I am very new to this spark python world so I have another question. It's good to know I can write spark commands in spark shell or python code, so:
pyspark --packages com.datastax.spark:spark-cassandra-connector_2.11:2.5.1\
--files path_to/secure-connect-test.zip \
--conf spark.cassandra.connection.config.cloud.path=secure-connect-test.zip \
--conf spark.cassandra.auth.username=UserName \
--conf spark.cassandra.auth.password=Password \
--conf spark.dse.continuousPagingEnabled=false
This part of code, if I want to write it inside python code, do I have to add it with os.environ command? I have seen some posts with that command but they add a variable. Thanks
Related
I am executing a spark job, via spark-submit using a wrapper script and a parameter file. The parameter file contains some config information in addition to a set of set of command line arguments needed by the spark job.
The parameter file, params.sh, look like this:
MODE="query"
QUERYCOND="Id==1 and customer==2"
PACKAGE="com.etl"
CLASS="ExecSparkETL"
DRIVER_CLASS_PATH="/home/jars/ojdbc8.jar"
JARS="/home/jars/ojdbc8.jar"
JAR="/home/jars/etl-scala_2.11-1.0.0.jar"
ARGS="$MODE \"$CUSTOM\""
The wrapper script, exec.sh, contains this:
source params.sh
spark2-submit \
--class ${PACKAGE}.${CLASS} \
--queue root.me \
--deploy-mode client \
--master yarn \
--driver-class-path $DRIVER_CLASS_PATH \
--jars $JARS \
--conf spark.network.timeout=1200000 \
$JAR \
$ARGS
If I echo $ARGS from the script the result is what I should be passing to the scala jar as two args, one of them having spaces in the arg which is wrapped in double-quotes:
query "Id==1 and customer==2"
But when I execute and print those two args from within the scala program I get:
arg1: query
arg2: "Id==1
So it is somehow not parsing my double-quote wrapped argument correctly.
Now if I execute the program without assigning the program paramters to the script variable ARGS, it works as expected:
source params.sh
spark2-submit \
--class ${PACKAGE}.${CLASS} \
--queue root.me \
--deploy-mode client \
--master yarn \
--driver-class-path $DRIVER_CLASS_PATH \
--jars $JARS \
--conf spark.network.timeout=1200000 \
$JAR \
query "Id==1 and customer==2"
Results...
arg1: query
arg2: "Id==1 and customer==2"
So it doesn't seem to like the fact that the two parameters passed to the scala program are stored in a variable, like it is dropping the double-quote wrap from the parameter that contains spaces.
Any ideas on how I can fix that?
I'm trying to put a command in a variable, but the complex cases always fail! -- the solution is to use an array:
source params.sh
spark_args=(
--class "${PACKAGE}.${CLASS}"
--queue root.me
--deploy-mode client
--master yarn
--driver-class-path "$DRIVER_CLASS_PATH"
--jars "$JARS"
--conf spark.network.timeout=1200000
"$JAR"
"$MODE"
"$CUSTOM"
)
spark2-submit "${spark_args[#]}"
Note how all the variables are quoted.
Arrays are available in bash/zsh/ksh but not plain sh.
I am trying to run a PySpark Job using Dataproc. The only different thing comparing to all the examples out there is that I want to submit the job from .egg instead of .py file.
In order to submit the PySpark job in a regular commodity cluster would be something like:
spark2-submit --master yarn \
--driver-memory 20g \
--deploy-mode client \
--conf parquet.compression=SNAPPY \
--jars spark-avro_2.11-3.2.0.jar \
--py-files dummyproject-1_spark-py2.7.egg \
dummyproject-1_spark-py2.7.egg#__main__.py "param1" "param2"
Now, I want to submit exactly the same job but using Dataproc.
In order to accomplish this I am using the following command:
gcloud dataproc jobs submit pyspark \
file:///dummyproject-1_spark-py2.7.egg#__main__.py \
--cluster=my-cluster-001 \
--py-files=file:///dummyproject-1_spark-py2.7.egg
The error I am getting is:
Error: Cannot load main class from JAR
file:/dummyproject-1_spark-py2.7.egg
It is important to mention that when I try to run a simple PySpark job using .py file, it is working correctly.
Can somebody tell me, how can I run a PySpark job from .egg file instead of .py file?
It looks like there is a bug on how gcloud dataproc is parsing the arguments and making Spark try to execute your file like a Java JAR file. A workaround is copy your __main__.py file outside of your egg file and execute it independently like this.
gcloud dataproc jobs submit pyspark \
--cluster=my-cluster-001 \
--py-files=file:///dummyproject-1_spark-py2.7.egg \
file:///__main__.py \
The following spark-submit script works:
nohup ./bin/spark-submit --jars ./ikoda/extrajars/ikoda_assembled_ml_nlp.jar,./ikoda/extrajars/stanford-corenlp-3.8.0.jar,./ikoda/extrajars/stanford-parser-3.8.0.jar \
--packages datastax:spark-cassandra-connector:2.0.1-s_2.11 \
--class ikoda.mlserver.Application \
--conf spark.cassandra.connection.host=192.168.0.33 \
--master local[*] ./ikoda/ikodaanalysis-mlserver-0.1.0.jar 1000 > ./logs/nohup.out &
Programatically, I can do the same by configuring SparkContext:
val conf = new SparkConf().setMaster("local[4]").setAppName("MLPCURLModelGenerationDataStream")
conf.set("spark.streaming.stopGracefullyOnShutdown", "true")
conf.set("spark.cassandra.connection.host", sparkcassandraconnectionhost)
conf.set("spark.driver.maxResultSize", sparkdrivermaxResultSize)
conf.set("spark.network.timeout", sparknetworktimeout)
Question
Can I add --packages datastax:spark-cassandra-connector:2.0.1-s_2.11 programatically? If yes, how?
The corresponding option is spark.jars.packages
conf.set(
"spark.jars.packages",
"datastax:spark-cassandra-connector:2.0.1-s_2.11")
After using Spark 1.2 for quite a long time, I have realised that you can no longer pass spark configuration to the driver via the --conf via command line.
I am thinking about using system properties and picking the config up using the following bit of code:
def getConfigOption(conf: SparkConf, name: String)
conf getOption name orElse sys.props.get(name)
How do i pass a config.file option and string version of the date specified as a start time to a spark-submit command?
I have attempted using the following in my start up shell script:
--conf "spark.executor.extraJavaOptions=-Dconfig.file=../conf/application.conf -DstartTime=2016-06-04 00:00:00"
but this fails at it space splits the command up.
Any idea how to do this successfully, or has anyone got any advice on this one?
I am EDITing this to show the bash script being used:
#!/bin/bash
export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-/etc/hadoop/conf}
LIB_DIRECTORY=/opt/app/latest/lib/
ANALYSIS_JAR=spark-fa-2.16.18-standalone.jar
ANALYSIS_DRIVER_CLASS=com.spark.fa.Main
OTHER_OPTIONS=""
KEYTAB="/opt/app/keytab/fa.keytab"
PRINCIPAL="spark_K"
CLUSTER_OPTIONS=" \
--master yarn-client \
--driver-memory 2000M \
--executor-memory 5G \
--num-executors 39 \
--executor-cores 5 \
--conf spark.default.parallelism=200 \
--driver-java-options=-Dconfig.file=../conf/application.conf \
--conf "spark.executor.extraJavaOptions=-DstartTime='2016-06-04 00:00:00'" \
--conf spark.storage.memoryFraction=0.9 \
--files /opt/app/latest/conf/application.conf \
--conf spark.storage.safetyFraction=0.9 \
--keytab ${KEYTAB} \
--principal ${PRINCIPAL} \
"
spark-submit --class ${ANALYSIS_DRIVER_CLASS} ${CLUSTER_OPTIONS} ${LIB_DIRECTORY}/${ANALYSIS_JAR} ${CONFIG} ${#}
Use quotes:
--conf "spark.executor.extraJavaOptions=-Dconfig.file=../conf/application.conf -DstartTime='2016-06-04 00:00:00'"
If your parameter contains both spaces and single quotes (for instance a query paramter) you should enclose it with in escaped double quote \"
Example:
spark-submit --master yarn --deploy-mode cluster --conf "spark.driver.extraJavaOptions=-DfileFormat=PARQUET -Dquery=\"select * from bucket where code in ('A')\" -Dchunk=yes" spark-app.jar
I want to change Typesafe config of a Spark job in dev/prod environment. It seems to me that the easiest way to accomplish this is to pass -Dconfig.resource=ENVNAME to the job. Then Typesafe config library will do the job for me.
Is there way to pass that option directly to the job? Or maybe there is better way to change job config at runtime?
EDIT:
Nothing happens when I add --conf "spark.executor.extraJavaOptions=-Dconfig.resource=dev" option to spark-submit command.
I got Error: Unrecognized option '-Dconfig.resource=dev'. when I pass -Dconfig.resource=dev to spark-submit command.
Change spark-submit command line adding three options:
--files <location_to_your_app.conf>
--conf 'spark.executor.extraJavaOptions=-Dconfig.resource=app'
--conf 'spark.driver.extraJavaOptions=-Dconfig.resource=app'
Here is my spark program run with addition java option
/home/spark/spark-1.6.1-bin-hadoop2.6/bin/spark-submit \
--files /home/spark/jobs/fact_stats_ad.conf \
--conf spark.executor.extraJavaOptions=-Dconfig.fuction.conf \
--conf 'spark.driver.extraJavaOptions=-Dalluxio.user.file.writetype.default=CACHE_THROUGH -Dalluxio.user.file.write.location.policy.class=alluxio.client.file.policy.MostAvailableFirstPolicy -Dconfig.file=/home/spark/jobs/fact_stats_ad.conf' \
--class jobs.DiskDailyJob \
--packages com.databricks:spark-csv_2.10:1.4.0 \
--jars /home/spark/jobs/alluxio-core-client-1.2.0-RC2-jar-with-dependencies.jar \
--driver-memory 2g \
/home/spark/jobs/convert_to_parquet.jar \
AD_COOKIE_REPORT FACT_AD_STATS_DAILY | tee /data/fact_ad_stats_daily.log
as you can see
the custom config file
--files /home/spark/jobs/fact_stats_ad.conf
the executor java options
--conf spark.executor.extraJavaOptions=-Dconfig.fuction.conf
the driver java options.
--conf 'spark.driver.extraJavaOptions=-Dalluxio.user.file.writetype.default=CACHE_THROUGH -Dalluxio.user.file.write.location.policy.class=alluxio.client.file.policy.MostAvailableFirstPolicy -Dconfig.file=/home/spark/jobs/fact_stats_ad.conf'
Hope it can helps.
I Had a lot of problems with passing -D parameters to spark executors and the driver, I've added a quote from my blog post about it:
"
The right way to pass the parameter is through the property:
“spark.driver.extraJavaOptions” and “spark.executor.extraJavaOptions”:
I’ve passed both the log4J configurations property and the parameter that I needed for the configurations. (To the Driver I was able to pass only the log4j configuration).
For example (was written in properties file passed in spark-submit with “—properties-file”):
“
spark.driver.extraJavaOptions –Dlog4j.configuration=file:///spark/conf/log4j.properties -
spark.executor.extraJavaOptions –Dlog4j.configuration=file:///spark/conf/log4j.properties -Dapplication.properties.file=hdfs:///some/path/on/hdfs/app.properties
spark.application.properties.file hdfs:///some/path/on/hdfs/app.properties
“
You can read my blog post about overall configurations of spark.
I'm am running on Yarn as well.
--files <location_to_your_app.conf>
--conf 'spark.executor.extraJavaOptions=-Dconfig.resource=app'
--conf 'spark.driver.extraJavaOptions=-Dconfig.resource=app'
if you write in this way, the later --conf will overwrite the previous one, you can verify this by looking at sparkUI after job started under Environment tab.
so the correct way is to put the options under same line like this:
--conf 'spark.executor.extraJavaOptions=-Da=b -Dc=d'
if you do this, you can find all your settings will be shown under sparkUI.
I am starting my Spark application via a spark-submit command launched from within another Scala application. So I have an Array like
Array(".../spark-submit", ..., "--conf", confValues, ...)
where confValues is:
for yarn-cluster mode:
"spark.driver.extraJavaOptions=-Drun.mode=production -Dapp.param=..."
for local[*] mode:
"run.mode=development"
It is a bit tricky to understand where (not) to escape quotes and spaces, though. You can check the Spark web interface for system property values.
spark-submit --driver-java-options "-Denv=DEV -Dmode=local" --class co.xxx.datapipeline.jobs.EventlogAggregator target/datapipeline-jobs-1.0-SNAPSHOT.jar
The above command works for me:
-Denv=DEV => to read DEV env properties file, and
-Dmode=local => to create SparkContext in local - .setMaster("local[*]")
Use the method like in below command, may be helpful for you -
spark-submit --master local[2] --conf
'spark.driver.extraJavaOptions=Dlog4j.configuration=file:/tmp/log4j.properties'
--conf 'spark.executor.extraJavaOptions=-Dlog4j.configuration=file:/tmp/log4j.properties'
--class com.test.spark.application.TestSparkJob target/application-0.0.1-SNAPSHOT-jar-with-dependencies.jar prod
I have tried and it worked for me, I would suggest also go through heading below spark post which is really helpful -
https://spark.apache.org/docs/latest/running-on-yarn.html
I originally had this config file:
my-app {
environment: dev
other: xxx
}
This is how I'm loading my config in my spark scala code:
val config = ConfigFactory.parseFile(File<"my-app.conf">)
.withFallback(ConfigFactory.load())
.resolve
.getConfig("my-app")
With this setup, despite what the Typesafe Config documentation and all the other answers say, the system property override didn't work for me when I launched my spark job like so:
spark-submit \
--master yarn \
--deploy-mode cluster \
--name my-app \
--driver-java-options='-XX:MaxPermSize=256M -Dmy-app.environment=prod' \
--files my-app.conf \
my-app.jar
To get it to work I had to change my config file to:
my-app {
environment: dev
environment: ${?env.override}
other: xxx
}
and then launch it like so:
spark-submit \
--master yarn \
--deploy-mode cluster \
--name my-app \
--driver-java-options='-XX:MaxPermSize=256M -Denv.override=prod' \
--files my-app.conf \
my-app.jar