By Zubair Nabi
Learn the fitting state-of-the-art abilities and information to leverage Spark Streaming to enforce a wide range of real-time, streaming functions. This e-book walks you thru end-to-end real-time program improvement utilizing real-world purposes, facts, and code. Taking an application-first procedure, each one bankruptcy introduces use instances from a selected and makes use of publicly on hand datasets from that area to resolve the intricacies of production-grade layout and implementation. The domain names lined in Pro Spark Streaming comprise social media, the sharing economic system, finance, web advertising, telecommunication, and IoT.
In the previous couple of years, Spark has turn into synonymous with giant facts processing. DStreams improve the underlying Spark processing engine to help streaming research with a unique micro-batch processing version. Pro Spark Streaming by Zubair Nabi will help you develop into a consultant of latency delicate purposes via leveraging the most important good points of DStreams, micro-batch processing, and practical programming. To this finish, the ebook contains ready-to-deploy examples and genuine code. Pro Spark Streaming will act because the bible of Spark Streaming.
What you will Learn
- Discover Spark Streaming program improvement and most sensible practices
- Work with the low-level info of discretized streams
- Optimize production-grade deployments of Spark Streaming through configuration recipes and instrumentation utilizing Graphite, collectd, and Nagios
- Ingest information from disparate assets together with MQTT, Flume, Kafka, Twitter, and a customized HTTP receiver
- Integrate and couple with HBase, Cassandra, and Redis
- Take benefit of layout styles for side-effects and holding country around the Spark Streaming micro-batch model
- Implement real-time and scalable ETL utilizing facts frames, SparkSQL, Hive, and SparkR
- Use streaming laptop studying, predictive analytics, and recommendations
- Mesh batch processing with flow processing through the Lambda architecture
Who This booklet Is For
Data scientists, mammoth info specialists, BI analysts, and knowledge architects.
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Extra resources for Pro Spark Streaming: The Zen of Real-Time Analytics Using Apache Spark
An overloaded variant also accepts a function to filter the contents of the fetched array. collectAsMap(): Map[K, V] Collects the data in PairRDD as a Map. count(): Long Counts the number of elements in the calling RDD. countByKey(): Map[K, Long] PairRDD variant of count(). countApproxDistinct(relativeSD: Double): Long Counts the number of distinct elements in the RDD by using the HyperLogLog algorithm. relativeSD decides the relative accuracy, with larger values requiring less space. 05): RDD[(K, Long)] The key-value variant of countApproxDistinct().
31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. map(word => dict. toMap } } Every Spark application needs an accompanying configuration object of type SparkConf. For instance, the application name and the JARs for cluster deployment are provided via this configuration. Typically the location of the master is picked up from the environment, but it can be explicitly provided by setting setMaster() on SparkConf. Chapter 4 discusses more configuration parameters. In this example, a SparkConf object is defined on line 19.
Internally, invokes hadoopFile() by passing SequenceFileInputFormat as the inputFormatClass. 10 wholeTextFiles(path: String): RDD[(String, String)] Returns an RDD for whole Hadoop files located at path. Under the hood, uses String as both the key and the value and WholeTextFileInputFormat as the InputFormatClass. Note that the key is the file path, whereas the value is the entire content of the file(s). Use this method if the files are small. For larger files, use textFile(). union[T](rdds: Seq[RDD[T]]): RDD[T] Returns an RDD that is the union of all input RDDs of the same type.