I m beginner in apache spark and have installed a prebuilt distribution of apache spark with hadoop. I look to get the consumption or the usage of memory while running the example PageRank implemented within spark. I have my cluster standalone mode with 1 maser and 4 workers (Virtual machines)
I have tried external tools like ganglia and graphite but they give the memory usage at resource or system level (more general) but what i need exactly is "to track the behavior of the memory (Storage, execution) while running the algorithm does it means, memory usage for a spark application-ID ". Is there anyway to get it into text-file for further exploitation? Please help me on this, Thanks
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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
Environments:
Hortonworks Sandbox running HDP 2.5
Hortonworks HDP 2.5 Hadoop cluster managed by Ambari
We are facing a tricky situation. We run Pig script from Hadoop tutorial. Script is working with tiny data. It works fine on a Sandbox. But fails in real cluster where it complains about insufficient memory for the container.
container is running beyond physical memory limit
message can be seen in the logs.
The tricky part is - Sandbox has way less memory available than real cluster (about 3 times less). Also most memory settings in Sandbox (MapReduce memory, Yarn memory, Yarn container sizes) allow much less memory than corresponding settings in a real cluster. Still it is sufficient for Pig in Sandbox but not sufficient in a real cluster.
Another note - Hive queries doing the similar job also work good (in both environements), they do not complain about memory.
Apparently there is some setting somewhere (within Environment 2), which makes Pig to request too much memory? Can please anybody recommend what parameter should be modified to stop Pig script to request too big memory?
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
Initially I had two machines to setup hadoop, spark, hbase, kafka, zookeeper, MR2. Each of those machines had 16GB of RAM. I used Apache Ambari to setup the two machines with the above mentioned services.
Now I have upgraded the RAM of each of those machines to 128GB.
How can I now tell Ambari to scale up all its services to make use of the additional memory?
Do I need to understand how the memory is configured for each of these services?
Is this part covered in Ambari documentation somewhere?
Ambari calculates recommended settings for memory usage of each service at install time. So a change in memory post install will not scale up. You would have to edit these settings manually for each service. In order to do that yes you would need an understanding of how memory should be configured for each service. I don't know of any Ambari documentation that recommends memory configuration values for each service. I would suggest one of the following routes:
1) Take a look at each services documentation (YARN, Oozie, Spark, etc.) and take a look at what they recommend for memory related parameter configurations.
2) Take a look at the Ambari code that calculates recommended values for these memory parameters and use those equations to come up with new values that account for your increased memory.
I used this https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.4.0/bk_installing_manually_book/content/determine-hdp-memory-config.html
Also, Smartsense is must http://docs.hortonworks.com/HDPDocuments/SS1/SmartSense-1.2.0/index.html
We need to define cores, memory, Disks and if we use Hbase or not then script will provide the memory settings for yarn and mapreduce.
root#ttsv-lab-vmdb-01 scripts]# python yarn-utils.py -c 8 -m 128 -d 3 -k True
Using cores=8 memory=128GB disks=3 hbase=True
Profile: cores=8 memory=81920MB reserved=48GB usableMem=80GB disks=3
Num Container=6
Container Ram=13312MB
Used Ram=78GB
Unused Ram=48GB
yarn.scheduler.minimum-allocation-mb=13312
yarn.scheduler.maximum-allocation-mb=79872
yarn.nodemanager.resource.memory-mb=79872
mapreduce.map.memory.mb=13312
mapreduce.map.java.opts=-Xmx10649m
mapreduce.reduce.memory.mb=13312
mapreduce.reduce.java.opts=-Xmx10649m
yarn.app.mapreduce.am.resource.mb=13312
yarn.app.mapreduce.am.command-opts=-Xmx10649m
mapreduce.task.io.sort.mb=5324
Apart from this, we have formulas there to do calculate it manually. I tried with this settings and it was working for me.
We have two systems- One Offline system(Performance is not critical here), where the MapReduce jobs run on the HBase Cluster. The Other is the Online System(Performace is very critical here), where the API reads from the same HBase Cluster. But As the MapReduce jobs running on the same cluster, there are performance issues on the online system. So we are trying to set up separate HBase cluster for Offline system which is the replication of few family names from the Source cluster.
So on the source heavy MapReduce job runs. On the replicated cluster only online system runs giving the best performance.
My Question here is :: Cant we use Snap shot feature in HBase for doing the Same? I also wanted to know what is the difference between them?
If you use snapshot feature for mapreduce, it will also spend cpu, memory and disk io on live hbase cluster nodes too. So if disk io or cpu is the bottleneck for you, a seperate cluster for mapreduce jobs is better solution.