PySpark java.io.IOException：方案没有文件系统：https
PySpark java.io.IOException: No FileSystem for scheme: https
我正在使用本地 windows 并尝试在 python 上使用以下代码加载 XML 文件，我遇到了这个错误，有谁知道如何解决它,
这是代码
df1 = sqlContext.read.format("xml").options(rowTag="IRS990EZ").load("https://irs-form-990.s3.amazonaws.com/201611339349202661_public.xml")
这是错误
Py4JJavaError Traceback (most recent call last)
<ipython-input-7-4832eb48a4aa> in <module>()
----> 1 df1 = sqlContext.read.format("xml").options(rowTag="IRS990EZ").load("https://irs-form-990.s3.amazonaws.com/201611339349202661_public.xml")
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\pyspark\sql\readwriter.py in load(self, path, format, schema, **options)
157 self.options(**options)
158 if isinstance(path, basestring):
--> 159 return self._df(self._jreader.load(path))
160 elif path is not None:
161 if type(path) != list:
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\java_gateway.py in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\pyspark\sql\utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling o38.load.
: java.io.IOException: No FileSystem for scheme: https
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)
at org.apache.hadoop.fs.FileSystem.access0(FileSystem.java:94)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.setInputPaths(FileInputFormat.java:500)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.setInputPaths(FileInputFormat.java:469)
at org.apache.spark.SparkContext$$anonfun$newAPIHadoopFile.apply(SparkContext.scala:1160)
at org.apache.spark.SparkContext$$anonfun$newAPIHadoopFile.apply(SparkContext.scala:1148)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.SparkContext.newAPIHadoopFile(SparkContext.scala:1148)
at com.databricks.spark.xml.util.XmlFile$.withCharset(XmlFile.scala:46)
at com.databricks.spark.xml.DefaultSource$$anonfun$createRelation.apply(DefaultSource.scala:62)
at com.databricks.spark.xml.DefaultSource$$anonfun$createRelation.apply(DefaultSource.scala:62)
at com.databricks.spark.xml.XmlRelation$$anonfun.apply(XmlRelation.scala:47)
at com.databricks.spark.xml.XmlRelation$$anonfun.apply(XmlRelation.scala:46)
at scala.Option.getOrElse(Option.scala:121)
at com.databricks.spark.xml.XmlRelation.<init>(XmlRelation.scala:45)
at com.databricks.spark.xml.DefaultSource.createRelation(DefaultSource.scala:65)
at com.databricks.spark.xml.DefaultSource.createRelation(DefaultSource.scala:43)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:306)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Unknown Source)
错误消息说明了一切：您不能使用数据框 reader 和 load 访问网络上的文件（http 或 htpps）。建议先下载到本地
请参阅 pyspark.sql.DataFrameReader docs 了解有关可用资源的更多信息（通常是本地文件系统、HDFS 和通过 JDBC 的数据库）。
与错误无关，请注意您似乎错误地使用了命令的 format 部分：假设您使用 XML Data Source for Apache Spark package, the correct usage should be format('com.databricks.spark.xml') (see the example).
pyspark 无法加载 http 或 https，我的一位同事找到了这个问题的答案，所以这里是解决方案，
在创建 spark 上下文和 sql 上下文之前，我们需要加载这两行代码
import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages com.databricks:spark-xml_2.11:0.4.1 pyspark-shell'
从 sc = pyspark.SparkContext.getOrCreate 和 sqlContext = SQLContext(sc)
创建 sparkcontext 和 sqlcontext 之后
使用 sc.addFile(url)
将 http 或 https url 添加到 sc
Data_XMLFile = sqlContext.read.format("xml").options(rowTag="anytaghere").load(pyspark.SparkFiles.get("*_public.xml")).coalesce(10).cache()
这个解决方案对我有用
我犯了一个类似但略有不同的错误：忘记了文件路径的 "s3://" 前缀。添加此前缀以形成 "s3://path/to/object" 后，以下代码有效：
my_data = spark.read.format("com.databricks.spark.csv")\
.option("header", "true")\
.option("inferSchema", "true")\
.option("delimiter", ",")\
.load("s3://path/to/object")
我对 CSV 文件也有类似的问题，基本上我们试图将 CSV 文件加载到 spark 中。
我们能够通过使用 pandas' 库成功加载文件，首先我们将文件加载到 pandas 数据框中，然后使用 pandas 我们能够将数据加载到 spark 数据框中。
from pyspark.sql import SparkSession
import pandas as pd
spark = SparkSession.builder.appName('appName').getOrCreate()
pdf = pd.read_csv('file patth with https')
sdf = spark.createDataFrame(pdf)
我正在使用本地 windows 并尝试在 python 上使用以下代码加载 XML 文件，我遇到了这个错误，有谁知道如何解决它,
这是代码
df1 = sqlContext.read.format("xml").options(rowTag="IRS990EZ").load("https://irs-form-990.s3.amazonaws.com/201611339349202661_public.xml")
这是错误
Py4JJavaError Traceback (most recent call last)
<ipython-input-7-4832eb48a4aa> in <module>()
----> 1 df1 = sqlContext.read.format("xml").options(rowTag="IRS990EZ").load("https://irs-form-990.s3.amazonaws.com/201611339349202661_public.xml")
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\pyspark\sql\readwriter.py in load(self, path, format, schema, **options)
157 self.options(**options)
158 if isinstance(path, basestring):
--> 159 return self._df(self._jreader.load(path))
160 elif path is not None:
161 if type(path) != list:
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\java_gateway.py in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\pyspark\sql\utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
C:\SPARK_HOME\spark-2.2.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling o38.load.
: java.io.IOException: No FileSystem for scheme: https
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)
at org.apache.hadoop.fs.FileSystem.access0(FileSystem.java:94)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.setInputPaths(FileInputFormat.java:500)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.setInputPaths(FileInputFormat.java:469)
at org.apache.spark.SparkContext$$anonfun$newAPIHadoopFile.apply(SparkContext.scala:1160)
at org.apache.spark.SparkContext$$anonfun$newAPIHadoopFile.apply(SparkContext.scala:1148)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.SparkContext.newAPIHadoopFile(SparkContext.scala:1148)
at com.databricks.spark.xml.util.XmlFile$.withCharset(XmlFile.scala:46)
at com.databricks.spark.xml.DefaultSource$$anonfun$createRelation.apply(DefaultSource.scala:62)
at com.databricks.spark.xml.DefaultSource$$anonfun$createRelation.apply(DefaultSource.scala:62)
at com.databricks.spark.xml.XmlRelation$$anonfun.apply(XmlRelation.scala:47)
at com.databricks.spark.xml.XmlRelation$$anonfun.apply(XmlRelation.scala:46)
at scala.Option.getOrElse(Option.scala:121)
at com.databricks.spark.xml.XmlRelation.<init>(XmlRelation.scala:45)
at com.databricks.spark.xml.DefaultSource.createRelation(DefaultSource.scala:65)
at com.databricks.spark.xml.DefaultSource.createRelation(DefaultSource.scala:43)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:306)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Unknown Source)
错误消息说明了一切：您不能使用数据框 reader 和 load 访问网络上的文件（http 或 htpps）。建议先下载到本地
请参阅 pyspark.sql.DataFrameReader docs 了解有关可用资源的更多信息（通常是本地文件系统、HDFS 和通过 JDBC 的数据库）。
与错误无关，请注意您似乎错误地使用了命令的 format 部分：假设您使用 XML Data Source for Apache Spark package, the correct usage should be format('com.databricks.spark.xml') (see the example).
pyspark 无法加载 http 或 https，我的一位同事找到了这个问题的答案，所以这里是解决方案，
在创建 spark 上下文和 sql 上下文之前，我们需要加载这两行代码
import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages com.databricks:spark-xml_2.11:0.4.1 pyspark-shell'
从 sc = pyspark.SparkContext.getOrCreate 和 sqlContext = SQLContext(sc)
使用 sc.addFile(url)
Data_XMLFile = sqlContext.read.format("xml").options(rowTag="anytaghere").load(pyspark.SparkFiles.get("*_public.xml")).coalesce(10).cache()
这个解决方案对我有用
我犯了一个类似但略有不同的错误：忘记了文件路径的 "s3://" 前缀。添加此前缀以形成 "s3://path/to/object" 后，以下代码有效：
my_data = spark.read.format("com.databricks.spark.csv")\
.option("header", "true")\
.option("inferSchema", "true")\
.option("delimiter", ",")\
.load("s3://path/to/object")
我对 CSV 文件也有类似的问题，基本上我们试图将 CSV 文件加载到 spark 中。
我们能够通过使用 pandas' 库成功加载文件，首先我们将文件加载到 pandas 数据框中，然后使用 pandas 我们能够将数据加载到 spark 数据框中。
from pyspark.sql import SparkSession
import pandas as pd
spark = SparkSession.builder.appName('appName').getOrCreate()
pdf = pd.read_csv('file patth with https')
sdf = spark.createDataFrame(pdf)