I know spark related configuration can be get via spark-env.sh file however what would be the command to get it from spark-shell ?
For example to get spark.driver.memory shall I use
set spark.driver.memory
above isn't working
You can provide the memory as a configuration while launching spark-shell
spark-shell --conf spark.driver.memory=2g
This will start a spark shell with 2g of driver memory. In order to access it in spark shell, you can do the following.
val conf = sparkContext.getConf
val driverMemory = conf.get("spark.driver.memory")
This will return String = 2g.
Related
I seem to be unable to assign cores to an application. This leads to the following (apparently common) error message:
Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
I have one master and two slaves in a Spark cluster. All are 8-core i7s with 16GB of RAM.
I have left the spark-env.sh virtually virgin on all three, just specifying the master's IP address.
My spark-submit is the following:
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 \
--conf spark.cores.max=4 \
--driver-memory 4g –num-executors 2 --executor-memory 2g --executor-cores 2 \
--master spark://192.168.0.141:7077 ./ikoda/ikodaanalysis-mlserver-0.1.0.jar 1000 > ./logs/nohup.out &
I suspect I am conflating the sparkConf initialization in my code with the spark-submit. I need this as the app involves SparkStreaming which can require reinitializing the SparkContext.
The sparkConf setup is as follows:
val conf = new SparkConf().setMaster(s"spark://$sparkmaster:7077").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)
conf.set("spark.jars.packages", "datastax:spark-cassandra-connector:"+datastaxpackageversion)
conf.set("spark.cores.max", sparkcoresmax)
The Spark UI shows the following:
OK, this is definitely a case of programmer error.
But maybe others will make a similar error. The Master had been used as a local Spark previously. I had put some executor settings in spark-defaults.conf and then months later had forgotten about this.
There is a cascading hierarchy whereby SparkConf settings get precedence, then spark-submit settings and then spark-defaults.conf. spark-defaults.conf overrides defaults set by Apache Spark team
Once I removed the settings from spark-defaults, all was fixed.
It is because of the maximum of your physical memory.
your spark memory in spark UI 14.6GB So you must request memory for each executor below the volume
14.6GB, for this you can add config to your spark conf something like this:
conf.set("spark.executor.memory", "10gb")
if you request more than your physical memory spark does't allocate cpu cores to your job and display 0 in Cores in spark UI and run NOTHING.
I set up a spark-yarn cluster environment, and try spark-SQL with spark-shell:
spark-shell --master yarn --deploy-mode client --conf spark.yarn.archive=hdfs://hadoop_273_namenode_ip:namenode_port/spark-archive.zip
One thing to mention is the Spark is in Windows 7. After spark-shell starts up successfully, I execute the commands as below:
scala> val sqlContext = new org.apache.spark.sql.SQLContext(sc)
scala> val df_mysql_address = sqlContext.read.format("jdbc").option("url", "jdbc:mysql://mysql_db_ip/db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "ADDRESS").option("user", "root").option("password", "root").load()
scala> df_mysql_address.show
scala> df_mysql_address.write.format("parquet").saveAsTable("address_local")
"show" command returns result-set correctly, but the "saveAsTable" ends in failure. The error message says:
java.io.IOException: Mkdirs failed to create file:/C:/jshen.workspace/programs/spark-2.2.0-bin-hadoop2.7/spark-warehouse/address_local/_temporary/0/_temporary/attempt_20171018104423_0001_m_000000_0 (exists=false, cwd=file:/tmp/hadoop/nm-local-dir/usercache/hduser/appcache/application_1508319604173_0005/container_1508319604173_0005_01_000003)
I expect and guess the table is to be saved in the hadoop cluster, but you can see that the dir (C:/jshen.workspace/programs/spark-2.2.0-bin-hadoop2.7/spark-warehouse) is the folder in my Windows 7, not in hdfs, not even in the hadoop ubuntu machine.
How could I do? Please advise, thanks.
The way to get rid of the problem is to provide "path" option prior to "save" operation as shown below:
scala> df_mysql_address.write.option("path", "/spark-warehouse").format("parquet").saveAsTable("address_local")
Thanks #philantrovert.
I wrote a Spark application, which sets sets some configuration stuff via SparkConf instance, like this:
SparkConf conf = new SparkConf().setAppName("Test App Name");
conf.set("spark.driver.cores", "1");
conf.set("spark.driver.memory", "1800m");
conf.set("spark.yarn.am.cores", "1");
conf.set("spark.yarn.am.memory", "1800m");
conf.set("spark.executor.instances", "30");
conf.set("spark.executor.cores", "3");
conf.set("spark.executor.memory", "2048m");
JavaSparkContext sc = new JavaSparkContext(conf);
JavaRDD<String> inputRDD = sc.textFile(...);
...
When I run this application with the command (master=yarn & deploy-mode=client)
spark-submit --class spark.MyApp --master yarn --deploy-mode client /home/myuser/application.jar
everything seems to work fine, the Spark History UI shows correct executor information:
But when running it with (master=yarn & deploy-mode=cluster)
my Spark UI shows wrong executor information (~512 MB instead of ~1400 MB):
Also my App name equals Test App Name when running in client mode, but is spark.MyApp when running in cluster mode. It seems that however some default settings are taken when running in Cluster mode. What am I doing wrong here? How can I make these settings for the Cluster mode?
I'm using Spark 1.6.2 on a HDP 2.5 cluster, managed by YARN.
OK, I think I found out the problem! In short form: There's a difference between running Spark settings in Standalone and in YARN-managed mode!
So when you run Spark applications in the Standalone mode, you can focus on the Configuration documentation of Spark, see http://spark.apache.org/docs/1.6.2/configuration.html
You can use the following settings for Driver & Executor CPU/RAM (just as explained in the documentation):
spark.executor.cores
spark.executor.memory
spark.driver.cores
spark.driver.memory
BUT: When running Spark inside a YARN-managed Hadoop environment, you have to be careful with the following settings and consider the following points:
orientate on the "Spark on YARN" documentation rather then on the Configuration documentation linked above: http://spark.apache.org/docs/1.6.2/running-on-yarn.html (the properties explained here have a higher priority then the ones explained in the Configuration docu (this seems to describe only the Standalone cluster vs. client mode, not the YARN cluster vs. client mode!!))
you can't use SparkConf to set properties in yarn-cluster mode! Instead use the corresponding spark-submit parameters:
--executor-cores 5
--executor-memory 5g
--driver-cores 3
--driver-memory 3g
In yarn-client mode you can't use the spark.driver.cores and spark.driver.memory properties! You have to use the corresponding AM properties in a SparkConf instance:
spark.yarn.am.cores
spark.yarn.am.memory
You can't set these AM properties via spark-submit parameters!
To set executor resources in yarn-client mode you can use
spark.executor.cores and spark.executor.memory in SparkConf
--executor-cores and executor-memory parameters in spark-submit
if you set both, the SparkConf settings overwrite the spark-submit parameter values!
This is the textual form of my notes:
Hope I can help anybody else with this findings...
Just to add on to D. Müller's answer:
Same issue happened to me and I tried the settings with some different combination. I am running Pypark 2.0.0 on YARN cluster.
I found that driver-memory must be written during spark submit but executor-memory can be written in script (i.e. SparkConf) and the application will still work.
My application will die if driver-memory is less than 2g. The error is:
ERROR yarn.ApplicationMaster: RECEIVED SIGNAL TERM
ERROR yarn.ApplicationMaster: User application exited with status 143
CASE 1:
driver & executor both written in SparkConf
spark = (SparkSession
.builder
.appName("driver_executor_inside")
.enableHiveSupport()
.config("spark.executor.memory","4g")
.config("spark.executor.cores","2")
.config("spark.yarn.executor.memoryOverhead","1024")
.config("spark.driver.memory","2g")
.getOrCreate())
spark-submit --master yarn --deploy-mode cluster myscript.py
CASE 2:
- driver in spark submit
- executor in SparkConf in script
spark = (SparkSession
.builder
.appName("executor_inside")
.enableHiveSupport()
.config("spark.executor.memory","4g")
.config("spark.executor.cores","2")
.config("spark.yarn.executor.memoryOverhead","1024")
.getOrCreate())
spark-submit --master yarn --deploy-mode cluster --conf spark.driver.memory=2g myscript.py
The job Finished with succeed status. Executor memory correct.
CASE 3:
- driver in spark submit
- executor not written
spark = (SparkSession
.builder
.appName("executor_not_written")
.enableHiveSupport()
.config("spark.executor.cores","2")
.config("spark.yarn.executor.memoryOverhead","1024")
.getOrCreate())
spark-submit --master yarn --deploy-mode cluster --conf spark.driver.memory=2g myscript.py
Apparently the executor memory is not set. Meaning CASE 2 actually captured executor memory settings despite writing it inside sparkConf.
I'm trying to start my Spark application in local mode using spark-submit. I am using Spark 2.0.2, Hadoop 2.6 & Scala 2.11.8 on Windows. The application runs fine from within my IDE (IntelliJ), and I can also start it on a cluster with actual, physical executors.
The command I'm running is
spark-submit --class [MyClassName] --master local[*] target/[MyApp]-jar-with-dependencies.jar [Params]
Spark starts up as usual, but then terminates with
java.io.Exception: Failed to connect to /192.168.88.1:56370
What am I missing here?
Check which port you are using: if on cluster: log in to master node and include:
--master spark://XXXX:7077
You can find it always in spark ui under port 8080
Also check your spark builder config if you have set master already as it takes priority when launching eg:
val spark = SparkSession
.builder
.appName("myapp")
.master("local[*]")
I have Spark running on a Cloudera CDH5.3 cluster, using YARN as the resource manager. I am developing Spark apps in Python (PySpark).
I can submit jobs and they run succesfully, however they never seem to run on more than one machine (the local machine I submit from).
I have tried a variety of options, like setting --deploy-mode to cluster and --master to yarn-client and yarn-cluster, yet it never seems to run on more than one server.
I can get it to run on more than one core by passing something like --master local[8], but that obviously doesn't distribute the processing over multiple nodes.
I have a very simply Python script processing data from HDFS like so:
import simplejson as json
from pyspark import SparkContext
sc = SparkContext("", "Joe Counter")
rrd = sc.textFile("hdfs:///tmp/twitter/json/data/")
data = rrd.map(lambda line: json.loads(line))
joes = data.filter(lambda tweet: "Joe" in tweet.get("text",""))
print joes.count()
And I am running a submit command like:
spark-submit atest.py --deploy-mode client --master yarn-client
What can I do to ensure the job runs in parallel across the cluster?
Can you swap the arguments for the command?
spark-submit --deploy-mode client --master yarn-client atest.py
If you see the help text for the command:
spark-submit
Usage: spark-submit [options] <app jar | python file>
I believe #MrChristine is correct -- the option flags you specify are being passed to your python script, not to spark-submit. In addition, you'll want to specify --executor-cores and --num-executors since by default it will run on a single core and use two executors.
Its not true that python script doesn't run in cluster mode. I am not sure about previous versions but this is executing in spark 2.2 version on Hortonworks cluster.
Command : spark-submit --master yarn --num-executors 10 --executor-cores 1 --driver-memory 5g /pyspark-example.py
Python Code :
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext
conf = (SparkConf()
.setMaster("yarn")
.setAppName("retrieve data"))
sc = SparkContext(conf = conf)
sqlContext = SQLContext(sc)
parquetFile = sqlContext.read.parquet("/<hdfs-path>/*.parquet")
parquetFile.createOrReplaceTempView("temp")
df1 = sqlContext.sql("select * from temp limit 5")
df1.show()
df1.write.save('/<hdfs-path>/test.csv', format='csv', mode='append')
sc.stop()
Output : Its big so i am not pasting. But it runs perfect.
It seems that PySpark does not run in distributed mode using Spark/YARN - you need to use stand-alone Spark with a Spark Master server. In that case, my PySpark script ran very well across the cluster with a Python process per core/node.