无法在azure databricks中使用spark read读取csv文件

sirbozc5 于 2021-05-27 发布在 Spark

关注(0)|答案(2)|浏览(393)

我的数据位于azurecosmosdb中，我已经在azuredatabricks上挂载了数据集。
我可以使用pandas读取csv文件并将其加载到sparkDataframe。

df = pd.read_csv('/dbfs/mnt/ajviswan/forest_efficiency/2020-04-26_2020-05-26.csv')
sdf = spark.createDataFrame(df)
sdf.head()

这与控制台的以下输出一起工作，我可以用这个Dataframe做进一步的处理。

(1) Spark Jobs
sdf:pyspark.sql.dataframe.DataFrame = [Forest: string, LoadBalanceMoveReason: string ... 4 more fields]
Out[34]: Row(Forest='AUSP282', LoadBalanceMoveReason='DefaultEncryption', CompletionDate='5/26/2020 12:00:00 AM', efficiencyRopCount=None, efficiencySize=0.9966470723725392, efficiencyIOPS=None)

但是当我尝试直接使用spark dataframe读取文件时，它失败了，出现了一个读取错误。

df = spark.read.csv('/dbfs/mnt/ajviswan/forest_efficiency/2020-04-26_2020-05-26.csv')
df

退货

Py4JJavaError                             Traceback (most recent call last)
<command-4117735793908621> in <module>
----> 1 df = spark.read.csv('/dbfs/mnt/ajviswan/forest_efficiency/2020-04-26_2020-05-26.csv')
      2 df

/databricks/spark/python/pyspark/sql/readwriter.py in csv(self, path, schema, sep, encoding, quote, escape, comment, header, inferSchema, ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace, nullValue, nanValue, positiveInf, negativeInf, dateFormat, timestampFormat, maxColumns, maxCharsPerColumn, maxMalformedLogPerPartition, mode, columnNameOfCorruptRecord, multiLine, charToEscapeQuoteEscaping, samplingRatio, enforceSchema, emptyValue, locale, lineSep, pathGlobFilter, recursiveFileLookup)
    533             path = [path]
    534         if type(path) == list:
--> 535             return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
    536         elif isinstance(path, RDD):
    537             def func(iterator):

/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1303         answer = self.gateway_client.send_command(command)
   1304         return_value = get_return_value(
-> 1305             answer, self.gateway_client, self.target_id, self.name)
   1306 
   1307         for temp_arg in temp_args:

/databricks/spark/python/pyspark/sql/utils.py in deco(*a,**kw)
     96     def deco(*a,**kw):
     97         try:
---> 98             return f(*a,**kw)
     99         except py4j.protocol.Py4JJavaError as e:
    100             converted = convert_exception(e.java_exception)

/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o3781.csv.
: java.lang.NoClassDefFoundError: org/apache/spark/sql/sources/v2/ReadSupport
    at java.lang.ClassLoader.defineClass1(Native Method)
    at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
    at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
    at java.net.URLClassLoader.defineClass(URLClassLoader.java:468)
    at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
    at com.databricks.backend.daemon.driver.ClassLoaders$LibraryClassLoader.loadClass(ClassLoaders.scala:151)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
    at java.lang.ClassLoader.defineClass1(Native Method)
    at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
    at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
    at java.net.URLClassLoader.defineClass(URLClassLoader.java:468)
    at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
    at com.databricks.backend.daemon.driver.ClassLoaders$LibraryClassLoader.loadClass(ClassLoaders.scala:151)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
    at com.databricks.backend.daemon.driver.ClassLoaders$ReplWrappingClassLoader.loadClass(ClassLoaders.scala:65)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:405)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:348)
    at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:370)
    at java.util.ServiceLoader$LazyIterator.access$700(ServiceLoader.java:323)
    at java.util.ServiceLoader$LazyIterator$2.run(ServiceLoader.java:407)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:409)
    at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
    at scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:44)
    at scala.collection.Iterator.foreach(Iterator.scala:941)
    at scala.collection.Iterator.foreach$(Iterator.scala:941)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
    at scala.collection.IterableLike.foreach(IterableLike.scala:74)
    at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
    at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
    at scala.collection.TraversableLike.filterImpl(TraversableLike.scala:255)
    at scala.collection.TraversableLike.filterImpl$(TraversableLike.scala:249)
    at scala.collection.AbstractTraversable.filterImpl(Traversable.scala:108)
    at scala.collection.TraversableLike.filter(TraversableLike.scala:347)
    at scala.collection.TraversableLike.filter$(TraversableLike.scala:347)
    at scala.collection.AbstractTraversable.filter(Traversable.scala:108)
    at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:696)
    at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSourceV2(DataSource.scala:780)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:317)
    at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:807)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
    at py4j.Gateway.invoke(Gateway.java:295)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:251)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ClassNotFoundException: org.apache.spark.sql.sources.v2.ReadSupport
    at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
    at com.databricks.backend.daemon.driver.ClassLoaders$LibraryClassLoader.loadClass(ClassLoaders.scala:151)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:351)

python apache-spark pyspark pandas azure-databricks

来源：https://stackoverflow.com/questions/62128625/unable-to-read-csv-file-using-spark-read-in-azure-databricks

2条答案

按热度按时间

wnavrhmk1#

要通过spark方法从装载的存储中读取数据，不应包括 /dbfs 前缀：

df = spark.read.csv('/mnt/ajviswan/forest_efficiency/2020-04-26_2020-05-26.csv')

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vulvrdjw2#

试试下面这个，

df=spark.read.format("com.databricks.spark.csv").option("header", "true").option("inferschema", "true").option("mode", "DROPMALFORMED").load("/dbfs/mnt/ajviswan/forest_efficiency/2020-04-26_2020-05-26.csv")
df.show()

编辑#1老实说，我相信在集群创建过程中存在一些配置问题。如果你的唯一目的是读取宇宙数据库数据。然后你可以试试下面的方法，

readConfig = {
  "Endpoint" : "https://<cosmos_end_point_name>.documents.azure.com:443/",
  "Masterkey" : "<master_key_value>",
  "Database" : "<database_name>",
  "preferredRegions" : "East US",
  "Collection": "<collection_name>",
  "schema_samplesize" : "1000",
  "query_pagesize" : "200000",
  "query_custom" : "SELECT * FROM c"
}
df = spark.read.format("com.microsoft.azure.cosmosdb.spark").options(**readConfig).load()

为此，在集群配置的maven包下添加maven库for spark cosmosdb。假设您的环境兼容，然后尝试使用“com.microsoft”。azure:azure-cosmosdb-spark_2.4.0_2.11:3.0.2'

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无法在azure databricks中使用spark read读取csv文件

2条答案

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