Storm 集成 Redis 详解

发表于 2022/04/28

作者 deathwhispers

13 分钟阅读

Storm 集成 Redis 详解

一、简介

Storm-Redis 提供了 Storm 与 Redis 的集成支持，你只需要引入对应的依赖即可使用：

  
<dependency>
  <groupId>org.apache.storm</groupId>
  <artifactId>storm-redis</artifactId>
  <version>${storm.version}</version>
  <type>jar</type>
</dependency>

Storm-Redis 使用 Jedis 为 Redis 客户端，并提供了如下三个基本的 Bolt 实现：

RedisLookupBolt ：从 Redis 中查询数据；
RedisStoreBolt ：存储数据到 Redis；
RedisFilterBolt
查询符合条件的数据；

RedisLookupBolt、RedisStoreBolt、RedisFilterBolt 均继承自 AbstractRedisBolt 抽象类。我们可以通过继承该抽象类，实现自定义 RedisBolt，进行功能的拓展。

二、集成案例

2.1 项目结构

这里首先给出一个集成案例：进行词频统计并将最后的结果存储到 Redis。项目结构如下：

用例源码下载地址：storm-redis-integration

2.2 项目依赖

项目主要依赖如下：

  
<properties>
  <storm.version>1.2.2</storm.version>
</properties>
<dependencies>
  <dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-core</artifactId>
    <version>${storm.version}</version>
  </dependency>
  <dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-redis</artifactId>
    <version>${storm.version}</version>
  </dependency>
</dependencies>

2.3 DataSourceSpout

  
/** * 产生词频样本的数据源 */public class DataSourceSpout extends BaseRichSpout {    private List<String> list = Arrays.asList("Spark", "Hadoop", "HBase", "Storm", "Flink", "Hive");    private SpoutOutputCollector spoutOutputCollector;    @Override    public void open(Map map, TopologyContext topologyContext, SpoutOutputCollector spoutOutputCollector) {        this.spoutOutputCollector = spoutOutputCollector;    }    @Override    public void nextTuple() {        // 模拟产生数据        String lineData = productData();        spoutOutputCollector.emit(new Values(lineData));        Utils.sleep(1000);    }    @Override    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {        outputFieldsDeclarer.declare(new Fields("line"));    }    /**     * 模拟数据     */    private String productData() {        Collections.shuffle(list);        Random random = new Random();        int endIndex = random.nextInt(list.size()) % (list.size()) + 1;        return StringUtils.join(list.toArray(), "\t", 0, endIndex);    }}

产生的模拟数据格式如下：

```plain text Spark HBase Hive Flink Storm Hadoop HBase Spark Flink HBase Storm HBase Hadoop Hive Flink HBase Flink Hive Storm Hive Flink Hadoop HBase Hive Hadoop Spark HBase Storm

### 2.4 SplitBolt

```java
/** * 将每行数据按照指定分隔符进行拆分 */public class SplitBolt extends BaseRichBolt {    private OutputCollector collector;    @Override    public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {        this.collector = collector;    }    @Override    public void execute(Tuple input) {        String line = input.getStringByField("line");        String[] words = line.split("\t");        for (String word : words) {            collector.emit(new Values(word, String.valueOf(1)));        }    }    @Override    public void declareOutputFields(OutputFieldsDeclarer declarer) {        declarer.declare(new Fields("word", "count"));    }}

2.5 CountBolt

  
/** * 进行词频统计 */public class CountBolt extends BaseRichBolt {    private Map<String, Integer> counts = new HashMap<>();    private OutputCollector collector;    @Override    public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {        this.collector=collector;    }    @Override    public void execute(Tuple input) {        String word = input.getStringByField("word");        Integer count = counts.get(word);        if (count == null) {            count = 0;        }        count++;        counts.put(word, count);        // 输出        collector.emit(new Values(word, String.valueOf(count)));    }    @Override    public void declareOutputFields(OutputFieldsDeclarer declarer) {        declarer.declare(new Fields("word", "count"));    }}

2.6 WordCountStoreMapper

实现 RedisStoreMapper 接口，定义 tuple 与 Redis 中数据的映射关系：即需要指定 tuple 中的哪个字段为 key，哪个字段为 value，并且存储到 Redis 的何种数据结构中。

  
/** * 定义 tuple 与 Redis 中数据的映射关系 */public class  WordCountStoreMapper implements RedisStoreMapper {    private RedisDataTypeDescription description;    private final String hashKey = "wordCount";    public WordCountStoreMapper() {        description = new RedisDataTypeDescription(            RedisDataTypeDescription.RedisDataType.HASH, hashKey);    }    @Override    public RedisDataTypeDescription getDataTypeDescription() {        return description;    }    @Override    public String getKeyFromTuple(ITuple tuple) {        return tuple.getStringByField("word");    }    @Override    public String getValueFromTuple(ITuple tuple) {        return tuple.getStringByField("count");    }}

2.7 WordCountToRedisApp

  
/** * 进行词频统计 并将统计结果存储到 Redis 中 */public class WordCountToRedisApp {    private static final String DATA_SOURCE_SPOUT = "dataSourceSpout";    private static final String SPLIT_BOLT = "splitBolt";    private static final String COUNT_BOLT = "countBolt";    private static final String STORE_BOLT = "storeBolt";    //在实际开发中这些参数可以将通过外部传入 使得程序更加灵活    private static final String REDIS_HOST = "192.168.200.226";    private static final int REDIS_PORT = 6379;    public static void main(String[] args) {        TopologyBuilder builder = new TopologyBuilder();        builder.setSpout(DATA_SOURCE_SPOUT, new DataSourceSpout());        // split        builder.setBolt(SPLIT_BOLT, new SplitBolt()).shuffleGrouping(DATA_SOURCE_SPOUT);        // count        builder.setBolt(COUNT_BOLT, new CountBolt()).shuffleGrouping(SPLIT_BOLT);        // save to redis        JedisPoolConfig poolConfig = new JedisPoolConfig.Builder()        .setHost(REDIS_HOST).setPort(REDIS_PORT).build();        RedisStoreMapper storeMapper = new WordCountStoreMapper();        RedisStoreBolt storeBolt = new RedisStoreBolt(poolConfig, storeMapper);        builder.setBolt(STORE_BOLT, storeBolt).shuffleGrouping(COUNT_BOLT);        // 如果外部传参 cluster 则代表线上环境启动否则代表本地启动        if (args.length > 0 && args[0].equals("cluster")) {            try {                StormSubmitter.submitTopology("ClusterWordCountToRedisApp", new Config(), builder.createTopology());            } catch (AlreadyAliveException | InvalidTopologyException | AuthorizationException e) {                e.printStackTrace();            }        } else {            LocalCluster cluster = new LocalCluster();            cluster.submitTopology("LocalWordCountToRedisApp",                                   new Config(), builder.createTopology());        }    }}

2.8 启动测试

可以用直接使用本地模式运行，也可以打包后提交到服务器集群运行。本仓库提供的源码默认采用 maven-shade-plugin 进行打包，打包命令如下：

```plain text

mvn clean package -D maven.test.skip=true

启动后，查看 Redis 中的数据：

![bc2769c4f7ee1fbc5ec30443a575660f](/assets/images/learning/bigdata/storm/storm-integration-redis-detail/bc2769c4f7ee1fbc5ec30443a575660f.png)

## 三、storm-redis 实现原理

### 3.1 AbstractRedisBolt

RedisLookupBolt、RedisStoreBolt、RedisFilterBolt 均继承自 AbstractRedisBolt 抽象类，和我们自定义实现 Bolt 一样，AbstractRedisBolt 间接继承自 BaseRichBolt。

![1a05281d39f48096baeac66668c73c90](/assets/images/learning/bigdata/storm/storm-integration-redis-detail/1a05281d39f48096baeac66668c73c90.png)

AbstractRedisBolt 中比较重要的是 prepare 方法，在该方法中通过外部传入的 jedis 连接池配置 ( jedisPoolConfig/jedisClusterConfig) 创建用于管理 Jedis 实例的容器 JedisCommandsInstanceContainer。

```java
public abstract class AbstractRedisBolt extends BaseTickTupleAwareRichBolt {    protected OutputCollector collector;    private transient JedisCommandsInstanceContainer container;    private JedisPoolConfig jedisPoolConfig;    private JedisClusterConfig jedisClusterConfig;    ......    @Override    public void prepare(Map map, TopologyContext topologyContext, OutputCollector collector) {        // FIXME: stores map (stormConf), topologyContext and expose these to derived classes        this.collector = collector;        if (jedisPoolConfig != null) {            this.container = JedisCommandsContainerBuilder.build(jedisPoolConfig);        } else if (jedisClusterConfig != null) {            this.container = JedisCommandsContainerBuilder.build(jedisClusterConfig);        } else {            throw new IllegalArgumentException("Jedis configuration not found");        }    }    .......}

JedisCommandsInstanceContainer 的 build() 方法如下，实际上就是创建 JedisPool 或 JedisCluster 并传入容器中。

  
public static JedisCommandsInstanceContainer build(JedisPoolConfig config) {JedisPool jedisPool = new JedisPool(DEFAULT_POOL_CONFIG, config.getHost(), config.getPort(), config.getTimeout(), config.getPassword(), config.getDatabase());return new JedisContainer(jedisPool);}public static JedisCommandsInstanceContainer build(JedisClusterConfig config) {JedisCluster jedisCluster = new JedisCluster(config.getNodes(), config.getTimeout(), config.getTimeout(), config.getMaxRedirections(), config.getPassword(), DEFAULT_POOL_CONFIG);return new JedisClusterContainer(jedisCluster);}

3.2 RedisStoreBolt和RedisLookupBolt

RedisStoreBolt 中比较重要的是 process 方法，该方法主要从 storeMapper 中获取传入 key/value 的值，并按照其存储类型 dataType 调用 jedisCommand 的对应方法进行存储。

RedisLookupBolt 的实现基本类似，从 lookupMapper 中获取传入的 key 值，并进行查询操作。

  
public class RedisStoreBolt extends AbstractRedisBolt {    private final RedisStoreMapper storeMapper;    private final RedisDataTypeDescription.RedisDataType dataType;    private final String additionalKey;    public RedisStoreBolt(JedisPoolConfig config, RedisStoreMapper storeMapper) {        super(config);        this.storeMapper = storeMapper;        RedisDataTypeDescription dataTypeDescription = storeMapper.getDataTypeDescription();        this.dataType = dataTypeDescription.getDataType();        this.additionalKey = dataTypeDescription.getAdditionalKey();    }    public RedisStoreBolt(JedisClusterConfig config, RedisStoreMapper storeMapper) {        super(config);        this.storeMapper = storeMapper;        RedisDataTypeDescription dataTypeDescription = storeMapper.getDataTypeDescription();        this.dataType = dataTypeDescription.getDataType();        this.additionalKey = dataTypeDescription.getAdditionalKey();    }    @Override    public void process(Tuple input) {        String key = storeMapper.getKeyFromTuple(input);        String value = storeMapper.getValueFromTuple(input);        JedisCommands jedisCommand = null;        try {            jedisCommand = getInstance();            switch (dataType) {                case STRING:                    jedisCommand.set(key, value);                    break;                case LIST:                    jedisCommand.rpush(key, value);                    break;                case HASH:                    jedisCommand.hset(additionalKey, key, value);                    break;                case SET:                    jedisCommand.sadd(key, value);                    break;                case SORTED_SET:                    jedisCommand.zadd(additionalKey, Double.valueOf(value), key);                    break;                case HYPER_LOG_LOG:                    jedisCommand.pfadd(key, value);                    break;                case GEO:                    String[] array = value.split(":");                    if (array.length != 2) {                        throw new IllegalArgumentException("value structure should be longitude:latitude");                    }                    double longitude = Double.valueOf(array[0]);                    double latitude = Double.valueOf(array[1]);                    jedisCommand.geoadd(additionalKey, longitude, latitude, key);                    break;                default:                    throw new IllegalArgumentException("Cannot process such data type: " + dataType);            }            collector.ack(input);        } catch (Exception e) {            this.collector.reportError(e);            this.collector.fail(input);        } finally {            returnInstance(jedisCommand);        }    }    .........}

3.3 JedisCommands

JedisCommands 接口中定义了所有的 Redis 客户端命令，它有以下三个实现类，分别是 Jedis、JedisCluster、ShardedJedis。Strom 中主要使用前两种实现类，具体调用哪一个实现类来执行命令，由传入的是 jedisPoolConfig 还是 jedisClusterConfig 来决定。

3.4 RedisMapper 和 TupleMapper

RedisMapper 和 TupleMapper 定义了 tuple 和 Redis 中的数据如何进行映射转换。

1. TupleMapper

TupleMapper 主要定义了两个方法：

getKeyFromTuple(ITuple tuple)：从 tuple 中获取那个字段作为 Key；
getValueFromTuple(ITuple tuple)：从 tuple 中获取那个字段作为 Value；

2. RedisMapper

定义了获取数据类型的方法 getDataTypeDescription(),RedisDataTypeDescription 中 RedisDataType 枚举类定义了所有可用的 Redis 数据类型：

  
public class RedisDataTypeDescription implements Serializable {
    public enum RedisDataType { STRING, HASH, LIST, SET, SORTED_SET, HYPER_LOG_LOG, GEO }    ......}

3. RedisStoreMapper

RedisStoreMapper 继承 TupleMapper 和 RedisMapper 接口，用于数据存储时，没有定义额外方法。

4. RedisLookupMapper

RedisLookupMapper 继承 TupleMapper 和 RedisMapper 接口：

定义了 declareOutputFields 方法，声明输出的字段。
定义了 toTuple 方法，将查询结果组装为 Storm 的 Values 的集合，并用于发送。

下面的例子表示从输入 Tuple 的获取 word 字段作为 key，使用 RedisLookupBolt 进行查询后，将 key 和查询结果 value 组装为 values 并发送到下一个处理单元。

  
class WordCountRedisLookupMapper implements RedisLookupMapper {    private RedisDataTypeDescription description;    private final String hashKey = "wordCount";    public WordCountRedisLookupMapper() {        description = new RedisDataTypeDescription(            RedisDataTypeDescription.RedisDataType.HASH, hashKey);    }    @Override    public List<Values> toTuple(ITuple input, Object value) {        String member = getKeyFromTuple(input);        List<Values> values = Lists.newArrayList();        values.add(new Values(member, value));        return values;    }    @Override    public void declareOutputFields(OutputFieldsDeclarer declarer) {        declarer.declare(new Fields("wordName", "count"));    }    @Override    public RedisDataTypeDescription getDataTypeDescription() {        return description;    }    @Override    public String getKeyFromTuple(ITuple tuple) {        return tuple.getStringByField("word");    }    @Override    public String getValueFromTuple(ITuple tuple) {        return null;    }}

5. RedisFilterMapper

RedisFilterMapper 继承 TupleMapper 和 RedisMapper 接口，用于查询数据时，定义了 declareOutputFields 方法，声明输出的字段。如下面的实现：

  
@Overridepublic void declareOutputFields(OutputFieldsDeclarer declarer) {declarer.declare(new Fields("wordName", "count"));}

四、自定义RedisBolt实现词频统计

4.1 实现原理

自定义 RedisBolt：主要利用 Redis 中哈希结构的 hincrby key field 命令进行词频统计。在 Redis 中 hincrby 的执行效果如下。hincrby 可以将字段按照指定的值进行递增，如果该字段不存在的话，还会新建该字段，并赋值为 0。通过这个命令可以非常轻松的实现词频统计功能。

```plain text redis> HSET myhash field 5 (integer) 1 redis> HINCRBY myhash field 1 (integer) 6 redis> HINCRBY myhash field -1 (integer) 5 redis> HINCRBY myhash field -10 (integer) -5 redis>

### 4.2 项目结构

![1a1599b8aa1e300ba26fb4d73da16f26](/assets/images/learning/bigdata/storm/storm-integration-redis-detail/1a1599b8aa1e300ba26fb4d73da16f26.png)

### 4.3 自定义RedisBolt的代码实现

```java
/** * 自定义 RedisBolt 利用 Redis 的哈希数据结构的 hincrby key field 命令进行词频统计 */public class RedisCountStoreBolt extends AbstractRedisBolt {    private final RedisStoreMapper storeMapper;    private final RedisDataTypeDescription.RedisDataType dataType;    private final String additionalKey;    public RedisCountStoreBolt(JedisPoolConfig config, RedisStoreMapper storeMapper) {        super(config);        this.storeMapper = storeMapper;        RedisDataTypeDescription dataTypeDescription = storeMapper.getDataTypeDescription();        this.dataType = dataTypeDescription.getDataType();        this.additionalKey = dataTypeDescription.getAdditionalKey();    }    @Override    protected void process(Tuple tuple) {        String key = storeMapper.getKeyFromTuple(tuple);        String value = storeMapper.getValueFromTuple(tuple);        JedisCommands jedisCommand = null;        try {            jedisCommand = getInstance();            if (dataType == RedisDataTypeDescription.RedisDataType.HASH) {                jedisCommand.hincrBy(additionalKey, key, Long.valueOf(value));            } else {                throw new IllegalArgumentException("Cannot process such data type for Count: " + dataType);            }            collector.ack(tuple);        } catch (Exception e) {            this.collector.reportError(e);            this.collector.fail(tuple);        } finally {            returnInstance(jedisCommand);        }    }    @Override    public void declareOutputFields(OutputFieldsDeclarer declarer) {    }}

4.4 CustomRedisCountApp

  
/** * 利用自定义的 RedisBolt 实现词频统计 */public class CustomRedisCountApp {    private static final String DATA_SOURCE_SPOUT = "dataSourceSpout";    private static final String SPLIT_BOLT = "splitBolt";    private static final String STORE_BOLT = "storeBolt";    private static final String REDIS_HOST = "192.168.200.226";    private static final int REDIS_PORT = 6379;    public static void main(String[] args) {        TopologyBuilder builder = new TopologyBuilder();        builder.setSpout(DATA_SOURCE_SPOUT, new DataSourceSpout());        // split        builder.setBolt(SPLIT_BOLT, new SplitBolt()).shuffleGrouping(DATA_SOURCE_SPOUT);        // save to redis and count        JedisPoolConfig poolConfig = new JedisPoolConfig.Builder()        .setHost(REDIS_HOST).setPort(REDIS_PORT).build();        RedisStoreMapper storeMapper = new WordCountStoreMapper();        RedisCountStoreBolt countStoreBolt = new RedisCountStoreBolt(poolConfig, storeMapper);        builder.setBolt(STORE_BOLT, countStoreBolt).shuffleGrouping(SPLIT_BOLT);        // 如果外部传参 cluster 则代表线上环境启动,否则代表本地启动        if (args.length > 0 && args[0].equals("cluster")) {            try {                StormSubmitter.submitTopology("ClusterCustomRedisCountApp", new Config(), builder.createTopology());            } catch (AlreadyAliveException | InvalidTopologyException | AuthorizationException e) {                e.printStackTrace();            }        } else {            LocalCluster cluster = new LocalCluster();            cluster.submitTopology("LocalCustomRedisCountApp",                                   new Config(), builder.createTopology());        }    }}

参考资料

Storm Redis Integration

BigData

Storm Redis

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