Apache Spark overview
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This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
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Apache Spark overview
- 1. Apache Spark
- 2. Agenda Hadoop vs Spark: Big ‘Big Data’ question Spark Ecosystem What is RDD Operations on RDD: Actions vs Transformations Running in cluster Task schedulers Spark Streaming Dataframes API
- 5. Hadoop VS/AND Spark Hadoop: DFS Spark: Speed (RAM)
- 9. RDD: Resilient Distributed Dataset Represents an immutable, partitioned collection of elements that can be operated in parallel with failure recovery possibilities.
- 10. Example Hadoop RDD getPartitions = HDFS blocks getDependencies = None compute = load block in memory getPrefferedLocations = HDFS block locations partitioner = None MapPartitions RDD getPartitions = same as parent getDependencies = parent RDD compute = compute parent and apply map() getPrefferedLocations = same as parent partitioner = None
- 11. RDD: Resilient Distributed Dataset
- 12. RDD Example
- 13. RDD Example
- 14. RDD Operations ● Transformations ○ Apply user function to every element in a partition ○ Apply aggregation function to a whole dataset (groupBy, sortBy) ○ Provide functionality for repartitioning (repartition, partitionBy) ● Actions ○ Materialize computation results (collect, count, take) ○ Store RDDs in memory or on disk (cache, persist)
- 15. RDD Dependencies
- 16. DAG: Directed Acyclic Graph All the operators in a job are used to construct a DAG (Directed Acyclic Graph). The DAG is optimized by rearranging and combining operators where possible.
- 17. DAG Example
- 18. DAG Scheduler The DAG scheduler divides operators into stages of tasks. A stage is comprised of tasks based on partitions of the input data. Pipelines operators together.
- 20. RDD Persistence: persist() & cache() When you persist an RDD, each node stores any partitions of it that it computes in memory and reuses them in other actions on that dataset (or datasets derived from it). Storage levels: MEMORY_ONLY (default), MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY, MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc. Removing data: least-recently-used (LRU) fashion or RDD.unpersist() method.
- 21. Job execution
- 22. Task Schedulers Standalone Default FIFO strategy Controls number of CPU cores and executor memory YARN Hadoop oriented Takes all available resources Was designed for stateless batch jobs that can be restarted easily if they fail. Mesos Resource oriented Dynamic sharing or CPU cores Less predictive latency
- 25. Memory usage • Execution memory • Storage for data needed during tasks execution • Shuffle-related data • Storage memory • Cached RDDs • Possible to borrow from execution memory • User memory • User data structures and internal metadata • Safeguarding against OOM • Reserved memory • Memory needed for running executor itself
- 26. Spark Streaming
- 27. Spark Streaming: Basic Concept
- 28. Spark Streaming: Architecture Spark Streaming receives live input data streams and divides the data into batches, which are then processed by the Spark engine to generate the final stream of results in batches.
- 31. Spark Streaming checkpoints • Create heavy objects in foreachRDD • Default persistence level of DStreams keeps the data serialized in memory. • Checkpointing (metadata and received data) • Automatic restart (task manager) • Max receiving rate • Level of Parallelism • Kryo serialization
- 34. Apache Hive • Hadoop product • Stores metadata in the relational database, but data only in HDFS • Is not suited for real time data processing • Best used for batch jobs over large datasets of immutable data (web logs) Is a good choice if you: • Want to query the data • When you’re familiar with SQL
- 35. About Spark SQL Part of Spark core since April 2014 Works with structured data Mixes SQL queries with Spark programs Connect to any datasource (files, Hive tables, external databases, RDDs)
- 36. Spark Dataframes
- 37. Spark Dataframes
- 38. Spark SQL
- 39. Spark SQL with schema
- 41. Q&A
Editor's Notes
- #4: Исполнение в кластере Параллельность Отказоустойчивость Скорость Различные форматы данных Мониторинг и распределение ресурсов
- #21: Spark’s cache is fault-tolerant – if any partition of an RDD is lost, it will automatically be recomputed using the transformations that originally created it. Пример: вычитываем данные из файла, берем имя работника, его должность, зарплату и возраст. Фильтруем по нужным должностям. Потом хоть аггрегировать: среднюю зп по должности и по возрасту.
- #31: window length - The duration of the window (3 in the figure). sliding interval - The interval at which the window operation is performed (2 in the figure).
- #32: Пример с созданием коннекшена к базе