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.
Related
I'll try to do my best to explain my self. I'm using JupyterHub to connect to the cluster of my university and write some code. Basically i'm using pyspark but, since i'va always used "yarn kernel" (i'm not sure of what i'm sying) i've never defined the spark context or the spark session. Now, for some reason, it doesn't work anymore and when i try to use spark this error appears:
Code ->
df = spark.read.csv('file:///%s/.....
Error ->
name 'spark' is not defined
It already happend to me but i just solved by installing another version of pyspark. Now i don't know what to do.
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.
This is what I wrote via intellij. I plan on eventually writing larger spark scala files.
Anyways, I uploaded it on an AWS cluster that I had made. The "master" line, line 11 was "master("local")". I ran into this error
The second picture is the error that was returned by AWS when it did not run successfully. i changed line 11 to "yarn" instead of local (see the first picture for its current state)
It still is returning the same error. I put in the following flags when I uploaded it manually
--steps Type=CUSTOM_JAR,Name="SimpleApp"
It worked two weeks ago. My friend did almost the exact same thing as me. I am not sure why it isn't working.
I am looking for both a brief explanation and an answer. Looks like I need a little more knowledge on how spark works.
I am working with amazon EMR.
I think on the line 9 you are creating SparkContext with "old way" approach in spark 1.6.x and older version - you need to set master in default configuration file (usually location conf/spark-defaults.conf) or pass it to spark-submit (it is required in new SparkConf())...
On line 10 you are creating "spark" context with SparkSesion which is approach in spark 2.0.0. So in my opinion your problem is line num. 9 and I think you should remove it and work with SparkSesion or set reqiered configuration for SparkContext In case when you need sc.
You can access to sparkContext with sparkSession.sparkContext();
If you still want to use SparkConf you need to define master programatically:
val sparkConf = new SparkConf()
.setAppName("spark-application-name")
.setMaster("local[4]")
.set("spark.executor.memory","512m");
or with declarative approach in conf/spark-defaults.conf
spark.master local[4]
spark.executor.memory 512m
or simply at runtime:
./bin/spark-submit --name "spark-application-name" --master local[4] --executor-memory 512m your-spark-job.jar
Try using the below code:
val spark = SparkSession.builder().master("spark://ec2-xx-xxx-xxx-xxx.compute-1.amazonaws.com:xxxx").appName("example").getOrCreate()
you need to provide the proper link to your aws cluster.
I hava a app and read data from MongoDB.
If I use local pattern, it runs well, however, it throws java.lang.illegalStateExcetion when I use standalone cluster pattern
With local pattern, the SparkContext is val sc = new SparkContext("local","Scala Word Count")
With Standalone cluster pattern, the SparkContext is val sc = new SparkContext() and submit shell is ./spark-submit --class "xxxMain" /usr/local/jarfile/xxx.jar --master spark://master:7077
It trys 4 times then throw error when it runs to the first action
My code
configOriginal.set("mongo.input.uri","mongodb://172.16.xxx.xxx:20000/xxx.Original")
configOriginal.set("mongo.output.uri","mongodb://172.16.xxx.xxx:20000/xxx.sfeature")
mongoRDDOriginal =sc.newAPIHadoopRDD(configOriginal,classOf[com.mongodb.hadoop.MongoInputFormat],classOf[Object], classOf[BSONObject])
I learned from this example
mongo-spark
I searched and someone said it was because of mongo-hadoop-core-1.3.2, but either I up the version to mongo-hadoop-core-1.4.0 or down to 'mongo-hadoop-core-1.3.1', it didn't work.
Please help me!
Finally, I got the solution.
Because each of my workers have many cores and mongo-hadoop-core-1.3.2 doesn't support multiple threads, however it fixed in mongo-hadoop-core-1.4.0. But why my app still get error is because of "intellij idea" cache. You should add mongo-java-driver dependency, too.
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.