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spark
I mainly used spark to process large amount of data in on-prem architectures and some elastic computing on cloud mainly based on hadoop.
This is a brief summary of the content of the data processing pipelines I wrote.
Overview
This document outlines a variety of Python scripts designed for different purposes related to data processing, machine learning, geospatial analysis, and image generation. Each script serves a specific function within the context of various applications.
Data Processing and Machine Learning Scripts
- proc_freq.py : Processes GPS trajectory data to calculate unique users, total events, and average events per user for each day.
- train_linear.py : Trains a linear regression model using the Cal_Housing dataset with Spark MLlib.
- etl_tankRef.py : Reads and processes reference curve data from log files, generating daily and hourly reference values.
These scripts typically involve reading and processing large datasets, performing statistical calculations, and applying machine learning models to extract insights or predict outcomes based on the data.
Geospatial Analysis Scripts
- proc_cronon.py : Processes trajectory data using Apache Spark.
- proc_traj.py : Processes GPS trajectory data to compute motion vectors, velocity quivers, and clustering ratios for each segment of a trajectory file.
- test_etl_matrix.py : Performs ETL operations on structured data using Apache Spark.
These scripts focus on geospatial analysis, including trajectory processing, spatial data manipulation, and visualization using geospatial libraries like GeoPandas and Shapely.
Image Generation Scripts
- gan_train_aws.py : Trains various types of Generative Adversarial Networks (GANs) on AWS using Keras and TensorFlow.
- gan_train.py : Another script for training GANs, possibly focusing on different domains or configurations.
- gan_deploy.py : Deploys trained GAN models as endpoints on Amazon SageMaker.
These scripts involve complex machine learning tasks such as image generation, model training, and deployment in cloud environments like AWS.
Miscellaneous Scripts
- proc_demoData.py : Processes data from a specified directory using Apache Spark.
- train_aws.py : Another script for training GANs on AWS.
- etl_tank.py : Contains functions for processing GPS trajectory data.
These scripts can be used for various purposes such as data processing, model training, and deployment, with each script addressing a specific aspect of these tasks.
Summary
Each script in this document is designed to perform different functions related to data processing, machine learning, geospatial analysis, and image generation. These scripts are typically part of larger applications or workflows that require handling large datasets, applying complex algorithms, and integrating with cloud services like AWS for scalability and performance. The use of libraries such as Apache Spark, TensorFlow, Pandas, and GeoPandas ensures robustness and efficiency in processing the data.
This Python script is designed to interact with Apache Kafka using PyKafka, a Python client for producing and consuming messages from Kafka topics. The main components and functions include:
- Importing Libraries :
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from kafka import KafkaProduceris used to create a producer instance for sending messages. -
from kafka.errors import KafkaErroris used for handling exceptions related to Kafka operations. -
Kafka Producer Configuration :
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The script sets up the producer by specifying the Kafka broker address (
brokers) and any additional configurations like key serialization (key_serializer), value serialization (value_serializer), and batch size (batch_size). -
Producer Initialization :
-
A
kafka_produceris initialized with the configuration settings defined in step 2. -
The producer can now be used to send messages to a Kafka topic.
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Sending Messages :
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The script includes a function named
send_messagethat takes three parameters: the message content, the key for serialization (optional), and the topic name. -
Inside the
send_messagefunction, the producer is used to send the message to the specified topic with optional key serialization. -
Error Handling :
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The script handles potential exceptions that might occur during message sending using a try-except block within the
send_messagefunction. -
If an error occurs, it prints an error message and logs the exception details for debugging purposes.
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Example Usage :
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There is an example usage of the
send_messagefunction in the main part of the script to demonstrate sending a sample message to a Kafka topic named "test_topic". -
The script also includes print statements to show the status of the message sending operation and the response from the producer.
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Cleanup :
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After sending messages, it is good practice to close the producer when done using
kafka_producer.close(), but in this example, it is not explicitly shown as the script exits after completing the example usage.
Overall, this script provides a simple and effective way to integrate Kafka into Python applications for real-time data processing and message passing. The use of PyKafka simplifies the process by providing a high-level API for interacting with Kafka brokers.