I am new to Pyspark. I would like to learn one while solving a Kaggle Challenge using a large dataset.
Does Pyspark offer performance advantage over Pandas when using on a local system? Or does it not matter?
When running locally, pyspark runs with as many worker threads as logical cores available on your machine - if you run spark.sparkContext.master, it should return local[*] (more information on local configurations can be found here). Since Pandas is single threaded (unless you're using something like Dask), for large datasets, Pyspark should be more performant. However, due to the overhead associated with using multiple threads, serializing data and sending to the JVM, etc., Pandas may be faster for smaller datasets.
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.
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!
I have a requirement to parallelize the Scala Data Frames to load various tables. I have a fact table that is having around 1.7 TB of data. This is taking around 5 minutes to load. I want to concurrently load my dimension tables so that I can reduce my overall scala . I am not well versed with Concurrent API in Scala?.
You need to read up on Spark - the whole point of it is to parallelize processing of data beyond the scope of single machine. Essentially Spark will parallelize the load by as many tasks you'd have running in parallel - It all depends on how you set your cluster - from the question I am guessing you only use on and that you ran it in local model in which case you should at least run it with local[number of processors you have]
If I didn't make it clear you shouldn't also use any other Scala concurrency APIs
I have a folder with 150 G of txt files (around 700 files, on average each 200 MB).
I'm using scala to process the files and calculate some aggregate statistics in the end. I see two possible approaches to do that:
manually loop through all the files, do the calculations per file and merge the results in the end
read the whole folder to one RDD, do all the operations on this single RDD and let spark do all the parallelization
I'm leaning towards the second approach as it seems cleaner (no need for parallelization specific code), but I'm wondering if my scenario will fit the constraints imposed by my hardware and data. I have one workstation with 16 threads and 64 GB of RAM available (so the parallelization will be strictly local between different processor cores). I might scale the infrastructure with more machines later on, but for now I would just like to focus on tunning the settings for this one workstation scenario.
The code I'm using:
- reads TSV files, and extracts meaningful data to (String, String, String) triplets
- afterwards some filtering, mapping and grouping is performed
- finally, the data is reduced and some aggregates are calculated
I've been able to run this code with a single file (~200 MB of data), however I get a java.lang.OutOfMemoryError: GC overhead limit exceeded
and/or a Java out of heap exception when adding more data (the application breaks with 6GB of data but I would like to use it with 150 GB of data).
I guess I would have to tune some parameters to make this work. I would appreciate any tips on how to approach this problem (how to debug for memory demands). I've tried increasing the 'spark.executor.memory' and using a smaller number of cores (the rational being that each core needs some heap space), but this didn't solve my problems.
I don't need the solution to be very fast (it can easily run for a few hours even days if needed). I'm also not caching any data, but just saving them to the file system in the end. If you think it would be more feasible to just go with the manual parallelization approach, I could do that as well.
Me and my team had processed a csv data sized over 1 TB over 5 machine #32GB of RAM each successfully. It depends heavily what kind of processing you're doing and how.
If you repartition an RDD, it requires additional computation that
has overhead above your heap size, try loading the file with more
paralelism by decreasing split-size in
TextInputFormat.SPLIT_MINSIZE and TextInputFormat.SPLIT_MAXSIZE
(if you're using TextInputFormat) to elevate the level of
paralelism.
Try using mapPartition instead of map so you can handle the
computation inside a partition. If the computation uses a temporary
variable or instance and you're still facing out of memory, try
lowering the number of data per partition (increasing the partition
number)
Increase the driver memory and executor memory limit using
"spark.executor.memory" and "spark.driver.memory" in spark
configuration before creating Spark Context
Note that Spark is a general-purpose cluster computing system so it's unefficient (IMHO) using Spark in a single machine
To add another perspective based on code (as opposed to configuration): Sometimes it's best to figure out at what stage your Spark application is exceeding memory, and to see if you can make changes to fix the problem. When I was learning Spark, I had a Python Spark application that crashed with OOM errors. The reason was because I was collecting all the results back in the master rather than letting the tasks save the output.
E.g.
for item in processed_data.collect():
print(item)
failed with OOM errors. On the other hand,
processed_data.saveAsTextFile(output_dir)
worked fine.
Yes, PySpark RDD/DataFrame collect() function is used to retrieve all the elements of the dataset (from all nodes) to the driver node. We should use the collect() on smaller dataset usually after filter(), group(), count() etc. Retrieving larger dataset results in out of memory.
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.