Redis 布隆过滤器存储查询千万级用户数据

红太狼 2021-09-24 00:48 395阅读 0赞

> 实时业务需要计算新增用户，从 Hive 加载用户表到内存存储，但是几千万数据放到内存占用资源，查询效率也低。使用 Redis 位图进行存储，可以有效减少内存占用，提高查询效率。这里使用 Spark 查询， Redis 位图存储。

这里是 Scala + Spark local模式 + Redis 单节点测试。

测试了几种 Hash 算法，单一 Hash 算法效果或者效率都不是很满意，在万级到几十万级时，出现了多个碰撞。经过几种算法多次Hash，结果仍然不理想，在千万数据时，仍会有大量碰撞。

测试了 Redissom，碰撞率接近的情况下，Redisson 效率会低很多（千万数据/小时级）。

测试了多个质数组合：（61），（7，61），（7，11，13，61），（5，7，11，13，61），（61，83，113，151，211），（61，83，113，151，211，379）...

最后选择 murmurhash，经过 5 次 Hash（37, 41, 61, 79, 83）， 使用 256m 存储 4kw 数据， 31 个碰撞，可以接受。

**主类**

import java.util.Properties
    
    import com.sm.bloom.Bloom
    import com.sm.constants.Constants
    import com.sm.utils.{DateUtils, RedisUtils}
    import org.apache.log4j.{Level, Logger}
    import org.apache.spark.sql.SparkSession
    import org.slf4j.LoggerFactory
    import redis.clients.jedis.Jedis
    
    /**
      * Spark查询用户表,导入Redis缓存
      *
      * create by LiuJinHe 2020/3/3
      */
    object ImportDataToRedis {
      private val warehouseLocation = Constants.WAREHOUSE
      private val logger = LoggerFactory.getLogger(this.getClass)
      private var prop: Properties = new Properties
      private var jedis: Jedis = _
      private var startDate: String = _
      private var endDate: String = _
      private var count: Long = 0L
    
      def main(args: Array[String]): Unit = {
        Logger.getLogger("org.apache.hadoop").setLevel(Level.WARN)
        Logger.getLogger("org.apache.spark").setLevel(Level.INFO)
        Logger.getLogger("org.spark_project.jetty").setLevel(Level.WARN)
        
        // hash 质数
        val seeds = Array(37, 41, 61, 79, 83)
    
        if (args.length == 2) {
          startDate = args(0)
          endDate = args(1)
        }
    
        val spark = initSparkSession
    
        logger.info(s"========== 全量导入新增用户表到 Redis ==========")
    
        val start = System.currentTimeMillis()
    
        // 加载 redis 配置,初始化 jedis
        val redisProp = loadProp(Constants.REDIS_PROP)
        val host = redisProp.getProperty("redis.host")
        val port = redisProp.getProperty("redis.port").toInt
        val timeout = redisProp.getProperty("redis.timeout").toInt
        jedis = RedisUtils.init(host, port, timeout)
    
        // 用户位图
        // 位图存储用户记录，key：用户表名， value：bitmap (offset,即 hash(core_account + game_id + package_id))
        val userBit = redisProp.getProperty("redis.user.bit")
    
        // 初始化布隆过滤器，128m
        lazy val bloom = new Bloom(1 << 31)
    
        // 临时参数
        var data: Array[String] = null
        var gamePackage: String = ""
        var userId: String = ""
        var offset: Array[Long] = null
        var isExist: Boolean = true
    
        // 读取用户表数据
        val sqlStr =
          s"""
             |select
             | core_account, game_id, package_id, time
             |from sm_data.dws_new_user_id
             | where add_date >= '$startDate' and add_date < '$endDate'
          """.stripMargin
    
        val dataFrame = spark.sql(sqlStr)
        dataFrame.foreach(line => {
          // 组合字段作为userId
          userId = line.getInt(1) + "," + line.getInt(2) + "," + line.getString(0)
    
          // hash
          offset = bloom.murmurHash3(userId, seeds)
    
          // 判断 redis 位图中是否存在
          isExist = bloom.getbit(jedis, userBit, offset)
          if (!isExist) {
            // 如果不存在，设置位图对应位置1
            bloom.setbit(jedis, userBit, offset)
            count += 1
          }
        })
    
        spark.stop()
        jedis.close()
    
        val end = System.currentTimeMillis()
        logger.info(s"========== 新增总数: $count ==========")
        logger.info(s"========== 耗时： ${(end - start) / 1000} 秒 ==========")
    
      }
    
      def initSparkSession: SparkSession = SparkSession.builder()
        .appName(this.getClass.getSimpleName)
        .master(Constants.SPARK_LOCAL_MODE)
        .config("spark.sql.warehouse.dir", warehouseLocation)
        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
        .config("spark.kryoserializer.buffer", "1024m")
        .config("spark.kryoserializer.buffer.max", "2046m")
        .config("spark.io.compression.codec", "snappy")
        .enableHiveSupport()
        .getOrCreate()
    }

** RedisUtils**

import org.apache.commons.pool2.impl.GenericObjectPoolConfig
    import redis.clients.jedis.{Jedis, JedisPool}
    
    /**
      * create by LiuJinHe 2020/3/3
      */
    object RedisUtils extends Serializable {
    
      // @transient 类型修饰符，只能用来修饰字段。
      // 在对象序列化过程中，被transient 标记的变量不会被序列化
      @transient private var redisPool: JedisPool = _
      var host: String = _
      var port: Int = _
      var timeout: Int = _
    
      def init(host: String, port: Int, timeout: Int): Jedis = {
        redisPool = new JedisPool(new GenericObjectPoolConfig, host, port, timeout)
        redisPool.getResource
      }
    }

**Bloom类**

import redis.clients.jedis.Jedis
    import scala.util.hashing.MurmurHash3
    
    
    /**
      * create by LiuJinHe 2020/3/3
      */
    class Bloom(size: Long) extends Serializable {
      // 位图大小，默认128m
      private val cap = if (size > 0) size else 1 << 31
      private val seeds = Array(37, 41, 61, 79, 83)
      private var indices:Array[Long] = _
    
      /**
        * BKDRHash
        * @param value 需要hash的字符串
        * @param seed 随机数种子
        * @return hash结果
        */
      def hash(str: String, seed: Int): Long = {
        var result = 0L
        val value = md5Hash(str)
        for (i <- 0 until value.length) {
          result = result * seed + value.charAt(i)
        }
        result = result & (cap - 1)
        println("result： " + result)
        result
      }
    
       /**
        * murmurHash
        * @param value 需要hash的字符串
        * @return hash后的结果数组
        */
      def murmurHash3(value: String): Long = {
        var result = 0L
        if (value != null && !value.isEmpty) {
          for (i <- seeds.indices) {
            result = Math.abs(MurmurHash3.stringHash(value, seeds(i))) % cap
          }
        }
        result
      }
    
      /**
        * murmurHash
        * @param seeds 传入murHash 中的 seed 的范围
        * @return hash后的结果数组
        */
      def murmurHash3(value: String, seeds: Array[Int]): Array[Long] = {
        indices = new Array[Long](seeds.length)
        if (value != null && !value.isEmpty) {
          for (i <- seeds.indices) {
            indices(i) = Math.abs(MurmurHash3.stringHash(value, seeds(i))) % cap
          }
        }
        indices
      }
    
      def setbit(jedis: Jedis, key: String, indices: Array[Long]): Unit = {
        for (index <- indices) {
          jedis.setbit(key, index, true)
        }
      }
    
      def getbit(jedis: Jedis, key: String, indices: Array[Long]): Boolean ={
        if (key == null && key.isEmpty ) return false
        var isExists = true
        for (index <- indices) {
          isExists = isExists && jedis.getbit(key, index)
        }
        isExists
      }
    }

**pom.xml**

<properties>
            <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
            <maven.compiler.source>1.8</maven.compiler.source>
            <maven.compiler.target>1.8</maven.compiler.target>
            <scala.version>2.11.12</scala.version>
            <scala.binary.version>2.11</scala.binary.version>
            <scala.complete.version>${scala.binary.version}.8</scala.complete.version>
            <hadoop.version>2.7.6</hadoop.version>
            <spark.version>2.4.0</spark.version>
            <slf4j.version>1.7.20</slf4j.version>
        </properties>
    
        <dependencies>
            <dependency>
                <groupId>org.scala-lang</groupId>
                <artifactId>scala-library</artifactId>
                <version>${scala.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-core_2.11</artifactId>
                <version>${spark.version}</version>
                <scope>provided</scope>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-sql_2.11</artifactId>
                <version>${spark.version}</version>
                <scope>provided</scope>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-hive_2.11</artifactId>
                <version>${spark.version}</version>
                <scope>provided</scope>
            </dependency>
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-client</artifactId>
                <version>${hadoop.version}</version>
                <scope>provided</scope>
            </dependency>
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-common</artifactId>
                <version>${hadoop.version}</version>
                <scope>provided</scope>
            </dependency>
            <dependency>
                <groupId>redis.clients</groupId>
                <artifactId>jedis</artifactId>
                <version>3.1.0</version>
            </dependency>
        </dependencies>
    
        <build>
            <pluginManagement>
                <plugins>
                    
                    <plugin>
                        <groupId>net.alchim31.maven</groupId>
                        <artifactId>scala-maven-plugin</artifactId>
                        <version>3.2.0</version>
                    </plugin>
                    <plugin>
                        <groupId>org.apache.maven.plugins</groupId>
                        <artifactId>maven-compiler-plugin</artifactId>
                        <version>3.8.1</version>
                    </plugin>
                </plugins>
            </pluginManagement>
            <plugins>
                <plugin>
                    <groupId>net.alchim31.maven</groupId>
                    <artifactId>scala-maven-plugin</artifactId>
                    <executions>
                        <execution>
                            <id>scala-compile-first</id>
                            <phase>process-resources</phase>
                            <goals>
                                <goal>add-source</goal>
                                <goal>compile</goal>
                            </goals>
                        </execution>
                        <execution>
                            <id>scala-test-compile</id>
                            <phase>process-test-resources</phase>
                            <goals>
                                <goal>testCompile</goal>
                            </goals>
                        </execution>
                    </executions>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <executions>
                        <execution>
                            <phase>compile</phase>
                            <goals>
                                <goal>compile</goal>
                            </goals>
                        </execution>
                    </executions>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-shade-plugin</artifactId>
                    <version>2.4.3</version>
                    <executions>
                        <execution>
                            <phase>package</phase>
                            <goals>
                                <goal>shade</goal>
                            </goals>
                            <configuration>
                                <filters>
                                    <filter>
                                        <artifact>*:*</artifact>
                                        <excludes>
                                            <exclude>META-INF/*.SF</exclude>
                                            <exclude>META-INF/*.DSA</exclude>
                                            <exclude>META-INF/*.RSA</exclude>
                                        </excludes>
                                    </filter>
                                </filters>
                            </configuration>
                        </execution>
                    </executions>
                </plugin>
            </plugins>
        </build>

Redis

![2020031214231376.png][]

RDB文件

![20200312142244776.png][]

**结果**

hash 5 {37, 41, 61, 79, 83}		256m
    
    原表		    导入		    差值
    17440258	17440227	31	
    6675384		6675384		0	
    11730951	11730951	0	
    3489212		3489212		0	
    
    总数
    39,335,805‬	39,335,774‬      31

[2020031214231376.png]: /images/20210923/dcef511ff56c4848a1e679e1a46cbb52.png
[20200312142244776.png]: /images/20210923/bdb2cf19aef140eab9e745041270f5c2.png