I am trying to write dataframe to sqlserver using spark. I am using the method write for dataframewriter to write to sql server.
Using DriverManager.getConnection I am able to get connection of sqlserver and able to write but when using jdbc method and passing uri I am getting "No suitable driver found".
I have passed the jtds jar in the --jars in spark-shell.
Spark version : 1.4
The issue is that spark is not finding driver jar file. So download jar and place in all worker nodes of spark cluster on the same path and add this path to SPARK_CLASSPATH in spark-env.sh file
as follow
SPARK_CLASSPATH=/home/mysql-connector-java-5.1.6.jar
hope it will help
Related
I'm trying to run my application using Livy that resides inside GCP Dataproc but I'm getting this: "Caused by: java.lang.ClassNotFoundException: bigquery.DefaultSource"
I'm able to run hadoop fs -ls gs://xxxx inside Dataproc and I checked if Spark is pointing to the right location in order to find gcs-connector.jar and that's ok too.
I included Livy in Dataproc using initialization (https://github.com/GoogleCloudDataproc/initialization-actions/blob/master/livy/)
How can I include bigquery-connector in Livy's classpath?
Could you help me, please?
Thank you all!
It looks like your application is depending on the BigQuery connector, not the GCS connector (bigquery.DefaultSource).
The GCS connector should always be included in the HADOOP classpath by default, but you will have to manually add the BigQuery connector jar to your application.
Assuming this is a Spark application, you can set the Spark jar property to pull in the bigquery connector jar from GCS at runtime: spark.jars='gs://spark-lib/bigquery/spark-bigquery-latest_2.12.jar'
For more installation options, see https://github.com/GoogleCloudDataproc/spark-bigquery-connector/blob/master/README.md
Is there a way, we can execute Spark code(package in jar) from Nifi, using Livy?
I can see in Nifi that using ExecuteSparkInteractive, we can submit custom code which can be run in spark cluster using livy. but what i want is , pass name of the jar file , main class in Nifi that connects Spark via Livy.
I see below article on this, but seems option like Session JARs is not available in plain Nifi installation.
https://community.cloudera.com/t5/Community-Articles/Apache-Livy-Apache-NiFi-Apache-Spark-Executing-Scala-Classes/ta-p/247985
I want to read data from cassandra node in my client node on :
This is what i tried :
spark-shell --jars /my-dir/spark-cassandra-connector_2.11-2.3.2.jar.
val df = spark.read.format("org.apache.spark.sql.cassandra")\
.option("keyspace","my_keyspace")\
.option("table","my_table")\
.option("spark.cassandra.connection.host","Hostname of my Cassandra node")\
.option("spark.cassandra.connection.port","9042")\
.option("spark.cassandra.auth.password","mypassword)\
.option("spark.cassandra.auth.username","myusername")\
.load
I'm getting this error: java.lang.NoClassDefFoundError: Could not initialize class org.apache.spark.sql.cassandra.DefaultSource$
and
java.lang.NoClassDefFoundError: org/apache/commons/configuration/ConfigurationException.
Am I missing any properties? What this error is for ? How would I resolve this ?
Spark-version:2.3.2, DSE version 6.7.8
The Spark Cassandra Connector itself depends on the number of other dependencies, that could be missing here - this happens because you're providing only one jar, and not all required dependencies.
Basically, in your case you need to have following choice:
If you're running this on the DSE node, then you can use built-in Spark, if the cluster has Analytics enabled - in this case, all jars and properties are already provided, and you only need to provide username and password when starting spark shell via dse -u user -p password spark
if you're using external Spark, then it's better to use so-called BYOS (bring your own spark) - special version of the Spark Cassandra Connector with all dependencies bundled inside, and you can download jar from DataStax's Maven repo, and use with --jars
you can still use open source Spark Cassandra Connector, but in this case, it's better to use --packages com.datastax.spark:spark-cassandra-connector_2.11:2.3.2 so Spark will able to fetch all dependencies automatically.
P.S. In case of open source Spark Cassandra Connector I would recommend to use version 2.5.1 or higher, although it requires Spark 2.4.x (although 2.3.x may work) - this version has improved support for DSE, plus a lot of the new functionality not available in the earlier versions. Plus for that version there is a version that includes all required dependencies (so-called assembly) that you can use with --jars if your machine doesn't have access to the internet.
I am trying to get access to HDFS files in Spark. Everything works fine when I run Spark in local mode, i.e.
SparkSession.master("local")
and get access to HDFS files by
hdfs://localhost:9000/$FILE_PATH
But when I am trying to run Spark in standalone cluster mode, i.e.
SparkSession.master("spark://$SPARK_MASTER_HOST:7077")
Error throws
java.lang.ClassCastException: cannot assign instance of java.lang.invoke.SerializedLambda to field org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction$1.fun$1 of type org.apache.spark.api.java.function.Function in instance of org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction$1
So far I have only
start-dfs.sh
in Hadoop and does not really config anything in Spark. Do I need to run Spark using YARN cluster manager instead so that Spark and Hadoop are using the same cluster manager, hence can get access to HDFS files?
I have tried to config yarn-site.xml in Hadoop following tutorialspoint https://www.tutorialspoint.com/hadoop/hadoop_enviornment_setup.htm, and specified HADOOP_CONF_DIR in spark-env.sh, but it does not seem to work and the same error throws. Am I missing some other configurations?
Thanks!
EDIT
The initial Hadoop version is 2.8.0 and the Spark version is 2.1.1 with Hadoop 2.7. Tried to download hadoop-2.7.4 but the same error still exists.
The question here suggests this as a java syntax issue rather than spark hdfs issue. I will try this approach and see if this solves the error here.
Inspired by the post here, solved the problem by myself.
This map-reduce job depends on a Serializable class, so when running in Spark local mode, this serializable class can be found and the map-reduce job can be executed dependently.
When running in Spark standalone cluster mode, the best is to submit the application through spark-submit, rather than running in an IDE. Packaged everything in jar and spark-submit the jar, works as a charm!
I am trying to deploy the Spark application to 4 node DSE spark cluster, and I have created a fat jar with all dependent Jars and I have created a property file under src/main/resources which has properties like batch interval master URL etc.
I have copied this fat jar to master and I am submitting the application with "spark-submit" and below is my submit command.
dse spark-submit --class com.Processor.utils.jobLauncher --supervise application-1.0.0-develop-SNAPSHOT.jar qa
everything works properly when I run on single node cluster, but if run on DSE spark standalone cluster, the properties mentioned above like batch interval become unavailable to executors. I have googled and found that is the common issue many has solved it. so I have followed one of the solutions and created a fat jar and tried to run, but still, my properties are unavailable to executors.
can someone please give any pointers on how to solve the issue ?
I am using DSE 4.8.5 and Spark 1.4.2
and this is how I am loading the properties
System.setProperty("env",args(0))
val conf = com.typesafe.config.ConfigFactory.load(System.getProperty("env") + "_application")
figured out the solution:
I am referring the property file name from system property(i am setting it main method with the command line parameter) and when the code gets shipped and executed on worker node the system property is not available (obviously..!!) , so instead of using typesafe ConfigFactory to load property file I am using simple Scala file reading.