Spark core

1. Starting Point:SparkContext

val conf = new SparkConf().setAppName(appName).setMaster(master)

new SparkContext(conf)

2.logic data:RDD

2.1.创建

2.1.1.Parallelized Collections

val data = Array(1, 2, 3, 4, 5)

val distData = sc.parallelize(data)

2.1.2.External Datasets

val distFile = sc.textFile("data.txt")

Spark SQL, DataFrames and Datasets

1. Starting Point:SparkSession

import org.apache.spark.sql.SparkSession

val spark = SparkSession

  .builder()

  .appName("Spark SQL basic example")

  .config("spark.some.config.option", "some-value")

  .getOrCreate()

// For implicit conversions like converting RDDs to DataFrames

import spark.implicits._

2.logic data:Structured RDD:Datasets and DataFrames

Spark Streaming

1. Starting Point:StreamingContext

import org.apache.spark._

import org.apache.spark.streaming._

val conf = new SparkConf().setAppName(appName).setMaster(master)

val ssc = new StreamingContext(conf, Seconds(1))

2.logic data:时间片的RDD（无状态的，有重复滑动窗口的，无重复滑动窗口的，有状态的）

Structured Streaming

1. Starting Point:SparkSessionval

import org.apache.spark.sql.functions._

import org.apache.spark.sql.SparkSession

val spark = SparkSession .builder .appName("StructuredNetworkWordCount") .getOrCreate()

import spark.implicits._

2.logic data:可存时间片的RDD的大表