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https://issues.apache.org/jira/browse/HADOOP-16360?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ladislav Jech updated HADOOP-16360:
-----------------------------------
Issue Type: Bug (was: Improvement)
> java.lang.NullPointerException: null uri host. This can be caused by
> unencoded / in the password string
> -------------------------------------------------------------------------------------------------------
>
> Key: HADOOP-16360
> URL: https://issues.apache.org/jira/browse/HADOOP-16360
> Project: Hadoop Common
> Issue Type: Bug
> Reporter: Ladislav Jech
> Priority: Blocker
>
> I am experiencing very old issue appearing now again on Cloudera cluster 6.2.
> I use following libraries with pyspark job:
> *
> /opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/hadoop/hadoop-common-3.0.0-cdh6.2.0.jar
> *
> /opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/hadoop/hadoop-aws-3.0.0-cdh6.2.0.jar
> While trying to write DF to S3 as CSV I get following error:
> {code:java}
> java.lang.NullPointerException: null uri host. This can be caused by
> unencoded / in the password string
> at java.util.Objects.requireNonNull(Objects.java:228)
> at
> org.apache.hadoop.fs.s3native.S3xLoginHelper.buildFSURI(S3xLoginHelper.java:69)
> at org.apache.hadoop.fs.s3a.S3AFileSystem.setUri(S3AFileSystem.java:467)
> at
> org.apache.hadoop.fs.s3a.S3AFileSystem.initialize(S3AFileSystem.java:234)
> at
> org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:3288)
> at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:123)
> at
> org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:3337)
> at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:3305)
> at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:476)
> at org.apache.hadoop.fs.Path.getFileSystem(Path.java:361)
> at
> org.apache.spark.sql.execution.datasources.DataSource.planForWritingFileFormat(DataSource.scala:423)
> at
> org.apache.spark.sql.execution.datasources.DataSource.planForWriting(DataSource.scala:523)
> at
> org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:281)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:270)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:228)
> 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:357)
> at py4j.Gateway.invoke(Gateway.java:282)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:238)
> at java.lang.Thread.run(Thread.java:748)
> // code placeholder
> {code}
> My code doesn't use secret key in s3 path, but as follows:
> {code:java}
> sparkSession = SparkSession.builder.getOrCreate()
> sparkContext = sparkSession.sparkContext
> #sparkContext._jsc.hadoopConfiguration().set("fs.s3a.multipart.size",
> "1000000")
> sparkContext._jsc.hadoopConfiguration().set("fs.s3a.access.key",
> AWS_ACCESS_KEY_ID)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3a.secret.key",
> AWS_SECRET_ACCESS_KEY)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3a.endpoint", AWS_HOST_BASE)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3.access.key",
> AWS_ACCESS_KEY_ID)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3.secret.key",
> AWS_SECRET_ACCESS_KEY)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3.endpoint", AWS_HOST_BASE)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3n.access.key",
> AWS_ACCESS_KEY_ID)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3n.secret.key",
> AWS_SECRET_ACCESS_KEY)
> sparkContext._jsc.hadoopConfiguration().set("fs.s3n.endpoint", AWS_HOST_BASE)
> sqlContext = SQLContext(sparkSession.sparkContext) # log4j =
> sparkContext._jvm.org.apache.log4j # pylint: disable=W0212 logger =
> sparkContext._jvm.org.apache.log4j.LogManager.getLogger("OracleToS3") #
> logger = log4j.LogManager.getlogger(__name__)
> sparkContext.setLogLevel('INFO') logger.info("Going to process Oracle
> tables...") for table in ADDCSource.table_list: logger.info("Reading oracle
> table into dataframe") oracle_table = sparkContext.read \ .format("jdbc") \
> .option("url", ADDCSource.jdbc_string) \ .option("dbtable", table) \
> .option("user", ADDCSource.user) \ .option("password", ADDCSource.password) \
> .option("driver", "oracle.jdbc.driver.OracleDriver") \ .load() # Display
> schema logger.info("Display table schema") oracle_table.show()
> logger.info("Display table top 5") oracle_table.head(5) output_file =
> "s3a://ADDC_ELICTRICITY_201906/" + "11/" + table + "_" +
> time.strftime("%Y%m%d_%H%M%S") +".csv" logger.info("Writing table into S3 to
> file: " + output_file) oracle_table\ .repartition(1)\ .write \
> .mode("overwrite")\ .format("csv")\ .option("header","true") \
> .save("s3a://ADDC_ELICTRICITY_201906/" + "11/" + table + "_" +
> time.strftime("%Y%m%d_%H%M%S") +".csv")
> {code}
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