Spark & Spark Streaming Internals - Nov 15 (1)
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This document summarizes Spark and Spark Streaming internals. It discusses the Resilient Distributed Dataset (RDD) model in Spark, which allows for fault tolerance through lineage-based recomputation. It provides an example of log mining using RDD transformations and actions. It then discusses Spark Streaming, which provides a simple API for stream processing by treating streams as series of small batch jobs on RDDs. Key concepts discussed include Discretized Stream (DStream), transformations, and output operations. An example Twitter hashtag extraction job is outlined.
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Spark & Spark Streaming Internals - Nov 15 (1)
- 1. Spark & Spark Streaming Internals Akhil Das [email protected]
- 2. Apache Spark Spark Stack
- 4. Resilient Distributed Dataset (RDD) Restricted form of distributed shared memory ➔ Immutable ➔ Can only be built through deterministic transformations (textFile, map, filter, join, …) Efficient fault recovery using lineage ➔ Recompute lost partitions on failure ➔ No cost if nothing fails
- 5. RDD Operations Transformations ➔ map/flatmap ➔ filter ➔ union/join/groupBy ➔ cache ……. Actions ➔ collect/count ➔ save ➔ take …….
- 6. Log Mining Example Load error messages from a log into memory, then interactively search for various patterns lines = spark.textFile(“hdfs://...”) Base RDD errors = lines.filter(_.startsWith(“ERROR”)) Transformed RDD messages = errors.map(_.split(‘t’)(2)) messages.persist() messages.filter(_.contains(“foo”)).count Action messages.filter(_.contains(“bar”)).count
- 7. What is Spark Streaming?
- 8. What is Spark Streaming? Framework for large scale stream processing ➔ Scales to 100s of nodes ➔ Can achieve second scale latencies ➔ Provides a simple batch-like API for implementing complex algorithm ➔ Can absorb live data streams from Kafka, Flume, ZeroMQ, Kinesis etc.
- 9. Overview Run a streaming computation as a series of very small, deterministic batch jobs SparkStreaming Spark - Chop up the live stream into batches of X seconds - Spark treats each batch of data as RDDs and processes them using RDD operations - Finally, the processed results of the RDD operations are returned in batches
- 10. Key Concepts ➔ DStream – sequence of RDDs representing a stream of data - Twitter, HDFS, Kafka, Flume, ZeroMQ, Akka Actor, TCP sockets ➔ Transformations – modify data from one DStream to another - Standard RDD operations – map, countByValue, reduce, join, … - Stateful operations – window, countByValueAndWindow, … ➔ Output Operations – send data to external entity - saveAsHadoopFiles – saves to HDFS - foreach – do anything with each batch of results
- 11. Eg: Get hashtags from Twitter val tweets = ssc.twitterStream(<Twitter username>, <Twitter password>) val hashTags = tweets.flatMap (status => status.getText.split(" ").filter(_. startsWith("#")))) hashTags.saveAsHadoopFiles("hdfs://...") Transformation #Ebola, #India, #Mars ...
- 12. Thank You