首页 > 代码库 > (四)storm-kafka源码走读之自定义Scheme

(四)storm-kafka源码走读之自定义Scheme

本文原创,转载请注明出处:

使用KafkaSpout需要子类实现Scheme,storm-kafka实现了StringScheme,KeyValueStringScheme等等,大家可以用。

这些Scheme主要负责从消息流中解析出所需要的数据。

public interface Scheme extends Serializable {
    public List<Object> deserialize(byte[] ser);
    public Fields getOutputFields();
}

需要实现反序列化方法和输出fields名称,来看简单StringScheme实现:

public class StringScheme implements Scheme {

    public static final String STRING_SCHEME_KEY = "str";

    public List<Object> deserialize(byte[] bytes) {
        return new Values(deserializeString(bytes));
    }

    public static String deserializeString(byte[] string) {
        try {
            return new String(string, "UTF-8");
        } catch (UnsupportedEncodingException e) {
            throw new RuntimeException(e);
        }
    }

    public Fields getOutputFields() {
        return new Fields(STRING_SCHEME_KEY);
    }
}

其实就是直接返回了一个String,在Spout往后发射时就一个字段,其名为“str”,如果采用StringScheme时,大家在Bolt中可以用

tuple.getStringByField("str")

来获取其值。有人有疑问前面为什么用new SchemeAsMultiScheme(new StringScheme())呐?来看SchemeAsMultiScheme代码
public class SchemeAsMultiScheme implements MultiScheme {
  public final Scheme scheme;

  public SchemeAsMultiScheme(Scheme scheme) {
    this.scheme = scheme;
  }

  @Override public Iterable<List<Object>> deserialize(final byte[] ser) {
    List<Object> o = scheme.deserialize(ser);
    if(o == null) return null;
    else return Arrays.asList(o);
  }

  @Override public Fields getOutputFields() {
    return scheme.getOutputFields();
  }
}

public interface MultiScheme extends Serializable {
  public Iterable<List<Object>> deserialize(byte[] ser);
  public Fields getOutputFields();
}

其实本身还是调用了传入的scheme方法,只不过返回结果组合成一个list而已,小弟觉得不用也可以。但是storm-kafka里面默认是需要的,在KafkaUtils解析message时调用scheme信息:

public static Iterable<List<Object>> generateTuples(KafkaConfig kafkaConfig, Message msg) {
        Iterable<List<Object>> tups;
        ByteBuffer payload = msg.payload();
        if (payload == null) {
            return null;
        }
        ByteBuffer key = msg.key();
        if (key != null && kafkaConfig.scheme instanceof KeyValueSchemeAsMultiScheme) {
            tups = ((KeyValueSchemeAsMultiScheme) kafkaConfig.scheme).deserializeKeyAndValue(Utils.toByteArray(key), Utils.toByteArray(payload));
        } else {
            tups = kafkaConfig.scheme.deserialize(Utils.toByteArray(payload));
        }
        return tups;
    }

所以没什么大的需求还是用storm-kafka默认的吧。


例子

kafka收到的message多种多样,而且往下发射的信息页多种多样,所以很多时候我们需要自己写scheme,下面举2个例子


example 1

第一:一般默认发射一个field,但是如果我需要多发几个fields的话,该怎么办呐,现在发射2个,其实网上已有大牛,把kafka的offset加到了发射的信息中去,分析的过程如下:

//returns false if it's reached the end of current batch
    public EmitState next(SpoutOutputCollector collector) {
        if (_waitingToEmit.isEmpty()) {
            fill();
        }
        while (true) {
            MessageAndRealOffset toEmit = _waitingToEmit.pollFirst();
            if (toEmit == null) {
                return EmitState.NO_EMITTED;
            }
            Iterable<List<Object>> tups = KafkaUtils.generateTuples(_spoutConfig, toEmit.msg);
            if (tups != null) {
                for (List<Object> tup : tups) {
                    collector.emit(tup, new KafkaMessageId(_partition, toEmit.offset));
                }
                break;
            } else {
                ack(toEmit.offset);
            }
        }
        if (!_waitingToEmit.isEmpty()) {
            return EmitState.EMITTED_MORE_LEFT;
        } else {
            return EmitState.EMITTED_END;
        }
    }

从上面看出,发射tuple时已经把offset作为messageId往下发射了,所以我们认为在下面接收tuple的Bolt中可以通过messageId获取offset,但是我们再来看看backtype.storm.daemon.executor 的代码:

(log-message"Opening spout " component-id ":" (keys task-datas))
        (doseq[[task-id task-data]task-datas
                :let[^ISpout spout-obj (:objecttask-data)
                      tasks-fn(:tasks-fntask-data)
                      send-spout-msg (fn[out-stream-id values message-id out-task-id]
                                       (.increment emitted-count)
                                       (let[out-tasks (ifout-task-id
                                                         (tasks-fnout-task-id out-stream-id values)
                                                         (tasks-fnout-stream-id values))
                                             rooted? (andmessage-id has-ackers?)
                                             root-id (ifrooted? (MessageId/generateId rand))
                                             out-ids (fast-list-for[t out-tasks](ifrooted? (MessageId/generateId rand)))]


从这段代码可以看出,messageId是随机生成的,跟之前kafkaSpout 锚定的new KafkaMessageId(_partition, toEmit.offset)一点关系都没有,所以需要自己手动把offset加到发射的tuple中去,这就需要我们自己实现Scheme了,代码如下:

publicclass KafkaOffsetWrapperScheme implements Scheme {
 
    public static final String SCHEME_OFFSET_KEY = "offset";
 
    private String _offsetTupleKeyName;
    private Scheme _localScheme;
 
    public KafkaOffsetWrapperScheme() {
        _localScheme = new StringScheme();
        _offsetTupleKeyName = SCHEME_OFFSET_KEY;
    }
 
 
    public KafkaOffsetWrapperScheme(Scheme localScheme,
                                    String offsetTupleKeyName) {
        _localScheme = localScheme;
        _offsetTupleKeyName = offsetTupleKeyName;
    }
 
    public KafkaOffsetWrapperScheme(Scheme localScheme) {
        this(localScheme, SCHEME_OFFSET_KEY);
    }
 
    public List<Object> deserialize(byte[] bytes) {
        return_localScheme.deserialize(bytes);
    }
 
    publicFields getOutputFields() {
        List<String> outputFields = _localScheme
                        .getOutputFields()
                        .toList();
        outputFields.add(_offsetTupleKeyName);
        returnnew Fields(outputFields);
    }
}



这里的scheme输出是两个fields,一个是str,由StringScheme负责反序列化,或者自己实现其他的scheme;一个是offset,但是offset如何加到发射的tuple中呐??我们从PartitionManager中找到被发射的tuple

public EmitState next(SpoutOutputCollector collector) {
    if (_waitingToEmit.isEmpty()) {
        fill();
    }
    while (true) {
        MessageAndRealOffset toEmit = _waitingToEmit.pollFirst();
        if (toEmit == null) {
            return EmitState.NO_EMITTED;
        }
        Iterable<List<Object>> tups = KafkaUtils.generateTuples(_spoutConfig, toEmit.msg);
        if (tups != null) {
            for (List<Object> tup : tups) {
                tup.add(toEmit.offset);
                collector.emit(tup, new KafkaMessageId(_partition, toEmit.offset));
            }
            break;
        } else {
            ack(toEmit.offset);
        }
    }
    if (!_waitingToEmit.isEmpty()) {
        return EmitState.EMITTED_MORE_LEFT;
    } else {
        return EmitState.EMITTED_END;
    }
}


KafkaUtils.generateTuples(xxx,xxx)

public static Iterable<List<Object>> generateTuples(KafkaConfig kafkaConfig, Message msg) {
        Iterable<List<Object>> tups;
        ByteBuffer payload = msg.payload();
        if (payload == null) {
            return null;
        }
        ByteBuffer key = msg.key();
        if (key != null && kafkaConfig.scheme instanceof KeyValueSchemeAsMultiScheme) {
            tups = ((KeyValueSchemeAsMultiScheme) kafkaConfig.scheme).deserializeKeyAndValue(Utils.toByteArray(key), Utils.toByteArray(payload));
        } else {
            tups = kafkaConfig.scheme.deserialize(Utils.toByteArray(payload));
        }
        return tups;
    }


目前我们已经成功把offset加到了发射的tuple中,在bolt中,可以通过tuple.getValue(1),或tuple.getStringByField("offset");来或者

唯一要做的就是在构建SpoutConfig时,指定scheme为KafkaOffsetWrapperScheme

example 2

第二,kafka里面的存的message是其他格式的,如thrift,avro,protobuf格式,那这样就需要自己实现反序列化的过程

这里以avro scheme格式为例(这里就不对avro扫盲了,自己google一下吧)

这时kafka中存放的是avro格式的message,如果avro schema如下

{"namespace": "example.avro",
 "type": "record",
 "name": "User",
 "fields": [
     {"name": "name", "type": "string"},
     {"name": "favorite_number",  "type": ["int", "null"]},
     {"name": "favorite_color", "type": ["string", "null"]}
 ]
}

那我们需要实现Scheme接口

public class AvroMessageScheme implements Scheme{

    private final static Logger logger = LoggerFactory.getLogger(AvroMessageScheme.class);

    private GenericRecord e2;
    private AvroRecord avroRecord;

    public AvroMessageScheme() {

        }

        @Override
        public List<Object> deserialize(byte[] bytes) {
                e2 = null;
                avroRecord = null;

        try {
            InputStream is = Thread.currentThread().getContextClassLoader().getResourceAsStream("examples.avsc");
            Schema schema = new Schema.Parser().parse(is);
            DatumReader<GenericRecord> datumReader = new GenericDatumReader<GenericRecord>(schema);
            Decoder decoder = DecoderFactory.get().binaryDecoder(bytes, null);
            e2 = datumReader.read(null, decoder);
            avroRecord = new AvroRecord(e2);
        } catch (Exception e) {
            e.printStackTrace();
            return new Values(avroRecord);
        }

        return new Values(avroRecord);
    }

        @Override
        public Fields getOutputFields() {
                 return new Fields("msg");  
        }

}


这里往下面发射的是一个POJO类,其实完全可以发射String。这样效率会更高一点。
其AvroRecord POJO如下

public class AvroRecord implements Serializable {
    private String name;
    private int favorite_number;
    private String favorite_color;

    public AvroRecord(GenericRecord gr) {
        try {
            this.name = String.valueOf(gr.get("name"));
            this.favorite_number = Integer.parseInt(gr.get("favorite_number"));
            this.favorite_color = gr.get("favorite_color").toString();
        } catch (Exception e) {
            logger.error("read AvroRecord error!");
        }
    }

    @Override
    public String toString() {
        return "AvroRecord{" +
                "name='" + name + '\'' +
                ", favorite_number=" + favorite_number +
                ", favorite_color='" + favorite_color + '\'' +
                '}';
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public String getFavorite_color() {
        return favorite_color;
    }

    public void setFavorite_color(String favorite_color) {
        this.favorite_color = favorite_color;
    }

    public int getFavorite_number() {
        return favorite_number;
    }

    public void setFavorite_number(int favorite_number) {
        this.favorite_number = favorite_number;
    }
}

该例子笔者未经过测试,望慎重使用


Reference

https://blog.deck36.de/no-more-over-counting-making-counters-in-apache-storm-idempotent-using-redis-hyperloglog/

(四)storm-kafka源码走读之自定义Scheme