Hi I am facing an error with providing dependency jars for spark-submit in kubernetes.
/usr/middleware/spark-3.1.1-bin-hadoop3.2/bin/spark-submit --master k8s://https://112.23.123.23:6443 --deploy-mode cluster --name spark-postgres-minio-kubernetes --jars file:///AirflowData/kubernetes/externalJars/postgresql-42.2.14.jar --driver-class-path file:///AirflowData/kubernetes/externalJars/postgresql-42.2.14.jar --conf spark.executor.instances=1 --conf spark.kubernetes.namespace=spark --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark --conf spark.kubernetes.file.upload.path=s3a://daci-dataintegration/spark-operator-on-k8s/code --conf spark.hadoop.fs.s3a.fast.upload=true --conf spark.kubernetes.container.image=hostname:5000/spark-py:spark3.1.2 file:///AirflowData/kubernetes/python/postgresminioKube.py
Below is the code to execute. The jars needed for the S3 minio and configurations are placed in the spark_home/conf and spark_home/jars and the docker image is created.
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql import functions as F
spark = SparkSession.builder.appName("Postgres-Minio-Kubernetes").getOrCreate()
import json
#spark = SparkSession.builder.config('spark.driver.extraClassPath', '/hadoop/externalJars/db2jcc4.jar').getOrCreate()
jdbcUrl = "jdbc:postgresql://{0}:{1}/{2}".format("hosnamme", "port", "db")
connectionProperties = {
"user" : "username",
"password" : "password",
"driver": "org.postgresql.Driver",
"fetchsize" : "100000"
}
pushdown_query = "(select * from public.employees) emp_als"
df = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, column="employee_id", lowerBound=1, upperBound=100, numPartitions=2, properties=connectionProperties)
df.write.format('csv').options(delimiter=',').mode('overwrite').save('s3a://daci-dataintegration/spark-operator-on-k8s/data/postgres-minio-csv/')
df.write.format('parquet').options(delimiter='|').options(header=True).mode('overwrite').save('s3a://daci-dataintegration/spark-operator-on-k8s/data/postgres-minio-csv/')
Error is below . It is trying to execute the jar for some reason
21/11/09 17:05:44 INFO SparkContext: Added JAR file:/tmp/spark-d987d7e7-9d49-4523-8415-1e438da1730e/postgresql-42.2.14.jar at spark://spark-postgres-minio-kubernetes-49d7d77d05a980e5-driver-svc.spark.svc:7078/jars/postgresql-42.2.14.jar with timestamp 1636477543573
21/11/09 17:05:49 ERROR TaskSchedulerImpl: Lost executor 1 on 192.168.216.12: Unable to create executor due to ./postgresql-42.2.14.jar
The external jars are getting added to the /opt/spark/work-dir and it didnt had access. So i changed the dockerfile to have access to the folder and then it worked.
RUN chmod 777 /opt/spark/work-dir
Related
I have a spark application which is being triggered from argo yaml via dockerized image.
The argo workflow yaml is as :
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: test-argo-spark
namespace: argo
spec:
entrypoint: sparkapp
templates:
- name: sparkapp
container:
name: main
command:
args: [
"/bin/sh",
"-c",
"/opt/spark/bin/spark-submit \
--master k8s://https://kubernetes.default.svc \
--deploy-mode cluster \
--conf spark.kubernetes.container.image=/test-spark:latest \
--conf spark.driver.extraJavaOptions='-Divy.cache.dir=/tmp -Divy.home=/tmp' \
--conf spark.app.name=test-spark-job \
--conf spark.jars.ivy=/tmp/.ivy \
--conf spark.kubernetes.driverEnv.HTTP2_DISABLE=true \
--conf spark.kubernetes.namespace=argo \
--conf spark.executor.instances=1 \
--conf spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension \
--packages org.postgresql:postgresql:42.1.4 \
--conf spark.kubernetes.driver.pod.name=custom-app \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=default \
--class SparkMain \
local:///opt/app/test-spark.jar"
]
image: /test-spark:latest
imagePullPolicy: IfNotPresent
resources: {}
This is calling in the spark submit which will invoke the jar file that has code residing in
The java code is as :
public class SparkMain {
public static void main(String args[]) {
SparkSession spark = SparkHelper.getSparkSession("SparkMain_Application");
System.out.println("Spark Java appn " + spark.logName());
}
}
The Spark session is being created as follows:
public static SparkSession getSparkSession(String appName) {
String sparkMode = System.getProperty("spark_mode");
if (sparkMode == null) {
sparkMode = "cluster";
}
if (sparkMode.equalsIgnoreCase("cluster")) {
return createSparkSession(appName);
} else if (sparkMode.equalsIgnoreCase("local")) {
return createLocalSparkSession(appName);
} else {
throw new RuntimeException("Invalid spark_mode option " + sparkMode);
}
}
If we see here there is a system property which needs to be passed in ,
String sparkMode = System.getProperty("spark_mode");
Can anyone tell how can we pass in these VM args from argo yaml when calling in the Spark submit.
Also how can we pass in multiple properties for a program
My end goal is to insert data from hdfs to elasticsearch but the issue i am facing is the connectivity
I am able to connect to my elasticsearch node using below curl command
curl -u username -X GET https://xx.xxx.xx.xxx:9200/_cat/indices?v' --insecure
but when it comes to connection with spark I am unable to do so. My command to insert data is
df.write.mode("append").format('org.elasticsearch.spark.sql').option("es.net.http.auth.user", "username").option("es.net.http.auth.pass", "password").option("es.index.auto.create","true").option('es.nodes', 'https://xx.xxx.xx.xxx').option('es.port','9200').save('my-index/my-doctype')
Error i am getting is
org.elastisearch.hadoop.EsHadoopIllegalArgumentException:Cannot detect ES version - typical this happens if then network/Elasticsearch cluster is not accessible or when targetting a Wan/Cloud instance without the proper setting 'es.nodes.wan.only'
....
....
Caused by: org.elasticseach.hadoop.rest.EsHadoopNoNodesLeftException: Connection error (check network and/or proy settings)- all nodes failed; tried [[xx.xxx.xx.xxx:9200]]
....
...
Here, What would be the pyspark equivalent of curl --insecure
Thanks
After many attempt and different config options. I found a way how to connect elastisearch running on https insecurely
dfToEs.write.mode("append").format('org.elasticsearch.spark.sql') \
.option("es.net.http.auth.user", username) \
.option("es.net.http.auth.pass", password) \
.option("es.net.ssl", "true") \
.option("es.net.ssl.cert.allow.self.signed", "true") \
.option("mergeSchema", "true") \
.option('es.index.auto.create', 'true') \
.option('es.nodes', 'https://{}'.format(es_ip)) \
.option('es.port', '9200') \
.option('es.batch.write.retry.wait', '100s') \
.save('{index}/_doc'.format(index=index))
with the
(es.net.ssl, true)
We also have to provide self signed certificate like below
(es.net.ssl.cert.allow.self.signed, true)
I did check a lot of things and finally i can write in AWS ElasticSearch service (ES), but with scala/spark.
In a VPC, create security groups to access from EMR to ES with port 443 (inbound rules in ES to SG of EMR and inbound rules in EMR to same port)
Check connectivity from EMR master node, with a telnet command
telnet xyz.eu-west-1.es.amazonaws.com 443
Once check above, check app level with curl command
curl https://xyz.eu-west-1.es.amazonaws.com:443/domainname/_search?pretty=true&?q=*```
After, goes to the code, in my case i did test with spark-shell, but server confs was included in start like this:
spark-shell --jars elasticsearch-spark-20_2.11-7.1.1.jar --conf spark.es.nodes="xyz.eu-west-1.es.amazonaws.com" --conf spark.es.port=443 --conf spark.es.nodes.wan.only=true --conf spark.es.nodes.discovery="false" --conf spark.es.index.auto.create="true" --conf spark.es.resource="domain/doc" --conf spark.es.scheme="https"
Finally the code to write:
import java.util.Date
import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}
import org.elasticsearch.spark._
import org.elasticsearch.spark.sql._
val dateformat = new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss")
val currentdate = dateformat.format(new Date)
val colorsDF = spark.read.json("multilinecolors.json")
val mcolors = colorsDF.withColumn("Date",lit(currentdate))
mcolors.write.mode("append").format("org.elasticsearch.spark.sql").option("es.net.http.auth.user", "").option("es.net.http.auth.pass", "").option("es.net.ssl", "true").option("es.net.ssl.cert.allow.self.signed", "true").option("mergeSchema", "true").option("es.index.auto.create", "true").option("es.nodes","https://xyz.eu-west-1.es.amazonaws.com").option("es.port", "443").option("es.batch.write.retry.wait", "100").save("domainname/_doc")```
can you try with the below sparkConfs,
val sparkConf = new SparkConf()
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.set("spark.es.index.auto.create", "true")
.set("spark.es.nodes", "yourESaddress")
.set("spark.es.port", "9200")
.set("spark.es.net.http.auth.user","")
.set("spark.es.net.http.auth.pass", "")
.set("spark.es.resource", indexName)
.set("spark.es.nodes.wan.only", "true")
still you face the problem then, es.net.ssl = true and see.
If still you get the error try adding the below configs,
'es.resource' = 'ctrl_rater_resumen_lla/hb',
'es.nodes' = 'localhost',
'es.port' = '9200',
'es.index.auto.create' = 'true',
'es.index.read.missing.as.empty' = 'true',
'es.nodes.discovery'='true',
'es.net.ssl'='false'
'es.nodes.client.only'='false',
'es.nodes.wan.only' = 'true'
'es.net.http.auth.user'='xxxxx',
'es.net.http.auth.pass' = 'xxxxx'
'es.nodes.discovery' = 'false'
When i submit like this, and in my application
spark-submit --master spark://ip:7077 --class mainClass myjar.jar 10}}
it will print out
10}}
but when i submit like this
spark-submit --master yarn --deploy-mode cluster --class mianClass myjar.jar 10}}
it only print out
10
So where is the "}}"? The same result when "10}}}" "10}}}}"
My code:
def main(args: Array[String]): Unit = {
println("start")
println(args(0))
println("args")
}
Spark version is 2.1.0
Facing one issue with Kerberos enabled Hadoop cluster.
We are trying to run a streaming job on yarn-cluster, which interacts with Kafka (direct stream), and hbase.
Somehow, we are not able to connect to hbase in the cluster mode. We use keytab to login to hbase.
This is what we do:
spark-submit --master yarn-cluster --keytab "dev.keytab" --principal "dev#IO-INT.COM" --conf "spark.executor.extraJavaOptions=-Dlog4j.configuration=log4j_executor_conf.properties -XX:+UseG1GC" --conf "spark.driver.extraJavaOptions=-Dlog4j.configuration=log4j_driver_conf.properties -XX:+UseG1GC" --conf spark.yarn.stagingDir=hdfs:///tmp/spark/ --files "job.properties,log4j_driver_conf.properties,log4j_executor_conf.properties" service-0.0.1-SNAPSHOT.jar job.properties
To connect to hbase:
def getHbaseConnection(properties: SerializedProperties): (Connection, UserGroupInformation) = {
val config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", HBASE_ZOOKEEPER_QUORUM_VALUE);
config.set("hbase.zookeeper.property.clientPort", 2181);
config.set("hadoop.security.authentication", "kerberos");
config.set("hbase.security.authentication", "kerberos");
config.set("hbase.cluster.distributed", "true");
config.set("hbase.rpc.protection", "privacy");
config.set("hbase.regionserver.kerberos.principal", “hbase/_HOST#IO-INT.COM”);
config.set("hbase.master.kerberos.principal", “hbase/_HOST#IO-INT.COM”);
UserGroupInformation.setConfiguration(config);
var ugi: UserGroupInformation = null;
if (SparkFiles.get(properties.keytab) != null
&& (new java.io.File(SparkFiles.get(properties.keytab)).exists)) {
ugi = UserGroupInformation.loginUserFromKeytabAndReturnUGI(properties.kerberosPrincipal,
SparkFiles.get(properties.keytab));
} else {
ugi = UserGroupInformation.loginUserFromKeytabAndReturnUGI(properties.kerberosPrincipal,
properties.keytab);
}
val connection = ConnectionFactory.createConnection(config);
return (connection, ugi);
}
and we connect to hbase:
….
foreachRDD { rdd =>
if (!rdd.isEmpty()) {
//var ugi: UserGroupInformation = Utils.getHbaseConnection(properties)._2
rdd.foreachPartition { partition =>
val connection = Utils.getHbaseConnection(propsObj)._1
val table = …
partition.foreach { json =>
}
table.put(puts)
table.close()
connection.close()
}
}
}
Keytab file is not getting copied to yarn staging/temp directory, we are not getting that in SparkFiles.get… and if we pass keytab with --files, spark-submit is failing because it’s there in --keytab already.
error is:
This server is in the failed servers list: myserver.test.com/120.111.25.45:60020
RpcRetryingCaller{globalStartTime=1497943263013, pause=100, retries=5}, org.apache.hadoop.hbase.ipc.FailedServerException: This server is in the failed servers list: myserver.test.com/120.111.25.45:60020
RpcRetryingCaller{globalStartTime=1497943263013, pause=100, retries=5}, org.apache.hadoop.hbase.ipc.FailedServerException: This server is in the failed servers list: myserver.test.com/120.111.25.45:60020 at org.apache.hadoop.hbase.client.RpcRetryingCaller.callWithRetries(RpcRetryingCaller.java:147)
at org.apache.hadoop.hbase.client.HTable.get(HTable.java:935)
Just started experimenting with the JobServer and would like to use it in our production environment.
We usually run spark jobs individually in yarn-client mode and would like to shift towards the paradigm offered by the Ooyala Spark JobServer.
I am able to run the WordCount examples shown in the official page.
I tried running submitting our custom spark job to the Spark JobServer and I got this error:
{
"status": "ERROR",
"result": {
"message": "null",
"errorClass": "scala.MatchError",
"stack": ["spark.jobserver.JobManagerActor$$anonfun$spark$jobserver$JobManagerActor$$getJobFuture$4.apply(JobManagerActor.scala:220)",
"scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)",
"scala.concurrent.impl.Future $PromiseCompletingRunnable.run(Future.scala:24)",
"akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:41)",
"akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)",
"scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)",
"scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java 1339)",
"scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)",
"scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)"]
}
I had made the necessary code modifications like extending SparkJob and implementing the runJob() method.
This is the dev.conf file that I used:
# Spark Cluster / Job Server configuration
spark {
# spark.master will be passed to each job's JobContext
master = "yarn-client"
# Default # of CPUs for jobs to use for Spark standalone cluster
job-number-cpus = 4
jobserver {
port = 8090
jar-store-rootdir = /tmp/jobserver/jars
jobdao = spark.jobserver.io.JobFileDAO
filedao {
rootdir = /tmp/spark-job-server/filedao/data
}
context-creation-timeout = "60 s"
}
contexts {
my-low-latency-context {
num-cpu-cores = 1
memory-per-node = 512m
}
}
context-settings {
num-cpu-cores = 2
memory-per-node = 512m
}
home = "/data/softwares/spark-1.2.0.2.2.0.0-82-bin-2.6.0.2.2.0.0-2041"
}
spray.can.server {
parsing.max-content-length = 200m
}
spark.driver.allowMultipleContexts = true
YARN_CONF_DIR=/home/spark/conf/
Also how can I give run-time parameters for the spark job, such as --files, --jars ?
For example, I usually run our custom spark job like this:
./spark-1.2.0.2.2.0.0-82-bin-2.6.0.2.2.0.0-2041/bin/spark-submit --class com.demo.SparkDriver --master yarn-cluster --num-executors 3 --jars /tmp/api/myUtil.jar --files /tmp/myConfFile.conf,/tmp/mySchema.txt /tmp/mySparkJob.jar
Number of executors and extra jars are passed in a different way, through the config file (see dependent-jar-uris config setting).
YARN_CONF_DIR should be set in the environment and not in the .conf file.
As for other issues, the google group is the right place to ask. You may want to search it for yarn-client issues, as several other folks have figured out how to get it to work.