背景

前段时间在为内部自研的计算框架设计算子层，参考对比了一些开源的计算框架的算子层，本文做一个粗粒度的梳理。

下面这张图是我对计算框架抽象层次的一个拆分，具体可以参考上周日杭州Spark meetup上我做的Spark SQL分享 slides。

Pig-latin

A = load ‘xx‘ AS (c1:int, c2:chararray, c3:float)
B = GROUP A BY c1
C = FOREACH B GENERATE group, COUNT(A)
C = FOREACH B GENERATE $0. $1.c2

X = COGROUP A by a1, B BY b1
Y = JOIN A by a1 (LEFT|FULL|LEFT OUTER), B BY b1

Cascading

// define source and sink Taps.
Scheme sourceScheme = new TextLine( new Fields( "line" ) );
Tap source = new Hfs( sourceScheme, inputPath );
Scheme sinkScheme = new TextLine( new Fields( "word", "count" ) );
Tap sink = new Hfs( sinkScheme, outputPath, SinkMode.REPLACE );

// the ‘head‘ of the pipe assembly
Pipe assembly = new Pipe( "wordcount" );

// For each input Tuple
// parse out each word into a new Tuple with the field name "word"
// regular expressions are optional in Cascading
String regex = "(?<!\\pL)(?=\\pL)[^ ]*(?<=\\pL)(?!\\pL)";
Function function = new RegexGenerator( new Fields( "word" ), regex );
assembly = new Each( assembly, new Fields( "line" ), function );

// group the Tuple stream by the "word" value
assembly = new GroupBy( assembly, new Fields( "word" ) );

// For every Tuple group
// count the number of occurrences of "word" and store result in
// a field named "count"
Aggregator count = new Count( new Fields( "count" ) );
assembly = new Every( assembly, count );

// initialize app properties, tell Hadoop which jar file to use
Properties properties = new Properties();
AppProps.setApplicationJarClass( properties, Main.class );

// plan a new Flow from the assembly using the source and sink Taps
// with the above properties
FlowConnector flowConnector = new HadoopFlowConnector( properties );
Flow flow = flowConnector.connect( "word-count", source, sink, assembly );

// execute the flow, block until complete
flow.complete();

Trident

TridentState urlToTweeters =
       topology.newStaticState(getUrlToTweetersState());
TridentState tweetersToFollowers =
       topology.newStaticState(getTweeterToFollowersState());

topology.newDRPCStream("reach")
       .stateQuery(urlToTweeters, new Fields("args"), new MapGet(), new Fields("tweeters"))
       .each(new Fields("tweeters"), new ExpandList(), new Fields("tweeter"))
       .shuffle()
       .stateQuery(tweetersToFollowers, new Fields("tweeter"), new MapGet(), new Fields("followers"))
       .parallelismHint(200)
       .each(new Fields("followers"), new ExpandList(), new Fields("follower"))
       .groupBy(new Fields("follower"))
       .aggregate(new One(), new Fields("one"))
       .parallelismHint(20)
       .aggregate(new Count(), new Fields("reach"));

RDD

scala> val textFile = sc.textFile("README.md")
textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3

scala> textFile.count() // Number of items in this RDD
res0: Long = 126

scala> textFile.first() // First item in this RDD
res1: String = # Apache Spark

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: spark.RDD[String] = spark.FilteredRDD@7dd4af09

scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15

scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res4: Long = 15

scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8

scala> wordCounts.collect()
res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)

SchemaRDD

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
import sqlContext.createSchemaRDD

// Define the schema using a case class.
case class Person(name: String, age: Int)

// Create an RDD of Person objects and register it as a table.
val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt))
people.registerAsTable("people")

// SQL statements can be run by using the sql methods provided by sqlContext.
val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

// DSL: where(), select(), as(), join(), limit(), groupBy(), orderBy() etc.
val teenagers = people.where(‘age >= 10).where(‘age <= 19).select(‘name)
teenagers.map(t => "Name: " + t(0)).collect().foreach(println)

Apache Crunch

public class WordCount extends Configured implements Tool, Serializable {
  public int run(String[] args) throws Exception {
    // Create an object to coordinate pipeline creation and execution.
    Pipeline pipeline = new MRPipeline(WordCount.class, getConf());
    // Reference a given text file as a collection of Strings.
    PCollection<String> lines = pipeline.readTextFile(args[0]);

    PCollection<String> words = lines.parallelDo(new DoFn<String, String>() {
      public void process(String line, Emitter<String> emitter) {
        for (String word : line.split("\\s+")) {
          emitter.emit(word);
        }
      }
    }, Writables.strings()); // Indicates the serialization format

    PTable<String, Long> counts = words.count();
    // Instruct the pipeline to write the resulting counts to a text file.
    pipeline.writeTextFile(counts, args[1]);
    // Execute the pipeline as a MapReduce.
    PipelineResult result = pipeline.done();

    return result.succeeded() ? 0 : 1;
  }

  public static void main(String[] args) throws Exception {
    int result = ToolRunner.run(new Configuration(), new WordCount(), args);
    System.exit(result);
  }
}

总结

最后这张图展示了Hadoop之上各种Data Pipeline项目的实现层次对比： 

全文完 :)

常见计算框架算子层对比