scala TraversableLike

scala TraversableLike

1. 由来

TraversableLike是Scala集合框架中的一个特质(trait),它提供了一组通用的集合操作方法。它定义了在可遍历集合上执行的基本操作,如映射、过滤、折叠等。

2. 示例

以下是使用TraversableLike的简单示例:

import scala.collection.immutable.List

val numbers: List[Int] = List(1, 2, 3, 4, 5)

// 使用map方法对每个元素进行平方操作
val squaredNumbers: List[Int] = numbers.map(x => x * x)
println(squaredNumbers) // 输出:List(1, 4, 9, 16, 25)

// 使用filter方法过滤出偶数
val evenNumbers: List[Int] = numbers.filter(_ % 2 == 0)
println(evenNumbers) // 输出:List(2, 4)

// 使用foldLeft方法求和
val sum: Int = numbers.foldLeft(0)(_ + _)
println(sum) // 输出:15

在上面的示例中,我们创建了一个整数列表numbers,然后使用TraversableLike提供的方法对其进行操作。我们使用map方法对每个元素进行平方操作,使用filter方法过滤出偶数,使用foldLeft方法对元素进行累加求和。

3. 其他类似概念

在Scala集合框架中,除了TraversableLike之外,还有其他类似的特质和类,如IterableSeqList等。这些特质和类提供了不同类型的集合操作方法和功能。

4. 联系

TraversableLike是Scala集合框架中定义通用集合操作的一个重要特质。它为可遍历集合提供了一组基本操作方法,可以方便地对集合进行转换、过滤、折叠等操作。

5. 区别

TraversableLike是Scala集合框架中的一个特质,它与其他特质(如IterableSeq等)有所不同。TraversableLike主要关注于可遍历集合的基本操作,而其他特质则更专注于特定类型的集合或序列。

6. 官方链接

您可以在Scala官方文档中查找有关TraversableLike的更多信息:TraversableLike - Scala Documentation

java.lang.NullPointerException at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:70) at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:47) at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:300) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:375) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:372) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:285) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:372) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:285) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:372) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:285) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:372) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:373) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:285) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:373) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:372) at org.apache.spark.sql.catalyst.plans.QueryPlan.sameResult(QueryPlan.scala:413) at org.apache.spark.sql.execution.ScalarSubquery.semanticEquals(subquery.scala:58) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$Expr.equals(EquivalentExpressions.scala:36) at scala.collection.mutable.HashTable$class.elemEquals(HashTable.scala:358) at scala.collection.mutable.HashMap.elemEquals(HashMap.scala:40) at scala.collection.mutable.HashTable$class.scala$collection$mutable$HashTable$$findEntry0(HashTable.scala:136) at scala.collection.mutable.HashTable$class.findEntry(HashTable.scala:132) at scala.collection.mutable.HashMap.findEntry(HashMap.scala:40) at scala.collection.mutable.HashMap.get(HashMap.scala:70) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExpr(EquivalentExpressions.scala:54) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExprTree(EquivalentExpressions.scala:96) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$subexpressionElimination$1.apply(CodeGenerator.scala:754) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$subexpressionElimination$1.apply(CodeGenerator.scala:754) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.subexpressionElimination(CodeGenerator.scala:754) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.generateExpressions(CodeGenerator.scala:808) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.createCode(GenerateUnsafeProjection.scala:309) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.create(GenerateUnsafeProjection.scala:373) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.generate(GenerateUnsafeProjection.scala:362) at org.apache.spark.sql.catalyst.expressions.UnsafeProjection$.create(Projection.scala:155) at org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:74) at org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:73) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:815) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:815) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)
08-30
[root@iot-0013 bin]# ./kafka-consumer-groups.sh --bootstrap-server 172.25.1.120:9092,172.25.1.121:9092,172.25.1.133:9092 --command-config ./kafka-client-jaas.conf --describe --group test Error: Executing consumer group command failed due to org.apache.kafka.common.errors.TimeoutException: Timed out waiting for a node assignment. Call: describeGroups(api=FIND_COORDINATOR) java.util.concurrent.ExecutionException: org.apache.kafka.common.errors.TimeoutException: Timed out waiting for a node assignment. Call: describeGroups(api=FIND_COORDINATOR) at java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:357) at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:1895) at org.apache.kafka.common.internals.KafkaFutureImpl.get(KafkaFutureImpl.java:165) at kafka.admin.ConsumerGroupCommand$ConsumerGroupService.$anonfun$describeConsumerGroups$1(ConsumerGroupCommand.scala:543) at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at scala.collection.AbstractIterator.foreach(Iterator.scala:1431) at scala.collection.IterableLike.foreach(IterableLike.scala:74) at scala.collection.IterableLike.foreach$(IterableLike.scala:73) at scala.collection.AbstractIterable.foreach(Iterable.scala:56) at scala.collection.TraversableLike.map(TraversableLike.scala:286) at scala.collection.TraversableLike.map$(TraversableLike.scala:279) at scala.collection.AbstractTraversable.map(Traversable.scala:108) at kafka.admin.ConsumerGroupCommand$ConsumerGroupService.describeConsumerGroups(ConsumerGroupCommand.scala:542) at kafka.admin.ConsumerGroupCommand$ConsumerGroupService.collectGroupsOffsets(ConsumerGroupCommand.scala:558) at kafka.admin.ConsumerGroupCommand$ConsumerGroupService.describeGroups(Consum
03-12
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

BigDataMLApplication

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值