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Auto Log

Insight Data Engineering
Session 2017A

Motivation

Traffic is always a headache for getting from point A to point B. It is especially worse in the Bay Area during the rush hours. Imagine you can have data from all vehicles driving on the road. What can you do with it? Simply visualize the traffic density and average speed of each region can help city planer make better roads and reduce congestions. Delivery services can better route their trucks and increase efficiency. Navigation softwares can incorporate these data and further imporve user experience.

Data Pipeline

Alt text

Architecture

The data is assumed pumping into Kafka cluster from mutiple devices. Each message contains lat, long, car id, time, and speed. Spark streaming then consume 20 seconds worth of messages and map a grid id to each message. This mapping can also be done on street id if the condition of specific road is perfered. The filtering of time will also be done in case of trandfer delay. The processed messages then being aggregate by grid id, calculate the unique count and average speed, and store the resulting calculations in Redis. Each message is also grouped by location and time for generate the graph. The graph information are stored in the db 1 of the same Redis server and being flush and recomputed every 20 seconds. In the frontend, when the map is being requested, Flask will query all data from db 0 and generate the location information for each grid for display on the map. Query one will convert the lat and long back to the grid id and make the query. Query all will query all data and generate json structure for all grid information. When the graph is requested, Flask will query all data from db 1 and generate the connection json file. The number of connection is bounded by 5000. Then the front end will read the newly generated file and display the traffic graph.

Product

  • All demo were presented at this point and AWS servers were taken down. Please refer to the Youtube video for the product.

Alt text

Project Setups

Clusters Setup

  1. Use pegasus and the yml files in cluster_setup folder to setup clusters. Install and start Zookeeper and Kafka for gene-su-kafka with pegasus. Insatll and start Hadoop and Spark for gene-su-spark.

  2. Login to master node of gene-su-kafka and create the auto_log topic with

    /usr/local/kafka/bin/kafka-topics.sh --create --zookeeper localhost:2181 --topic auto_log --partitions 4 --replication-factor 2

Simulator Setup

  1. Create a new AWS EC2 for traffic simulation (or simulate on local machine).

  2. Install the required libraries on the new EC2 instance (follow for Ubuntu 14.04).

    sudo apt-get update
sudo apt-get install python-pip
sudo apt-get install build-essential python-dev
sudo apt-get install gfortran
sudo pip install numpy
sudo apt-get install libjpeg-dev
sudo apt-get install libjpeg8-dev
sudo apt-get install libpng3 
sudo apt-get install libfreetype6-dev
sudo pip install Pillow
sudo pip install kafka-python

  3. Clone this repository

    sudo apt-get install git
git clone https://github.com/GeneDer/Auto-Log

  4. Know the IP of master node of gene-su-kafka and start the simulation.

    cd Auto-Log/traffic_simulator
bash spawn_kafka_streams.sh <ip address> <number of session> <session name> <number of car simulated>

e.g. bash spawn_kafka_streams.sh 35.167.53.204 10 k1 100000

  5. You can check the message produced by the simulator with the following commend in master node of gene-su-kafka.

    /usr/local/kafka/bin/kafka-simple-consumer-shell.sh --broker-list localhost:9092 --topic auto_log --partition 0

Redis and Spark Setup

  1. Follow this guide to install Redis on the master node of gene-su-spark. Make the following change to the redis.conf file before start, so it can be accessed from outside.

    #bind 127.0.0.1
requirepass <your password here>
daemonize yes

  2. Clone this repository to the master node of gene-su-spark.

    git clone https://github.com/GeneDer/Auto-Log

  3. Change the DNS of Kafka server and ip and password of Redis server in Auto-Log/spark_streaming/src/main/scala/traffic_data.scala on line 15, 42, and 58.

  4. Compile and start spark job.

    cd Auto-Log/spark_streaming/
sbt assembly
sbt package
spark-submit --class TrafficDataStreaming --master spark://<spark master node private DNS>:7077 --jars target/scala-2.11/traffic_data-assembly-1.0.jar target/scala-2.11/traffic_data-assembly-1.0.jar

e.g.
spark-submit --class TrafficDataStreaming --master spark://ip-172-31-0-69:7077 --jars target/scala-2.11/traffic_data-assembly-1.0.jar target/scala-2.11/traffic_data-assembly-1.0.jar

Flask Setup (was modified from this guide)

  1. In master node of gene-su-spark, replace the key.txt file under flaskapp with your redis ip and password.

  2. Install the apache webserver, mod_wsgi, flask, and redis client.

    sudo apt-get update
sudo apt-get install apache2
sudo apt-get install libapache2-mod-wsgi
sudo pip install flask
sudo pip install redis

  3. Create the flask directory for apache

    sudo ln -sT /home/ubuntu/Auto-Log/flaskapp /var/www/html/flaskapp

  4. Enable mod_wsgi.

    sudo nano /etc/apache2/sites-enabled/000-default.conf

# add the following under `DocumentRoot /var/www/html`
WSGIDaemonProcess flaskapp threads=5
WSGIScriptAlias / /var/www/html/flaskapp/flaskapp.wsgi

<Directory flaskapp>
    WSGIProcessGroup flaskapp
    WSGIApplicationGroup %{GLOBAL}
    Order deny,allow
    Allow from all
</Directory>

  5. Restart the webserver.

    sudo apachectl restart

  6. Change the file owner of data.json so that flask can rewrite new data.

    sudo chown www-data:www-data Auto-Log/flaskapp/static/data.json

  7. Now you can check the traffic density and average speed of simulated San Francisco traffics with the IP of the master node of gene-su-spark. As a note to myself, the apache error log can be found

    nano /var/log/apache2/error.log