I have developed a Scala Spark application for streaming data directly into Google BigQuery, using the spark-bigquery connector by Spotify.
Locally it works correctly, I have configured my application as described here https://github.com/spotify/spark-bigquery
val ssc = new StreamingContext(sc, Seconds(120))
val sqlContext = new SQLContext(sc)
sqlContext.setGcpJsonKeyFile("/opt/keyfile.json")
sqlContext.setBigQueryProjectId("projectid")
sqlContext.setBigQueryGcsBucket("gcsbucketname")
sqlContext.setBigQueryDatasetLocation("US")
but when I submit the application on my Spark on YARN cluster the job fails looking for GOOGLE_APPLICATION_CREDENTIALS environment variable...
The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials.
I set the variable as OS env var for root user to the .json file containing the credentials required, but it still fails.
I have also tried with the following line
System.setProperty("GOOGLE_APPLICATION_CREDENTIALS", "/opt/keyfile.json")
without success.
Any idea on what I'm missing?
Thank you,
Leonardo
the documentation suggests:
"Environment variables need to be set using the spark.yarn.appMasterEnv.[EnvironmentVariableName] property in your conf/spark-defaults.conf file.
Environment variables that are set in spark-env.sh will not be reflected in the YARN Application Master process in cluster mode."
Related
I have a spark setup currently running in PROD.
In which we are calling spark submit using shell script.
We export some variables inside shell script before spark submit in yarn client mode.
Those exported variable will refer inside scala program using "System.getenv(<variable_name_exportrd>)".
Problem:
Now the problem is we are switching to yarn cluster mode in spark-submit.
If we submit that job using Cluster mode. Those exported variables coming as null inside the program.
As per below blog, if I use "spark.yarn.appMasterEnv." i am able to access those exported variables. We are exporting nearly 40 variables in shell script. So buliding --conf for 40 variables is tedious task. (Variable changes dynamically)
How to pass environment variables to spark driver in cluster mode with spark-submit
Now my question is : Is there a way to specify multiple environment variables in a file and pass that in spark-submit.
This makes code change very less.
Please help. Thanks in advance.
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.
My existing project is kafka-spark-cassandra. Now I have got gcp account and have to migrate spark jobs to dataproc. In my existing spark jobs parameters like masterip,memory,cores etc are passed through command line which is triggerd by a linux shell script and create new sparkConf.
val conf = new SparkConf(true)
.setMaster(master)
.setAppName("xxxx")
.setJars(List(path+"/xxxx.jar"))
.set("spark.executor.memory", memory)
.set("spark.cores.max",cores)
.set("spark.scheduler.mode", "FAIR")
.set("spark.cassandra.connection.host", cassandra_ip)
1) How this can configure in dataproc?
2) Wheather there will be any compatibility issue b/w Spark 1.3(existing project) and Spark 1.6 provided by dataproc ? How it can resolve?
3) Is there any other connector needed for dataproc to get connected with Kafka and cassandra? I couldnt find any.
1) When submitting a job, you can specify arguments and properties: https://cloud.google.com/sdk/gcloud/reference/dataproc/jobs/submit/spark. When determining which properties to set, keep in mind that Dataproc submits Spark jobs in yarn-client mode.
In general, this means you should avoid specifying master directly in code, instead letting it come from the spark.master value inside of spark-defaults.conf, and then your local setup would have that config set to local while Dataproc would automatically have it set to yarn-client with the necessary yarn config settings alongside it.
Likewise, keys like spark.executor.memory, etc., should make use of Spark's first-class command-line if running spark-submit directly:
spark-submit --conf spark.executor.memory=42G --conf spark.scheduler.mode=FAIR
or if submitting to Dataproc with gcloud:
gcloud dataproc jobs submit spark \
--properties spark.executor.memory=42G,spark.scheduler.mode=FAIR
You'll also want to look at the equivalent --jars flags for jars instead of specifying it in code.
2) When building your project to deploy, ensure you exclude spark (e.g., in maven, mark spark as provided). You may hit compatibility issues, but without knowing all APIs in use, I can't say one way or the other. The simplest way to find out is to bump Spark to 1.6.1 in your build config and see what happens.
In general Spark core is considered GA and should thus be mostly backwards compatible in 1.X versions, but the compatibility guidelines didn't apply yet to subprojects like mllib and SparkSQL, so if you use those you're more likely to need to recompile against the newer Spark version.
3) Connectors should either be included in a fat jar, specified as --jars, or installed onto the cluster at creation via initialization actions.
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.