Spark SQL：无需秒即可解析时间戳

Posted 2023-04-18

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【中文标题】Spark SQL：无需秒即可解析时间戳【英文标题】：Spark SQL: parse timestamp without seconds 【发布时间】：2016-10-24 01:09:46 【问题描述】：

我不拥有的一些数据带有一个应该是 timestamp 的字段，但有时似乎不符合 ISO 8601 标准。

在我的代码中，我定义了一个模式，然后当 Spark SQL 解析我的 json 数据时，我收到以下错误：

java.lang.IllegalArgumentException: 2016-10-07T11:15Z

源数据有以下内容：

"transaction_date_time": "2016-10-07T11:15Z"

我的架构是这样定义的：

 val schema = (new StructType)
      .add("transaction_date_time", TimestampType)

我相信这是因为它错过了秒数。我怎样才能正确解析时间戳？

编辑：例如，使用

读取数据

spark.read.schema(schema).json(rdd).show()

会触发以下错误

16/10/24 13:06:27 ERROR Executor: Exception in task 6.0 in stage 5.0 (TID 23)
java.lang.IllegalArgumentException: 2016-10-07T11:15Z
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.skip(Unknown Source)
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.parse(Unknown Source)
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl.<init>(Unknown Source)
    at org.apache.xerces.jaxp.datatype.DatatypeFactoryImpl.newXMLGregorianCalendar(Unknown Source)
    at javax.xml.bind.DatatypeConverterImpl._parseDateTime(DatatypeConverterImpl.java:422)
    at javax.xml.bind.DatatypeConverterImpl.parseDateTime(DatatypeConverterImpl.java:417)
    at javax.xml.bind.DatatypeConverter.parseDateTime(DatatypeConverter.java:327)
    at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:140)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:114)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertObject(JacksonParser.scala:215)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:182)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertRootField(JacksonParser.scala:73)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:288)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:285)
    at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2366)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:285)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:280)
    at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
    at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
    at org.apache.spark.scheduler.Task.run(Task.scala:85)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    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)
16/10/24 13:06:27 WARN TaskSetManager: Lost task 6.0 in stage 5.0 (TID 23, localhost): java.lang.IllegalArgumentException: 2016-10-07T11:15Z
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.skip(Unknown Source)
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.parse(Unknown Source)
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl.<init>(Unknown Source)
    at org.apache.xerces.jaxp.datatype.DatatypeFactoryImpl.newXMLGregorianCalendar(Unknown Source)
    at javax.xml.bind.DatatypeConverterImpl._parseDateTime(DatatypeConverterImpl.java:422)
    at javax.xml.bind.DatatypeConverterImpl.parseDateTime(DatatypeConverterImpl.java:417)
    at javax.xml.bind.DatatypeConverter.parseDateTime(DatatypeConverter.java:327)
    at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:140)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:114)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertObject(JacksonParser.scala:215)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:182)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertRootField(JacksonParser.scala:73)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:288)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:285)
    at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2366)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:285)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:280)
    at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
    at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
    at org.apache.spark.scheduler.Task.run(Task.scala:85)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    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)

16/10/24 13:06:27 ERROR TaskSetManager: Task 6 in stage 5.0 failed 1 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage 5.0 failed 1 times, most recent failure: Lost task 6.0 in stage 5.0 (TID 23, localhost): java.lang.IllegalArgumentException: 2016-10-07T11:15Z
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.skip(Unknown Source)
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.parse(Unknown Source)
    at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl.<init>(Unknown Source)
    at org.apache.xerces.jaxp.datatype.DatatypeFactoryImpl.newXMLGregorianCalendar(Unknown Source)
    at javax.xml.bind.DatatypeConverterImpl._parseDateTime(DatatypeConverterImpl.java:422)
    at javax.xml.bind.DatatypeConverterImpl.parseDateTime(DatatypeConverterImpl.java:417)
    at javax.xml.bind.DatatypeConverter.parseDateTime(DatatypeConverter.java:327)
    at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:140)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:114)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertObject(JacksonParser.scala:215)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:182)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertRootField(JacksonParser.scala:73)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:288)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:285)
    at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2366)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:285)
    at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:280)
    at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
    at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
    at org.apache.spark.scheduler.Task.run(Task.scala:85)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    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)

Driver stacktrace:
  at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
  at scala.Option.foreach(Option.scala:257)
  at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
  at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
  at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:347)
  at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
  at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2183)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
  at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2532)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2182)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2189)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1925)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1924)
  at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2562)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:1924)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2139)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:526)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:486)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:495)
  ... 54 elided
Caused by: java.lang.IllegalArgumentException: 2016-10-07T11:15Z
  at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.skip(Unknown Source)
  at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl$Parser.parse(Unknown Source)
  at org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl.<init>(Unknown Source)
  at org.apache.xerces.jaxp.datatype.DatatypeFactoryImpl.newXMLGregorianCalendar(Unknown Source)
  at javax.xml.bind.DatatypeConverterImpl._parseDateTime(DatatypeConverterImpl.java:422)
  at javax.xml.bind.DatatypeConverterImpl.parseDateTime(DatatypeConverterImpl.java:417)
  at javax.xml.bind.DatatypeConverter.parseDateTime(DatatypeConverter.java:327)
  at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:140)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:114)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertObject(JacksonParser.scala:215)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertField(JacksonParser.scala:182)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$.convertRootField(JacksonParser.scala:73)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:288)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$2.apply(JacksonParser.scala:285)
  at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2366)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:285)
  at org.apache.spark.sql.execution.datasources.json.JacksonParser$$anonfun$parseJson$1.apply(JacksonParser.scala:280)
  at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
  at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
  at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
  at org.apache.spark.scheduler.Task.run(Task.scala:85)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
  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)

【问题讨论】：

【参考方案1】：

你可以改变

val schema = (new StructType)
      .add("transaction_date_time", TimestampType)

到

val schema = (new StructType)
      .add("transaction_date_time", StringType)

然后使用df.withColumn("columnTimeWithOutSec", unix_timestamp($"time", format))

where format = "format time with out seconds ex HH:mm "

就像this...

另外，请查看 DateTimeUtils.scala 以内联日期和时间戳的 Spark 样式转换。

【讨论】：

【参考方案2】：

看起来apache.spark.Timestamp 只是java.sql.Timestamp 的包装。至少这是this 让我相信的。

因此，我们可以使用SimpleDateFormat 解析日期并提取毫秒，然后将其传递给Timestamp 构造函数。

您可以在此示例中执行类似的操作来预处理数据：

import java.sql.Timestamp;
import java.text.*;
import java.util.Date;

public class Test 
    public static void main(String[] args) 
        String timestamp = "2016-10-07T11:15Z";
        DateFormat df = new SimpleDateFormat("yyyy-MM-dd'T'HH:mmXXX");
        Date parsedDate = null;
        try
                parsedDate = df.parse(timestamp);
        catch(Exception e)
                //do nothing
        
        Timestamp ts = new Timestamp(parsedDate.getTime());
        System.out.println(parsedDate);
        System.out.println(ts);

哪些输出

Fri Oct 07 04:15:00 PDT 2016
2016-10-07 04:15:00.0

我搜索了一下“日期格式的可选部分”，发现 this SO 说你应该只制作两个 SimpleDateFormats。

【讨论】：

我认为解析该日期并不难，即使使用 Java 8 Time API 也可以开箱即用。我想问题出在 Spark 上，挑战在于将您的代码与上面的代码一起使用（我只是进行了编辑以更好地解释）我查看了文档并找到了encoder trait。我认为您需要定义一个客户encoder 并使用它来代替TimeStamp。 Here's another link that looks more useful。仍在四处寻找，但我想我会发布我发现的内容看起来如果你只是扩展dataType，你可以将它传递给你已经从StructType使用的同一个add函数

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