Skip to content

Latest commit

 

History

History
91 lines (62 loc) · 3.44 KB

README.md

File metadata and controls

91 lines (62 loc) · 3.44 KB
Raw

MAPD2021

Lectures for Management and Analysis of Physics Datasets course Msc in Physic of Data at University of Padua 2020-2021.

Docker

It is possible to run all the exercises using docker. This is particularly usefull if you are experiencing problems with virtual or you don't want to go to the trouble of setting up the environment.

In this way, the only thing you need to install is docker itself (instructions for Mac, Windows and linux can be found here)

You can choose to run it in two ways: using a single container, similarly to what you were doing when setting up a local cluster, or by using docker compose to simulate the behaviour of a real cluster, where master and workers will be on different nodes.

Clone this repo if has not already been done, i.e. git clone https://github.com/Mmiglio/MAPD2021.git

Single container

Run the following command, from inside MAPD2021 directory, to create a container with spark and jupyter already installed. The first time you run it may take a while because it needs to download all the necessary components and libraries. However, the next times you will need this container it will be created instantly since all the components are cached.

docker run --rm -it \
        -p 8888:8888 \
        -p 4040:4040 \
        -p 8080:8080 \
        -v $PWD:/opt/workspace \
        miglio/mapd:spark-jupyter

Once it has been created, you will see some logs from the jupyter server. At this point you can connect to jupyter from your browser typing localhost:8888. Make sure that other jupyter are not using this port.

Test if everything is working with the notebook notebooks/docker/Docker.ipynb

Docker single container

Docker cluster

We can simulate multiple nodes by using multiple docker containers: A container hosting jupyter server and acting as the client node of the cluster, a container with the resource manager and one container for each worker. In this case you can think each container as a virtual machine. This setup is simulating what you experience on a real cluster.

Spark cluster with docker

This cluster can be spawned with

docker compose up

By defalult only one worker is created. If you want to use N workers you can scale the cluster with

docker compose up --scale spark-worker=N

Again, after the cluster has been created you can navigate to localhost:8888 to open the jupyter dashboard. In this case the master has already been created, you don't need to start it manually. Indeed the cluster dashboard will be already available on localhost:8080. See the example notebook on notebooks/docker/Docker-Cluster.ipynb.

If you want to use more resources per each worker, edit the file docker-compose.yml and change SPARK_WORKER_CORES and SPARK_WORKER_MEMORY to the value you prefer.

The cluster can be shut down either by pressing ctrl+C or by typing

docker compose down

In case you have stopped it by pressing ctrl+C remove the containers and network with docker compose rm -f.

Lecture 1

  • Creation of a Spark standalone cluster
  • Basic commands (parallelize, map, reduce, ...)
  • Spark application WebUI
  • Word count, computing pi and distributed gradient descent

Lecture 2

  • Spark dataframe
  • Basic dataframe operations
  • UDF and PandasUDF

Lecture 3

  • Spark Streaming
  • Streaming from TCP Socket
  • Dataframes in spark streaming