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
Related
I am working with a program that needs a lot RAM. Currently I am running it on a SLURM cluster. Each node has 125GB RAM. When submitting the job to a single node it eventually fails as it runs out of memory. My rather naive question, as I am new to working on servers, is:
Does assigning more nodes with the command --nodes flag increase available RAM for the submitted job?
For example:
When assigning 10 nodes instead of 1, with the command below, the program fails at the same point as with with one node.
#SBATCH --nodes=10
Is there some other way to combine RAM from multiple nodes for a single job?
Any and all advice is welcome!
That depends on your program, but most likely no.
To use multiple nodes on a Slurm Cluster (or any cluster, for that matter), your program needs to be set up in very specific way, ie. you need inter node communictaion. This is usually done via MPI and the whole program has to be designed around it.
So if your program uses MPI it may be able to split the workload over several nodes. And even that does not guarantee lower memory as that is usually not the goal of such a parallelization.
I found some code to start spark locally with:
val conf = new SparkConf().setAppName("test").setMaster("local[*]")
val ctx = new SparkContext(conf)
What does the [*] mean?
From the doc:
./bin/spark-shell --master local[2]
The --master option specifies the master URL for a distributed
cluster, or local to run locally with one thread, or local[N] to run
locally with N threads. You should start by using local for testing.
And from here:
local[*] Run Spark locally with as many worker threads as logical
cores on your machine.
Master URL Meaning
local : Run Spark locally with one worker thread (i.e. no parallelism at all).
local[K] : Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
local[K,F] : Run Spark locally with K worker threads and F maxFailures (see spark.task.maxFailures for an explanation of this variable)
local[*] : Run Spark locally with as many worker threads as logical cores on your machine.
local[*,F] : Run Spark locally with as many worker threads as logical cores on your machine and F maxFailures.
spark://HOST:PORT : Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default.
spark://HOST1:PORT1,HOST2:PORT2 : Connect to the given Spark standalone cluster with standby masters with Zookeeper. The list must have all the master hosts in the high availability cluster set up with Zookeeper. The port must be whichever each master is configured to use, which is 7077 by default.
mesos://HOST:PORT : Connect to the given Mesos cluster. The port must be whichever you have configured to use, which is 5050 by default. Or, for a Mesos cluster using ZooKeeper, use mesos://zk://.... To submit with --deploy-mode cluster, the HOST:PORT should be configured to connect to the MesosClusterDispatcher.
yarn : Connect to a YARN cluster in client or cluster mode depending on the value of --deploy-mode. The cluster location will be found based on the HADOOP_CONF_DIR or YARN_CONF_DIR variable.
https://spark.apache.org/docs/latest/submitting-applications.html
Some additional Info
Do not run Spark Streaming programs locally with master configured as "local" or "local[ 1]". This allocates only one CPU for tasks and if a receiver is running on it, there is no resource left to process the received data. Use at least "local[ 2]" to have more cores.
From -Learning Spark: Lightning-Fast Big Data Analysis
Master URL
You can run Spark in local mode using local, local[n] or the most general local[*] for the master URL.
The URL says how many threads can be used in total:
local uses 1 thread only.
local[n] uses n threads.
local[*] uses as many threads as the number of processors available to the Java virtual machine (it uses Runtime.getRuntime.availableProcessors() to know the number).
local[N, maxFailures] (called local-with-retries) with N being * or the number of threads to use (as explained above) and maxFailures being the value of spark.task.maxFailures.
You can run Spark in local mode using local, local[n] or the most general local[*] for the master URL.
The URL says how many threads can be used in total:-
local uses 1 thread only.
local[n] uses n threads.
local[*] uses as many threads as your spark local machine have, where you are running your application.
you can check by lscpu in your Linux machine
[ie#mapr2 ~]$ lscpu
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 56
On-line CPU(s) list: 0-55
Thread(s) per core: 2
if your machine has 56 cores means CPU then your spark jobs will be partitioned in 56 part.
NOTE:- there may be the case that in your spark cluster the spark-defaults.conf file has limited the partition value with the default value (like 10 or else) then your partitioned will be the same as default value has been set in config.
local[N, maxFailures] (called local-with-retries) with N being * or the number of threads to use (as explained above) and maxFailures being the value of spark.task.maxFailures.
without * spark will use single thread.
With * spark will use all the available threads the run this program
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!)
Is it possible to set multiple executors for Spark Streaming application in a local mode using some Spark Conf settings?
For now, I can not see any changes in Spark UI in terms of performance or executors number increase when I change spark.executor.instances parameter to 4, for example.
Local mode is a development tool, where all components are simulated in a single machine. Since single JVM mean single executor changing of the number of executors is simply not possible, and spark.executor.instances is not applicable.
All you can do in local mode is to increase number of threads by modifying the master URL - local[n] where n is the number of threads.
local mode is by definition "pseudo-cluster" that runs in Single JVM. That means maximum number of executors is 1.
If you want to experiment with multiple executors on local machine, what you can do is to create cluster with couple workers running on your local machine. Number of running instances is max number of executors for your tasks.
spark.executor.instances is not honoured in local mode.
Reference - https://books.japila.pl/apache-spark-internals/local/?h=local
Local-Mode: In this non-distributed single-JVM deployment mode, Spark spawns all the execution components - driver, executor, LocalSchedulerBackend, and master - in the same single JVM. The default parallelism is the number of threads as specified in the master URL. This is the only mode where a driver is used for execution.
So you can increase number of threads in JVM to n by passing master url as local[n].