sparksql：无秒解析时间戳

suzh9iv8  于 2021-05-27  发布在  Spark
关注(0)|答案(2)|浏览(425)

一些我不拥有的数据附带了一个应该是 timestamp ，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，我得到以下错误：

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

源数据包含以下内容：

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

我的模式定义如下：

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

我相信这是因为它错过了几秒钟。如何正确解析时间戳？
编辑：例如，使用

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

将触发以下错误

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)
JSONapache-sparkapache-spark-sqltimestamp

2条答案

按热度按时间
ijnw1ujt

ijnw1ujt1#

你可以改变

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

至

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 "

就这样。。。
另外，还可以查看datetimeutils.scala，它与日期和时间戳的spark样式转换相结合。
赞(0）分享  回复(0）举报  2021-05-27
fjaof16o

fjaof16o2#

看起来像 apache.spark.Timestamp 只是 Package 纸而已 java.sql.Timestamp . 至少这让我相信。
因此，我们可以使用 SimpleDateFormat 并提取毫秒数，将其传递给 Timestamp 建造师。
您可以执行以下示例中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“日期格式中的可选部分”，发现这个意思是说你应该只做两个 SimpleDateFormat s。

赞(0）分享  回复(0）举报  2021-05-27
我来回答

相关问题

    查看更多

    热门标签

    更多
    JavaquerypythonNode开发语言requestUtil数据库Table后端算法LoggerMessageElementParser

    最新问答

    更多
    • 蜀ICP备13028337号-1 大数据知识库 https://www.saoniuhuo.com © All rights reserved
    • 本站内容来源互联网,如果侵犯您的权益请联系我们删除, 联系方式：448109455@qq.com