Install Spark on the cluster: Difference between revisions
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Test run with: | Test run with: | ||
spark-submit spark-test.py | spark-submit spark-test.py | ||
<!-- | |||
# must upload tweets-id-text-100000.jl to spark-driver and then into hdfs | |||
hdfs dfs -put ~/tweets_id_text_100000.jl raw-data | |||
# working directory for python | |||
mkdir -p ~/volume/python | |||
cd ~/volume/python | |||
sudo apt install emacs | |||
# edit exercise1.py, first to read form big file, then from hdfs | |||
sudo apt install python3-pip python3-dev python3-venv | |||
python3 -m venv venv | |||
. venv/bin/activate | |||
python3 -m pip install --upgrade pip | |||
pip install findspark | |||
# because SPARK_HOME is set, findspark is not needed | |||
->> | |||
'''Task:''' Run ''exercise1.py'' from Exercise 1, for example with '''spark-submit exercise1.py''. A few tips: | |||
* Use the large dataset in ''tweets_id_text_100000.jl'' (i.e., '''small_dataset = False'''). | |||
* Because SPARK_HOME is set, you do not need ''findspark''. | |||
* WHen you run on top of YARN, Spark expects to find files in HDFS, not in the regular file system. | |||
* Use the HDFS and YARN web UIs to look a little at what is going on. | |||
* The default replication factors and other settings are not clever. Don't worry about that now: they are enough to get you started. | |||
'''Task:''' Run the full Twitter pipeline from Exercise 3. | |||
== Web UIs == | == Web UIs == | ||
While spark is running, you can attempt to access Spark's web UI at http://158.39.201.197:4040 (assuming that 158.39.201.197 is the IPv4 address of ''spark-driver''). But when you run on top of YARN it just attempts to redirect to YARN's web UI at http://158.39.201.197:8088 , which you have accessed already. | While spark is running, you can attempt to access Spark's web UI at http://158.39.201.197:4040 (assuming that 158.39.201.197 is the IPv4 address of ''spark-driver''). But when you run on top of YARN it just attempts to redirect to YARN's web UI at http://158.39.201.197:8088 , which you have accessed already. | ||
Revision as of 16:42, 17 October 2022
Install Spark on the cluster
Install Spark
Go to Apache Spark Downloads. Download and unpack a recent Spark binary. For example, on each instance:
cd ~/volume wget https://dlcdn.apache.org/spark/spark-3.3.0/spark-3.3.0-bin-hadoop3.tgz tar xzvf spark-3.3.0-bin-hadoop3.tgz rm spark-3.3.0-bin-hadoop3.tgz ln -fns spark-3.3.0-bin-hadoop3 spark
Set $SPARK_HOME and add Spark binaries to path:
export SPARK_HOME=/home/ubuntu/volume/spark
export PATH=${PATH}:${SPARK_HOME}/bin:${SPARK_HOME}/sbin
cp ~/.bashrc ~/.bashrc-bkp-$(date --iso-8601=minutes --utc)
echo "export SPARK_HOME=/home/ubuntu/volume/spark" >> ~/.bashrc
echo "export PATH=\${PATH}:\${SPARK_HOME}/bin:\${SPARK_HOME}/sbin" >> ~/.bashrc
On your local machine, create the file spark-defaults.conf:
spark.master yarn spark.driver.memory 512m spark.yarn.am.memory 512m spark.executor.memory 512m
From the local machine, upload spark-defaults.conf to each instance:
scp spark-defaults.conf spark-driver:volume/spark/conf scp spark-defaults.conf spark-worker-1:volume/spark/conf ...
Test run Spark
On spark-driver, start Spark with:
SPARK_PUBLIC_DNS=$MASTER_NODE pyspark
SPARK_PUBLIC_DNS is the public IP address that Spark's web UI listens to at port 4040. To set it permanently:
export SPARK_PUBLIC_DNS=$MASTER_NODE echo "export SPARK_PUBLIC_DNS=$MASTER_NODE" >> ~/.bashrc
Create a test program, e.g., spark-test.py:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.master("local") \
.appName("Word Count") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
print('*** The result row is',
spark.range(5000).where("id > 500").selectExpr("sum(id)").collect(),
'***')
Test run with:
spark-submit spark-test.py
