1. Crowd DetectorCrowd Detector
Reza Asad
Insight Data Engineering June 2015

2. Motivation
● Avoid waiting time in crowded areas.

3. Data
● Lets imagine we had data about people's location.
● This could be collected form people's cell phones.
● How can we use such data?

4. Naive Approach

5. Demo

6. Data
● But such data is not available to me ...
● Solution : Engineer the data!
● Take data from yelp
● Perform a random walk

7. Pipeline
Data

8. Engineering Challenges
● Choosing K?

9. Engineering Challenges
● The area of SF: 46.87 mi ²
● For the purpose of this project each cluster is 0.09 mi ²
● This means k is roughly 500

10. Engineering Challenges
● Parameters to tune:
– Time it takes to produce the messages
– Processing time for k-means in Spark Streaming
– The update interval for a fixed data point in the
database

11. Goal
● Tune the parameters in order to have a stable system
● The total delay after processing each batch must be
constant and comparable to the batch interval.
● You can check this in the Spark API

12. Tackling Challenges
●
Having multiple producers and consumers ✔
● Kafka is fast with sending messages and is not the bottleneck
● Establishing some safe limits:
– Using spark.streaming.receiver.maxRate to control
the input rate ✔
– Understanding the complexity of the process in Spark
Streaming ✔
– Choosing the right batch interval ✔

13. Raw Data

14. Data Process
● Data filteration in spark streaming

15. Data Process

16. About Me
● Long time ago - B.S in pure math, University of Toronto
● More recent - M.S in applied math, University of British Columbia
● The exciting now - A data engieer who wants to go camping with other
data engineers