how do I change my MapReduce v1 configuration (eg number of slots) to yarn configuration (eg container virtual memory and virtual cpu)?
There is no longer the concept of a "slot". Instead there are containers, and each container is configured to have a certain amount of RAM and virtual CPU. Go to http://tiny.cloudera.com/yarn-tuning for a detailed writeup on the conversion process.
Related
I am using cloud Dataproc as a cloud service for my research. Running Hadoop and spark job on this platform(cloud) is a bit slower than that of running the same job on a lower capacity virtual machine. I am running my Hadoop job on 3-node cluster(each with 7.5gb RAM and 50GB disk) on the cloud which took 4min49sec, while the same job took 3min20sec on the single node virtual machine(my pc) having 3gb RAM and 27GB disk. Why is the result slower in the cloud with multi-node clustering than on normal pc?
First of all:
not easy to answer without knowing the complete configuration and the type of job your running.
possible reasons are:
missconfiguration
http://HOSTNAME:8080
open ressourcemanager webapp and compare available vcores and memory
job type
Job adds more overhead when running parallelized so that it is slower
hardware
Selected virtual Hardware is slower than the local one. Thourgh low disk io and network overhead
I would say it is something like 1. and 2.
For more detailed answer let me know:
size and type of the job and how you run it.
hadoop configuration
cloud architecture
br
to be a bit more detailed here the numbers/facts which are interesting to find out the reason for the "slower" cloud environment:
job type &size:
size of data 1mb or 1TB
xml , parquet ....
what kind of process (e.g wordcount, format change, ml,....)
and of course the options (executors and drivers ) for your spark-submit or spark-shell
Hadoop Configuration:
do you use a distribution (hortonworks or cloudera?)
spark standalone or in yarn mode
how are nodemangers configured
is there a mapping/translation for the number of hardware systems, cpu cores and their associated memory to the spark-submit tunables of:
executor-memory
executor-cores
num-executors
The application is certaionly bound to have something to do with these tunables, I am however looking for a "basic rule of thumb"
Apache spark is running on yarn with hdfs in cluster mode.
Not all the hardware systems in the spark/hadoop yarn cluster have the same number of cpu cores or RAM.
There is no thumb rule, but after considering
off heap memory
Number of applications and other hadoop dameons running
Resource manager needs
HDFS IO
etc.
You can derive a suitable configuration. Please check this url
I'm trying to run Spark jobs on a Dataproc cluster, but Spark will not start due to Yarn being misconfigured.
I receive the following error when running "spark-shell" from the shell (locally on the master), as well as when uploading a job through the web-GUI and the gcloud command line utility from my local machine:
15/11/08 21:27:16 ERROR org.apache.spark.SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Required executor memory (38281+2679 MB) is above the max threshold (20480 MB) of this cluster! Please increase the value of 'yarn.s
cheduler.maximum-allocation-mb'.
I tried modifying the value in /etc/hadoop/conf/yarn-site.xml but it didn't change anything. I don't think it pulls the configuration from that file.
I've tried with multiple cluster combinations, at multiple sites (mainly Europe), and I only got this to work with the low memory version (4-cores, 15 gb memory).
I.e. this is only a problem on the nodes configured for memory higher than the yarn default allows.
Sorry about these issues you're running into! It looks like this is part of a known issue where certain memory settings end up computed based on the master machine's size rather than the worker machines' size, and we're hoping to fix this in an upcoming release soon.
There are two current workarounds:
Use a master machine type with memory either equal to or smaller
than worker machine types.
Explicitly set spark.executor.memory and spark.executor.cores either using the --conf flag if running from an SSH connection like:
spark-shell --conf spark.executor.memory=4g --conf spark.executor.cores=2
or if running gcloud beta dataproc, use --properties:
gcloud beta dataproc jobs submit spark --properties spark.executor.memory=4g,spark.executor.cores=2
You can adjust the number of cores/memory per executor as necessary; it's fine to err on the side of smaller executors and letting YARN pack lots of executors onto each worker, though you can save some per-executor overhead by setting spark.executor.memory to the full size available in each YARN container and spark.executor.cores to all the cores in each worker.
EDIT: As of January 27th, new Dataproc clusters will now be configured correctly for any combination of master/worker machine types, as mentioned in the release notes.
My company runs hadoop on mesos, and I’m new to mesos. The current limiting rate of the hadoop application I’m in charge of is the speed of reducer tasks, so I was hoping to play around with mesos and hadoop memory settings to speed up the reducer.
Unfortunately, I don’t understand the relationship between hadoop memory settings and mesos memory configuration, and I suspect that mesos may be overriding some of my hadoop memory settings.
Is changing the value of mapreduce.reduce.java.opts or mapreduce.reduce.memory.mb (in /etc/hadoop/conf/mapred-site.xml) affected by mesos? Does mesos limit the amount of memory that I can allocate to the reducer?
If so, where are the config files in mesos so I can change those settings?
Thanks!
9/30/2015 Update:
The file at https://github.com/mesos/hadoop/blob/master/configuration.md lists parameters that you can put in your mapred-site.xml file.
I'm still not sure how those parameters affect the memory-associated hadoop configuration parameters in mapred-site.xml.
The configuration is described in the respective GitHub repo mesos/hadoop.
I am running Spark using YARN(Hadoop 2.6) as cluster manager. YARN is running in Pseudo distributed mode. I have started the spark shell with 6 executors and was expecting the same
spark-shell --master yarn --num-executors 6
But whereas in the Spark Web UI, I see only 4 executors
Any reason for this?
PS : I ran the nproc command in my Ubuntu(14.04) and give below is the result. I believe this mean, my system has 8 cores
mountain#mountain:~$ nproc
8
did you take in account spark.yarn.executor.memoryOverhead?
possobly it creates hiden memory requrement and finaly yarn could not provide whole resources.
also, note that yarn round container size to yarn.scheduler.increment-allocation-mb.
all detail here:
http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/
This happens when there are not enough resources on your cluster to start more executors. Following things are taken into account
Spark executor runs inside a yarn container. This container size is determined from the value of yarn.scheduler.minimum-allocation-mb in yarn-site.xml. Check this property. If your existing containers consume all available memory then more memory will not be available for new containers. so no new executors will be started
The storage memory column in the UI displays the amount of memory used for execution and RDD storage. By default, this equals (HEAP_SPACE - 300MB) * 75%. The rest of the memory is used for internal metadata, user data structures and other stuffs. ref(Spark on YARN: Less executor memory than set via spark-submit)
I hope this helps.