Here the steps i should do it
1- Download a Storm release , unpack it, and put the unpacked bin/
directory on your PATH
2- To be able to start and stop topologies on a remote cluster
, put the cluster information in ~/.storm/storm.yaml
i downloaded storm release and setting up it
i want to do that
"put the unpacked bin/ directory on your PATH"
cause i can't use storm as a command
and the second step what's the cluster info should i do in storm.yaml ?
Let us say your storm is unpacked at /home/your/download/storm
define that as an env variable by
STORM_HOME="/home/your/download/storm"
export this variable by
export STORM_HOME
do not forget to import the path
export PATH=$PATH:$STORM_HOME/bin
then you can use storm command in it.
Making it more simple with a single export command.
I downloaded apache storm from here and Lets say My download path is /home/username/Downloads/apache-storm-1.0.5
Change your actual path in this command and hit this at terminal.
export PATH=$PATH:/home/username/Downloads/apache-storm-1.0.5/bin
all done.
Related
While analyzing the yarn launch_container.sh logs for a spark job, I got confused by some part of log.
I will point out those asks step by step here
When you will submit a spark job with spark-submit having --pyfiles and --files on cluster mode on YARN:
The config files passed in --files , executable python files passed in --pyfiles are getting uploaded into .sparkStaging directory created under user hadoop home directory.
Along with these files pyspark.zip and py4j-version_number.zip from $SPARK_HOME/python/lib is also getting copied
into .sparkStaging directory created under user hadoop home directory
After this launch_container.sh is getting triggered by yarn and this will export all env variables required.
If we have exported anything explicitly such as PYSPARK_PYTHON in .bash_profile or at the time of building the spark-submit job in a shell script or in spark_env.sh , the default value will be replaced by the value which we
are providing
This PYSPARK_PYTHON is a path in my edge node.
Then how a container launched in another node will be able to use this python version ?
The default python version in data nodes of my cluster is 2.7.5.
So without setting this pyspark_python , containers are using 2.7.5.
But when I will set pyspark_python to 3.5.x , they are using what I have given.
It is defining PWD='/data/complete-path'
Where this PWD directory resides ?
This directory is getting cleaned up after job completion.
I have even tried to run the job in one session of putty
and kept the /data folder opened in another session of putty to see
if any directories are getting created on run time. but couldn't find any?
It is also setting the PYTHONPATH to $PWD/pyspark.zip:$PWD/py4j-version.zip
When ever I am doing a python specific operation
in spark code , its using PYSPARK_PYTHON. So for what purpose this PYTHONPATH is being used?
3.After this yarn is creating softlinks using ln -sf for all the files in step 1
soft links are created for for pyspark.zip , py4j-<version>.zip,
all python files mentioned in step 1.
Now these links are again pointing to '/data/different_directories'
directory (which I am not sure where they are present).
I know soft links can be used for accessing remote nodes ,
but here why the soft links are created ?
Last but not the least , whether this launch_container.sh will run for each container launch ?
Then how a container launched in another node will be able to use this python version ?
First of all, when we submit a Spark application, there are several ways to set the configurations for the Spark application.
Such as:
Setting spark-defaults.conf
Setting environment variables
Setting spark-submit options (spark-submit —help and —conf)
Setting a custom properties file (—properties-file)
Setting values in code (exposed in both SparkConf and SparkContext APIs)
Setting Hadoop configurations (HADOOP_CONF_DIR and spark.hadoop.*)
In my environment, the Hadoop configurations are placed in /etc/spark/conf/yarn-conf/, and the spark-defaults.conf and spark-env.sh is in /etc/spark/conf/.
As the order of precedence for configurations, this is the order that Spark will use:
Properties set on SparkConf or SparkContext in code
Arguments passed to spark-submit, spark-shell, or pyspark at run time
Properties set in /etc/spark/conf/spark-defaults.conf, a specified properties file
Environment variables exported or set in scripts
So broadly speaking:
For properties that apply to all jobs, use spark-defaults.conf,
for properties that are constant and specific to a single or a few applications use SparkConf or --properties-file,
for properties that change between runs use command line arguments.
Now, regarding the question:
In Cluster mode of Spark, the Spark driver is running in container in YARN, the Spark executors are running in container in YARN.
In Client mode of Spark, the Spark driver is running outside of the Hadoop cluster(out of YARN), and the executors are always in YARN.
So for your question, it is mostly relative with YARN.
When an application is submitted to YARN, first there will be an ApplicationMaster container, which nigotiates with NodeManager, and is responsible to control the application containers(in your case, they are Spark executors).
NodeManager will then create a local temporary directory for each of the Spark executors, to prepare to launch the containers(that's why the launch_container.sh has such a name).
We can find the location of the local temporary directory is set by NodeManager's ${yarn.nodemanager.local-dirs} defined in yarn-site.xml.
And we can set yarn.nodemanager.delete.debug-delay-sec to 10 minutes and review the launch_container.sh script.
In my environment, the ${yarn.nodemanager.local-dirs} is /yarn/nm, so in this directory, I can find the tempory directories of Spark executor containers, they looks like:
/yarn/nm/nm-local-dir/container_1603853670569_0001_01_000001.
And in this directory, I can find the launch_container.sh for this specific container and other stuffs for running this container.
Where this PWD directory resides ?
I think this is a special Environment Variable in Linux OS, so better not to modify it unless you know how it works percisely in your application.
As per above, if you export this PWD environment at the runtime, I think it is passed to Spark as same as any other Environment Variables.
I'm not sure how the PYSPARK_PYTHON Environment Variable is used in Spark's launch scripts chain, but here you can find the instruction in the official documentation, showing how to set Python binary executable while you are using spark-submit:
spark-submit --conf spark.pyspark.python=/<PATH>/<TO>/<FILE>
As for the last question, yes, YARN will create a temp dir for each of the containers, and the launch_container.sh is included in the dir.
I am in the process of installing Spark in a shared cluster environment. We've decided to go with spark standalone mode, and are using the "start-all.sh" command included in sbin to launch the Spark workers. Due to the shared architecture of the cluster, SPARK_HOME is in a common directory not writeable by users. Therefore, we're creating "run" directories in the user's scratch, into which SPARK_CONF_DIR, log directory, and work directories can be pointed.
The problem is that SPARK_CONF_DIR is never set on the worker nodes, so they default to $SPARK_HOME/conf, which has only the templates. What I want to do is pass through SPARK_CONF_DIR from the master node to the slave nodes. I've identified a solution, but it requires a patch to sbin/start-slaves.sh:
sbin/start_slaves.sh
46c46
< "${SPARK_HOME}/sbin/slaves.sh" cd "${SPARK_HOME}" \; export SPARK_CONF_DIR=${SPARK_CONF_DIR} \; "$SPARK_HOME/sbin/start-slave.sh" "spark://$SPARK_MASTER_HOST:$SPARK_MASTER_PORT"
---
> "${SPARK_HOME}/sbin/slaves.sh" cd "${SPARK_HOME}" \; "${SPARK_HOME}/sbin/start-slave.sh" "spark://$SPARK_MASTER_HOST:$SPARK_MASTER_PORT"
Are there are any better solutions here that do not require a patch to the Spark source code?
One solution, of course, would be to copy and rename start-all.sh and start-slaves.sh and use those instead of sbin/start-all.sh. But is there anything more elegant?
Thank you for your time.
If you want to run standalone mode, you can try to setup SPARK_CONF_DIR on your program. Take pyspark for example:
import os
from pyspark.sql import SparkSession
os.environ["SPARK_CONF_DIR"] = "/path/to/configs/conf1"
spark = SparkSession.builder.getOrCreate()
If I start the Cassandra service everything is ok, but when I try to start Cassandra on the foreground using "cassandra -f" I get the following error:
Error: Could not find or load main class
Files\DataStax-DDC\apache-cassandra.logs.gc.log
Do I need to configure anything in particular to run Cassandra in the foreground?
Looks like the space in your "Program Files" directory is not escaped in your CASSANDRA_HOME environment variable. It gets set in your cassandra-env.ps1 (in conf/) config file, you could manually set it.
I want to do some ip to location computation on spark, after exploring the net ,find IPLocator https://github.com/miraclesu/IPLocator,
the IP to location need to use a file which contains the mapping information.
After packaging the jar, I can run it through on using local java, the package just runs with the IPLocator.jar and qqwry.dat in the same directory.
But I want to use this jar using spark , I tryed to use --jars IPLocator.jar qqwry.dat when starting spark-shell, but when launching , the functions still can not read get the file .
the file reading code is like
QQWryFile.class.getClassLoader().getResource("qqwry.dat")
I also tried to package qqwry.dat file into the jar, and It did not work.
You need to use --files and then SparkFiles.get inside of your program
Try to use comma delimitor and check if IPLocator.jar and qqwry.dat are distributed to spark staging folder(.sparkStaging/application_xxx).
--jars IPLocator.jar,qqwry.dat
Deploy the presto on single node . when running Presto-cli ,i got following errors:
presto:default> select * from test1;
Query 20131116_233859_00005_5a2yh failed: MetaException(message:org.apache.hadoop.hive.serde2.SerDeException SerDe org.apache.hadoop.hive.contrib.serde2.RegexSerDe does not exist)
hive is Operating normally. why prestodb was failed?
my profile:
export JAVA_HOME=/usr/java
export JRE_HOME=/usr/java/jre
export HADOOP_HOME=/usr/hadoop
export HIVE_HOME=/usr/hive
export PRESTO_HOME=/usr/presto
export CLASSPATH=:.:$CLASSPATH:$JAVA_HOME/lib:$JRE_HOME:`find /usr/hadoop -name '*.jar' | grep -v 'test' | grep -v 'example' | perl -e '#jars=<STDIN>;chomp #jars; print join(":",#jars);'`:$PRESTO_HOME/lib:$HADOOP/lib:$HIVE_HOME/lib
export PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PRESTO_HOME/bin:$HIVE_HOME/bin
export HADOOP_HEAPSIZE=4096
I got it,when the table of hive was created by RegEx, the presto-cli running failed.
I had no idea. somebody help me please!
On Master node:
Place your RegexSerDe.jar into hive connectors plugin directory related to your hadoop distribution. (Ex: for hadoop2 distribution you may place JAR file to ../presto/plugin/hive-hadoop2/)
Make sure the RegexSerDe.jar file has correct ownership as other jar files present in this directory.
Restart the presto-server process. (sudo service presto-server restart). If this do not work, you may need to restart with launcher sudo /usr/lib/presto/bin/launcher restart
Repeat this on all slave nodes !
Followed the same process as you mentioned above getting same error any suggestions please.
Query failed: org/apache/hadoop/hive/serde2/SerDe
Using hive-hadoop2 version placed the serde jar in the following path
presto/plugin/hive-hadoop2/hive-serde-1.0.0.jar and restarted presto.
You have to put the SerDe jar inside the plugin directory (plugin/hive-cdh4).
I didn't use RegexSerDe but it worked for CSVSerDe.