spark notes
  • Introduction
  • Databricks
  • Concepts
  • Spark Execution Flow
    • SparkContext and SparkSession
  • Resilient Distributed Dataset (RDD)
    • Caching
    • Pair RDDs
    • Transformations
      • Depedency Resolution
    • Actions
    • Persistence
    • RDD lineage
    • Types of RDDs
    • Loading Data into RDDs
    • Data Locality with RDDs
    • How Many Partitions Does An RDD Have
  • Spark job submission breakdown
  • Why Cluster Manager
  • SparkContext and its components
  • Spark Architecture
    • Stages
    • Tasks
    • Executors
    • RDD
    • DAG
    • Jobs
    • Partitions
  • Spark Deployment Modes
  • Running Modes
  • Spark Execution Flow
  • DataFrames, Datasets,RDDs
  • SparkSQL
    • Architecture
    • Spark Session
  • Where Does Map Reduce Does not Fit
  • Actions
    • reduceByKey
    • count
    • collect, take, top, and first Actions
    • take
    • top
    • first
    • The reduce and fold Actions
  • DataSets
  • Spark Application Garbage Collector
  • How Mapreduce works in spark
  • Notes
  • Scala
  • Spark 2.0
  • Types Of RDDs
    • MapPartitionsRDD
  • Spark UI
  • Optimization
    • Tungsten
  • Spark Streaming
    • Notes
    • Flow
  • FlatMap - Different Variations
  • Examples
  • Testing Spark
  • Passing functions to Spark
  • CONFIGURATION, MONITORING, AND TUNING
  • References
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  1. Spark Streaming

Notes

Notes

If you use --master local with only one thread, you may notice that your log lines are missing from the driver’s output. That’s because in Spark Streaming, for example, that single thread is used to read streaming data from a source, and the driver wouldn’t have any threads left to print out the results of your program. If you want the output printed to your log file, be sure to specify at least two threads (local[2]) when running spark streaming in local mode.

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Last updated 5 years ago

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