I'm naively testing for concurrency in local mode, with the following spark context
SparkSession
.builder
.appName("local-mode-spark")
.master("local[*]")
.config("spark.executor.instances", 4)
.config("spark.executor.cores", 2)
.config("spark.network.timeout", "10000001") // to avoid shutdown during debug, avoid otherwise
.config("spark.executor.heartbeatInterval", "10000000") // to avoid shutdown during debug, avoid otherwise
.getOrCreate()
and a mapPartitions API call like follows:
import spark.implicits._
val inputDF : DataFrame = spark.read.parquet(inputFile)
val resultDF : DataFrame =
inputDF.as[T].mapPartitions(sparkIterator => new MyIterator)).toDF
On the surface of it, this did surface one concurrency bug in my code contained in MyIterator (not a bug in Spark's code). However, I'd like to see that my application will crunch all available machine resources both in production, and also during this testing so that the chances of spotting additional concurrency bugs will improve.
That is clearly not the case for me so far: my machine is only at very low CPU utilization throughout the heavy processing of the inputDF, while there's plenty of free RAM and the JVM Xmx poses no real limitation.
How would you recommend testing for concurrency using your local machine? the objective being to test that in production, Spark will not bump into thread-safety or other concurrency issues in my code applied by spark from within MyIterator?
Or can it even in spark local mode, process separate partitions of my input dataframe in parallel? Can I get spark to work concurrently on the same dataframe on a single machine, preferably in local mode?
Max parallelism
You are already running spark in local mode using .master("local[*]").
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).
Max memory available to all executors/threads
I see that you are not setting the driver memory explicitly. By default the driver memory is 512M. If your local machine can spare more than this, set this explicitly. You can do that by either:
setting it in the properties file (default is spark-defaults.conf),
spark.driver.memory 5g
or by supplying configuration setting at runtime
$ ./bin/spark-shell --driver-memory 5g
Note that this cannot be achieved by setting it in the application, because it is already too late by then, the process has already started with some amount of memory.
Nature of Job
Check number of partitions in your dataframe. That will essentially determine how much max parallelism you can use.
inputDF.rdd.partitions.size
If the output of this is 1, that means your dataframe has only 1 partition and so you won't get concurrency when you do operations on this dataframe. In that case, you might have to tweak some config to create more number of partitions so that you can concurrently run tasks.
Running local mode cannot simulate a production environment for the following reasons.
There are lots of code which gets bypassed when code is run in local mode, which would normally run with any other cluster manager. Amongst various issues, few things that i could think
a. Inability to detect bugs from the way shuffle get handled.(Shuffle data is handled in a completely different way in local mode.)
b. We will not be able to detect serialization related issues, since all code is available to the driver and task runs in the driver itself, and hence we would not result in any serialization issues.
c. No speculative tasks(especially for write operations)
d. Networking related issues, all tasks are executed in same JVM. One would not be able detect issues like communication between driver/executor, codegen related issues.
Concurrency in local mode
a. Max concurrency than can be attained will be equal to the number of cores in your local machine.(Link to code)
b. The Job, Stage, Task metrics shown in Spark UI are not accurate since it will incur the overhead of running in the JVM where the driver is also running.
c: As for CPU/Memoryutilization, it depends on operation being performed. Is the operation CPU/memory intensive?
When to use local mode
a. Testing of code that will run only on driver
b. Basic sanity testing of the code that will get executed on the executors
c. Unit testing
tl; dr The concurrency bugs that occur in local mode might not even be present in other cluster resource managers, since there are lot of special handling in Spark code for local mode(There are lots of code which checks isLocal in code and control goes to a different code flow altogether)
Yes!
Achieving parallelism in local mode is quite possible.
Check the amount of memory and cpu available in your local machine and supply values to the driver-memory and driver-cores conf while submitting your spark job.
Increasing executor-memory and executor-cores will not make a difference in this mode.
Once the application is running, open up the SPARK UI for the job. You can now go to the EXECUTORS tab to actually check the amount of resources your spark job is utilizing.
You can monitor various tasks that get generated and the number of tasks that your job runs concurrently using the JOBS and STAGES tab.
In order to process data which is way larger than the resources available, ensure that you break your data into smaller partitions using repartition. This should allow your job to complete successfully.
Increase the default shuffle partitions in case your job has aggregations or joins. Also, ensure sufficient space on the local file system since spark creates intermediate shuffle files and writes them to disk.
Hope this helps!
Related
As mentioned into below link Spark Local vs Cluster
Spark Local vs Cluster
It means, spark local run on local machine with number of threads,
Can I assume it is similar to create a thread from threading module. and we do not need to bother any other thing,
Can I explore this way,
convert large list into dataframe
use udf function and apply manipulation on frame
convert dataframe to list.
will it better approach or efficient.
Not fully understand your question.
It means, spark local run on local machine with number of threads,
Yes, both local mode and cluster mode, you can set the configs to let multiple threads run on each node. Each executor would be one thread.
Can I assume it is similar to create a thread from threading module. and we do not need to bother any other thing,
I think so. I believe they are just different threads each performing a different executor's job as if they are different machines.
Can I explore this way,
I do not understand which method you compare to. Sorry.
In the famous word count example for spark streaming, the spark configuration object is initialized as follows:
/* Create a local StreamingContext with two working thread and batch interval of 1 second.
The master requires 2 cores to prevent from a starvation scenario. */
val sparkConf = new SparkConf().
setMaster("local[2]").setAppName("WordCount")
Here if I change the master from local[2] to local or does not set the Master, I do not get the expected output and in fact word counting doesn't happen at all.
The comment says:
"The master requires 2 cores to prevent from a starvation scenario" that's why they have done setMaster("local[2]").
Can somebody explain me why it requires 2 cores and what is starvation scenario ?
From the documentation:
[...] note that a Spark worker/executor is a long-running task, hence it occupies one of the cores allocated to the Spark Streaming application. Therefore, it is important to remember that a Spark Streaming application needs to be allocated enough cores (or threads, if running locally) to process the received data, as well as to run the receiver(s).
In other words, one thread will be used to run the receiver and at least one more is necessary for processing the received data. For a cluster, the number of allocated cores must be more than the number of receivers, otherwise the system can not process the data.
Hence, when running locally, you need at least 2 threads and when using a cluster at least 2 cores need to be allocated to your system.
Starvation scenario refers to this type of problem, where some threads are not able to execute at all while others make progress.
There are two classical problems where starvation is well known:
Dining philosophers
Readers-writer problem, here it's possible to synchronize the threads so the readers or writers starve. It's also possible to make sure that no starvation occurs.
I'm looking for a way to use Spark on Dataproc built with Scala 2.11. I want to use 2.11 since my jobs pulls in ~10 BigQuery tables and I'm using the new reflection libraries to map the corresponding objects to case classes. (There's a bug with the new reflection classes and concurrency which is only fixed in Scala 2.11) I've tried working around this issues by setting executor-cores to 1 but the performance decrease is painful. Is there a better way?
In general, setting executor-cores to 1 is a reasonable way to work around concurrency issues, since it can often happen that third-party libraries you may incorporate into your Spark jobs also have thread-safety problems; the key here is that you should be able to resize the executors to each only have 1 core without really sacrificing performance (the larger scheduling overhead and yarn overhead might mean o the order of, say ~10% performance decrease, but certainly nothing unmanageable).
I'm assuming you're referring to some multiplicative factor performance decrease due to, say, only using 2 out of 8 cores on an 8-core VM (Dataproc packs 2 executors per VM by default). The way to fix this is simply to also adjust spark.executor.memory down proportionally to match up with the 1 core. For example, in your cluster config (gcloud dataproc clusters describe your-cluster-name) if you use 4-core VMs you might see something like:
spark:spark.executor.cores: '2'
spark:spark.executor.memory: 5586m
YARN packs entirely based on memory, not cores, so this means 5586m is designed to fit in half a YARN node, and thus correspond to 2 cores. If you turn up your cluster like:
gcloud dataproc clusters create \
--properties spark:spark.executor.cores=1,spark:spark.executor.memory=2000m
Then you should end up with a setup which still uses all the cores, but without concurrency issues (one worker thread in each executor process only).
I didn't just use 5586/2 in this case because you have to factor in spark:spark.yarn.executor.memoryOverhead as well, so basically you have to add in the memoryOverhead, then divide by two, then subtract the memoryOverhead again to determine the new executor size, and beyond that the allocations also round to the next multiple of a base chunk size, which I believe is 512m.
In general, you can use trial-and-error by starting a bit lower on the memory allocation per core, and then increasing it if you find you need more memory headroom.
You don't have to redeploy a cluster to check these either; you can specify these at job submission time instead for faster turnaround:
gcloud dataproc jobs submit spark \
--properties spark.executor.cores=1,spark.executor.memory=2000m
I am trying to get to know how Spark splits a single job (a scala file built using sbt package and the jar is run using spark-submit command) across multiple workers.
For example : I have two workers (512MB memory each). I submit a job and it gets allocated to one worker only (if driver memory is less than the worker memory). In case the driver memory is more than the worker memory, it doesn't get allocated to any worker (even though the combined memory of both workers is higher than the driver memory) and goes to submitted state. This job then goes to running state only when a worker with the required memory is available in the cluster.
I want to know whether one job can be split up across multiple workers and can be run in parallel. If so, can anyone help me with the specific steps involved in it.
Note : the scala program requires a lot of jvm memory since I would be using a large array buffer and hence trying to split the job across multiple workers
Thanks in advance!!
Please check if the array you would be using is parallelized. Then when you do some action on it, it should work in parallel across the nodes.
Check out this page for reference : http://spark.apache.org/docs/0.9.1/scala-programming-guide.html
Make sure your RDD has more than one partition (rdd.partitions.size). Make sure you have more than one executor connected to the driver (http://localhost:4040/executors/).
If both of these are fulfilled, your job should run on multiple executors in parallel. If not, please include code and logs in your question.
I have some confusion about parallelism in Spark and Scala. I am running an experiment in which I have to read many (csv) files from the disk change/ process certain columns and then write it back to the disk.
In my experiments, if I use SparkContext's parallelize method only then it does not seem to have any impact on the performance. However simply using Scala's parallel collections (through par) reduces the time almost to half.
I am running my experiments in localhost mode with the arguments local[2] for the spark context.
My question is when should I use scala's parallel collections and when to use spark context's parallelize?
SparkContext will have additional processing in order to support generality of multiple nodes, this will be constant on the data size so may be negligible for huge data sets. On 1 node this overhead will make it slower than Scala's parallel collections.
Use Spark when
You have more than 1 node
You want your job to be ready to scale to multiple nodes
The Spark overhead on 1 node is negligible because the data is huge, so you might as well choose the richer framework
SparkContext's parallelize may makes your collection suitable for processing on multiple nodes, as well as on multiple local cores of your single worker instance ( local[2] ), but then again, you probably get too much overhead from running Spark's task scheduler an all that magic. Of course, Scala's parallel collections should be faster on single machine.
http://spark.incubator.apache.org/docs/latest/scala-programming-guide.html#parallelized-collections - are your files big enough to be automatically split to multiple slices, did you try setting slices number manually?
Did you try running the same Spark job on single core and then on two cores?
Expect best result from Spark with one really big uniformly structured file, not with multiple smaller files.