I'm trying to set spark.local.dir from spark-shell using sc.getconf.set("spark.local.dir","/temp/spark"), But it is not working. Is there any other way to set this property from sparkshell.
You can't do it from inside the shell - since the Spark context was already created, so the local dir was already set (and used). You should pass it as parameter when starting the shell:
./spark-shell --conf spark.local.dir=/temp/spark
#Tzach Zohar solution seems to be the right answer.
However, if you insist to set spark.local.dir from spark-shell you can do it:
1) close the current spark context
sc.stop()
2) updated the sc configuration, and restart it.
The updated code was kindly provided by #Tzach-Zohar:
SparkSession.builder.config(sc.getConf).config("spark.local.dir","/temp/spark").getOrCreate())
#Tzach Zohar note: "but you get a WARN SparkContext: Use an existing SparkContext, some configuration may not take effect, which suggests this isn't the recommended path to take.
Related
What is the difference between Spark-submit "--master" defined in the CLI and spark application code, defining the master?
In Spark we can specify the master URI in either the application code like below:
Or we can specify the master URI in the spark-submit as an argument to a parameter, like below:
Does one take precendence over the other? Do they have to agree contractually, so I have two instances of the same URI referenced in the program spark-submit and the spark application code, creating the SparkSession? Will one override the other? What will the SparkSession do differently with the master argument, and what will the spark-submit master parameter do differently?
Any help would be greatly appreciated. Thank you!
To quote the official documentation
The spark-submit script can load default Spark configuration values from a properties file and pass them on to your application. By default, it will read options from conf/spark-defaults.conf in the Spark directory. For more detail, see the section on loading default configurations.
Loading default Spark configurations this way can obviate the need for certain flags to spark-submit. For instance, if the spark.master property is set, you can safely omit the --master flag from spark-submit. In general, configuration values explicitly set on a SparkConf take the highest precedence, then flags passed to spark-submit, then values in the defaults file.
If you are ever unclear where configuration options are coming from, you can print out fine-grained debugging information by running spark-submit with the --verbose option.
So all are valid options, and there is a well defined hierarchy which defines precedence if the same option is set in multiple place. From highest to lowest:
Explicit settings in the application.
Commandline arguments.
Options from the configuration files.
From the Spark documentation:
In general,
configuration values explicitly set on a SparkConf take the highest precedence,
then flags passed to spark-submit,
then values in the defaults file.
It strikes me the most flexible approach is flags passed to spark-submit.
In pyspark I'm able to change to a fair scheduler within zeppelin (on AWS EMR) by doing the following:
conf = sc.getConf()
conf.set('spark.scheduler.allocation.file',
'/etc/spark/conf.dist/fairscheduler.xml.template')
sc.setLocalProperty("spark.scheduler.pool", 'production')
However if I try something similar in a scala cell it then things continue to run in the FIFO pool
val conf = sc.getConf()
conf.set("spark.scheduler.allocation.file",
"/etc/spark/conf.dist/fairscheduler.xml.template")
sc.setLocalProperty("spark.scheduler.pool", "FAIR")
I've tried so many combinations, but nothing has worked. Any advice is appreciated.
I ran into a similar issue with Spark 2.4. In my case, the problem was resolved by removing the default "spark.scheduler.pool" option in my Spark config. It might be that your Scala Spark interpreter is set up with spark.scheduler.pool but your python isn't.
I traced the issue to a bug in Spark - https://issues.apache.org/jira/browse/SPARK-26988. The problem is that if you set the config property "spark.scheduler.pool" in the base configuration, you can't then override it using setLocalProperty. Removing it from the base configuration made it work correctly. See the bug description for more detail.
When I do a spark-submit, the defaults conf set up in the SPARK_HOME directory is found and loaded into the System properties.
I want to stop the defaults conf from being loaded, and just get the command line arguments, so that I may re-order how spark is configured before creating my spark context.
Is this possible?
There are a couple ways to modify configurations.
According to the spark docs, you can modify configs at runtime with flags (http://spark.apache.org/docs/latest/configuration.html):
The Spark shell and spark-submit tool support two ways to load
configurations dynamically. The first are command line options, such
as --master, as shown above. spark-submit can accept any Spark
property using the --conf flag... Any values specified as flags or in the properties file will be passed on to the application and merged with those specified through SparkConf.
which means you can kick off your jobs like this:
./bin/spark-submit --conf spark.eventLog.enabled=false --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar
OR, you can go edit the spark-defaults.conf and not have to pass additional flags in your spark-submit command.
Here's a solution I found acceptable for my issue:
Create a blank "blank.conf" file, and supply it to spark using --properties
${SPARK_HOME}/bin/spark-submit --master local --properties-file "blank.conf" # etc
Spark will use the conf in its configuration instead of finding the defaults conf. You can then manually load up the defaults conf later, before creating your SparkContext, if that's your desire.
I'm trying to understand the importance of setting the master property when running a spark application.
The cluster location is at the default port of 7077. I'm running this app from a testmachine where it will hit an s3 bucket.
Currently spark configuration in the app reads:
val sparkConf = new SparkConf()
.setMaster("spark://127.0.0.1:7077")
but I'm also setting the flag on the command line with spark submit:
--master spark://127.0.0.1:7077
So, does having both of these set cause problems? Does one get overridden by the other? Are they both necessary?
So, does having both of these set cause problems? Does one get
overridden by the other? Are they both necessary?
The Spark Configuration page is very clear (emphasis mine):
Any values specified as flags or in the properties file will be passed
on to the application and merged with those specified through
SparkConf. Properties set directly on the SparkConf take highest
precedence, then flags passed to spark-submit or spark-shell, then
options in the spark-defaults.conf file. A few configuration keys have
been renamed since earlier versions of Spark; in such cases, the older
key names are still accepted, but take lower precedence than any
instance of the newer key.
I am new to spark and we are running spark on yarn. I can run my test applications just fine. I am trying to collect the spark metrics in Graphite. I know what changes to make to metrics.properties file. But how will my spark application see this conf file?
/xxx/spark/spark-0.9.0-incubating-bin-hadoop2/bin/spark-class org.apache.spark.deploy.yarn.Client --jar /xxx/spark/spark-0.9.0-incubating-bin-hadoop2/examples/target/scala-2.10/spark-examples_2.10-assembly-0.9.0-incubating.jar --addJars "hdfs://host:port/spark/lib/spark-assembly_2.10-0.9.0-incubating-hadoop2.2.0.jar" --class org.apache.spark.examples.Test --args yarn-standalone --num-workers 50 --master-memory 1024m --worker-memory 1024m --args "xx"
Where should I be specifying the metrics.properties file?
I made these changes to it:
*.sink.Graphite.class=org.apache.spark.metrics.sink.GraphiteSink
*.sink.Graphite.host=machine.domain.com
*.sink.Graphite.port=2003
master.source.jvm.class=org.apache.spark.metrics.source.JvmSource
worker.source.jvm.class=org.apache.spark.metrics.source.JvmSource
driver.source.jvm.class=org.apache.spark.metrics.source.JvmSource
executor.source.jvm.class=org.apache.spark.metrics.source.JvmSource
I have found a different solution to the same problem. It looks like that Spark can also take these metric settings from its config properties. For example the following line from metrics.properties:
*.sink.Graphite.class=org.apache.spark.metrics.sink.GraphiteSink
Can also be specified as a Spark property with key spark.metrics.conf.*.sink.graphite.class and value org.apache.spark.metrics.sink.GraphiteSink. You just need to prepend spark.metrics.conf. to each key.
I have ended up putting all these settings in the code like this:
val sparkConf = new spark.SparkConf()
.set("spark.metrics.conf.*.sink.graphite.class", "org.apache.spark.metrics.sink.GraphiteSink")
.set("spark.metrics.conf.*.sink.graphite.host", graphiteHostName)
// etc.
val sc = new spark.SparkContext(sparkConf)
This way I've got the metrics sink set up for both the driver and the executors. I was using Spark 1.6.0.
I struggled with the same thing. I have it working using these flags:
--files=/path/to/metrics.properties --conf spark.metrics.conf=metrics.properties
It's tricky because the --files flag makes it so your /path/to/metrics.properties file ends up in every executor's local disk space as metrics.properties; AFAIK there's no way to specify more complex directory structure there, or have two files with the same basename.
Related, I filed SPARK-5152 about letting the spark.metrics.conf file be read from HDFS, but that seems like it would require a fairly invasive change, so I'm not holding my breath on that one.