最近要在 Spark job 中通过 Spark SQL 的方式读取 Elasticsearch 数据，踩了一些坑，总结于此。
Spark job 的编写语言为 Scala，scala-library 的版本为 2.11.8。
Spark 相关依赖包的版本为 2.3.2，如 spark-core、spark-sql。
Elasticsearch 数据
schema
{ "settings": { "number_of_replicas": 1 }, "mappings": { "label": { "properties": { "docId": { "type": "keyword" }, "labels": { "type": "nested", "properties": { "id": { "type": "long" }, "label": { "type": "keyword" } } }, "itemId": { "type": "long" } } } }}sample data
{ "took" : 141, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 17370929, "max_score" : 1.0, "hits" : [ { "_index" : "aen-label-v1", "_type" : "label", "_id" : "123_ITEM", "_score" : 1.0, "_source" : { "docId" : "123_ITEM", "labels" : [ { "id" : 7378, "label" : "1kg" } ], "itemId" : 123 } }, { "_index" : "aen-label-v1", "_type" : "label", "_id" : "456_ITEM", "_score" : 1.0, "_source" : { "docId" : "456_ITEM", "labels" : [ { "id" : 7378, "label" : "2kg" } ], "itemId" : 456 } } ] }}既然要用 Spark SQL，当然少不了其对应的依赖，
dependencies { implementation 'org.apache.spark:spark-core_2.11:2.3.2' implementation 'org.apache.spark:spark-sql_2.11:2.3.2'}对于 ES 的相关库，如同 官网 所说，要在 Spark 中访问 ES，需要将 elasticsearch-hadoop 依赖包加入到 Spark job 运行的类路径中，具体而言就是添加到 Spark job 工程的依赖中，公司的 nexus 中当前最新的版本为 7.15.0，且目前我们是使用 gradle 管理依赖，故添加依赖的代码如下，
dependencies { implementation 'org.elasticsearch:elasticsearch-hadoop:7.15.0'}对于 Spark，基于资源管理器的不同，可以在两种模式下运行：本地模式和集群模式，可通过 --master 参数来指定资源管理器的方式。本地模式时，不依赖额外的 Spark 集群，Spark 将在同一台机器上运行所有内容，非常方便用于本地测试，对于 Spark SQL，只需要在创建 SparkSession 时采用 local 的模式即可，
class MyUtils extends Serializable { def esHost() = s"es.sherlockyb.club" // local mode def getLocalSparkSession: SparkSession = SparkSession.builder() .master("local") .getOrCreate() // cluster mode def getSparkSession: SparkSession = SparkSession.builder() .enableHiveSupport() .config("spark.sql.broadcastTimeout", "3600") .getOrCreate()}object LocalTest extends LazyLogging { def main(args: Array[String]): Unit = { new LocalTest().run() }}class LocalTest { def run(): Unit = { val myUtils = new MyUtils val spark = myUtils.getLocalSparkSession import spark.implicits._ var start = System.currentTimeMillis() val attributeId = 7378L val labelNames = Array("aen-label-retail", "aen-label-seller") spark.read .format("es") .option("es.nodes", myUtils.esHost()) .option("es.port", "9200") .option("es.nodes.wan.only", value = true) .option("es.resource", Joiner.on(",").join(java.util.Arrays.asList(labelNames:_*)) + "/label") .option("es.scroll.size", 2000) .load() .createOrReplaceTempView("temp_labels") val sqlDf = spark.sql("select itemId, labels from temp_labels where itemId in (123, 456)") val newDf = sqlDf .map(row => { val labels = row.getAs[Seq[Row]]("labels") val labelValue = labels.find(p => p.getAs[Long]("id") == attributeId).map(p => p.getAs[String]("label")) (row.getAs[Long]("itemId"), attributeId, labelValue.orNull) }) .withColumn("final_result", lit("PASS")) .toDF("itemId", "attributeId", "label", "final_result") val finalDf = newDf.toDF("itemId", "attributeId", "label", "result") finalDf.printSchema() finalDf.show() var emptyDf = newDf .filter(col("label").isNotNull) .toDF("itemId", "attributeId", "label", "result") emptyDf = emptyDf.union(finalDf) emptyDf.printSchema() emptyDf.show() emptyDf.filter(col("itemId") === 6238081929L and col("label").notEqual(col("result"))) .show() val attributeTypeIds = Array.fill(3)(100) val attributeTypeIdsStr = Joiner.on(",").join(java.util.Arrays.asList(attributeTypeIds:_*)) println(attributeTypeIdsStr) import scala.collection.JavaConverters._ emptyDf = emptyDf.filter(!col("itemId").isin(trainItemIds.asScala.map(Long2long).toList:_*)) emptyDf.show(false) }}Spark SQL 通过 DataFrameReader 类支持读取各种类型的数据源，比如 Parquet、ORC、JSON、CSV 等格式的文件，Hive table，以及其他 database。而 Elasticsearch 只不过是众多数据源中的一种，DataFrameReader 通过 format(...) 指定数据源格式，通过 option(...) 定制对应数据源下的配置，最后通过 load() 加载生成 DataFrame，也就是 Dataset[Row] 的类型别名。有了 DataFrame，就可以创建一个临时表，然后就能以 SQL 的方式读取数据。
在 Spark 1.5 以前，Elasticsearch 在 format(...) 中对应的 source 名需要是全包名 org.elasticsearch.spark.sql，而在 Spark 1.5 以及之后的版本，source 名称简化为 es。
lit 添加常量列。import scala.collection.JavaConverters._newDf = df.filter(!col("itemId").isin(trainItemIds.asScala.map(Long2long).toList:_*)): _*:_* 是 type ascription 的一个特例，它会告诉编译器将序列类型的单个参数视为变参数序列，即 varargs。应用例子，
val indices = Array("aen-label", "aen-label-seller")Joiner.on(",").join(java.util.Arrays.asList(indices:_*))该配置项表示连接器是否用于 WAN 上的云或受限环境如 AWS 中的 Elasticsearch 实例，默认为 false，而公司的 Elasticsearch 集群是在 AWS 上的，endpoint 只能在内网访问，因而刚开始测试时，遇到如下报错，
Exception in thread "main" org.elasticsearch.hadoop.EsHadoopIllegalArgumentException: No data nodes with HTTP-enabled available at org.elasticsearch.hadoop.rest.InitializationUtils.filterNonDataNodesIfNeeded(InitializationUtils.java:159) at org.elasticsearch.hadoop.rest.RestService.findPartitions(RestService.java:223) at org.elasticsearch.spark.rdd.AbstractEsRDD.esPartitions$lzycompute(AbstractEsRDD.scala:73) at org.elasticsearch.spark.rdd.AbstractEsRDD.esPartitions(AbstractEsRDD.scala:72) at org.elasticsearch.spark.rdd.AbstractEsRDD.getPartitions(AbstractEsRDD.scala:44) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:46) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:46) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:46) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:46) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:340) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3278) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2489) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2489) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3259) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3258) at org.apache.spark.sql.Dataset.head(Dataset.scala:2489) at org.apache.spark.sql.Dataset.take(Dataset.scala:2703) at org.apache.spark.sql.Dataset.showString(Dataset.scala:254) at org.apache.spark.sql.Dataset.show(Dataset.scala:723)通过 option("es.nodes.wan.only", value = true) 将配置项设置为 true 后恢复正常。
在遍历 DataFrame 时遇到如下编译错误，
Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._在处理 DataFrame 之前需要加上 importing spark.implicits._，用于将常见的 Scala 对象转换为 DataFrame，通常在获取 SparkSession 后立马 import。
WrappedArray 而不是 Array当我们通过 createOrReplaceTempView("temp_labels") 构建一个临时表视图后，就可以通过 SQL 像操作 hive 表那样读取数据。例如读取指定的列，
val sqlDf = spark.sql("select itemId, labels from temp_labels where itemId in (123, 456)")通过 sqlDf.printSchema() 可以看到 sqlDf 的 schema 长这样，
root |-- itemId: long (nullable = true) |-- labels: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- id: long (nullable = true) | | |-- label: string (nullable = true)labels 是包含 struct 的数组，于是从 row 中将 labels 列读出时想尝试转换为 Array，
val newDf = sqlDf.map( row => { val labels = row.getAs[Array[Row]]("labels") val labelValue = labels.find(p => p.getAs[Long]("id") == attributeId).map(p => p.getAs[String]("label")) (row.getAs[Long]("itemId"), attributeId, labelValue.orNull) })结果报错如下，
java.lang.ClassCastException: scala.collection.mutable.WrappedArray$ofRef cannot be cast to [Lorg.apache.spark.sql.Row;可以看到 Spark SQL 在读取表中数组列时，是用的 scala.collection.mutable.WrappedArray 来存储结果的，看其类定义可知，它是间接实现 Seq 接口的，所以也可用 row.getAs[Seq[Row]]("labels") 来读取。这里需要注意的是，Array[T] 虽然在 Scala 源码定义中是 class，但其对标的 Java 类型是原生数组 T[]。
is null 或 is not null，而不是 === 或 !==对于错误的用法，filter 并不会生效，就像下面这样
newDf.filter(col("label") !== null)这一点和 hive 表以及 MySQL 表判断字段是否为 null，是保持一致的，应该像下面这样，
newDf.filter(col("label").isNotNull)import com.google.common.base.Joinerimport com.typesafe.scalalogging.LazyLoggingimport org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession}object TestMain extends LazyLogging { def main(args: Array[String]): Unit = { val myUtils = new MyUtils new TestApp(myUtils).run() }}class TestApp(myUtils: MyUtils) extends Serializable with LazyLogging { def esDf(spark: SparkSession, indices: Array[String]): DataFrame = { spark.read .format("es") .option("es.nodes", myUtils.esHost()) .option("es.port", "9200") .option("es.nodes.wan.only", value = true) .option("es.resource", Joiner.on(",").join(java.util.Arrays.asList(indices:_*)) + "/label") .option("es.scroll.size", 2000) .load() } def run(): Unit = { val spark = myUtils.getSparkSession import spark.implicits._ val esTempView = "es_label" val labelNames = Array("aen-label-retail", "aen-label-seller") esDf(spark, labelNames).createOrReplaceTempView(esTempView) val labelDf = getLabelDf(spark, itemIdsStr, attributeTypeIds, esTempView) println("debug log") labelDf.printSchema() labelDf.show() labelDf.createOrReplaceTempView("final_labels") val data = spark.sql( s""" |select cc.*, pp.final_result, pp.label, null as remark |from temp_request cc |left join final_labels pp |on cc.itemid = pp.itemId |and cc.attributetypeid = pp.attributeId |where cc.profile = '$jobId' |""".stripMargin) data.distinct().write.mode(SaveMode.Overwrite) .option("compression", "gzip") .json(s"s3://sherlockyb-test/check-precision/job_id=$jobId") } def getLabelDf(spark: SparkSession, itemIdsStr: String, attributeTypeIds: Array[String], esTempView: String): DataFrame = { import spark.implicits._ val sqlDf = spark.sql(s"select itemId, labels from $esTempView where itemId in ($itemIdsStr)") val emptyDf = spark.emptyDataFrame var labelDf = emptyDf attributeTypeIds.foreach(attributeTypeId => { val attributeDf = sqlDf .map(row => { val labels = row.getAs[Seq[Row]]("labels") val labelValue = labels.find(p => p.getAs[Long]("id") == attributeTypeId.toLong).map(p => p.getAs[String]("label")) (row.getAs[Long]("itemId"), attributeTypeId.toLong, labelValue.orNull) }) .withColumn("final_result", lit("PASS")) .toDF("itemId", "attributeId", "label", "final_result") .filter(col("label").isNotNull) if (labelDf == emptyDf) { labelDf = attributeDf } else { labelDf = labelDf.union(attributeDf) } }) labelDf.dropDuplicates("itemId","attributeId") }}将 job 工程打包为 Jar，上传到 AWS 的 s3，比如 s3://sherlockyb-test/1.0.0/artifacts/spark/ 目录下，然后通过 Genie 提交 spark job 到 Spark 集群运行。Genie 是 Netflix 研发的联合作业执行引擎，提供 REST-full API 来运行各种大数据作业，如 Hadoop、Pig、Hive、Spark、Presto、Sqoop 等。
def run_spark(job_name, spark_jar_name, spark_class_name, arg_str, spark_param=''): import pygenie pygenie.conf.DEFAULT_GENIE_URL = "genie.sherlockyb.club" job = pygenie.jobs.GenieJob() \ .genie_username('sherlockyb') \ .job_name(job_name) \ .job_version('0.0.1') \ .metadata(teamId='team_account') \ .metadata(teamCredential='team_password') job.cluster_tags(['type:yarn-kerberos', 'sched:default']) job.command_tags(['type:spark-submit-kerberos', 'ver:2.3.2']) job.command_arguments( f"--class {spark_class_name} {spark_param} " f"s3a://sherlockyb-test/1.0.0/artifacts/spark/{spark_jar_name} " f"{arg_str}" ) # Submit the job to Genie running_job = job.execute() running_job.wait() return running_job.status首发链接： https://www.yangbing.club/2022/06/03/Spark-reading-elasticsearch-guide/
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