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SF-Crime-Statistics

Project Overview:

In this project, we will use a real-world dataset, extracted from Kaggle, on San Francisco crime incidents, and we will provide statistical analyses of the data using Apache Spark Structured Streaming. We will create a Kafka server to produce data, and ingest data through Spark Structured Streaming.

Environment Prerequisites:

  1. Spark 2.4.3
  2. Scala 2.11.x
  3. Java 1.8.x
  4. Kafka build with Scala 2.11.x
  5. Python 3.6.x or 3.7.x

How to run the project:

  1. Run ./start.sh to install project requirements. If you use pip rather than conda, then use pip install -r requirements.txt

  2. Start zookeeper server: /usr/bin/zookeeper-server-start config/zookeeper.properties

  3. Open a new terminal and start kafka server: /usr/bin/kafka-server-start.sh config/server.properties

  4. Open a new terminal and run python kafka_server.py

  5. Open a new terminal and start kafka-consumer-console: /usr/bin/kafka-consumer-console --bootstrap-server localhost:9091 --topic service.calls --from-beginning

  6. Run spark job: spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.4 --master local[*] data_stream.py

Questions:

  1. How did changing values on the SparkSession property parameters affect the throughput and latency of the data?

Kafka emits at the rate of 1/second and with the current sparkSession allowing maxOffsetsPerTrigger=200, changing property parameters will not make a noticeable difference of performace due to the size of the dataset.

  1. What were the 2-3 most efficient SparkSession property key/value pairs? Through testing multiple variations on values, how can you tell these were the most optimal?

Properties: spark.sql.shuffle.partitions spark.streaming.kafka.maxRatePerPartition spark.default.parallelism

To determine if we have reached the optimal group of the above property values we will need to reach the largest possible processedRowsPerSecond.