Given below is the version of Spark & Hive I have installed in my system
Spark : spark-1.4.0-bin-hadoop2.6
Hive : apache-hive-1.0.0-bin
I have configured the Hive installation to use MySQL as Metastore. The goal is to access the MySQL Metastore & execute HiveQL queries inside spark-shell(using HiveContext)
So far I am able to execute the HiveQL queries by accessing the Derby Metastore(As described here, believe Spark-1.4 comes bundled with Hive 0.13.1 which in turn uses the internal Derby database as Metastore)
Then I tried to point spark-shell to my external Metastore(MySQL in this case) by setting the property(as suggested here) given below in $SPARK_HOME/conf/spark-defaults.conf,
spark.sql.hive.metastore.jars /home/mountain/hv/lib:/home/mountain/hp/lib
I have also copied $HIVE_HOME/conf/hive-site.xml into $SPARK_HOME/conf. But I am getting the following exception when I start the spark-shell
mountain#mountain:~/del$ spark-shell
Spark context available as sc.
java.lang.ClassNotFoundException: java.lang.NoClassDefFoundError:
org/apache/hadoop/hive/ql/session/SessionState when creating Hive client
using classpath: file:/home/mountain/hv/lib/, file:/home/mountain/hp/lib/
Please make sure that jars for your version of hive and hadoop are
included in the paths passed to spark.sql.hive.metastore.jars.
Am I missing something (or) not setting the property spark.sql.hive.metastore.jars correctly?
Note: In Linux Mint verified.
If you are setting properties in spark-defaults.conf, spark will take those settings only when you submit your job using spark-submit.
file: spark-defaults.conf
spark.driver.extraJavaOptions -Dlog4j.configuration=file:log4j.properties -Dspark.yarn.app.container.log.dir=app-logs -Dlogfile.name=hello-spark
spark.jars.packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1,org.apache.spark:spark-avro_2.12:3.0.1
In the terminal run your job say wordcount.py
spark-submit /path-to-file/wordcount.py
If you want to run your job in development mode from an IDE then you should use config() method. Here we will set Kafka jar packages
spark = SparkSession.builder \
.appName('Hello Spark') \
.master('local[3]') \
.config("spark.streaming.stopGracefullyOnShutdown", "true") \
.config("spark.jars.packages", "org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1") \
.getOrCreate()
Corrupted version of hive-site.xml will cause this... please copy the correct hive-site.xml
Related
I have been trying to read/write with synapse spark pools into a mongodb atlas server, i have tried PyMongo but im more interested in using the mongodb spark connector but in the install procedure they use this command:
./bin/pyspark --conf "spark.mongodb.input.uri=mongodb://127.0.0.1/test.myCollection?readPreference=primaryPreferred" \
--conf "spark.mongodb.output.uri=mongodb://127.0.0.1/test.myCollection" \
--packages org.mongodb.spark:mongo-spark-connector_2.12:3.0.1
The problem im facing is that Synapse Spark Pools allow for spark session configuration but not for packacges command or use of the spark shell, how can i acomplish this instalation inside a spark pool?
This can be solved by installing the jar directly under workspace packages. Download the jar of the connector and then upload it to synapse, then to the spark pool.
I'm currently running PySpark via local mode. I want to be able to efficiently output parquet files to S3 via the S3 Directory Committer. This PySpark instance is using the local disk, not HDFS, as it is being submitted via spark-submit --master local[*].
I can successfully write to my S3 Instance without enabling the directory committer. However, this involves writing staging files to S3 and renaming them, which is slow and unreliable. I would like for Spark to write to my local filesystem as a temporary store, and then copy to S3.
I have the following configuration in my PySpark conf:
self.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
self.spark.conf.set("spark.hadoop.fs.s3a.committer.name", "directory")
self.spark.conf.set("spark.sql.sources.commitProtocolClass", "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol")
self.spark.conf.set("spark.sql.parquet.output.committer.class", "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter")
My spark-submit command looks like this:
spark-submit --master local[*] --py-files files.zip --packages com.amazonaws:aws-java-sdk:1.11.375,org.apache.hadoop:hadoop-aws:3.2.0,org.apache.spark.internal.io.cloud.PathOutputCommitProtocol --driver-memory 4G --name clean-raw-recording_data main.py
spark-submit gives me the following error, due to the requisite JAR not being in place:
java.lang.ClassNotFoundException: org.apache.spark.internal.io.cloud.PathOutputCommitProtocol
My questions are:
Which JAR (specifically, the maven coordinates) do I need to include in spark-submit --packages in order to be able to reference PathOutputCommitProtocol?
Once I have (1) working, will I be able to use PySpark's local mode to stage temporary files on the local filesystem? Or is HDFS a strict requirement?
I need this to be running in local mode, not cluster mode.
EDIT:
I got this to work with the following configuration:
Using pyspark version 3.1.2 and the package
org.apache.spark:spark-hadoop-cloud_2.12:3.1.1.3.1.7270.0-253.
I needed to add the cloudera repository using the --repositories option for spark-submit:
spark-submit --repositories https://repository.cloudera.com/artifactory/cloudera-repos/ --packages com.amazonaws:aws-java-sdk:1.11.375,org.apache.hadoop:hadoop-aws:3.2.0,org.apache.spark:spark-hadoop-cloud_2.12:3.1.1.3.1.7270.0-253
you need the spark-hadoop-cloud module for the release of spark you are using
the committer is happy using the local fs (it's now the public integration test suites work https://github.com/hortonworks-spark/cloud-integration. all that's needed is a "real" filesystem shared across all workers and the spark driver, so the driver gets the manifests of each pending commit.
print the _SUCCESS file after a job to see what the committer did: 0 byte file == old committer, JSON with diagnostics == new one
BACKGROUND
I am trying to run a spark-submit command that streams from Kafka and performs a JDBC sink into a postgres DB in AWS EMR (version 5.23.0) and using scala (version 2.11.12). The errors I see are
INFO TaskSetManager: Lost task 0.3 in stage 0.0 (TID 6) on <master-public-dns-name>, executor 1: java.sql.SQLException (No suitable driver found for jdbc:postgres://...
ERROR WriteToDataSourceV2Exec: Data source writer org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter#44dd5258 is aborting.
19/06/20 06:11:26 ERROR WriteToDataSourceV2Exec: Data source writer org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter#44dd5258 aborted.
HYPOTHESIS PROBLEM
I think the error is telling me that the jdbc postgres driver cannot be found on the executors, which is why it cannot sink to postgres.
PREVIOUS ATTEMPTS
I have already done the following:
Identified my driver in my structured streaming job as Class.forName("org.postgresql.Driver")
added --jars postgresql-42.1.4.jar \ to my spark-submit job in order to send the jars to the driver and executors. In this attempt, this postgres driver jar exists in my local /home/user_name/ directory
Also tried --jars /usr/lib/spark/jars/postgresql-42.1.4.jar \ to my spark-submit job, which is the location that spark in emr finds all the jars for execution
started my spark-submit job with spark-submit --driver-class-path /usr/lib/spark/jars/postgresql-42.1.4.jar:....
added the /usr/lib/spark/jars/postgresql-42.1.4.jar to the spark.driver.extraClassPath, spark.executor.extraClassPath, spark.yarn.dist.jars, spark.driver.extraLibraryPath, spark.yarn.secondary.jars, java.library.path, and to the System Classpath in general
My jdbc connection, while working in Zeppelin, does not work in spark-submit. It is jdbc:postgres://master-public-dns-name:5432/DBNAME"
EXPECTED RESULT:
I expect my executors to recognize the postgres driver and sink the data to the postgres DB.
PREVIOUS ATTEMPTS:
I've already used the following suggestions to no avail:
Adding JDBC driver to Spark on EMR
No Suitable Driver found Postgres JDBC
No suitable driver found for jdbc:postgresql://192.168.1.8:5432/NexentaSearch
use -- packages org.postgresql:postgresql:<VERSION>
I have existing EMR cluster running and wish to create DF from Postgresql DB source.
To do this, it seems you need to modify the spark-defaults.conf with the updated spark.driver.extraClassPath and point to the relevant PostgreSQL JAR that has been already downloaded on master & slave nodes, or you can add these as arguments to a spark-submit job.
Since I want to use existing Jupyter notebook to wrangle the data, and not really looking to relaunch cluster, what is the most efficient way to resolve this?
I tried the following:
Create new directory (/usr/lib/postgresql/ on master and slaves and copied PostgreSQL jar to it. (postgresql-9.41207.jre6.jar)
Edited spark-default.conf to include wildcard location
spark.driver.extraClassPath :/usr/lib/postgresql/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/$
Tried to create dataframe in Jupyter cell using the following code:
SQL_CONN = "jdbc:postgresql://some_postgresql_db:5432/dbname?user=user&password=password"
spark.read.jdbc(SQL_CONN, table="someTable", properties={"driver":'com.postgresql.jdbc.Driver'})
I get a Java error as per below:
Py4JJavaError: An error occurred while calling o396.jdbc.
: java.lang.ClassNotFoundException: com.postgresql.jdbc.Driver
Help appreciated.
I think you don't need to copy postgres jar in slaves as the driver programme and cluster manager take care everything. I've created dataframe from Postgres external source by the following way:
Download postgres driver jar:
cd $HOME && wget https://jdbc.postgresql.org/download/postgresql-42.2.5.jar
Create dataframe:
atrribute = {'url' : 'jdbc:postgresql://{host}:{port}/{db}?user={user}&password={password}' \
.format(host=<host>, port=<port>, db=<db>, user=<user>, password=<password>),
'database' : <db>,
'dbtable' : <select * from table>}
df=spark.read.format('jdbc').options(**attribute).load()
Submit to spark job:
Add the the downloaded jar to driver class path while submitting the spark job.
--properties spark.driver.extraClassPath=$HOME/postgresql-42.2.5.jar,spark.jars.packages=org.postgresql:postgresql:42.2.5
Check the github repo of the Driver. The class path seems to be something like this org.postgresql.Driver. Try using the same.
EDIT: See the edit at the end
First of all, I am using Spark 1.5.2 on Amazon EMR and using Amazon RDS for my postgres database. Second is that I am a complete newbie in this world of Spark and Hadoop and MapReduce.
Essentially my problem is the same as for this guy:
java.sql.SQLException: No suitable driver found when loading DataFrame into Spark SQL
So the dataframe is loaded, but when I try to evaluate it (doing df.show(), where df is the dataframe) gives me the error:
java.sql.SQLException: No suitable driver found for jdbc:postgresql://mypostgres.cvglvlp29krt.eu-west-1.rds.amazonaws.com:5432/mydb
I should note that I start spark like this:
spark-shell --driver-class-path /home/hadoop/postgresql-9.4.1207.jre7.jar
The solutions suggest delivering the jar onto the worker nodes and setting the classpath on them somehow, which I don't really understand how to do. But then they say that apparently the issue was fixed in Spark 1.4, and I'm using 1.5.2, and still having this issue, so what is going on?
EDIT: Looks like I resolved the issue, however I still don't quite understand why this works and the thing above doesn't, so I guess my questions is now why does doing this:
spark-shell --driver-class-path /home/hadoop/postgresql-9.4.1207.jre7.jar --conf spark.driver.extraClassPath=/home/hadoop/postgresql-9.4.1207.jre7.jar --jars /home/hadoop/postgresql-9.4.1207.jre7.jar
solve the problem? I just added the path as a parameter into some more of the flags it seems.
spark-shell --driver-class-path .... --jars ... works because all jar files listed in --jars are automatically distributed over the cluster.
Alternatively you could use
spark-shell --packages org.postgresql:postgresql:9.4.1207.jre7
and specify driver class as an option for DataFrameReader / DataFrameWriter
val df = sqlContext.read.format("jdbc").options(Map(
"url" -> url, "dbtable" -> table, "driver" -> "org.postgresql.Driver"
)).load()
or even manually copy required jars to the workers and place these somewhere on the CLASSPATH.