I want to run a simple spark program, but i am restricted by some errors.
My Environment is:
CentOS:6.6
Java: 1.7.0_51
Scala: 2.10.4
Spark: spark-1.4.0-bin-hadoop2.6
Mesos: 0.22.1
All are installed and nodes are up.Now i have one Mesos master and Mesos slave node. My spark properties are below:
spark.app.id 20150624-185838-2885789888-5050-1291-0005
spark.app.name Spark shell
spark.driver.host 192.168.1.172
spark.driver.memory 512m
spark.driver.port 46428
spark.executor.id driver
spark.executor.memory 512m
spark.executor.uri http://192.168.1.172:8080/spark-1.4.0-bin-hadoop2.6.tgz
spark.externalBlockStore.folderName spark-91aafe3b-01a8-4c86-ac3b-999e278807c5
spark.fileserver.uri http://192.168.1.172:51240
spark.jars
spark.master mesos://zk://192.168.1.172:2181/mesos
spark.mesos.coarse true
spark.repl.class.uri http://192.168.1.172:51600
spark.scheduler.mode FIFO
Now when I started spark, it comes to scala prompt(scala>).
After that I am getting following error: mesos task 1 is now TASK_FAILED, blacklisting mesos slave value due to too many failures is Spark installed on it
How to resolve this.
With only 900MB and spark.driver.memory = 512m, you will be able to launch the scheduler/REPL, but you won't have enough memory for spark.executor.memory = 512m, so any tasks will fail. Either increasing your VM memory size or reducing the driver/executor memory requirements will help you get around these memory limits.
Could you check the mesos slave logs/ task information for more output on why the task failed. You could have a look at :5050.
Probably unrelated question: Do you actually have zookeeper:
spark.master mesos://zk://192.168.1.172:2181/mesos
running (as you mentioned you only have one master)?
Related
I have 2 machines with 32gb ram and 8core each machine. So how can i configure the yarn with spark and which properties i have to use to tune the resources according to our dataset. I have 8gb dataset, So can anyone suggest the configuration of yarn with spark in parallel jobs running?
Here is the yarn configuration:
I'm using hadoop 2.7.3,spark 2.2.0 and ubuntu 16
`yarn scheduler minimum-allocation-mb--2048
yarn scheduler maximum-allocation-mb--5120
yarn nodemanager resource.memory-mb--30720
yarn scheduler minimum-allocation-vcores--1
yarn scheduler maximum-allocation-vcores--6
yarn nodemanager resource.cpu-vcores--6`
Here is the spark configuration:
spark master master:7077
spark yarn am memory 4g
spark yarn am cores 4
spark yarn am memoryOverhead 412m
spark executor instances 3
spark executor cores 4
spark executor memory 4g
spark yarn executor memoryOverhead 412m
but my question is with 32gb ram and 8core each machine. how many applications i can run whether this conf is correct? bcoz only two applications running parallely.
I seem to be unable to assign cores to an application. This leads to the following (apparently common) error message:
Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
I have one master and two slaves in a Spark cluster. All are 8-core i7s with 16GB of RAM.
I have left the spark-env.sh virtually virgin on all three, just specifying the master's IP address.
My spark-submit is the following:
nohup ./bin/spark-submit
--jars ./ikoda/extrajars/ikoda_assembled_ml_nlp.jar,./ikoda/extrajars/stanford-corenlp-3.8.0.jar,./ikoda/extrajars/stanford-parser-3.8.0.jar \
--packages datastax:spark-cassandra-connector:2.0.1-s_2.11 \
--class ikoda.mlserver.Application \
--conf spark.cassandra.connection.host=192.168.0.33 \
--conf spark.cores.max=4 \
--driver-memory 4g –num-executors 2 --executor-memory 2g --executor-cores 2 \
--master spark://192.168.0.141:7077 ./ikoda/ikodaanalysis-mlserver-0.1.0.jar 1000 > ./logs/nohup.out &
I suspect I am conflating the sparkConf initialization in my code with the spark-submit. I need this as the app involves SparkStreaming which can require reinitializing the SparkContext.
The sparkConf setup is as follows:
val conf = new SparkConf().setMaster(s"spark://$sparkmaster:7077").setAppName("MLPCURLModelGenerationDataStream")
conf.set("spark.streaming.stopGracefullyOnShutdown", "true")
conf.set("spark.cassandra.connection.host", sparkcassandraconnectionhost)
conf.set("spark.driver.maxResultSize", sparkdrivermaxResultSize)
conf.set("spark.network.timeout", sparknetworktimeout)
conf.set("spark.jars.packages", "datastax:spark-cassandra-connector:"+datastaxpackageversion)
conf.set("spark.cores.max", sparkcoresmax)
The Spark UI shows the following:
OK, this is definitely a case of programmer error.
But maybe others will make a similar error. The Master had been used as a local Spark previously. I had put some executor settings in spark-defaults.conf and then months later had forgotten about this.
There is a cascading hierarchy whereby SparkConf settings get precedence, then spark-submit settings and then spark-defaults.conf. spark-defaults.conf overrides defaults set by Apache Spark team
Once I removed the settings from spark-defaults, all was fixed.
It is because of the maximum of your physical memory.
your spark memory in spark UI 14.6GB So you must request memory for each executor below the volume
14.6GB, for this you can add config to your spark conf something like this:
conf.set("spark.executor.memory", "10gb")
if you request more than your physical memory spark does't allocate cpu cores to your job and display 0 in Cores in spark UI and run NOTHING.
I'm trying to setup a standalone Spark 2.0 server to process an analytics function in parallel. To do this I want to run 8 workers, with a single core per each worker. However, the Spark Master/Worker UI doesn't seem to be reflecting my configuration.
I'm using :
Standalone Spark 2.0
8 Cores 24gig RAM
windows server 2008
pyspark
spark-env.sh file is configured as follows:
SPARK_WORKER_INSTANCES = 8
SPARK_WORKER_CORES = 1
SPARK_WORKER_MEMORY = 2g
spark-defaults.conf is configured as follows:
spark.cores.max = 8
I start the master:
spark-class org.apache.spark.deploy.master.Master
I start the workers by running this command 8 times within a batch file:
spark-class org.apache.spark.deploy.worker.Worker spark://10.0.0.10:7077
The problem is that the UI shows up as follows:
As you can see each worker has 8 cores instead of the 1 core I have assigned it via the SPARK_WORKER_CORES setting. Also the memory is reflective of the entire machine memory not the 2g assigned to each worker. How can I configure Spark to run with 1 core/2g per each worker in standalone mode?
I fixed this to adding the cores and memory arguments to the worker itself.
start spark-class org.apache.spark.deploy.worker.Worker --cores 1 --memory 2g spark://10.0.0.10:7077
I have a apache spark full stack+ Apache zeppelin running on a machine with very little resources (512MB) which is crashing.
Spark Command: /usr/lib/jvm/java/bin/java -cp /home/ec2-user/spark-1.4.1-bin-hadoop2.6/sbin/../conf/:/home/ec2-user/spark-1.4.1-bin-hadoop2.6/lib/spark-assembly-1.4.1-hadoop2.6.0.jar:/home/ec2-user/spark-1.4.1-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/home/ec2-user/spark-1.4.1-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/home/ec2-user/spark-1.4.1-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar -Xms512m -Xmx512m -XX:MaxPermSize=256m org.apache.spark.deploy.master.Master --ip ip-172-31-24-107 --port 7077 --webui-port 8080
========================================
OpenJDK 64-Bit Server VM warning: INFO: os::commit_memory(0x00000000daaa0000, 357957632, 0) failed; error='Cannot allocate memory' (errno=12)
#
# There is insufficient memory for the Java Runtime Environment to continue.
# Native memory allocation (malloc) failed to allocate 357957632 bytes for committing reserved memory.
# An error report file with more information is saved as:
# /tmp/jvm-17290/hs_error.log
I know this is a bad idea, but I don't have anywhere else to test it and would like to be able to learn some code in scala + apache spark...
Is there a way I can reduce the memory footprint on spark so I can do my tests?
thanks
Apache zeppelin is a great tool, but I have seen the same thing, takes up a lot of RAM. You could use the command like, in the spark home folder, bin/spark-shell will give you a spark scala shell, but its not pretty and intuitive to use.
You can use Eclipse (scala IDE) or IntelliJ (has a scala plugin)for spark scala development, just need to add the jars with maven or sbt.
You can do your prototyping in the scala shell and copy and paste into the IDE.
Also check out https://github.com/andypetrella/spark-notebook, it takes a smaller RAM foot print. Spark by it self takes less, but zeppelin takes a lot of space, from what I have seen.
Also for scala notebook: https://github.com/alexarchambault/jupyter-scala, then you can add the spark jars to the env, create sparkContext object, and use it.
While I'm starting Spark shell:
bin>./spark-shell
I get the following error :
Spark assembly has been built with Hive, including Data nucleus jars on classpath
Welcome to SPARK VERSION 1.3.0
Using Scala version 2.10.4 (Java HotSpot(TM) Server VM, Java 1.7.0_75)
Type in expressions to have them evaluated.
Type :help for more information.
15/05/10 12:12:21 ERROR SparkDeploySchedulerBackend: Application has been killed. Reason: All masters are unresponsive! Giving up.
15/05/10 12:12:21 ERROR TaskSchedulerImpl: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.
I have installed spark by follow below link :- http://www.philchen.com/2015/02/16/how-to-install-apache-spark-and-cassandra-stack-on-ubuntu
You should supply your Spark Cluster's Master URL when start a spark-shell
At least:
bin/spark-shell --master spark://master-ip:7077
All the options make up a long list and you can find the suitable ones yourself:
bin/spark-shell --help
I am assuming that you are running this in standalone/local mode.
Run your spark shell with following line. That indicates you are using all the available cores of your master which is local machine.
bin/spark-shell --master local[*]
http://spark.apache.org/docs/1.2.1/submitting-applications.html#master-urls
You also need to start spark master and slave before giving spark-submit command
start-master.sh
start-slave.sh spark://spark:7077
then use
spark-submit --master spark://spark:7077
Look at your log files for "permission denied" errors... It may happens that your client service doesn't have the proper authority to access your Master folders.