pyspark connection to MariaDB fails with ClassNotFoundException - apache-spark

I'm trying to retrieve data from MariaDB with pyspark.
I created spark_session with configuration to include jdbc jar file, but couldn't solve problem. Current code to create session looks like below.
path = "hdfs://nameservice1/user/PATH/TO/JDBC/mariadb-java-client-2.7.1.jar"
# or path = "/home/PATH/TO/JDBC/mariadb-java-client-2.7.1.jar"
spark = SparkSession.config("spark.jars", path)\
.config("spark.driver.extraClassPath", path)\
.config("spark.executor.extraClassPath", path)\
.enableHiveSupport()
.getOrCreate()
Note that I've tried every case of configuration I know
(Check Permission, change directory both hdfs or local, add or remove configuration ...)
And then, code to load data is.
sql = "SOME_SQL_TO_RETRIEVE_DATA"
spark = spark.read.format('jdbc').option('dbtable', sql)
.option('url', 'jdbc:mariadb://{host}:{port}/{db}')\
.option("user", SOME_USER)
.option("password", SOME_PASSWORD)
.option("driver", 'org.mariadb.jdbc.Driver')
.load()
But it fails with java.lang.ClassNotFoundException: org.mariadb.jdbc.Driver
When I tried this with spark-submit, I saw log message.
... INFO SparkContext: Added Jar /PATH/TO/JDBC/mariadb-java-client-2.7.1.jar at spark://SOME_PATH/jars/mariadb-java-client-2.7.1.jar with timestamp SOME_TIMESTAMP
What is wrong?

For anyone who suffers from same problem.
I figured out. Spark Document says that
Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-class-path command line option or in your default properties file.
So instead setting configuration on python code, I added arguments on spark-submit following this document.
spark-submit {other arguments ...} \
--driver-class-path PATH/TO/JDBC/my-jdbc.jar \
--jars PATH/TO/JDBC/my-jdbc.jar \
MY_PYTHON_SCRIPT.py

Related

How to use Apache Spark to query Hive table with Kerberos?

I am attempting to use Scala with Apache Spark locally to query Hive table which is secured with Kerberos. I have no issues connecting and querying the data programmatically without Spark. However, the problem comes when I try to connect and query in Spark.
My code when run locally without spark:
Class.forName("org.apache.hive.jdbc.HiveDriver")
System.setProperty("kerberos.keytab", keytab)
System.setProperty("kerberos.principal", keytab)
System.setProperty("java.security.krb5.conf", krb5.conf)
System.setProperty("java.security.auth.login.config", jaas.conf)
val conf = new Configuration
conf.set("hadoop.security.authentication", "Kerberos")
UserGroupInformation.setConfiguration(conf)
UserGroupInformation.createProxyUser("user", UserGroupInformation.getLoginUser)
UserGroupInformation.loginUserFromKeytab(user, keytab)
UserGroupInformation.getLoginUser.checkTGTAndReloginFromKeytab()
if (UserGroupInformation.isLoginKeytabBased) {
UserGroupInformation.getLoginUser.reloginFromKeytab()
}
else if (UserGroupInformation.isLoginTicketBased) UserGroupInformation.getLoginUser.reloginFromTicketCache()
val con = DriverManager.getConnection("jdbc:hive://hdpe-hive.company.com:10000", user, password)
val ps = con.prepareStatement("select * from table limit 5").executeQuery();
Does anyone know how I could include the keytab, krb5.conf and jaas.conf into my Spark initialization function so that I am able to authenticate with Kerberos to get the TGT?
My Spark initialization function:
conf = new SparkConf().setAppName("mediumData")
.setMaster(numCores)
.set("spark.driver.host", "localhost")
.set("spark.ui.enabled","true") //enable spark UI
.set("spark.sql.shuffle.partitions",defaultPartitions)
sparkSession = SparkSession.builder.config(conf).enableHiveSupport().getOrCreate()
I do not have files such as hive-site.xml, core-site.xml.
Thank you!
Looking at your code, you need to set the following properties in the spark-submit command on the terminal.
spark-submit --master yarn \
--principal YOUR_PRINCIPAL_HERE \
--keytab YOUR_KEYTAB_HERE \
--conf spark.driver.extraJavaOptions="-Djava.security.auth.login.config=JAAS_CONF_PATH" \
--conf spark.driver.extraJavaOptions="-Djava.security.krb5.conf=KRB5_PATH" \
--conf spark.executor.extraJavaOptions="-Djava.security.auth.login.config=JAAS_CONF_PATH" \
--conf spark.executor.extraJavaOptions="-Djava.security.krb5.conf=KRB5_PATH" \
--class YOUR_MAIN_CLASS_NAME_HERE code.jar

Loading data from GCS using Spark Local

I am trying to read data from GCS buckets on my local machine, for testing purposes. I would like to sample some of the data in the cloud
I have downloaded the GCS Hadoop Connector JAR.
And setup the sparkConf as follow:
conf = SparkConf() \
.setMaster("local[8]") \
.setAppName("Test") \
.set("spark.jars", "path/gcs-connector-hadoop2-latest.jar") \
.set("spark.hadoop.google.cloud.auth.service.account.enable", "true") \
.set("spark.hadoop.google.cloud.auth.service.account.json.keyfile", "path/to/keyfile")
sc = SparkContext(conf=conf)
spark = SparkSession.builder \
.config(conf=sc.getConf()) \
.getOrCreate()
spark.read.json("gs://gcs-bucket")
I have also tried to set the conf like so:
sc._jsc.hadoopConfiguration().set("fs.AbstractFileSystem.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS")
sc._jsc.hadoopConfiguration().set("fs.gs.auth.service.account.json.keyfile", "path/to/keyfile")
sc._jsc.hadoopConfiguration().set("fs.gs.auth.service.account.enable", "true")
I am using PySpark install via PIP and running the code using the unit test module from IntelliJ
py4j.protocol.Py4JJavaError: An error occurred while calling o128.json.
: java.io.IOException: No FileSystem for scheme: gs
What should I do?
Thanks!
To solve this issue, you need to add configuration for fs.gs.impl property in addition to properties that you already configured:
sc._jsc.hadoopConfiguration().set("fs.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem")

With sbt accembly pkg, but still got Err "java.sql.SQLException: No suitable driver found for jdbc:mysql" [duplicate]

I am using
df.write.mode("append").jdbc("jdbc:mysql://ip:port/database", "table_name", properties)
to insert into a table in MySQL.
Also, I have added Class.forName("com.mysql.jdbc.Driver") in my code.
When I submit my Spark application:
spark-submit --class MY_MAIN_CLASS
--master yarn-client
--jars /path/to/mysql-connector-java-5.0.8-bin.jar
--driver-class-path /path/to/mysql-connector-java-5.0.8-bin.jar
MY_APPLICATION.jar
This yarn-client mode works for me.
But when I use yarn-cluster mode:
spark-submit --class MY_MAIN_CLASS
--master yarn-cluster
--jars /path/to/mysql-connector-java-5.0.8-bin.jar
--driver-class-path /path/to/mysql-connector-java-5.0.8-bin.jar
MY_APPLICATION.jar
It doens't work. I also tried setting "--conf":
spark-submit --class MY_MAIN_CLASS
--master yarn-cluster
--jars /path/to/mysql-connector-java-5.0.8-bin.jar
--driver-class-path /path/to/mysql-connector-java-5.0.8-bin.jar
--conf spark.executor.extraClassPath=/path/to/mysql-connector-java-5.0.8-bin.jar
MY_APPLICATION.jar
but still get the "No suitable driver found for jdbc" error.
I had to add the driver option when using the sparkSession's read function.
.option("driver", "org.postgresql.Driver")
var jdbcDF - sparkSession.read
.option("driver", "org.postgresql.Driver")
.option("url", "jdbc:postgresql://<host>:<port>/<DBName>")
.option("dbtable", "<tableName>")
.option("user", "<user>")
.option("password", "<password>")
.load()
Depending on how your dependencies are setup, you'll notice that when you include something like compile group: 'org.postgresql', name: 'postgresql', version: '42.2.8' in Gradle, for example, this will include the Driver class at org/postgresql/Driver.class, and that's the one you want to instruct spark to load.
There is 3 possible solutions,
You might want to assembly you application with your build manager (Maven,SBT) thus you'll not need to add the dependecies in your spark-submit cli.
You can use the following option in your spark-submit cli :
--jars $(echo ./lib/*.jar | tr ' ' ',')
Explanation : Supposing that you have all your jars in a lib directory in your project root, this will read all the libraries and add them to the application submit.
You can also try to configure these 2 variables : spark.driver.extraClassPath and spark.executor.extraClassPath in SPARK_HOME/conf/spark-default.conf file and specify the value of these variables as the path of the jar file. Ensure that the same path exists on worker nodes.
I tried the suggestions shown here which didn't work for me (with mysql). While debugging through the DriverManager code, I realized that I needed to register my driver since this was not happening automatically with "spark-submit". I therefore added
Driver driver = new Driver();
The constructor registers the driver with the DriverManager, which solved the SQLException problem for me.

Connect to Oracle DB using PySpark

I am trying to connect to an Oracle DB using PySpark.
spark_config = SparkConf().setMaster(config['cluster']).setAppName('sim_transactions_test').set("jars", "..\Lib\ojdbc7.jar")
sc = SparkContext(conf=spark_config)
sqlContext = SQLContext(sc)
df_sim_input = self.sqlContext.read\
.format("jdbc")\
.option("driver", "oracle.jdbc.driver.OracleDriver")\
.option("url", config["db.url"])\
.option("dbtable", query)\
.option("user", config["db.user"])\
.option("password", config["db.password"])\
.load()
This gives me a
py4j.protocol.Py4JJavaError: An error occurred while calling o31.load.
: java.lang.ClassNotFoundException: oracle.jdbc.driver.OracleDriver
So it seems it cannot find the jar file in the SparkContext. It seems to be possible to load a PySpark shell with external jars, but I want to load them from the Python code.
Can someone explain to me how you can add this external jar from Python and make a query to an Oracle DB?
Extra question, how come that for a postgres DB the code works fine without importing an external jdbc? Is that because if it is installed on your system, it will automatically find it?
You should probably also set driver-class-path as jars sends the jar file only to workers, not the driver.
That said, you should be very careful when setting JVM configuration in the python code as you need to make sure the JVM loads with them (you can't add them later). You can try setting PYSPARK_SUBMIT_ARGS e.g.:
export PYSPARK_SUBMIT_ARGS="--jars jarname --driver-class-path jarname pyspark-shell"
This will tell pyspark to add these options to the JVM loading the same as if you would have added it in the command line

How to specify multiple dependencies using --packages for spark-submit?

I have the following as the command line to start a spark streaming job.
spark-submit --class com.biz.test \
--packages \
org.apache.spark:spark-streaming-kafka_2.10:1.3.0 \
org.apache.hbase:hbase-common:1.0.0 \
org.apache.hbase:hbase-client:1.0.0 \
org.apache.hbase:hbase-server:1.0.0 \
org.json4s:json4s-jackson:3.2.11 \
./test-spark_2.10-1.0.8.jar \
>spark_log 2>&1 &
The job fails to start with the following error:
Exception in thread "main" java.lang.IllegalArgumentException: Given path is malformed: org.apache.hbase:hbase-common:1.0.0
at org.apache.spark.util.Utils$.resolveURI(Utils.scala:1665)
at org.apache.spark.deploy.SparkSubmitArguments.parse$1(SparkSubmitArguments.scala:432)
at org.apache.spark.deploy.SparkSubmitArguments.parseOpts(SparkSubmitArguments.scala:288)
at org.apache.spark.deploy.SparkSubmitArguments.<init>(SparkSubmitArguments.scala:87)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:105)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
I've tried removing the formatting and returning to a single line, but that doesn't resolve the issue. I've also tried a bunch of variations: different versions, added _2.10 to the end of the artifactId, etc.
According to the docs (spark-submit --help):
The format for the coordinates should be groupId:artifactId:version.
So what I have should be valid and should reference this package.
If it helps, I'm running Cloudera 5.4.4.
What am I doing wrong? How can I reference the hbase packages correctly?
A list of packages should be separated using commas without whitespaces (breaking lines should work just fine) for example
--packages org.apache.spark:spark-streaming-kafka_2.10:1.3.0,\
org.apache.hbase:hbase-common:1.0.0
I found it worthy to use SparkSession in spark version 3.0.0 for mysql and postgres
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('mysql-postgres').config('spark.jars.packages', 'mysql:mysql-connector-java:8.0.20,org.postgresql:postgresql:42.2.16').getOrCreate()
#Mohammad thanks for this input. This worked for me too. I had to load the Kafka and msql packages in a single sparksession. I did something like this:
spark = (SparkSession .builder ... .appName('myapp') # Add kafka and msql package .config("spark.jars.packages", "org.apache.spark:spark-sql-kafka-0-10_2.12:3.1.2,mysql:mysql-connector-java:8.0.26") .getOrCreate())

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