常见计算框架算子层对比

jopen 10年前

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

Hadoop MR上的DSL，面向过程，适用于large-scale的数据分析。语法很美，可惜只适合CLI 。

    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

Hadoop MR上的封装，推ter Summingbird正是基于Cascading的。 每个算子都是new出来的，Pipe实例被"迭代式"地传入新的算子里。

// 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

在Storm上提供高级的抽象原语，延续Transactional Topology的exactly-once的语义，满足事务性。 原语过于抽象，构造过程充斥重复性的字段定义。

    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

Spark上的分布式弹性数据集，具备丰富的原语。 RDD原语的灵活性归功于Scala语言本身的FP性质以及语法糖，而其丰富性又源自Scala语言本身API的丰富性。Java难以实现如此强大的表达能力。但RDD确实是非常有参考价值的。

    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

Spark SQL里的"Table"型RDD，额外为SQL提供了一套DSL。 但是这套DSL只适合SQL，表达能力不够，偏"垂直"。

    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

Google FlumeJava论文的开源实现，是一个标准的算子层，现在支持Hadoop任务和Spark任务。
Crunch 符合FlumeJava的设定，实现了PCollection和PTable这样的分布式、不可变数据表示集，实现了 parallelDo()，groupByKey()，combineValues()，flattern()四种基本原语，且基于此原语可以衍生出 count()，join()，top()。也实现了Deffered Evalution 以及针对MSCR(MapShuffleCombineReduce) Operation的优化。
Crunch的任务编写严重依赖Hadoop，其本质是为了在批量计算框架上写MapReduce Pipeline。原语方面不够丰富，且parallelDo()不太适合流式语境。此外，其很多特性和功能是我们不需要具备的，但是抽象数据表示、接口模型、流程控制是可以参考的。

    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 :)

来自：http://blog.csdn.net/pelick/article/details/39076223