I am trying to read a text file from on-prem s3 compatible object storage using Spark and I am getting an error stating: UsupportedOperationException. I am unsure what this is pointing to and have tried to adjust code thinking maybe it was the spark.read command. I have tried read.text and read.csv both of which should work, but result in the same error. Full stack trace is below along with code:
Code being used:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("s3reader") \
.getOrCreate()\
sc = spark.sparkContext
sc._jsc.hadoopConfiguration().set("fs.s3a.path.style.access", "true")
sc._jsc.hadoopConfiguration().set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
sc._jsc.hadoopConfiguration().set("fs.s3a.access.key","xxxxxxxxxxxx")
sc._jsc.hadoopConfiguration().set("fs.s3a.secret.key", "xxxxxxxxxxxxxx")
sc._jsc.hadoopConfiguration().set("fs.s3a.connection.ssl.enabled", "true")
df = spark.read.text("https://s3a.us-east-1.xxxx.xxxx.xxxx.com/bronze/xxxxxxx/test.txt")
print(df)
Stack trace:
Traceback (most recent call last):
File "/home/cloud/sparks3test.py", line 19, in <module>
df = spark.read.text("https://s3a.us-east-1.tpavcps3ednrg1.vici.verizon.com/bronze/CoreMetrics/test.txt")
File "/usr/local/bin/spark-3.1.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 516, in text
File "/usr/local/bin/spark-3.1.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1304, in __call__
File "/usr/local/bin/spark-3.1.2-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/sql/utils.py", line 111, in deco
File "/usr/local/bin/spark-3.1.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 326, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o31.text.
: java.lang.UnsupportedOperationException
at org.apache.hadoop.fs.http.AbstractHttpFileSystem.listStatus(AbstractHttpFileSystem.java:91)
at org.apache.hadoop.fs.http.HttpsFileSystem.listStatus(HttpsFileSystem.java:23)
at org.apache.spark.util.HadoopFSUtils$.listLeafFiles(HadoopFSUtils.scala:225)
at org.apache.spark.util.HadoopFSUtils$.$anonfun$parallelListLeafFilesInternal$1(HadoopFSUtils.scala:95)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFilesInternal(HadoopFSUtils.scala:85)
at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFiles(HadoopFSUtils.scala:69)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.bulkListLeafFiles(InMemoryFileIndex.scala:158)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.listLeafFiles(InMemoryFileIndex.scala:131)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:94)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:66)
at org.apache.spark.sql.execution.datasources.DataSource.createInMemoryFileIndex(DataSource.scala:581)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:417)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:325)
at org.apache.spark.sql.DataFrameReader.$anonfun$load$3(DataFrameReader.scala:307)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:307)
at org.apache.spark.sql.DataFrameReader.text(DataFrameReader.scala:944)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
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.base/java.lang.Thread.run(Thread.java:829)```
Try reading file from S3 like below.
s3a://bucket/bronze/xxxxxxx/test.txt
Related
I was able to read Cassandra tables. I created Cassandra table according to spark dataframe schema. But when I tried to write spark dataframe to Cassandra table. I got following error. Environment: pyspark 3.0.1 local shell, Cassandra 3.11.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/spark/python/pyspark/sql/readwriter.py", line 825, in save
self._jwrite.save()
File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/opt/spark/python/pyspark/sql/utils.py", line 128, in deco
return f(*a, **kw)
File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o62.save.
: com.datastax.spark.connector.datasource.CassandraCatalogException: Attempting to write to C* Table but missing
primary key columns: [logicalref]
at com.datastax.spark.connector.datasource.CassandraWriteBuilder.<init>(CassandraWriteBuilder.scala:44)
at com.datastax.spark.connector.datasource.CassandraTable.newWriteBuilder(CassandraTable.scala:69)
at org.apache.spark.sql.execution.datasources.v2.BatchWriteHelper.newWriteBuilder(WriteToDataSourceV2Exec.scala:346)
at org.apache.spark.sql.execution.datasources.v2.BatchWriteHelper.newWriteBuilder$(WriteToDataSourceV2Exec.scala:341)
at org.apache.spark.sql.execution.datasources.v2.AppendDataExec.newWriteBuilder(WriteToDataSourceV2Exec.scala:253)
at org.apache.spark.sql.execution.datasources.v2.AppendDataExec.run(WriteToDataSourceV2Exec.scala:259)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.doExecute(V2CommandExec.scala:54)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:171)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:122)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:121)
at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:963)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:963)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:354)
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)
First I read emty cassandra table. I got columns. I select these columns and assigned another dataframe like
df = spark.read.format("org.apache.spark.sql.cassandra")...
df2 = df.select(*df.columns)
Then I was able to write
df2.write.format("org.apache.spark.sql.cassandra")....
I'm using Pyspark Spark 3.0.1 on Ubuntu 18.04 and want to export data to a MariaDB server using JDBC.
I'm specifying the Connector/J jar on the pyspark command line like this:
$ pyspark --jars /usr/share/java/mariadb-java-client.jar
However, when I to use the JDBC connection I get the following error:
>>> df1 = sc.parallelize([[1,2,3], [2,3,4]]).toDF(("a", "b", "c"))
>>> df1.write.format("jdbc") \
... .mode("overwrite") \
... .option("url", "jdbc:mariadb://localhost:3306/testDatabase?user=foo&password=bar") \
... .option("dbtable", "example") \
... .save()
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "/opt/spark/python/pyspark/sql/readwriter.py", line 825, in save
self._jwrite.save()
File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/opt/spark/python/pyspark/sql/utils.py", line 128, in deco
return f(*a, **kw)
File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o60.save.
: java.sql.SQLException: No suitable driver
at java.sql.DriverManager.getDriver(DriverManager.java:315)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.$anonfun$driverClass$2(JDBCOptions.scala:105)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:105)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:194)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:198)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:45)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:90)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:171)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:122)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:121)
at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:963)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:963)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:415)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:399)
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)
>>>
Because of java.sql.SQLException: No suitable driver I assume I need some additional configuration for Connector/J to be invoked. I'm not seeing how to to it though. What's the trick?
You need to specifiy the mariadb driver class org.mariadb.jdbc.Driver using driver option when writing:
df1.write.format("jdbc") \
.mode("overwrite") \
.option("driver", "org.mariadb.jdbc.Driver") \
.option("url", "jdbc:mysql://localhost:3306/testDatabase?user=foo&password=bar") \
.option("dbtable", "example") \
.save()
See Usage in the docs.
For anyone still facing this error,
use mysql in url, not mariadb.
the jdbc url should be like jdbc:mysql:{host} ... in place of jdbc:mariadb:{host} ....
Please note that I am new to pySpark, and feel free to let me know if I am missing any detail.
Running on Windows 10, with python3.7 installed
Command being used to run pyspark: pyspark --jars "C:\spark\spark-2.4.5-bin-hadoop2.7\jars\ojdbc6.jar"
Code that I am trying to execute in pyspark shell:
from pyspark import SparkConf, SparkContext
sqlctx = SQLContext(sc)
with open("new1", "r") as f:
query = f.read()
df = sqlctx.read.format("jdbc").options(url="jdbc:oracle:thin:#host:port:sid",
driver="oracle.jdbc.driver.OracleDriver", dbtable=query).load()
I am pretty sure, url is correct, window login that i'm using has access to database as it works fine with cx_Oracle and I can access DB using PL/SQL client.
Error:
File "<stdin>", line 1, in <module>
File "C:\spark\spark-2.4.5-bin-hadoop2.7\python\pyspark\sql\readwriter.py", line 172, in load
return self._df(self._jreader.load())
File "C:\spark\spark-2.4.5-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\java_gateway.py", line 1257, in __call__
File "C:\spark\spark-2.4.5-bin-hadoop2.7\python\pyspark\sql\utils.py", line 63, in deco
return f(*a, **kw)
File "C:\spark\spark-2.4.5-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o39.load.
: java.sql.SQLException: ORA-01017: invalid username/password; logon denied
at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:447)
at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:389)
at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:382)
at oracle.jdbc.driver.T4CTTIfun.processError(T4CTTIfun.java:675)
at oracle.jdbc.driver.T4CTTIoauthenticate.processError(T4CTTIoauthenticate.java:448)
at oracle.jdbc.driver.T4CTTIfun.receive(T4CTTIfun.java:513)
at oracle.jdbc.driver.T4CTTIfun.doRPC(T4CTTIfun.java:227)
at oracle.jdbc.driver.T4CTTIoauthenticate.doOAUTH(T4CTTIoauthenticate.java:383)
at oracle.jdbc.driver.T4CTTIoauthenticate.doOAUTH(T4CTTIoauthenticate.java:776)
at oracle.jdbc.driver.T4CConnection.logon(T4CConnection.java:432)
at oracle.jdbc.driver.PhysicalConnection.<init>(PhysicalConnection.java:553)
at oracle.jdbc.driver.T4CConnection.<init>(T4CConnection.java:254)
at oracle.jdbc.driver.T4CDriverExtension.getConnection(T4CDriverExtension.java:32)
at oracle.jdbc.driver.OracleDriver.connect(OracleDriver.java:528)
at org.apache.spark.sql.execution.datasources.jdbc.DriverWrapper.connect(DriverWrapper.scala:45)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:63)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:54)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:56)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation$.getSchema(JDBCRelation.scala:210)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:35)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:318)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:167)
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: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(Unknown Source)
this is the example way to access oracle from spark, where you are using user and pwd seperately.
see read-data-from-oracle-database-with-apache-spark
myDF = spark.read \
.format("jdbc") \
.option("url", "jdbc:oracle:thin:username/password#//hostname:portnumber/SID") \
.option("dbtable", "hr.emp") \
.option("user", "db_user_name") \
.option("password", "password") \
.option("driver", "oracle.jdbc.driver.OracleDriver") \
.load()
I'm using spark 2.4 and I've run pyspark like this:
./bin/pyspark --packages org.apache.bahir:spark-sql-streaming-mqtt_2.11:2.3.2
pyspark runs successfully.
(But when I run spark-sql-streaming-mqtt_2.11:2.4.0-SNAPSHOT, got an error)
I'm trying to get data from a MQTT broker using structured streaming.
so, I've run this
>>> from pyspark.sql import SparkSession
>>> from pyspark.sql.functions import explode
>>> from pyspark.sql.functions import split
>>> spark = SparkSession \
... .builder \
... .appName("Test") \
... .getOrCreate()
>>> lines = spark.readStream\
... .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")\
... .option("topic", "/sensor")\
... .option("brokerUrl", "tcp://localhost:1883")\
... .load()
the error shown:
2019-03-22 01:24:43 WARN MQTTUtils:51 - If `clientId` is not set, a random value is picked up.
Recovering from failure is not supported in such a case.
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "/opt/spark/python/pyspark/sql/streaming.py", line 400, in load
return self._df(self._jreader.load())
File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/opt/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o43.load.
: MqttException (0)
at org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence.checkIsOpen(MqttDefaultFilePersistence.java:130)
at org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence.getFiles(MqttDefaultFilePersistence.java:247)
at org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence.close(MqttDefaultFilePersistence.java:142)
at org.apache.bahir.sql.streaming.mqtt.MQTTStreamSource.stop(MQTTStreamSource.scala:228)
at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:190)
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)
I tried to stream MQTT data for a week. But I don't think there is a way to solve it and it is really desperate. Is there no way I can solve it?
Thank you.
Try to set the persistence option.
Example :
val lines = spark.readStream.format("datasource.mqtt.MQTTStreamSourceProvider")
.option("topic", topic)
.option("persistence","memory")
.option("brokerUrl",broker)
.option("cleanSession", "true")
.load()
I'm trying to read data from sql server using pyspark. Below mentioned code works fine when executed using following command (where i'm passing sqljdbc driver path) but it fails when i try to run it using PyCharm IDE(on windows).
spark-submit --driver-class-path C:\drivers\sqljdbc_6.0.8112.100_enu\sqljdbc_6.0\enu\jre8\sqljdbc42.jar ReadSQLServerData.py
How to include or set the driver path while running same code through PyCharm IDE?
Code:
from pyspark.sql import SQLContext, Row
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName("ReadSQLServerData")
sc = SparkContext(conf=conf)
query = "(SELECT top 10 * from users) as users"
sqlctx = SQLContext(sc)
df = sqlctx.read.format("jdbc").options(url="jdbc:sqlserver://mssqlserver:1433;database=user_management;user=pyspark;password=pyspark", dbtable=query).load()
Exception:
Traceback (most recent call last):
File "H:/Mine/OneDrive/Python/PySpark01/ReadSQLServerData.py", line 9, in <module>
df = sqlctx.read.format("jdbc").options(url="jdbc:sqlserver://mssqlserver:1433;database=user_management;user=pyspark;password=pyspark", dbtable=query).load()
File "C:\spark\python\pyspark\sql\readwriter.py", line 155, in load
return self._df(self._jreader.load())
File "C:\spark\python\lib\py4j-0.10.4-src.zip\py4j\java_gateway.py", line 1133, in __call__
File "C:\spark\python\pyspark\sql\utils.py", line 63, in deco
return f(*a, **kw)
File "C:\spark\python\lib\py4j-0.10.4-src.zip\py4j\protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o27.load.
: java.sql.SQLException: No suitable driver
at java.sql.DriverManager.getDriver(DriverManager.java:315)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:84)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:84)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:83)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:34)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:32)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:330)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:152)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:125)
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: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(Thread.java:748)
Not sure if you figured this out but figured I could help others.
You have to set the driver-class-path and you can pass it in as a config option like below
spark = SparkSession \
.builder \
.appName("Python Spark SQL basic example") \
.config("spark.driver.extraClassPath","/Users/Desktop/drivers/sqljdbc42.jar") \
.getOrCreate()