I am running into the same issue as in this thread with my Scala Spark Streaming application: Why does Spark job fail with "too many open files"?
But given that I am using Azure HDInsights to deploy my YARN cluster, and I don't think I can log into that machine and update the ulimit in all machines.
Is there any other way to solve this problem? I cannot reduce the number of reducers by too much either, or my job will become much slower.
You can ssh into all nodes from the head node (ambari ui show fqdn of all nodes).
ssh sshuser#nameofthecluster.azurehdinsight.net
You can the write a custom action that alters the settings on the necessary nodes if you want to automate this action.
Related
I have three Ubuntu VMs (clones) in my local machine which i wanted to use to make a simple cluster. One VM to be used as a master and the other two as slaves. I can ssh every VM from every other one succesfully and i have the ip's of the two slaves in the conf/slaves file of the master and the master's ip in the spark-env.sh of every VM.When I run
start-slave.sh spark://master-ip:7077
from the slaves,they appear in the spark UI. But when i try to run things in parallel i always get the message about the resources. For testing code i use the scala shell
spark-shell --master://master-ip:7077 and sc.parallelize(1 until 10000).count.
Do You mean that warn: WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster ui to ensure that workers are registered and have sufficient memory
This message will pop up any time an application is requesting more resources from the cluster than the cluster can currently provide.
Spark is only looking for two things: Cores and Ram. Cores represents the number of open executor slots that your cluster provides for execution. Ram refers to the amount of free Ram required on any worker running your application.
Note for both of these resources the maximum value is not your System's max, it is the max as set by the your Spark configuration.
If you need to run multiple Spark apps simultaneously then you’ll need to adjust the amount of cores being used by each app.
If you are working with applications on the same node you need to assign cores to each application to make them work in parallel: ResourceScheduling
If you use VMs (as in your situation): assign only one core to each VM
when you first create it or whatever relevant to your system
resource capacity as by now spark request 4 cores for each * 2 VMs = 8 core which you don't have.
This is a tutorial i find that could help you: Install Spark on Ubuntu: Standalone Cluster Mode
Further Reading: common-spark-troubleshooting
I have researched this for a significant amount of time and find answers that seem to be for a slightly different question than mine.
UPDATE: Spark docs say the Driver runs on a cluster Worker in deployMode: cluster. This does not seem to be true when you don't use spark-submit
My Spark 2.3.3 cluster is running fine. I see the GUI on “http://master-address:8080", there are 2 idle workers, as configured.
I have a Scala application that creates a context and starts a Job. I do not use spark-submit, I start the Job programmatically and this is where many answers diverge from my question.
In "my-app" I create a new SparkConf, with the following code (slightly abbreviated):
conf.setAppName(“my-job")
conf.setMaster(“spark://master-address:7077”)
conf.set(“deployMode”, “cluster”)
// other settings like driver and executor memory requests
// the driver and executor memory requests are for all mem on the slaves, more than
// mem available on the launching machine with “my-app"
val jars = listJars(“/path/to/lib")
conf.setJars(jars)
…
When I launch the job I see 2 executors running on the 2 nodes/workers/slaves. The logs show their IP address and calls them executor 0 and 1.
With a Yarn cluster I would expect the “Driver" to run on/in the Yarn Master but I am using the Spark Standalone Master, where is the Driver part of the Job running? If it runs on a random worker or elsewhere, is there a way to find it from logs
Where is my Spark Driver executing? Does deployMode = cluster work when not using spark-submit? Evidence shows a cluster with one master (on the same machine as executor 0) and 2 Workers. It also show identical memory usage on both Workers during the job. From logs I know both Workers are running Executors. Where is the Driver?
The “Driver” creates and broadcasts some large data structures so the need for an answer is more critical than with more typical tiny Drivers.
Where is the driver running? How do I find it given logs and monitoring? I can't reconcile what I see with the docs, they contradict each other.
This is answered by the official documentation:
In cluster mode, however, the driver is launched from one of the Worker processes inside the cluster, and the client process exits as soon as it fulfills its responsibility of submitting the application without waiting for the application to finish.
In other words driver uses arbitrary worker node, hence it it is likely to co-locate with one on the executors, on such small cluster. And to anticipate the follow-up question - this behavior is not configurable - you just have to make sure that the cluster has capacity to start both required executors, and the driver with it's requested memory and cores.
I have an Apache spark standalone set up.
I wish to start 3 workers to run in parallel:
I use the commands below.
./start-master.sh
SPARK_WORKER_INSTANCES=3 SPARK_WORKER_CORES=2 ./start-slaves.sh
I tried to run a few jobs and below are the apache UI results:
Ignore the last three applications that failed: Below are my questions:
Why do I have just one worker displayed in the UI despite asking spark to start 3 each with 2 cores?
I want to partition my input RDD for better performance. So for the first two jobs with no partions, I had a time of 2.7 mins. Here my Scala source code had the following.
val tweets = sc.textFile("/Users/soft/Downloads/tweets").map(parseTweet).persist()
In my third job (4.3 min) I had the below:
val tweets = sc.textFile("/Users/soft/Downloads/tweets",8).map(parseTweet).persist()
I expected a shorter time with more partitions(8). Why was this the opposite of what was expected?
Apparently you have only one active worker, which you need to investigate why other workers are not reported by checking the spark logs.
More partitions doesn't always mean that the application runs faster, you need to check how you are creating partitions from source data, the amount of data parition'd and how much data is being shuffled, etc.
In case you are running on a local machine it is quite normal to just start a single worker with several CPU's as shown in the output. It will still split you task of the available CPU's in the machine.
Partitioning your file will happen automatically depending on the amount of available resources, it works quite well most of the time. Spark (and partitioning the files) comes with some overhead, so often, especially on a single machine Spark adds so much overhead it will slowdown you process. The added values comes with large amounts of data on a cluster of machines.
Assuming that you are starting a stand-alone cluster, I would suggest using the configuration files to setup a the cluster and use start-all.sh to start a cluster.
first in your spark/conf/slaves (copied from spark/conf/slaves.template add the IP's (or server names) of you worker nodes.
configure the spark/conf/spark-defaults.conf (copied from spark/conf/spark-defaults.conf.template Set at least the master node to the server that runs your master.
Use the spark-env.sh (copied from spark-env.sh.template) to configure the cores per worker, memory etc:
export SPARK_WORKER_CORES="2"
export SPARK_WORKER_MEMORY="6g"
export SPARK_DRIVER_MEMORY="4g"
export SPARK_REPL_MEM="4g"
Since it is standalone (and not hosted on a Hadoop environment) you need to share (or copy) the configuration (or rather the complete spark directory) to all nodes in your cluster. Also the data you are processing needs to be available on all nodes e.g. directly from a bucket or a shared drive.
As suggested by the #skjagini checkout the various log files in spark/logs/ to see what's going on. Each node will write their own log files.
See https://spark.apache.org/docs/latest/spark-standalone.html for all options.
(we have a setup like this running for several years and it works great!)
Installed spark cluster on standalone mode with 2 nodes on first node there is spark master running and on another node spark worker. When i try to run spark shell on worker node with word count code it runs fine but when i try to run spark shell on the master node it gives following output :
WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
Executor is not triggered to run the job. Even though there is worker available to spark master its giving such a problem . Any help is appriciated , thanks
You use client deploy mode so the best bet is that executor nodes cannot connect to the driver port on the local machine. It could be firewall issue or problem with advertised IP / hostname. Please make sure that:
spark.driver.bindAddress
spark.driver.host
spark.driver.port
use expected values. Please refer to the networking section of Spark documentation.
Less likely it is a lack of resources. Please check if you don't request more resources than provided by workers.
Is that even possible? I've searched quite a bit and I'd say it's not possible, but I think it's so strange a so basilar functionality has not been foreseen.
If i have a cluster of 3 machine and 1 is relative to a part (Let's say an action i Oozie) of the bigger process, can't i say to Oozie to run that job only on node X and not in the other nodes?
I don not think you can enforce Oozie launcher mapper to run on a certain node.