I am facing some issue while running a spark java program that reads a file, do some manipulation and then generates output file at a given path.
Every thing works fine when master and slaves are on same machine .ie: in Standalone-cluster mode.
But problem started when I deployed same program in multi machine multi node cluster set up. That means the master is running at x.x.x.102 and slave is running on x.x.x.104.
Both the master -slave have shared their SSH keys and are reachable from each other.
Initially slave was not able to read input file , for that I came to know I need to call sc.addFile() before sc.textFile(). that solved issue. But now I see output is being generated on slave machine in a _temporary folder under the output path. ie: /tmp/emi/_temporary/0/task-xxxx/part-00000
In local cluster mode it works fine and generates output file in /tmp/emi/part-00000.
I came to know that i need to use SparkFiles.get(). but i am not able to understand how and where to use this method.
till now I am using
DataFrame dataobj = ...
dataObj.javaRDD().coalesce(1).saveAsTextFile("file:/tmp/emi");
Can any one please let me know how to call SparkFiles.get()?
In short how can I tell slave to create output file in the machine where driver is running?
Please help.
Thanks a lot in advance.
There is nothing unexpected here. Each worker writes its own part of the data separately. Using file scheme only means that data is writer to a file in the file system local from the worker perspective.
Regarding SparkFiles it is not applicable in this particular case. SparkFiles can be used to distribute common files to the worker machines not to deal with the results.
If for some reason you want to perform writes on the machine used to run driver code you'll have to fetch data to the driver machine first (either collect which requires enough memory to fit all data or toLocalIterator which collects partition at the time and requires multiple jobs) and use standard tools to write results to local file system. In general though writing to driver is not a good practice and most of the time is simply useless.
Related
I am currently running/experimenting with Spark in a Windows environment and have noticed a large number of orphaned blockmgr folders and rdd files. These are being created when I have insufficient memory to cache a full data set.
I suspect that they are being left behind when processes fail.
At the moment, I am manually deleting them from time to time (when I run out of disk space..... ). I have also toyed around with a simple file operation script.
I was wondering, is there any pyspark functions or scripts available that would clean these up, or any way to check for them when a process is initiated?
Thanks
As per #cronoik, this was solved by setting the following properties:
spark.worker.cleanup.enabled true
In my instance, using both 'local' and 'standalone' modes on a single node Windows environment, I have set this within spark-defaults.conf file.
Refer to the documentation for more information: Spark Standalone Mode
I have a spark application which runs in local mode currently and writes an output to a file in a local UNIX directory.
Now, I want to run the same job in yarn cluster mode and still want to write into that UNIX folder.
Can I use the same saveAsTextFile(path)?
Yes, you can. But it is not the best practice to do that. The spark itself can run standalone and on distributed file system. The reason we are using distributed file system is that the data is huge and the expected output might be huge.
So, if you are compeltely sure that the output will fit in to your local file system, go for it or you can save it to your local storage using the below command.
bin/hadoop fs -copyToLocal /hdfs/source/path /localfs/destination/path
I am new to spark and just wanted to check on a problem that i am facing. My objective was to read a nested xml file, flatten it out and save it as a csv file. I wrote the code. It works really fine in pyspark in my cluster. When i write the code line by line in pyspark i can see the executors from different nodes being assigned the worker processes. Now the problem is that when i run the same code as a python script, the executors from different nodes are not allotted. The worker process starts on the node i am running the script on and does not get parallelized. Consequently it is taking much longer time to do the processing. I am attaching the screenshot of the warning with this post.
Has anyone faced it as well? Thank you in anticipation.
Also i dont own this cluster but i am working on it for someone. So i have no idea about how many nodes are there.
I did get it working. I was not initializing the configuration properly.
I had specified setmaster to local in the spark configuration.
I just removed that property tag and the app started distributing across the available executors even while running the script.
I'm just getting started using Apache Spark. I'm using cluster mode (master, slave1, slave2) and I want to process a big file which is kept in Hadoop (hdfs). I am using the textFile method from SparkContext; while the file is being processing I monitorize the nodes and I can see that just the slave2 is working. After processing, slave2 has tasks but slave1 has no task.
If instead of using a hdfs I use a local file then both slaves work simultaneously.
I don't get why this behaviour. Please, can anybody give me a clue?
The main reason of that behavior is the concept of data locality. When Spark's Application Master asks for the creation of new executors, they are tried to be allocated in the same node where data resides.
I.e. in your case, HDFS is likely to have written all the blocks of the file on the same node. Thus Spark will instantiate the executors on that node. Instead, if you use a local file, it will be present in all nodes, so data locality won't be an issue anymore.
I am using Spark 1.3.1 in standalone mode (No YARN/HDFS involved - Only Spark) on a cluster with 3 machines. I have a dedicated node for master (no workers running on it) and 2 separate worker nodes.
The cluster starts healthy, and I am just trying to test my installation by running some simple examples via spark-shell (CLI - which I started on the master machine) : I simply put a file on the localfs on the master node (workers do NOT have a copy of this file) and I simply run:
$SPARKHOME/bin/spark-shell
...
scala> val f = sc.textFile("file:///PATH/TO/LOCAL/FILE/ON/MASTER/FS/file.txt")
scala> f.count()
and it returns the words count results correctly.
My Questions are:
1) This contradicts with what spark documentation (on using External Datasets) say as:
"If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system."
I am not using NFS and I did not copy the file to workers, so how does it work ? (Is it because spark-shell does NOT really launch jobs on the cluster, and does the computation locally (It is weird as I do NOT have a worker running on the node, I started shell on)
2) If I want to run SQL scripts (in standalone mode) against some large data files (which do not fit into one machine) through Spark's thrift server (like the way beeline or hiveserver2 is used in Hive) , do I need to put the files on NFS so each worker can see the whole file, or is it possible that I create chunks out of the files, and put each smaller chunk (which would fit on a single machine) on each worker, and then use multiple paths (comma separated) to pass them all to the submitted queries ?
The problem is that you are running the spark-shell locally. The default for running a spark-shell is as --master local[*], which will run your code locally on as many cores as you have. If you want to run against your workers, then you will need to run with the --master parameter specifying the master's entry point. If you want to see the possible options you can use with spark-shell, just type spark-shell --help
As to whether you need to put the file on each server, the short answer is yes. Something like HDFS will split it up across the nodes and the manager will handle the fetching as appropriate. I am not as familiar with NFS and if it has this capability, though