal Solution Architect for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is the organizer of the Global Data Science on AWS meetup. He is co-author of the O'Reilly Book, "Data Science on AWS."
Related Links
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Ray AI Runtime (AIR) on AWS - Data Science On AWS MeetupChris Fregly
RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154
Talk #0: Introductions and Meetup Announcements By Chris Fregly and Antje Barth
Talk #1: Ray Overview, Ray AI Runtime on AWS using Amazon SageMaker, EC2, EMR, EKS by Chris Fregly, Principal Specialist Solution Architect, AI and Machine Learning @ AWS
Talk #2: Deep-dive Blueprints for Amazon Elastic Kubernetes Service (EKS) including Ray and Spark by Apoorva Kulkarni, Sr. Specialist Solution Architect, Containers and Kubernetes @ AWS
RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Amazon reInvent 2020 Recap: AI and Machine LearningChris Fregly
Amazon reInvent 2020 Recap: AI and Machine Learning
Video here: https://youtu.be/YSXe02Y5pHM
NEW RELEASE! Build, Automate, Manage, and Scale ML Workflows with the NEW Amazon SageMaker Pipelines by Hallie Crosby Weishahn.
Description of Talk and Demo
AWS recently announced Amazon SageMaker Pipelines (https://aws.amazon.com/sagemaker/pipelines/), the first purpose-built, easy-to-use Continuous Integration and Continuous Delivery (CI/CD) service for machine learning.
SageMaker Pipelines has three main components which improve the operational resilience and reproducibility of your workflows: 1) pipelines, 2) model registry, and 3) projects.
In this talk and demo, Hallie will walk us through the new Amazon SageMaker Pipelines feature including MLOps support.
Date/Time
9-10am US Pacific Time (Third Monday of Every Month)
RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154
Meetup:
https://www.meetup.com/Data-Science-on-AWS/
Zoom:
https://zoom.us/j/690414331
Webinar ID: 690 414 331
Phone:
+1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll)
Related Links
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
O'Reilly Book: https://datascienceonaws.com
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai
Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227
RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154
Waking the Data Scientist at 2am: Detect Model Degradation on Production Mod...Chris Fregly
Waking the Data Scientist at 2am:
Detect Model Degradation on Production Models with Amazon SageMaker Endpoints & Model Monitor
In this talk, I describe how to deploy a model into production and monitor its performance using SageMaker Model Monitor. With Model Monitor, I can detect if a model's predictive performance has degraded - and alert an on-call data scientist to take action and improve the model at 2am while the DevOps folks sleep soundly through the night.
Topics: AI and Machine Learning, Model Deployment, Anomaly Detection, Amazon SageMaker Endpoints, and Model Monitor
Quantum Computing with Amazon Braket
In this talk, I describe some fundamental principles of quantum computing including qu-bits, superposition, and entanglement. I will demonstrate how to perform secure quantum computing tasks across many Quantum Processing Units (QPUs) using Amazon Braket, IAM, and S3.
AI and Machine Learning, Quantum Computing, Amazon Braket, QPU
15 Tips to Scale a Large AI/ML Workshop - Both Online and In-PersonChris Fregly
In this talk, we present tips and best practices for scaling a large workshop for 1,000's of simultaneous attendees - both online and in-person. While our workshop is focused on AI and machine learning on AWS, we generalize our learnings for any domain or specialization.
Video: https://youtu.be/T0L0JxDaPkc
RSVP Here: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, Airflow, and MLflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning and data engineering.
MLflow is a lightweight experiment-tracking system recently open-sourced by Databricks, the creators of Apache Spark. MLflow supports Python, Java/Scala, and R - and offers native support for TensorFlow, Keras, and Scikit-Learn.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
The link will be sent a few hours before the start of the workshop.
Only registered users will receive the link.
If you do not receive the link a few hours before the start of the workshop, please send your Eventbrite registration confirmation to support@pipeline.ai for help.
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Run Multiple Experiments with MLflow Experiment Tracking
12. Reproduce Model Training with TFX Metadata Store
13. Deploy the Model to Production with TensorFlow Serving and Istio
14. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
RSVP Here: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227
https://youtu.be/T0L0JxDaPkc
Title
Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU
Video
https://youtu.be/vaB4IM6ySD0
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, and Airflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Reproduce Model Training with TFX Metadata Store and Pachyderm
12. Deploy the Model to Production with TensorFlow Serving and Istio
13. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
Related Links
1. PipelineAI Home: https://pipeline.ai
2. PipelineAI Community Edition: http://community.pipeline.ai
3. PipelineAI GitHub: https://github.com/PipelineAI/pipeline
4. Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
5. YouTube Videos: https://youtube.pipeline.ai
6. SlideShare Presentations: https://slideshare.pipeline.ai
7. Slack Support: https://joinslack.pipeline.ai
8. Web Support and Knowledge Base: https://support.pipeline.ai
9. Email Support: support@pipeline.ai
Speaker: Umayah Abdennabi
Agenda
* Intro Grammarly (Umayah Abdennabi, 5 mins)
* Meetup Updates and Announcements (Chris, 5 mins)
* Custom Functions in Spark SQL (30 mins)
Speaker: Umayah Abdennabi
Spark comes with a rich Expression library that can be extended to make custom expressions. We will look into custom expressions and why you would want to use them.
* TF 2.0 + Keras (30 mins)
Speaker: Francesco Mosconi
Tensorflow 2.0 was announced at the March TF Dev Summit, and it brings many changes and upgrades. The most significant change is the inclusion of Keras as the default model building API. In this talk, we'll review the main changes introduced in TF 2.0 and highlight the differences between open source Keras and tf.keras
* SQUAD Deep-Dive: Question & Answer with Context (45 mins)
Speaker: Brett Koonce (https://quarkworks.co)
SQuAD (Stanford Question Answer Dataset) is an NLP challenge based around answering questions by reading Wikipedia articles, designed to be a real-world machine learning benchmark. We will look at several different ways to tackle the SQuAD problem, building up to state of the art approaches in terms of time, complexity, and accuracy.
https://rajpurkar.github.io/SQuAD-explorer/
https://dawn.cs.stanford.edu/benchmark/#squad
Food and drinks will be provided. The event will be held at Grammarly's office at One Embarcadero Center on the 9th floor. When you arrive at One Embarcadero, take the escalator to the second floor where you will find the lobby and elevators to the office suites. Come on up to the 9th floor (no need to check in at security), and ring the Grammarly doorbell.
PipelineAI Continuous Machine Learning and AI - Rework Deep Learning Summit -...Chris Fregly
Traditional machine learning pipelines end with life-less models sitting on disk in the research lab. These traditional models are typically trained on stale, offline, historical batch data. Static models and stale data are not sufficient to power today's modern, AI-first Enterprises that require continuous model training, continuous model optimizations, and lightning-fast model experiments directly in production. Through a series of open source, hands-on demos and exercises, we will use PipelineAI to breathe life into these models using 4 new techniques that we’ve pioneered:
* Continuous Validation (V)
* Continuous Optimizing (O)
* Continuous Training (T)
* Continuous Explainability (E).
The Continuous "VOTE" techniques has proven to maximize pipeline efficiency, minimize pipeline costs, and increase pipeline insight at every stage from continuous model training (offline) to live model serving (online.)
Attendees will learn to create continuous machine learning pipelines in production with PipelineAI, TensorFlow, and Kafka.
PipelineAI Real-Time Machine Learning - Global Artificial Intelligence Confer...Chris Fregly
Perform Online Predictions using Slack
A/B and multi-armed bandit model compare
Train Online Models with Kafka Streams
Create new models quickly
Deploy to production safely
Mirror traffic to validate online performance
Any Framework, Any Hardware, Any Cloud
Dashboard to manage the lifecycle of models from local development to live production
Generates optimized runtimes for the models
Custom targeting rules, shadow mode, and percentage-based rollouts to safely test features in live production
Continuous model training, model validation, and pipeline optimization
https://youtu.be/zpkH9oiIovU
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/258276286/
Related Links
PipelineAI Home: https://pipeline.ai
PipelineAI Community Edition: https://community.pipeline.ai
PipelineAI GitHub: https://github.com/PipelineAI/pipeline
PipelineAI Quick Start: https://quickstart.pipeline.ai
Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
YouTube Videos: https://youtube.pipeline.ai
SlideShare Presentations: https://slideshare.pipeline.ai
Slack Support:
https://joinslack.pipeline.ai
Web Support and Knowledge Base: https://support.pipeline.ai
Email Support: help@pipeline.ai
Hyper-Parameter Tuning Across the Entire AI Pipeline GPU Tech Conference San ...Chris Fregly
Chris Fregly, Founder @ PipelineAI, will walk you through a real-world, complete end-to-end Pipeline-optimization example. We highlight hyper-parameters - and model pipeline phases - that have never been exposed until now.
While most Hyperparameter Optimizers stop at the training phase (ie. learning rate, tree depth, ec2 instance type, etc), we extend model validation and tuning into a new post-training optimization phase including 8-bit reduced precision weight quantization and neural network layer fusing - among many other framework and hardware-specific optimizations.
Next, we introduce hyperparameters at the prediction phase including request-batch sizing and chipset (CPU v. GPU v. TPU).
Lastly, we determine a PipelineAI Efficiency Score of our overall Pipeline including Cost, Accuracy, and Time. We show techniques to maximize this PipelineAI Efficiency Score using our massive PipelineDB along with the Pipeline-wide hyper-parameter tuning techniques mentioned in this talk.
Bio
Chris Fregly is Founder and Applied AI Engineer at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco.
He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
PipelineAI Optimizes Your Enterprise AI Pipeline from Distributed Training to...Chris Fregly
https://pipeline.ai
With PipelineAI, You Can…
* Generate Hardware-Specific Model Optimizations
* Deploy and Compare Models in Live Production
* Optimize Complete AI Pipeline Across Many Models
* Hyper-Parameter Tune Both Training & Predicting Phases
Advanced Spark and TensorFlow Meetup - Dec 12 2017 - Dong Meng, MapR + Kubern...Chris Fregly
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/244971261/
Based on this blog post: https://mengdong.github.io/2017/07/15/distributed-tensorflow-with-gpu-on-kubernetes-and-mapr/
youtube video:
https://www.youtube.com/watch?v=3phz1_B-rR4
http://pipeline.ai
High Performance Distributed TensorFlow in Production with GPUs - NIPS 2017 -...Chris Fregly
Online Workshop
Note: A GPU-based cloud instance will be provided to each attendee for the duration of this event!!
At 8am PT on the morning of this workshop, we will email the Webinar details to your email address registered with Eventbrite.
If this email address is not up to date - or you do not get the email by 8am PT - please email your Eventbrite confirmation to help@pipeline.ai and we'll send you the details.
http://pipeline.ai
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
Time
Start: 9am PT Time
End: 1pm PT Time
Highlights
We will each build an end-to-end, continuous Tensorflow AI model training and deployment pipeline on our own GPU-based cloud instance.
At the end, we will combine our cloud instances to create the LARGEST Distributed Tensorflow AI Training and Serving Cluster in the WORLD!
Pre-requisites
Just a modern browser, internet connection, and a good night's sleep! We'll provide the rest.
Agenda
Spark ML
TensorFlow AI
Storing and Serving Models with HDFS
Trade-offs of CPU vs. *GPU, Scale Up vs. Scale Out
CUDA + cuDNN GPU Development Overview
TensorFlow Model Checkpointing, Saving, Exporting, and Importing
Distributed TensorFlow AI Model Training (Distributed Tensorflow)
TensorFlow's Accelerated Linear Algebra Framework (XLA)
TensorFlow's Just-in-Time (JIT) Compiler, Ahead of Time (AOT) Compiler
Centralized Logging and Visualizing of Distributed TensorFlow Training (Tensorboard)
Distributed Tensorflow AI Model Serving/Predicting (TensorFlow Serving)
Centralized Logging and Metrics Collection (Prometheus, Grafana)
Continuous TensorFlow AI Model Deployment (TensorFlow, Airflow)
Hybrid Cross-Cloud and On-Premise Deployments (Kubernetes)
High-Performance and Fault-Tolerant Micro-services (NetflixOSS)
More Info including GitHub and Docker Repos
http://pipeline.ai
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
1. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
After Dark 1.5+End-to-End, Real-time, Advanced Analytics:
Reference Big Data Processing Pipeline
Melbourne Spark Meetup
Dec 09, 2015
Chris Fregly
Principal Data Solutions Engineer
advancedspark.com
2. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Who Am I?
2
Streaming Data Engineer
Open Source Committer
Data Solutions Engineer
Apache Contributor
Principal Data Solutions Engineer
IBM Technology Center
Founder
Advanced Apache Meetup
Author
Advanced .
Due 2016
3. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Advanced Apache Spark Meetup
Meetup Metrics
Top 5 Most Active Spark Meetup!
1700+ Members in just 4 mos!!
2100+ Downloads of Docker Image
with many Meetup Demos!!!
@ advancedspark.com
Meetup Goals
Code dive deep into Spark and related open source code bases
Study integrations with Cassandra, ElasticSearch,Tachyon, S3,
BlinkDB, Mesos, YARN, Kafka, R, etc
Surface and share patterns and idioms of well-designed,
distributed, big data processing systems
4. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Upcoming Meetups and Conferences
London Spark Meetup (Oct 12th)
Scotland Data Science Meetup (Oct 13th)
Dublin Spark Meetup (Oct 15th)
Barcelona Spark Meetup (Oct 20th)
Madrid Big Data Meetup (Oct 22nd)
Paris Spark Meetup (Oct 26th)
Amsterdam Spark Summit (Oct 27th)
Brussels Spark Meetup (Oct 30th)
Zurich Big Data Meetup (Nov 2nd)
Geneva Spark Meetup (Nov 5th)
San Francisco Datapalooza (Nov 10th)
San Francisco Advanced Spark (Nov 12th)
4
Oslo Big Data Hadoop Meetup (Nov 19th)
Helsinki Spark Meetup (Nov 20th)
Stockholm Spark Meetup (Nov 23rd)
Copenhagen Spark Meetup (Nov 25th)
Budapest Spark Meetup (Nov 26th)
Istanbul Spark Meetup (Nov 28th)
Singapore Pre-Strata Spark Meetup (Dec 1st)
Sydney Spark Meetup (Dec 8th)
Melbourne Spark Meetup (Dec 9th)
San Francisco Advanced Spark (Dec 10th)
San Francisco Univ of San Francisco (Dec 11th)
Toronto Spark Meetup (Dec 14th)
5. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Topics of This Talk (15-20 mins each)
Spark Streaming and Spark ML
Kafka, Cassandra, ElasticSearch, Redis, Docker
Spark Core
Tuning and Profiling
Spark SQL
Tuning and Customizing
5
6. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Live, Interactive Demo!
6
7. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Audience Participation Required!
7
You ->
Audience Instructions
Navigate to
sparkafterdark.com
Click
3 actresses & 3 actors
Data ->
Scientist
This is Totally Anonymous!!
8. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Topics of This Talk (15-20 mins each)
Spark Streaming and Spark ML
Kafka, Cassandra, ElasticSearch, Redis, Docker
Spark Core
Tuning and Profiling
Spark SQL
Tuning and Customizing
8
9. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Mechanical Sympathy
“Hardware and software working together.”
- Martin Thompson
http://mechanical-sympathy.blogspot.com
“Whatever your data structure, my array will beat it.”
- Scott Meyers
Every C++ Book, basically
9
10. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Spark and Mechanical Sympathy
10
Project
Tungsten
(Spark 1.4-1.6+)
GraySort
Challenge
(Spark 1.1-1.2)
Minimize Memory & GC
Maximize CPU Cache
Saturate Network I/O
Saturate Disk I/O
11. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
CPU Cache Line and Cache Sizes
11
My
Laptop
My
SoftLayer
BareMetal
aka
“LLC”
12. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
CPU Cache Sympathy (AlphaSort paper)
Key (10 bytes) + Pointer (4 bytes) = 14 bytes
12
Key
Ptr
Pre-process & Pull Key From Record
Ptr
Key-Prefix
2x CPU Cache-line Friendly!
Key-Prefix (4 bytes) + Pointer (4 bytes) = 8 bytes
Key (10 bytes) + Pad (2 bytes) + Pointer (4 bytes) = 16 bytes
Ptr
Must Dereference to Compare Key
Key
Pointer (4 bytes) = 4 bytes
Key
Ptr
Pad
/Pad
CPU Cache-line Friendly!
Dereference full key only
to resolve prefix duplicates
13. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Performance Comparison
13
14. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Sequential vs Random Memory Access
14
15. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Demo!
Performance Impact of Random Pointer vs. Sequential Key Sort
15
16. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Instrumenting and Monitoring CPU
Use Linux perf command!
16
http://www.brendangregg.com/blog/2015-11-06/java-mixed-mode-flame-graphs.html
17. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Results of Random vs. Sequential Sort
17
Naïve Random Access
Pointer Sort
Cache Friendly Sequential
Key/Pointer Sort
Ptr
Key
Must Dereference to Compare Key
Key
Ptr
Pre-process & Pull Key From Record
-35%
-90%
-68%
-26%
% Change
-55%
perf stat –event
L1-dcache-load-misses,L1-dcache-prefetch-misses,LLC-load-misses,LLC-prefetch-misses
18. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Demo!
Performance Impact of CPU Cache & Prefetch Misses
18
19. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
CPU Cache Naïve Matrix Multiplication
// Dot product of each row & column vector
for (i <- 0 until numRowA)
for (j <- 0 until numColsB)
for (k <- 0 until numColsA)
res[ i ][ j ] += matA[ i ][ k ] * matB[ k ][ j ];
19
Bad: Row-wise traversal,
not using full CPU cache line,
ineffective pre-fetching
20. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
CPU Cache Friendly Matrix Multiplication
// Transpose B
for (i <- 0 until numRowsB)
for (j <- 0 until numColsB)
matBT[ i ][ j ] = matB[ j ][ i ];
20
Good: Full CPU Cache Line,
Effective Prefetching
OLD: matB [ k ][ j ];
// Modify algo for Transpose B
for (i <- 0 until numRowsA)
for (j <- 0 until numColsB)
for (k <- 0 until numColsA)
res[ i ][ j ] += matA[ i ][ k ] * matBT[ j ][ k ];
21. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Results Of Matrix Multiply Challenge
Cache-Friendly Matrix Multiply
21
Naïve Matrix Multiply
perf stat –event
L1-dcache-load-misses,L1-dcache-prefetch-misses,LLC-load-misses,
LLC-prefetch-misses,cache-misses,stalled-cycles-frontend
-96%
-93%
-93%
-70%
-53%
% Change
-63%
+8543%?
22. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Thread and Context Switch Sympathy
Problem
Atomically Increment 2 Counters
(each at different increments) by
1000’s of Simultaneous Threads
22
Possible Solutions
Synchronized Immutable
Synchronized Mutable
AtomicReference CAS
Volatile???
Context Switches are Expen$ive!!
aka
“LLC”
23. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Synchronized Immutable Counters
case class Counters(left: Int, right: Int)
object SynchronizedImmutableCounters {
var counters = new Counters(0,0)
def getCounters(): Counters = {
this.synchronized { counters }
}
def increment(leftIncrement: Int, rightIncrement: Int): Unit = {
this.synchronized {
counters = new Counters(counters.left + leftIncrement,
counters.right + rightIncrement)
}
}
}
23
Locks whole
outer object!!
24. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Synchronized Mutable Counters
class MutableCounters(left: Int, right: Int) {
def increment(leftIncrement: Int, rightIncrement: Int): Unit={
this.synchronized {…}
}
def getCountersTuple(): (Int, Int) = {
this.synchronized{ (counters.left, counters.right) }
}
}
object SynchronizedMutableCounters {
val counters = new MutableCounters(0,0)
…
def increment(leftIncrement: Int, rightIncrement: Int): Unit = {
counters.increment(leftIncrement, rightIncrement)
}
}
24
Locks just
MutableCounters
25. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Lock-Free AtomicReference Counters
object LockFreeAtomicReferenceCounters {
val counters = new AtomicReference[Counters](new Counters(0,0))
…
def increment(leftIncrement: Int, rightIncrement: Int) : Long = {
var originalCounters: Counters = null
var updatedCounters: Counters = null
do {
originalCounters = getCounters()
updatedCounters = new Counters(originalCounters.left+ leftIncrement,
originalCounters.right+ rightIncrement)
}
// Retry lock-free, optimistic compareAndSet() until AtomicRef updates
while !(counters.compareAndSet(originalCounters, updatedCounters))
}
}
25
Lock Free!!
26. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Lock-Free AtomicLong Counters
object LockFreeAtomicLongCounters {
// a single Long (64-bit) will maintain 2 separate Ints (32-bits each)
val counters = new AtomicLong()
…
def increment(leftIncrement: Int, rightIncrement: Int): Unit = {
var originalCounters = 0L
var updatedCounters = 0L
do {
originalCounters = counters.get()
…
// Store two 32-bit Int into one 64-bit Long
// Use >>> 32 and << 32 to set and retrieve each Int from the Long
}
// Retry lock-free, optimistic compareAndSet() until AtomicLong updates
while !(counters.compareAndSet(originalCounters, updatedCounters))
}
}
26
Q: Why not
@volatile long?
A: The JVM does not
guarantee atomic
updates of 64-bit
longs and doubles
Lock Free!!
27. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Demo!
Performance Impact of Thread Blocking & Context Switching
27
28. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Results of 2 Counter Synchronization
Immutable Case Class
28
Lock-Free AtomicLong
-64%
-46%
-17%
-33%
perf stat –event
context-switches,L1-dcache-load-misses,L1-dcache-prefetch-misses,
LLC-load-misses, LLC-prefetch-misses,cache-misses,stalled-cycles-frontend
% Change
-31%
-32%
-33%
-27%
case class Counters(left: Int, right: Int)
...
this.synchronized {
counters = new Counters(counters.left + leftIncrement,
counters.right + rightIncrement)
}
val counters = new AtomicLong()
…
do {
…
} while !(counters.compareAndSet(originalCounters,
updatedCounters))
29. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Profile Visualizations: Flame Graphs
29
Example: Spark Word Count
Java Stack Traces are Good! JDK 1.8+
(-XX:-Inline -XX:+PreserveFramePointer)
Plateaus are Bad!
I/O stalls, Heavy CPU
serialization, etc
30. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Project Tungsten: CPU and Memory
Create Custom Data Structures & Algorithms
Operate on serialized and compressed ByteArrays!
Minimize Garbage Collection
Reuse ByteArrays
In-place updates for aggregations
Maximize CPU Cache Effectiveness
8-byte alignment
AlphaSort-based Key-Prefix
Utilize Catalyst Dynamic Code Generation
Dynamic optimizations using entire query plan
Developer implements genCode() to create Scala source code (String)
Project Janino compiles source code into JVM bytecode
30
31. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Why is CPU the Bottleneck?
CPU for serialization, hashing, & compression
Spark 1.2 updates saturated Network, Disk I/O
10x increase in I/O throughput relative to CPU
More partition, pruning, and pushdown support
Newer columnar file formats help reduce I/O
31
32. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Custom Data Structs & Algos: Aggs
UnsafeFixedWidthAggregationMap
Uses BytesToBytesMap internally
In-place updates of serialized aggregation
No object creation on hot-path
TungstenAggregate & TungstenAggregationIterator
Operates directly on serialized, binary UnsafeRow
2 steps to avoid single-key OOMs
Hash-based (grouping) agg spills to disk if needed
Sort-based agg performs external merge sort on spills
32
33. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Custom Data Structs & Algos: Sorting
o.a.s.util.collection.unsafe.sort.
UnsafeSortDataFormat
UnsafeExternalSorter
UnsafeShuffleWriter
UnsafeInMemorySorter
RecordPointerAndKeyPrefix
33
Ptr
Key-Prefix
2x CPU Cache-line Friendly!
SortDataFormat<RecordPointerAndKeyPrefix, Long[ ]>
Note: Mixing multiple subclasses of SortDataFormat
simultaneously will prevent JIT inlining.
Supports merging compressed records
(if compression CODEC supports it, ie. LZF)
In-place external sorting of spilled BytesToBytes data
AlphaSort-based, 8-byte aligned sort key
In-place sorting of BytesToBytesMap data
34. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Code Generation
Problem
Boxing creates excessive objects
Expression tree evaluations are costly
JVM can’t inline polymorphic impls
Lack of polymorphism == poor code design
Solution
Code generation enables inlining
Rewrite and optimize code using overall plan, 8-byte align
Defer source code generation to each operator, UDF, UDAF
Use Janino to compile generated source code into bytecode
(More IDE friendly than Scala quasiquotes)
34
35. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Autoscaling Spark Workers (Spark 1.5+)
Scaling up is easy J
SparkContext.addExecutors() until max is reached
Scaling down is hard L
SparkContext.removeExecutors()
Lose RDD cache inside Executor JVM
Must rebuild active RDD partitions in another Executor JVM
Uses External Shuffle Service from Spark 1.1-1.2
If Executor JVM dies/restarts, shuffle keeps shufflin’!
35
36. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
“Hidden” Spark Submit REST API
http://arturmkrtchyan.com/apache-spark-hidden-rest-api
Submit Spark Job
curl -X POST http://127.0.0.1:6066/v1/submissions/create
--header "Content-Type:application/json;charset=UTF-8"
--data ’{"action" : "CreateSubmissionRequest”,
"mainClass" : "org.apache.spark.examples.SparkPi”,
"sparkProperties" : {
"spark.jars" : "file:/spark/lib/spark-examples-1.5.1.jar",
"spark.app.name" : "SparkPi",…
}}’
Get Spark Job Status
curl http://127.0.0.1:6066/v1/submissions/status/<job-id-from-submit-request>
Kill Spark Job
curl -X POST http://127.0.0.1:6066/v1/submissions/kill/<job-id-from-submit-request>
36
(the snitch)
37. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Outline
Spark Streaming and Spark ML
Kafka, Cassandra, ElasticSearch, Redis, Docker
Spark Core
Tuning and Profiling
Spark SQL
Tuning and Customizing
37
38. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Parquet Columnar File Format
Based on Google Dremel paper ~2010
Collaboration with Twitter and Cloudera
Columnar storage format for fast columnar aggs
Supports evolving schema
Supports pushdowns
Support nested partitions
Tight compression
Min/max heuristics enable file and chunk skipping
38
Min/Max Heuristics
For Chunk Skipping
39. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Partitions
Partition Based on Data Access Patterns
/genders.parquet/gender=M/…
/gender=F/… <-- Use Case: Access Users by Gender
/gender=U/…
Dynamic Partition Creation (Write)
Dynamically create partitions on write based on column (ie. Gender)
SQL: INSERT TABLE genders PARTITION (gender) SELECT …
DF: gendersDF.write.format("parquet").partitionBy("gender")
.save("/genders.parquet")
Partition Discovery (Read)
Dynamically infer partitions on read based on paths (ie. /gender=F/…)
SQL: SELECT id FROM genders WHERE gender=F
DF: gendersDF.read.format("parquet").load("/genders.parquet/").select($"id").
.where("gender=F")
39
40. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Pruning
Partition Pruning
Filter out rows by partition
SELECT id, gender FROM genders WHERE gender = ‘F’
Column Pruning
Filter out columns by column filter
Extremely useful for columnar storage formats (ie. Parquet)
Skip entire blocks of columns
SELECT id, gender FROM genders
40
41. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Pushdowns
aka. Predicate or Filter Pushdowns
Predicate returns true or false for given function
Filters rows deep into the data source
Reduces number of rows returned
Data Source must implement PrunedFilteredScan
def buildScan(requiredColumns: Array[String],
filters: Array[Filter]): RDD[Row]
41
42. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Demo!
Compare Performance of File Formats, Partitions, and Pushdowns
42
43. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Filter Pushdown and Collapsing
43
Filter pushdown (Parquet)
Filter collapsing (next best thing)
44. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Join Between Partitioned & Unpartitioned
44
45. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Join Between Partitioned & Partitioned
45
46. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
46
Cartesian Join vs. Inner Join
47. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
47
Broadcast Join vs. Normal Shuffle Join
48. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Visualizing the Query Plan
48
Effectiveness
of Filter
Cost-based
Join Optimization
Similar to
MapReduce
Map-side Join
& DistributedCache
Peak Memory for
Joins and Aggs
UnsafeFixedWidthAggregationMap
getPeakMemoryUsedBytes()
49. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Data Source API
Relations (o.a.s.sql.sources.interfaces.scala)
BaseRelation (abstract class): Provides schema of data
TableScan (impl): Read all data from source
PrunedFilteredScan (impl): Column pruning & predicate pushdowns
InsertableRelation (impl): Insert/overwrite data based on SaveMode
RelationProvider (trait/interface): Handle options, BaseRelation factory
Filters (o.a.s.sql.sources.filters.scala)
Filter (abstract class): Handles all filters supported by this source
EqualTo (impl)
GreaterThan (impl)
StringStartsWith (impl)
49
50. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Native Spark SQL Data Sources
50
51. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
JSON Data Source
DataFrame
val ratingsDF = sqlContext.read.format("json")
.load("file:/root/pipeline/datasets/dating/ratings.json.bz2")
-- or –
val ratingsDF = sqlContext.read.json
("file:/root/pipeline/datasets/dating/ratings.json.bz2")
SQL Code
CREATE TABLE genders USING json
OPTIONS
(path "file:/root/pipeline/datasets/dating/genders.json.bz2")
51
json() convenience method
52. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Parquet Data Source
Configuration
spark.sql.parquet.filterPushdown=true
spark.sql.parquet.mergeSchema=false (unless your schema is evolving)
spark.sql.parquet.cacheMetadata=true (requires sqlContext.refreshTable())
spark.sql.parquet.compression.codec=[uncompressed,snappy,gzip,lzo]
DataFrames
val gendersDF = sqlContext.read.format("parquet")
.load("file:/root/pipeline/datasets/dating/genders.parquet")
gendersDF.write.format("parquet").partitionBy("gender")
.save("file:/root/pipeline/datasets/dating/genders.parquet")
SQL
CREATE TABLE genders USING parquet
OPTIONS
(path "file:/root/pipeline/datasets/dating/genders.parquet")
52
53. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
ElasticSearch Data Source
Github
https://github.com/elastic/elasticsearch-hadoop
Maven
org.elasticsearch:elasticsearch-spark_2.10:2.1.0
Code
val esConfig = Map("pushdown" -> "true", "es.nodes" -> "<hostname>",
"es.port" -> "<port>")
df.write.format("org.elasticsearch.spark.sql”).mode(SaveMode.Overwrite)
.options(esConfig).save("<index>/<document-type>")
53
54. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Cassandra Data Source (DataStax)
Github
https://github.com/datastax/spark-cassandra-connector
Maven
com.datastax.spark:spark-cassandra-connector_2.10:1.5.0-M1
Code
ratingsDF.write
.format("org.apache.spark.sql.cassandra")
.mode(SaveMode.Append)
.options(Map("keyspace"->"<keyspace>",
"table"->"<table>")).save(…)
54
55. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Mythical New Cassandra Data Source
By-pass Cassandra CQL “front door”
(which is optimized for transactional data)
Bulk read and write directly against SSTables
(Similar to 5 year old NetflixOSS project Aegisthus)
Promotes Cassandra to first-class analytics option
Replicated analytics cluster not needed
55
56. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
DB2 and BigSQL Data Sources (IBM)
Coming Soon!
56
57. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Creating a Custom Data Source
① Study existing implementations
o.a.s.sql.execution.datasources.jdbc.JDBCRelation
② Extend base traits & implement required methods
o.a.s.sql.sources.{BaseRelation,PrunedFilterScan}
Spark JDBC (o.a.s.sql.execution.datasources.jdbc)
class JDBCRelation extends BaseRelation
with PrunedFilteredScan
with InsertableRelation
DataStax Cassandra (o.a.s.sql.cassandra)
class CassandraSourceRelation extends BaseRelation
with PrunedFilteredScan
with InsertableRelation!
57
58. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Demo!
Create a Custom Data Source
58
59. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Publishing Custom Data Sources
59
spark-packages.org
60. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
IBM | spark.tc
Spark SQL UDF Code Generation
100+ UDFs now generating code
More to come in Spark 1.6+
Details in
SPARK-8159, SPARK-9571
Every UDF must use Expressions and
implement Expression.genCode()
to participate in the fun
Lambdas (RDD or Dataset API)
and sqlContext.udf.registerFunction()
are not enough!!
61. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Creating a Custom UDF with Code Gen
① Study existing implementations
o.a.s.sql.catalyst.expressions.Substring
② Extend and implement base trait
o.a.s.sql.catalyst.expressions.Expression.genCode
③ Don’t forget about Python!
python.pyspark.sql.functions.py
61
62. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Demo!
Creating a Custom UDF participating in Code Generation
62
63. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Spark 1.6 Improvements
63
64. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Adaptive Query Execution
Adapt query execution using data from previous stages
Dynamically choose spark.sql.shuffle.partitions (default 200)
64
Broadcast Join
(popular keys)
Shuffle Join
(not-so-popular keys)
Hybrid
Join
65. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Adaptive Memory Management
Spark <1.6
Manual configure between 2 memory regions
Spark execution engine (shuffles, joins, sorts, aggs)
spark.shuffle.memoryFraction
RDD Data Cache
spark.storage.memoryFraction
Spark 1.6+
Unified memory regions
Dynamically expand/contract memory regions
Supports minimum for RDD storage (LRU Cache)
65
66. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Metrics
Shows exact memory usage per operator & per node
Helps debugging and identifying skew
66
67. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Spark SQL API
Datasets type safe API (similar to RDDs) utilizing Tungsten
val ds = sqlContext.read.text("ratings.csv").as[String]
val df = ds.flatMap(_.split(",")).filter(_ != "").toDF() // RDD API, convert to DF
val agg = df.groupBy($"rating").agg(count("*") as "ct”).orderBy($"ct" desc)
Typed Aggregators used alongside UDFs and UDAFs
val simpleSum = new Aggregator[Int, Int, Int] with Serializable {
def zero: Int = 0
def reduce(b: Int, a: Int) = b + a
def merge(b1: Int, b2: Int) = b1 + b2
def finish(b: Int) = b
}.toColumn
val sum = Seq(1,2,3,4).toDS().select(simpleSum)
Query files directly without registerTempTable()
%sql SELECT * FROM json.`/datasets/movielens/ml-latest/movies.json`
67
68. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Spark Streaming State Management
New trackStateByKey()
Store deltas, compaction later
No longer scan every key
Sessionization TTL
Integrated A/B Testing
Shows failure output in Admin UI
68
69. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Thank You!!!
Chris Fregly
IBM Spark Tech Center
(http://spark.tc)
San Francisco, California, USA
advancedspark.com
Sign up for the Meetup and Book
Clone, Contribute, Commit on Github
Run All Demos in Docker
2100+ Docker Downloads!!
Find me on LinkedIn, Twitter, Github, Email, Fax
69
70. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
What’s Next?
70
After Dark 1.6+
71. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
What’s Next?
Autoscaling Docker/Spark Workers
Completely Docker-based
Docker Compose, Google Kubernetes
Lots of Demos and Examples
More Zeppelin & IPython/Jupyter notebooks
More advanced analytics use cases
More Performance Profiling
Work closely with Netflix & Databricks
Identify & fix performance bottlenecks
71
72. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
Upcoming Meetups and Conferences
London Spark Meetup (Oct 12th)
Scotland Data Science Meetup (Oct 13th)
Dublin Spark Meetup (Oct 15th)
Barcelona Spark Meetup (Oct 20th)
Madrid Big Data Meetup (Oct 22nd)
Paris Spark Meetup (Oct 26th)
Amsterdam Spark Summit (Oct 27th)
Brussels Spark Meetup (Oct 30th)
Zurich Big Data Meetup (Nov 2nd)
Geneva Spark Meetup (Nov 5th)
San Francisco Datapalooza (Nov 10th)
San Francisco Advanced Spark (Nov 12th)
72
Oslo Big Data Hadoop Meetup (Nov 19th)
Helsinki Spark Meetup (Nov 20th)
Stockholm Spark Meetup (Nov 23rd)
Copenhagen Spark Meetup (Nov 25th)
Budapest Spark Meetup (Nov 26th)
Istanbul Spark Meetup (Nov 28th)
Singapore Pre-Strata Spark Meetup (Dec 1st)
Sydney Spark Meetup (Dec 8th)
Melbourne Spark Meetup (Dec 9th)
San Francisco Advanced Spark (Dec 10th)
San Francisco Univ of San Francisco (Dec 11th)
Toronto Spark Meetup (Dec 14th)
73. Power of data. Simplicity of design. Speed of innovation.
IBM Spark
spark.tc
Power of data. Simplicity of design. Speed of innovation.
IBM Spark
advancedspark.com
@cfregly