Introducción al flujo de Spark

package example;

import org.apache.log4j.LogManager;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder;
import org.apache.spark.sql.catalyst.encoders.RowEncoder;
import org.apache.spark.sql.functions;
import org.apache.spark.sql.streaming.StreamingQuery;
import org.apache.spark.sql.streaming.Trigger;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StringType$;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.sql.types.StructType$;
import org.apache.spark.sql.types.TimestampType$;

import java.sql.Timestamp;
import java.text.SimpleDateFormat;
import java.util.Arrays;
import java.util.concurrent.TimeoutException;

public class StructuredKafkaWordCount {

  public static void main(String[] args) throws Exception {

    Logger log = LogManager.getLogger(StructuredKafkaWordCount.class);
    log.info("Started StructuredKafkaWordCount");

    Thread.setDefaultUncaughtExceptionHandler((thread, e) -> {
      log.error("Exception uncaught: ", e);
    });

    //Uncomment following line to enable debug log level.
    //Logger.getRootLogger().setLevel(Level.DEBUG);

    if (args.length < 4) {
      printUsage();
    }

    String bootstrapServers = args[0];
    String topics = args[1];
    String checkpointLocation = args[2];
    String type = args[3];
    String outputLocation = null;

    switch (type) {
      case "console":
        System.err.println("Using console output sink");
        break;

      case "csv":
        if (args.length < 5) {
          printUsage();
        }
        outputLocation = args[4];
        System.err.println("Using csv output sink, output location = " + outputLocation);
        break;

      default:
        printUsage();
    }

    SparkSession spark;

    SparkConf conf = new SparkConf();
    if (conf.contains("spark.master")) {
      spark = SparkSession.builder()
          .appName("StructuredKafkaWordCount")
          .config("spark.sql.streaming.minBatchesToRetain", "10")
          .config("spark.sql.shuffle.partitions", "1")
          .config("spark.sql.streaming.stateStore.maintenanceInterval", "300")
          .getOrCreate();
    } else {
      spark = SparkSession.builder()
          .appName("StructuredKafkaWordCount")
          .master("local[*]")
          .config("spark.sql.streaming.minBatchesToRetain", "10")
          .config("spark.sql.shuffle.partitions", "1")
          .config("spark.sql.streaming.stateStore.maintenanceInterval", "300")
          .getOrCreate();
    }

    // Create DataFrame representing the stream of input lines from Kafka
    Dataset<Row> lines = spark
        .readStream()
        .format("kafka")
        .option("kafka.bootstrap.servers", bootstrapServers)
        .option("subscribe", topics)
        .option("kafka.security.protocol", "SASL_SSL")
        .option("kafka.sasl.mechanism", "OCI-RSA-SHA256")
        .option("kafka.sasl.jaas.config",
            "com.oracle.bmc.auth.sasl.ResourcePrincipalsLoginModule required intent=\"streamPoolId:ocid1.streampool.oc1.phx.amaaaaaaep4fdfaartcuoi5y72mkrcg7hzogcx2jnygbmgik3hqwssxqa6pq\";")
        .option("kafka.max.partition.fetch.bytes", 1024 * 1024) // limit request size to 1MB per partition
        .option("startingOffsets", "latest")
        .load()
        .selectExpr("CAST(value AS STRING)");

    // Split the lines into timestamp and words
    StructType wordsSchema = StructType$.MODULE$.apply(
        new StructField[]{
            StructField.apply("timestamp", TimestampType$.MODULE$, true, Metadata.empty()),
            StructField.apply("value", StringType$.MODULE$, true, Metadata.empty())
        }
    );
    ExpressionEncoder<Row> encoder = RowEncoder.apply(wordsSchema);
    final SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss");

    Dataset<Row> words = lines
        .flatMap(
            (FlatMapFunction<Row, Row>) row -> {
              // parse Kafka record in format: "timestamp(iso8601) text"
              String text = row.getString(0);
              String timestampString = text.substring(0, 25);
              String message = text.substring(26);
              Timestamp timestamp = new Timestamp(dateFormat.parse(timestampString).getTime());

              return Arrays.asList(message.split(" ")).stream()
                  .map(word -> RowFactory.create(timestamp, word)).iterator();
            }
            , encoder);

    // Time window aggregation
    Dataset<Row> wordCounts = words
        .withWatermark("timestamp", "1 minutes")
        .groupBy(
            functions.window(functions.col("timestamp"), "1 minutes", "1 minutes"),
            functions.col("value")
        )
        .count()
        .selectExpr("CAST(window.start AS timestamp) AS START_TIME",
            "CAST(window.end AS timestamp) AS END_TIME",
            "value AS WORD", "CAST(count AS long) AS COUNT");

    wordCounts.printSchema();

    // Reducing to a single partition
    wordCounts = wordCounts.coalesce(1);

    // Start streaming query
    StreamingQuery query = null;
    switch (type) {
      case "console":
        query = outputToConsole(wordCounts, checkpointLocation);
        break;
      case "csv":
        query = outputToCsv(wordCounts, checkpointLocation, outputLocation);
        break;
      default:
        System.err.println("Unknown type " + type);
        System.exit(1);
    }

    query.awaitTermination();
  }

  private static void printUsage() {
    System.err.println("Usage: StructuredKafkaWordCount <bootstrap-servers> " +
        "<subscribe-topics> <checkpoint-location> <type> ...");
    System.err.println("<type>: console");
    System.err.println("<type>: csv <output-location>");
    System.err.println("<type>: adw <wallet-path> <wallet-password> <tns-name>");
    System.exit(1);
  }

  private static StreamingQuery outputToConsole(Dataset<Row> wordCounts, String checkpointLocation)
      throws TimeoutException {
    return wordCounts
        .writeStream()
        .format("console")
        .outputMode("complete")
        .option("checkpointLocation", checkpointLocation)
        .start();
  }

  private static StreamingQuery outputToCsv(Dataset<Row> wordCounts, String checkpointLocation,
      String outputLocation) throws TimeoutException {
    return wordCounts
        .writeStream()
        .format("csv")
        .outputMode("append")
        .option("checkpointLocation", checkpointLocation)
        .trigger(Trigger.ProcessingTime("1 minutes"))
        .option("path", outputLocation)
        .start();
  }
}

#!/usr/bin/env python3
 
from pyspark.sql import SparkSession, SQLContext
from pyspark.sql.functions import concat, col, current_timestamp, lit, window, \
  substring, to_timestamp, explode, split, length
 
import argparse
import os
 
 
def main():
  parser = argparse.ArgumentParser()
  parser.add_argument('--auth-type', default='PLAIN')
  parser.add_argument('--bootstrap-port', default='9092')
  parser.add_argument('--bootstrap-server')
  parser.add_argument('--checkpoint-location')
  parser.add_argument('--encryption', default='SASL_SSL')
  parser.add_argument('--ocid')
  parser.add_argument('--output-location')
  parser.add_argument('--output-mode', default='file')
  parser.add_argument('--stream-password')
  parser.add_argument('--raw-stream')
  parser.add_argument('--stream-username')
  args = parser.parse_args()
 
  if args.bootstrap_server is None:
    args.bootstrap_server = os.environ.get('BOOTSTRAP_SERVER')
  if args.raw_stream is None:
    args.raw_stream = os.environ.get('RAW_STREAM')
  if args.stream_username is None:
    args.stream_username = os.environ.get('STREAM_USERNAME')
  if args.stream_password is None:
    args.stream_password = os.environ.get('STREAM_PASSWORD')
 
  assert args.bootstrap_server is not None, "Kafka bootstrap server (--bootstrap-server) name must be set"
  assert args.checkpoint_location is not None, "Checkpoint location (--checkpoint-location) must be set"
  assert args.output_location is not None, "Output location (--output-location) must be set"
  assert args.raw_stream is not None, "Kafka topic (--raw-stream) name must be set"
 
  spark = (
    SparkSession.builder
      .appName('StructuredKafkaWordCount')
      .config('failOnDataLoss', 'true')
      .config('spark.sql.streaming.minBatchesToRetain', '10')
      .config('spark.sql.shuffle.partitions', '1')
      .config('spark.sql.streaming.stateStore.maintenanceInterval', '300')
      .getOrCreate()
  )
 
  # Uncomment following line to enable debug log level.
  # spark.sparkContext.setLogLevel('DEBUG')
 
  # Configure Kafka connection settings.
  if args.ocid is not None:
    jaas_template = 'com.oracle.bmc.auth.sasl.ResourcePrincipalsLoginModule required intent="streamPoolId:{ocid}";'
    args.auth_type = 'OCI-RSA-SHA256'
  else:
    jaas_template = 'org.apache.kafka.common.security.plain.PlainLoginModule required username="{username}" password="{password}";'
 
  raw_kafka_options = {
    'kafka.sasl.jaas.config': jaas_template.format(
      username=args.stream_username, password=args.stream_password,
      ocid=args.ocid
    ),
    'kafka.sasl.mechanism': args.auth_type,
    'kafka.security.protocol': args.encryption,
    'kafka.bootstrap.servers': '{}:{}'.format(args.bootstrap_server,
                                              args.bootstrap_port),
    'subscribe': args.raw_stream,
    'kafka.max.partition.fetch.bytes': 1024 * 1024,
    'startingOffsets': 'latest'
  }
 
  # Reading raw Kafka stream.
  raw = spark.readStream.format('kafka').options(**raw_kafka_options).load()
 
  # Cast raw lines to a string.
  lines = raw.selectExpr('CAST(value AS STRING)')
 
  # Split value column into timestamp and words columns.
  parsedLines = (
    lines.select(
      to_timestamp(substring('value', 1, 25))
        .alias('timestamp'),
      lines.value.substr(lit(26), length('value') - 25)
        .alias('words'))
  )
 
  # Split words into array and explode single record into multiple.
  words = (
    parsedLines.select(
      col('timestamp'),
      explode(split('words', ' ')).alias('word')
    )
  )
 
  # Do time window aggregation
  wordCounts = (
    words
      .withWatermark('timestamp', '1 minutes')
      .groupBy('word', window('timestamp', '1 minute'))
      .count()
      .selectExpr('CAST(window.start AS timestamp) AS START_TIME',
                  'CAST(window.end AS timestamp) AS END_TIME',
                  'word AS WORD',
                  'CAST(count AS long) AS COUNT')
  )
 
  # Reduce partitions to a single.
  wordCounts = wordCounts.coalesce(1)
 
  wordCounts.printSchema()
 
  # Output it to the chosen channel.
  if args.output_mode == 'console':
    print("Writing aggregates to console")
    query = (
      wordCounts.writeStream
        .option('checkpointLocation', args.checkpoint_location)
        .outputMode('complete')
        .format('console')
        .option('truncate', False)
        .start()
    )
  else:
    print("Writing aggregates to Object Storage")
    query = (
      wordCounts.writeStream
        .format('csv')
        .outputMode('append')
        .trigger(processingTime='1 minutes')
        .option('checkpointLocation', args.checkpoint_location)
        .option('path', args.output_location)
        .start()
    )
 
  query.awaitTermination()
 
 
main()