The document provides information about key-value RDD transformations and actions in Spark. It defines transformations like keys(), values(), groupByKey(), combineByKey(), sortByKey(), subtractByKey(), join(), leftOuterJoin(), rightOuterJoin(), and cogroup(). It also defines actions like countByKey() and lookup() that can be performed on pair RDDs. Examples are given showing how to use these transformations and actions to manipulate key-value RDDs.
Twitter Scalding is built on top of Cascading, which is built on top of Hadoop. It's basically a very nice to read and extend DSL for writing map reduce jobs.
此課程專為 Spark 入門者設計,在六小時帶您從無到有建置 Spark 開發環境,並以實作方式帶領您了解 Spark 機器學習函式庫 (MLlib) 的應用及開發。課程實作將以 Spark 核心之實作語言 - Scala 為主,搭配 Scala IDE eclipse 及相關 Library 建置本機開發環境,透過 IDE 強大的開發及偵錯功能加速開發流程;並介紹如何佈置至 Spark 平台,透過 Spark-submit 執行資料分析工作。本課程涵蓋機器學習中最常使用之分類、迴歸及分群方法,歡迎對 Spark 感興趣,卻不知從何下手;或想快速的對 Spark 機器學習有初步的了解的您參與!
Dean Wampler presents on using Scalding, which leverages Cascading, to write MapReduce jobs in a more productive way. Cascading provides higher-level abstractions for building data pipelines and hides much of the boilerplate of the Hadoop MapReduce framework. It allows expressing jobs using concepts like joins and group-bys in a cleaner way focused on the algorithm rather than infrastructure details. Word count is shown implemented in the lower-level MapReduce API versus in Cascading Java code to demonstrate how Cascading minimizes boilerplate and exposes the right abstractions.
This document discusses various functions in R for exporting data, including print(), cat(), paste(), paste0(), sprintf(), writeLines(), write(), write.table(), write.csv(), and sink(). It provides descriptions, syntax, examples, and help documentation for each function. The functions can be used to output data to the console, files, or save R objects. write.table() and write.csv() convert data to a data frame or matrix before writing to a text file or CSV. sink() diverts R output to a file instead of the console.
This is an quick introduction to Scalding and Monoids. Scalding is a Scala library that makes writing MapReduce jobs very easy. Monoids on the other hand promise parallelism and quality and they make some more challenging algorithms look very easy. The talk was held at the Helsinki Data Science meetup on January 9th 2014.
You all know what RDD stands for, right? You have the mental model of a distributed collection. But have you ever consider writing your own RDD? During this talk we will do just that. We will start by explaining essence of how RDDs are implemented internally, following by semi-live demo (*), where we will implement few RDDs from the scratch. After this talk you will not only be able to write your own RDD, but you will also have a deeper understanding of how Apache Spark works under the hood. I guarantee fun during the talk and profit during your next job interview. (*) by 'semi-live' author means not really code live because that almost never works, but slowly pulling small commits from the repo :)
This document discusses Scoobi, a Scala library for developing MapReduce applications on Hadoop. Some key points: 1) Scoobi allows developers to write Hadoop MapReduce jobs using a functional programming style in Scala, inspired by Google's FlumeJava. It provides abstractions like DList and DObject to represent distributed datasets and computations. 2) Under the hood, Scoobi compiles Scala code into Java MapReduce jobs that run on Hadoop. It handles partitioning, parallelization, and distribution of data and computation across clusters. 3) Examples show how common operations like filtering, mapping, reducing can be expressed concisely using the Scoobi API, mirroring Scala
Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. To aid this effort, we built Titian, a library that enables data provenance tracking data through transformations in Apache Spark.
This document discusses common technical problems encountered when building products with Spark and provides solutions. It covers Spark exceptions like out of memory errors and shuffle file problems. It recommends increasing partitions and memory configurations. The document also discusses optimizing Spark code using functional programming principles like strong and weak pipelining, and leveraging monoid structures to reduce shuffling. Overall it provides tips to debug issues, optimize performance, and productize Spark applications.
Dr. Hsieh is teaching how to use the state-of-the-art libraries, Spark by Apache, to conduct data analysis on hadoop platform in ISSNIP 2015, Singapore. He started with teaching the basic operations like “map, reduce, flatten, and more,” followed by explaining the extension of Spark, including MLib, GraphX, and SparkSQL.
Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
This document provides an overview of key concepts for working with D3, including: - D3 uses standard web technologies like HTML, SVG, and CSS rather than introducing new representations. Learning D3 largely means learning web standards. - Visualization with D3 requires mapping data to visual elements using scales. Scales are functions that map from data values to visual values like pixel positions. - Selections in D3 correspond to elements in the DOM. Data joins allow binding data to selections to drive attribute updates. The enter, update, exit pattern is used to handle new, existing and removed data. - Common scale types include linear, log, quantize and quantile for quantitative data, and
This document summarizes the new features and changes in Ring 1.5.2. It introduces natural language processing capabilities through defining commands for a MyLanguage. It shows code for Hello and Count commands. It also describes improvements to Ring Notepad styles, the RingREPL interactive shell, number conversion functions, standard and web libraries, RingQt classes and functions, and a Qt class converter tool.
The document provides an overview of the different Spark APIs for working with structured data: RDDs, DataFrames, and Datasets. It discusses the timeline and key features of each API. RDDs were introduced in Spark 1.0 and represent resilient distributed datasets. DataFrames were added in Spark 1.3 and introduce schema support and SQL-like capabilities. Datasets, introduced in Spark 1.6, provide a type-safe interface but are still experimental. DataFrames are now considered the most stable and flexible API due to built-in optimizations and support for dynamic languages.
This document summarizes a user's journey developing a custom aggregation function for Apache Spark using a T-Digest sketch. The user initially implemented it as a User Defined Aggregate Function (UDAF) but ran into performance issues due to excessive serialization/deserialization. They then worked to resolve it by implementing the function as a custom Aggregator using Spark 3.0's new aggregation APIs, which avoided unnecessary serialization and provided a 70x performance improvement. The story highlights the importance of understanding how custom functions interact with Spark's execution model and optimization techniques like avoiding excessive serialization.
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.
The document discusses various built-in functions in Python including numeric, string, and container data types. It provides examples of using list comprehensions, dictionary comprehensions, lambda functions, enumerate, zip, filter, any, all, map and reduce to manipulate data in Python. It also includes references to online resources for further reading.
Defining customized scalable aggregation logic is one of Apache Spark’s most powerful features. User Defined Aggregate Functions (UDAF) are a flexible mechanism for extending both Spark data frames and Structured Streaming with new functionality ranging from specialized summary techniques to building blocks for exploratory data analysis.
The document describes various transformations and actions that can be performed on RDDs in Apache Spark. It explains functions like map(), filter(), reduceByKey() for transformations. Actions to extract data from RDDs like collect(), count(), take() are also covered. Examples of working with key-value pairs and performing joins on pair RDDs are provided. The document also includes code examples to analyze sales data from a CSV file using Spark RDD functions.
This document discusses collections and queries in Java, including associative arrays (maps), lambda expressions, and the stream API. It provides examples of using maps like HashMap, LinkedHashMap and TreeMap to store key-value pairs. Lambda expressions are introduced as anonymous functions. The stream API is shown processing collections through methods like filter, map, sorted and collect. Examples demonstrate common tasks like finding minimum/maximum values, summing elements, and sorting lists.
This document discusses using Erlang for data operations involving multiple relational databases with different schemas located in different locations. Erlang is well-suited for this due to its concurrency, scalability, and ability to act as "glue" between systems. An approach is described using Erlang thin clients to enforce and maintain the relational data model across databases while global state resides in a traditional database. Asynchronous message passing via RabbitMQ is used to route processing jobs between agents located at different sites.
This document contains 30 programming exercises involving Scheme functions and data structures. The exercises cover topics like defining functions, manipulating lists, recursive functions, structures, and more. For each exercise, the reader is asked to write Scheme code to solve programming problems related to topics like reversing lists, finding elements in lists, defining recursive functions, and representing geometric and other relationships with nested data structures.
SOURCE: • https://gist.github.com/hadley/439761 (hadley/clustergram-had.r) http://www.r-statistics.com/tag/large-data/