Apache Spark has driven a lot of adoption of both Scala and functional programming concepts in non-traditionally industries. For many programmers in the big data world they coming looking for a solution to scaling their code, and quickly find themselves dealing with immutable data structures and lambdas, and those who love it stay. However, there is a dark side (of escape), much of Spark’s functional programming is changing, and even though it encourages functional programming it’s in a variety of languages with different expectations (in-line XML as a valid part of your language is fun!). This talk will look at how Spark does a good job of introduce folks to concepts like immutability, but also places where we maybe don’t do a great job of setting up developers for a life of functional programming. Things like accumulators, our three different models for streaming data, and an “interesting” approach to closures (come to find out what the ClosuerCleaner does, stay to find out why). The talk will close out with a look at how the functional inspired API is in exposed in the different languages, and how this impacts the kind of code written (Scala, Java, and Python – other languages are supported by Spark but I don’t want to re-learn Javascript or learn R just for this talk). Pictures of cute animals will be included in the slides to distract from the sad parts.
Video: https://www.youtube.com/watch?v=EDJfpkDpoE4
Powering Tensorflow with big data using Apache Beam, Flink, and Spark - OSCON...Holden Karau
TensorFlow is all kinds of fancy, from helping startups raising their series A in Silicon Valley to detecting if something is a cat. However, when things start to get “real,” you may find yourself no longer just dealing with mnist.csv but instead needing do large-scale data prep as well as training.
Holden Karau details how to use TensorFlow in conjunction with Apache Spark, Flink, and Beam to create a full machine learning pipeline—including the annoying feature engineering and data prep components that we like to pretend don’t exist. Holden also explains why these feature prep stages need to be integrated into the serving layer. She concludes by examining changing industry trends, like Apache Arrow, and how they impact cross-language development for things like deep learning. Even if you’re not trying to raise a round of funding in Silicon Valley, this talk will give you tools to do interesting machine learning problems at scale.
Simplifying training deep and serving learning models with big data in python...Holden Karau
More Serious Business Kitty Description:
While some deep learning systems have promised to not require any kind of data preparation or cleaning, in practice many folks find that effectively training their models requires some amount of data preparation and often we spend more time on our data preparation than anything else. This talk will examine tools for data preparation that can be used at scale on "big-data" and then how to use their results on-line at serving time (where we hopefully no longer require a cluster to predict every new user).
Less Serious Business Kitty Description:
Deep Learning, in addition to being a world class tool for detecting the presence of cats, requires large amounts of data for training. As much vendors may say "no data prep required", they are all lying*. This talk will look tools to build a deep learning pipeline with feature prep on top of existing big data technologies without rewriting your code for serving.
Traditionally feature prep done in a big data system, like Spark, Flink, or Beam, would have to be rewritting for the on-line serving component. This is about as much fun as when we have to rewrite our sample Python code into Java, as for some reason that's what a lot companies associate with "production." Come for the deep learning buzz-words, stay for the how to perform on-line serving without writing Java code.
*All vendors are optimists when it comes to their own products, including the vendors who pay Holden and Gris but they pay us so its ok.
Making the big data ecosystem work together with Python & Apache Arrow, Apach...Holden Karau
Slides from PyData London exploring how the big data ecosystem (currently) works together as well as how different parts of the ecosystem work with Python. Proof-of-concept examples are provided using nltk & spacy with Spark. Then we look to the future and how we can improve.
Accelerating Big Data beyond the JVM - Fosdem 2018Holden Karau
Many popular big data technologies (such as Apache Spark, BEAM, Flink, and Kafka) are built in the JVM, and many interesting tools are built in other languages (ranging from Python to CUDA). For simple operations the cost of copying the data can quickly dominate, and in complex cases can limit our ability to take advantage of specialty hardware. This talk explores how improved formats are being integrated to reduce these hurdles to co-operation.
Many popular big data technologies (such as Apache Spark, BEAM, and Flink) are built in the JVM, and many interesting AI tools are built in other languages, and some requiring copying to the GPU. As many folks have experienced, while we may wish that we spend all of our time playing with cool algorithms -- we often need to spend more of our time working on data prep. Having to copy our data slowly between the JVM and the target language of computation can remove much of the benefit of being able to access our specialized tooling. Thankfully, as illustrated in the soon to be released Spark 2.3, Apache Arrow and related tools offer the ability to reduce this overhead. This talk will explore how Arrow is being integrated into Spark, and how it can be integrated into other systems, but also limitations and places where Apache Arrow will not magically save us.
Link: https://fosdem.org/2018/schedule/event/big_data_outside_jvm/
Sharing (or stealing) the jewels of python with big data & the jvm (1)Holden Karau
With the new Apache Arrow integration in PySpark 2.3, it is now starting become reasonable to look to the Python world and ask “what else do we want to steal besides tensorflow”, or as a Python developer look and say “how can I get my code into production without it being rewritten into a mess of Java?”
Regardless of your specific side(s) in the JVM/Python divide, collaboration is getting a lot faster, so lets learn how to share! In this brief talk we will examine sharing some of the wonders of Spacy with the Java world, which still has a somewhat lackluster set of options for NLP.
Big Data Beyond the JVM - Strata San Jose 2018Holden Karau
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Powering Tensorflow with big data using Apache Beam, Flink, and Spark - OSCON...Holden Karau
TensorFlow is all kinds of fancy, from helping startups raising their series A in Silicon Valley to detecting if something is a cat. However, when things start to get “real,” you may find yourself no longer just dealing with mnist.csv but instead needing do large-scale data prep as well as training.
Holden Karau details how to use TensorFlow in conjunction with Apache Spark, Flink, and Beam to create a full machine learning pipeline—including the annoying feature engineering and data prep components that we like to pretend don’t exist. Holden also explains why these feature prep stages need to be integrated into the serving layer. She concludes by examining changing industry trends, like Apache Arrow, and how they impact cross-language development for things like deep learning. Even if you’re not trying to raise a round of funding in Silicon Valley, this talk will give you tools to do interesting machine learning problems at scale.
Simplifying training deep and serving learning models with big data in python...Holden Karau
More Serious Business Kitty Description:
While some deep learning systems have promised to not require any kind of data preparation or cleaning, in practice many folks find that effectively training their models requires some amount of data preparation and often we spend more time on our data preparation than anything else. This talk will examine tools for data preparation that can be used at scale on "big-data" and then how to use their results on-line at serving time (where we hopefully no longer require a cluster to predict every new user).
Less Serious Business Kitty Description:
Deep Learning, in addition to being a world class tool for detecting the presence of cats, requires large amounts of data for training. As much vendors may say "no data prep required", they are all lying*. This talk will look tools to build a deep learning pipeline with feature prep on top of existing big data technologies without rewriting your code for serving.
Traditionally feature prep done in a big data system, like Spark, Flink, or Beam, would have to be rewritting for the on-line serving component. This is about as much fun as when we have to rewrite our sample Python code into Java, as for some reason that's what a lot companies associate with "production." Come for the deep learning buzz-words, stay for the how to perform on-line serving without writing Java code.
*All vendors are optimists when it comes to their own products, including the vendors who pay Holden and Gris but they pay us so its ok.
Making the big data ecosystem work together with Python & Apache Arrow, Apach...Holden Karau
Slides from PyData London exploring how the big data ecosystem (currently) works together as well as how different parts of the ecosystem work with Python. Proof-of-concept examples are provided using nltk & spacy with Spark. Then we look to the future and how we can improve.
Accelerating Big Data beyond the JVM - Fosdem 2018Holden Karau
Many popular big data technologies (such as Apache Spark, BEAM, Flink, and Kafka) are built in the JVM, and many interesting tools are built in other languages (ranging from Python to CUDA). For simple operations the cost of copying the data can quickly dominate, and in complex cases can limit our ability to take advantage of specialty hardware. This talk explores how improved formats are being integrated to reduce these hurdles to co-operation.
Many popular big data technologies (such as Apache Spark, BEAM, and Flink) are built in the JVM, and many interesting AI tools are built in other languages, and some requiring copying to the GPU. As many folks have experienced, while we may wish that we spend all of our time playing with cool algorithms -- we often need to spend more of our time working on data prep. Having to copy our data slowly between the JVM and the target language of computation can remove much of the benefit of being able to access our specialized tooling. Thankfully, as illustrated in the soon to be released Spark 2.3, Apache Arrow and related tools offer the ability to reduce this overhead. This talk will explore how Arrow is being integrated into Spark, and how it can be integrated into other systems, but also limitations and places where Apache Arrow will not magically save us.
Link: https://fosdem.org/2018/schedule/event/big_data_outside_jvm/
Sharing (or stealing) the jewels of python with big data & the jvm (1)Holden Karau
With the new Apache Arrow integration in PySpark 2.3, it is now starting become reasonable to look to the Python world and ask “what else do we want to steal besides tensorflow”, or as a Python developer look and say “how can I get my code into production without it being rewritten into a mess of Java?”
Regardless of your specific side(s) in the JVM/Python divide, collaboration is getting a lot faster, so lets learn how to share! In this brief talk we will examine sharing some of the wonders of Spacy with the Java world, which still has a somewhat lackluster set of options for NLP.
Big Data Beyond the JVM - Strata San Jose 2018Holden Karau
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. This talk will examine how to debug Apache Spark applications, the different options for logging in PySpark, as well as some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose, and this talk will examine how to effectively search logs from Apache Spark to spot common problems. In addition to the internal logging, this talk will look at options for logging from within our program itself.
Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but this talk will look at how to effectively use Spark’s current accumulators for debugging as well as a look to future for data property type accumulators which may be coming to Spark in future version.
In addition to reading logs, and instrumenting our program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems.
Debuggers are a wonderful tool, however when you have 100 computers the “wonder” can be a bit more like “pain”. This talk will look at how to connect remote debuggers, but also remind you that it’s probably not the easiest path forward.
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Using Spark ML on Spark Errors - What do the clusters tell us?Holden Karau
If you’re subscribed to user@spark.apache.org, or work in a large company, you may see some common Spark error messages. Even attending Spark Summit over the past few years you have seen talks like the “Top K Mistakes in Spark.” While cool non-machine learning based tools do exist to examine Spark’s logs — they don’t use machine learning and therefore are not as cool but also limited in by the amount of effort humans can put into writing rules for them. This talk will look what happens when we train “regular” clustering models on stack traces, and explore DL models for classifying user message to the Spark list. Come for the reassurance that the robots are not yet able to fix themselves, and stay to learn how to work better with the help of our robot friends. The tl;dr of this talk is Spark ML on Spark output, plus a little bit of Tensorflow is fun for the whole family, but probably shouldn’t automatically respond to user list posts just yet.
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
A peek into Python's Metaclass and Bytecode from a Smalltalk UserKoan-Sin Tan
Understanding object model and bytecode is a crucial part in understanding an interpreted object-oriented language. Smalltalk, one of the oldest object-oriented programming languages, has a great object model and has been used bytecode and VM since 1970s. It is interesting to compare Smalltalk's and Python's object model and bytecode. Guido once said "I remember being surprised by its use of metaclasses (which is quite different from that in Python or Ruby!) when I read about them much later. " and "Smalltalk's bytecode was a bigger influence of Python's bytecode though." It is interesting to compare Smalltalk's and Python's metacalss and bytecode.
Devel::NYTProf v3 - 200908 (OUTDATED, see 201008)Tim Bunce
Slides of my talk on Devel::NYTProf and optimizing perl code at the Italian Perl Workshop (IPW09). It covers the new features in NYTProf v3 and a new section outlining a multi-phase approach to optimizing your perl code.
30 mins long plus 10 mins of questions. Best viewed fullscreen.
Outline of my experience introducing and championing the usage of Julia Language inside a medium sized Financial organization. Cover reason for using it, reason why I thought it was a good fit and some advice on how to improve other first experience
Keeping the fun in functional w/ Apache Spark @ Scala Days NYCHolden Karau
Apache Spark has been a great driver of not only Scala adoption, but introducing a new generation of developers to functional programming concepts. As Spark places more emphasis on its newer DataFrame & Dataset APIs, it’s important to ask ourselves how we can benefit from this while still keeping our fun functional roots. We will explore the cases where the Dataset APIs empower us to do cool things we couldn’t before, what the different approaches to serialization mean, and how to figure out when the shiny new API is actually just trying to steal your lunch money (aka CPU cycles).
A fast introduction to PySpark with a quick look at Arrow based UDFsHolden Karau
This talk will introduce Apache Spark (one of the most popular big data tools), the different built ins (from SQL to ML), and, of course, everyone's favorite wordcount example. Once we've got the nice parts out of the way, we'll talk about some of the limitations and the work being undertaken to improve those limitations. We'll also look at the cases where Spark is more like trying to hammer a screw. Since we want to finish on a happy note, we will close out with looking at the new vectorized UDFs in PySpark 2.3.
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. This talk will examine how to debug Apache Spark applications, the different options for logging in PySpark, as well as some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose, and this talk will examine how to effectively search logs from Apache Spark to spot common problems. In addition to the internal logging, this talk will look at options for logging from within our program itself.
Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but this talk will look at how to effectively use Spark’s current accumulators for debugging as well as a look to future for data property type accumulators which may be coming to Spark in future version.
In addition to reading logs, and instrumenting our program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems.
Debuggers are a wonderful tool, however when you have 100 computers the “wonder” can be a bit more like “pain”. This talk will look at how to connect remote debuggers, but also remind you that it’s probably not the easiest path forward.
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Using Spark ML on Spark Errors - What do the clusters tell us?Holden Karau
If you’re subscribed to user@spark.apache.org, or work in a large company, you may see some common Spark error messages. Even attending Spark Summit over the past few years you have seen talks like the “Top K Mistakes in Spark.” While cool non-machine learning based tools do exist to examine Spark’s logs — they don’t use machine learning and therefore are not as cool but also limited in by the amount of effort humans can put into writing rules for them. This talk will look what happens when we train “regular” clustering models on stack traces, and explore DL models for classifying user message to the Spark list. Come for the reassurance that the robots are not yet able to fix themselves, and stay to learn how to work better with the help of our robot friends. The tl;dr of this talk is Spark ML on Spark output, plus a little bit of Tensorflow is fun for the whole family, but probably shouldn’t automatically respond to user list posts just yet.
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
A peek into Python's Metaclass and Bytecode from a Smalltalk UserKoan-Sin Tan
Understanding object model and bytecode is a crucial part in understanding an interpreted object-oriented language. Smalltalk, one of the oldest object-oriented programming languages, has a great object model and has been used bytecode and VM since 1970s. It is interesting to compare Smalltalk's and Python's object model and bytecode. Guido once said "I remember being surprised by its use of metaclasses (which is quite different from that in Python or Ruby!) when I read about them much later. " and "Smalltalk's bytecode was a bigger influence of Python's bytecode though." It is interesting to compare Smalltalk's and Python's metacalss and bytecode.
Devel::NYTProf v3 - 200908 (OUTDATED, see 201008)Tim Bunce
Slides of my talk on Devel::NYTProf and optimizing perl code at the Italian Perl Workshop (IPW09). It covers the new features in NYTProf v3 and a new section outlining a multi-phase approach to optimizing your perl code.
30 mins long plus 10 mins of questions. Best viewed fullscreen.
Outline of my experience introducing and championing the usage of Julia Language inside a medium sized Financial organization. Cover reason for using it, reason why I thought it was a good fit and some advice on how to improve other first experience
Keeping the fun in functional w/ Apache Spark @ Scala Days NYCHolden Karau
Apache Spark has been a great driver of not only Scala adoption, but introducing a new generation of developers to functional programming concepts. As Spark places more emphasis on its newer DataFrame & Dataset APIs, it’s important to ask ourselves how we can benefit from this while still keeping our fun functional roots. We will explore the cases where the Dataset APIs empower us to do cool things we couldn’t before, what the different approaches to serialization mean, and how to figure out when the shiny new API is actually just trying to steal your lunch money (aka CPU cycles).
A fast introduction to PySpark with a quick look at Arrow based UDFsHolden Karau
This talk will introduce Apache Spark (one of the most popular big data tools), the different built ins (from SQL to ML), and, of course, everyone's favorite wordcount example. Once we've got the nice parts out of the way, we'll talk about some of the limitations and the work being undertaken to improve those limitations. We'll also look at the cases where Spark is more like trying to hammer a screw. Since we want to finish on a happy note, we will close out with looking at the new vectorized UDFs in PySpark 2.3.
A super fast introduction to Spark and glance at BEAMHolden Karau
Apache Spark is one of the most popular general purpose distributed systems, with built in libraries to support everything from ML to SQL. Spark has APIs across languages including Scala, Java, Python, and R -- with more 3rd party language support (like Julia & C#). Apache BEAM is a cross-platform tool for building on top of different distributed systems, but its in it’s early stages. This talk will introduce the core concepts of Apache Spark, and look to the potential future of Apache BEAM.
Apache Spark has two core abstractions for representing distributed data and computations. This talk will introduce the basics of RDDs and Spark DataFrames & Datasets, and Spark’s method for achieving resiliency. Since it’s a big data talk, we will include the almost required wordcount example, and end the Spark part with follow up pointers on Spark’s new ML APIs. For folks who are interested we’ll then talk a bit about portability, and how Apache BEAM aims to improve portability (as well it’s unique approach to cross-language support).
Slides from Holden's talk at https://www.meetup.com/Wellington-Data-Scaling-Chats/events/mdcsdpyxcbxb/
An introduction into Spark ML plus how to go beyond when you get stuckData Con LA
Abstract:-
This talk will introduce Spark new machine learning frame work (Spark ML) and how to train basic models with it. A companion Jupyter notebook for people to follow along with will be provided. Once we've got the basics down we'll look at what to do when we find we need more than the tools available in Spark ML (and I'll try and convince people to contribute to my latest side project -- Sparkling ML).
Bio:-
Holden Karau is a transgender Canadian, Apache Spark committer, an active open source contributor, and coauthor of Learning Spark and High Performance Spark. When not in San Francisco working as a software development engineer at IBM’s Spark Technology Center, Holden speaks internationally about Spark and holds office hours at coffee shops at home and abroad. She makes frequent contributions to Spark, specializing in PySpark and machine learning. Prior to IBM, she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She holds a bachelor of mathematics in computer science from the University of Waterloo. Outside of computers she enjoys scootering and playing with fire.
Are general purpose big data systems eating the world?Holden Karau
Every-time there is a new piece of big data technology we often see many different specific implementations of the concepts, which often eventually consolidate down to a few viable options, and then frequently end up getting rolled into part of another larger project. This talk will examine this trend in big data ecosystem, look at the exceptions to the "rule", and look at how better interchange formats like Apache Arrow have the potential to change this going forward. In addition to general vague happy feelings (or sad depending on your ideas about how software should be made), this talk will look at some specific examples with deep learning, so if anyone is looking for a little bit of pixie dust to sprinkle on a failing business plan to take to silicon valley to raise a series A, you'll get something out this as well.
Video - https://www.youtube.com/watch?v=P_YKrLFZQJo
Beyond Wordcount with spark datasets (and scalaing) - Nide PDX Jan 2018Holden Karau
Apache Spark is one of the most popular big data systems, but once the shiny finish starts to wear off you can find yourself wondering if you've accidentally deployed a Ford Pinto into production. This talk will look at the challenges that come with scaling Spark jobs. Also, the talk will explore Spark's new(ish) Dataset/DataFrame API, as well as how it’s evolving in Spark 2.3 with improved Python support.
If you're already a Spark user, come to find out why it’s not all your fault. If you aren't already a Spark user, come to find out how to save yourself from some of the pitfalls once you move beyond the example code.
Check out Holden's newest book, High Performance Spark, for more information!
From https://niketechtalksjan2018.splashthat.com/
Improving PySpark performance: Spark Performance Beyond the JVMHolden Karau
This talk covers a number of important topics for making scalable Apache Spark programs - from RDD re-use to considerations for working with Key/Value data, why avoiding groupByKey is important and more. We also include Python specific considerations, like the difference between DataFrames/Datasets and traditional RDDs with Python. We also explore some tricks to intermix Python and JVM code for cases where the performance overhead is too high.
Introduction to Spark ML Pipelines WorkshopHolden Karau
Introduction to Spark ML Pipelines Workshop slides - companion IJupyter notebooks in Python & Scala are available from my github at https://github.com/holdenk/spark-intro-ml-pipeline-workshop
Introduction to Spark Datasets - Functional and relational together at lastHolden Karau
Spark Datasets are an evolution of Spark DataFrames which allow us to work with both functional and relational transformations on big data with the speed of Spark.
Testing and validating spark programs - Strata SJ 2016Holden Karau
Apache Spark is a fast, general engine for big data processing. As Spark jobs are used for more mission-critical tasks, it is important to have effective tools for testing and validation. Expanding her Strata NYC talk, “Effective Testing of Spark Programs,” Holden Karau details reasonable validation rules for production jobs and best practices for creating effective tests, as well as options for generating test data.
Holden explores best practices for generating complex test data, setting up performance testing, as well as basic unit testing. The validation component will focus on how to create reasonable validation rules given the constraints of Spark’s accumulators.
Unit testing of Spark programs is deceptively simple. Holden looks at how unit testing of Spark itself is accomplished and distills a number of best practices into traits we can use. This includes dealing with local mode cluster creation and tear down during test suites, factoring our functions to increase testability, mock data for RDDs, and mock data for Spark SQL. A number of interesting problems also arise when testing Spark Streaming programs, including handling of starting and stopping the streaming context, providing mock data, and collecting results, and Holden pulls out simple takeaways for dealing with these issues.
Holden also explores Spark’s internal methods for generating random data, as well as options using external libraries to generate effective test datasets (for both small- and large-scale testing). And while acceptance tests are not always thought of as part of testing, they share a number of similarities, so Holden discusses which counters Spark programs generate that we can use for creating acceptance tests, best practices for storing historic values, and some common counters we can easily use to track the success of our job, all while working within the constraints of Spark’s accumulators.
Debugging PySpark - Spark Summit East 2017Holden Karau
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. This talk will examine how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, as well as some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose, and this talk will examine how to effectively search logs from Apache Spark to spot common problems. In addition to the internal logging, this talk will look at options for logging from within our program itself.
Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but this talk will look at how to effectively use Spark’s current accumulators for debugging as well as a look to future for data property type accumulators which may be coming to Spark in future version.
In addition to reading logs, and instrumenting our program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems.
Video: https://www.youtube.com/watch?v=A0jYQlxc2FU&feature=youtu.be
Debugging PySpark: Spark Summit East talk by Holden KarauSpark Summit
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. This talk will examine how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, as well as some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose, and this talk will examine how to effectively search logs from Apache Spark to spot common problems. In addition to the internal logging, this talk will look at options for logging from within our program itself.
Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but this talk will look at how to effectively use Spark’s current accumulators for debugging as well as a look to future for data property type accumulators which may be coming to Spark in future version.
In addition to reading logs, and instrumenting our program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems.
The magic of (data parallel) distributed systems and where it all breaks - Re...Holden Karau
Distributed systems can seem magical, and sometimes all of the magic works and our job succeeds. However, if you've worked with them for a long enough time you've found a few places where the magic starts to break down and the fact that it's actually a collection of several hundred garden gnomes* rather than a single large garden gnome.
This talk will use Apache Spark, Beam, Flink, Kafka, and Map Reduce to explore the world of data parallel distributed systems. We'll start with some happy pieces of magic, like how we can combine different transformations into a single pass over the data, working between different languages, data partitioning, and lambda serialization. After each new piece of magic is introduced we'll look at how it breaks in one (or two) of the systems.
Come to be told it's not your fault everything is broken, or if your distributed software still works an exciting preview of everything that's going to go wrong. Don't work with distributed systems? Come to be reassured you've made good life choices.
Debugging Apache Spark - Scala & Python super happy fun times 2017Holden Karau
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. Holden Karau and Joey Echeverria explore how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, and some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose. Holden and Joey demonstrate how to effectively search logs from Apache Spark to spot common problems and discuss options for logging from within your program itself. Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but Holden and Joey look at how to effectively use Spark’s current accumulators for debugging before gazing into the future to see the data property type accumulators that may be coming to Spark in future versions. And in addition to reading logs and instrumenting your program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems. Holden and Joey cover how to quickly use the UI to figure out if certain types of issues are occurring in our job.
Beyond Shuffling and Streaming Preview - Salt Lake City Spark MeetupHolden Karau
This talk starts with a focus on "How to not make Spark Explode" as a developer, and then shifts to look towards the future of all of the cool nifty things we will be able to do with structured streaming.
Apache Spark: The Analytics Operating SystemAdarsh Pannu
This presentation was delivered by Adarsh Pannu at IBM's Insight Conference in Nov 2015. For a recording, visit: https://www.youtube.com/watch?v=Tbm7HIlmwJQ
The presentation provides an overview of Apache Spark, a general-purpose big data processing engine built around speed, ease of use and sophisticated analytics. It enumerates the benefits of incorporating Spark in the enterprise, including how it allows developers to write fully-featured distributed applications ranging from traditional data processing pipelines to complex machine learning. The presentation uses the Airline "On Time" data set to explore various components of the Spark stack.
Similar to Apache spark as a gateway drug to FP concepts taught and broken - Curry On 2018 (20)
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
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This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Apache spark as a gateway drug to FP concepts taught and broken - Curry On 2018
1. Apache Spark as a gateway
drug to functional programming
Concepts taught & broken
@holdenkarau
2. Holden:
● My name is Holden Karau
● Prefered pronouns are she/her
● Developer Advocate at Google
● Apache Spark PMC, Beam contributor
● previously IBM, Alpine, Databricks, Google, Foursquare & Amazon
● co-author of Learning Spark & High Performance Spark
● Twitter: @holdenkarau
● Slide share http://www.slideshare.net/hkarau
● Code review livestreams: https://www.twitch.tv/holdenkarau /
https://www.youtube.com/user/holdenkarau
● Spark Talk Videos http://bit.ly/holdenSparkVideos
3.
4. Who is Boo?
● Boo uses she/her pronouns (as I told the Texas house committee)
● Best doge
● Lot’s of experience barking at computers to make them go faster
● Author of “Learning to Bark” & “High Performance Barking”
○ Currently out of print, discussing a reprint re-run with my wife
● On twitter @BooProgrammer
5. Why Google Cloud cares about Spark?
● Lots of data!
○ We mostly use different, although similar FP inspired, tools internally
● We have two hosted solutions for using Spark (dataproc & GKE)
○ I have a blog post on how to try out custom/new versions of Spark if you want to help us test
the current RC - https://cloud.google.com/blog/big-data/2018/03/testing-future-apache-spark-
releases-and-changes-on-google-kubernetes-engine-and-cloud-dataproc
6. Who do I think y’all are?
● Friendly[ish] people
● Don’t mind pictures of cats or stuffed animals
● Like functional programming
● Want to keep growing the functional programming community
Lori Erickson
7. What will be covered?
● What is Spark (super brief) & how it’s helped drive FP to enterprise
● Wordcount example (as required by license)
● What concepts Spark does a good of teaching folks
● What concepts Spark does a so-so job of teaching folks
● Some examples
● Format is going to be happy-sad (repeating) so we can end on a downer
● But with lots of cat pictures I promise
8. What is Spark?
● General purpose distributed system
○ Built in Scala with an FP inspired API
● Apache project (one of the most
active)
● Must faster than Hadoop
Map/Reduce
● Good when too big for a single
machine
● Built on top of two abstractions for
distributed data: RDDs & Datasets
10. Why people come to Spark:
Well this MapReduce
job is going to take
16 hours - how long
could it take to learn
Spark?
dougwoods
11. Why people come to Spark:
My DataFrame won’t fit
in memory on my
cluster anymore, let
alone my MacBook Pro
:( Maybe this Spark
business will solve
that...
brownpau
13. What is the “magic” of Spark?
● Automatically distributed functional programming :)
● DAG / “query plan” is the root of much of it
● Optimizer to combine steps
● Resiliency: recover from failures rather than protecting
from failures.
● “In-memory” + “spill-to-disk”
● Functional programming to build the DAG for “free”
● Select operations without deserialization
● The best way to trick people into learning functional
programming
Richard Gillin
14. The different pieces of Spark
Apache Spark
SQL, DataFrames & Datasets
Structured
Streaming
Scala,
Java,
Python, &
R
Spark ML
bagel &
Graph X
MLLib
Scala,
Java,
PythonStreaming
Graph
Frames
Paul Hudson
15. What Spark got right (for Scala/FP):
● Strong enforced[ish] requirement for immutable data
○ Use recompute for failure so a core part of the logic
● Functional operators (map, filter, flatMap, etc.)
● Lambdas for everyone!
○ Sometime too many….
● Solved a “business need”
○ Even if that need was imaginary
● Made it hard to have side effects against external variables without being very
explicit & verbose
○ Even then discouraged strongly through lack of documentation :p
Stuart
16. What Spark got … less right (for Scala/FP):
● Serialization… complications
○ Makes people think closures are more limited than they can be
● Lots of Map[String, String] (equivalent) settings
○ Hey buddy can you spare a type checker?
● Hard to debug, could be confused with Scala hard to debug
○ Not completely unjustified sometimes
● New ML & SQL APIs without “any” types (initially)
● Heavy mutation focused API for Machine Learning
● Internals are…. very not functional programming best practices
indamage
17. Before the technical details: positioning
● “100x faster than hadoop” sounds nice
● wc(wc) < 17 also sounds nice if you’re current wordcount doesn’t fit on a page
● “Integrated solution” - sounds nice if you have to write your data to disk
between querying and training
● “Works with your existing JVM or Python code base.”
● Part of the Apache Software Foundation
○ e.g. you already run software from these folks, don’t worry about lock-in they’ve got it handled
● Like all positioning each of these have some implied “*”s associated with
them
● Focused on the benefits made possible with FP rather than the fact it was FP
● Lead to large commercial install base of folks being exposed to functional
programming
18. Hello World (Word count) - 1 of ?
val lines = sc.textFile("boop")
val words = lines.flatMap(line => line.split(" "))
val wordPairs = words.map(word => (word, 1))
val wordCounts = wordPairs.reduceByKey(c1, c2: c1 + c2)
wordCounts.saveAsTextFile("snoop")
Photo By: Will
Keightley
19. Spark & Lazy Evaluation: True BFFs
● Allows Spark to build a graph of operations and use it for recomputing on
failure
● Less passes over the data without thinking*
● Except…. Debates around limiting it due to developer confusion with
debugging
● And library level rather than macro or compiler level means needing to
kcxd
20. Spark & Immutability: Really good friends
● Many systems enforce some level of immutability but...
● Scala allows mutability (vars & friends), but Spark get’s not so happy
● Execution model is recompute on failure, and mutation would break this
● Provides a clear benefit (e.g. “Instead of writing data out to 3 network disks
we just recompute on failure leading to 100x improvement”*)
Bartlomiej Mostek
21. Even if you try to use, no mutation for you*
scala> var x = 0
x: Int = 0
scala> val rdd = sc.parallelize(1.to(100))
scala> val added = rdd.map{e => x += e}
scala> added.count()
res0: Long = 100
scala> x
res1: Int = 0
PROJennifer C.
24. Spark & Immutability: A few small challenges
● Accumulators:
○ Accumulators “leak” on recompute and partial compute (another Spark trick)
● You _can_ mutate state inside of the worker or master it just won’t propagate
○ No error messages here, just unexpected future “happiness”
Caroline
25. Spark & Lambdas: Everyone gets a lambda!
● Maps, flatMaps, filters, reducers oh my (and that’s just wordcount :p )
● Writing in Java 7?
○ You get a weird looking lambda!
Mark Jensen
26. Spark & Lambdas - closures & the dark side
● Python & Scala lambda serialization is (understandably) cautious
● Referencing a variable in the class brings the whole class with you
● Can create the impression closures are limited to serializable data
● ClosureCleaner.scala - “You weren’t using that class right?” & CloudPickle -
“Welllll…. What about if we stored this differently?”
● pre-Java 8 (new FunctionX<A, B, C>... ugh)
● SQL API custom aggregates don’t currently support lambdas :(
David Goehring
27. Oh right: serialization :(
● Many things in JVM & Python are not well designed to be rehydrated on
another VM let alone another machine
● Space efficiency: well…. At least its not XML?
● Oh wait, we have to parse Python serialized data in the JVM? Ruh roh!
● Oh hey let’s make it configurable… oh wait...
28. Reaching non-“traditional” FP languages
● Spark works in many languages - Python, R, Java, etc.
○ Allows Spark to have a wider audience that doesn’t have the time (or tooling) to learn another
language
○ e.g. Numerical Scala libraries are… rough
● Meeting developers where they are
● Some overhead (often) pushes systemy folks towards learning Scala for
performance
● Sometimes we could do a better job of working with the existing FP tools
○ e.g. we could do more to make an RDD look like “normal” Python and maybe I’d stop typing
.flatMap on Python itrs
31. ML: Enough getters and setters for the 90s!
● Took inspiration from ski-kit-learn
○ Which is a cool system, just not super functionally oriented
● Added hidden metadata, which got dropped in lots of places (not quite as bad
as global state buuuut….)
● Threw away compile time type information
● I really don’t have a + for this one in the FP column teaching
32. Basic Dataprep pipeline for “ML”
// Combines a list of double input features into a vector
val assembler = new VectorAssembler().setInputCols(Array("age",
"education-num")).setOutputCol("features")
// String indexer converts a set of strings into doubles
val indexer = StringIndexer().setInputCol("category")
.setOutputCol("category-index")
// Can be used to combine pipeline components together
val pipeline = Pipeline().setStages(Array(assembler, indexer))
Huang
Yun
Chung
33. Adding some ML (no longer cool -- DL)
// Specify model
val dt = new DecisionTreeClassifier()
.setLabelCol("category-index")
.setFeaturesCol("features")
// Add it to the pipeline
val pipeline_and_model = Pipeline().setStages(
List(assembler, indexer, dt))
val pipeline_model = pipeline_and_model.fit(df)
34. The internals… are fairly imperative
● Not everywhere and not without reason (at the time they were created)
● No macros for historic performance reasons
○ Code generation is done by concating strings of Java code
● This would matter less, but Spark is pretty aggressive at trying to hide things,
enough that some major projects end up having to peek a little bit at the code
● Can feel as a “do what I say not what I do” sometimes
○ Especially in ML lot’s of hacks to make things go a little bit faster using internal APIs
ivva
36. Let’s end on a happy-ish note
● Yes Spark isn’t perfect, but it’s APIs are teaching a generation of “big data”
developers & data scientists functional programming without calling it that (at
the start)
● Once we get them hooked we can show them cool things!
● If we can find other areas where we make tools that expose FP apis to more
non-FP folks (and not just for the sake of FP) we can make better software
● Or we can all go back to writing 90s style enterprise Java
37. Learning Spark
Fast Data
Processing with
Spark
(Out of Date)
Fast Data
Processing with
Spark
(2nd edition)
Advanced
Analytics with
Spark
Spark in Action
High Performance SparkLearning PySpark
38. High Performance Spark!
Available today! A great second Spark book to read (but
please buy it first)
You can buy it from that scrappy Seattle bookstore, Jeff
Bezos needs another newspaper and I want a cup of
coffee.
http://bit.ly/hkHighPerfSpark
39. And some upcoming talks:
● July
○ OSCON Portland & meetup
● August
○ JupyterCon NYC
● September
○ Strata NYC
○ Strangeloop STL
● October
○ Spark Summit London
○ Reversim Tel Aviv
40. k thnx bye :)
If you care about Spark testing and
don’t hate surveys:
http://bit.ly/holdenTestingSpark
Will tweet results
“eventually” @holdenkarau
Pssst: Have feedback on the presentation? Give me a shout
(holden@pigscanfly.ca or http://bit.ly/holdenTalkFeedback ) if
you feel comfortable doing so :)
Feedback (if you are so inclined):
http://bit.ly/holdenTalkFeedback
We can examine how RDD’s work in practice with the traditonal word count example. If you’ve taken another intro to big data class, or just worked with mapreduce you’ll notice that this is a lot less code than we normally have to do.
https://www.flickr.com/photos/feverblue/1166368091/in/photolist-2M4WAF-HKi9Wb-bPUuHF-bAZRBu-bAZRJs-cPgNi9-cPgM5y-ecdXE4-qiy3fi-ece5nR-8DSTJT-ekXn2J-ekXkQA-ekRMVx-p5EdCT-424qe7-41ZhFv-cHJ84A-ekRLzc-cS67Hu-cS6sKq-cEcmt5-9ae42r-eoqvBm-HH9A1M-846gJQ-dk6J-nqxTYN-J1PZUH-5q2iLC-HVDkA7-g6xvD3-96MiqV-hbZene-46uZse-6SUYhp-dGaXzD-oUC1ic-DayAdv-aoPJSZ-73L8Lm-5qVCiG-5qVC5q-3Kyyzj-Bf5UzP-4CbKo9-9ae8KM-5CXLKZ-pjFpPT-eccLSR
The 3rd cat hiding in the background is serialization
The 3rd cat hiding in the background is serialization
The 3rd cat hiding in the background is serialization
The 3rd cat hiding in the background is serialization
Red panda image is public domain https://www.flickr.com/photos/mathiasappel/25901445745/in/photolist-FsPFqM-8V1c5-EfF7ct-P6AMfB-oUV39B-EenZCo-oVbE3K-DDMMtE-295N7-dtVySZ-dtVzLP-7yXK47-6DEn2P-oY28m2-DnHaur-qtt3m9-DzTW6Q-E6JQmA-7yNdac-8gAHMU-8469Y8-pCUWfm-qLtvZk-E1BDYY-A5UiNX-bEw84A-yXwV8w-dAyief-z7erYS-BgXdnN-E2yyjM-fhXmyG-Dvyp78-qW9LEZ-qDHaZv-B9BzuN-nL11Wn-C4spLb-8wnAdN-6S1ECS-ntw1gj-7zbXAA-sanjgW-Ci72XP-oNYs5j-rBErd-awBvQG-4YDCuv-G82zkD-zwcfSu