Python scripts to process, and analyze log files using PySpark.
👋 This repository's maintainer is available to hire for Python/PySpark/Spark NLP consulting projects. To get a cost estimate, send email to [email protected] (for projects of any size or complexity).
- Clone this repository
git clone https://github.com/lykmapipo/Python-Spark-Log-Analysis.git
cd Python-Spark-Log-Analysis
- Install all dependencies
pip install -r requirements.txt
- To
parse and prepare
raw log files, run:
python prepare.py
- To get
basic summary report
of structured logs, run:
python summarize.py
- To perform
word frequency analysis
on structured logs, run:
python count_words.py
- To perform
keyword extraction analysis
on structured logs, run:
python keywords.py
-
Check data/raw directory for all
raw logs
data. Each file is intext format
, and each log entry is format as[timestamp] level: message
. -
Check data/interim/structured-logs directory for
structured logs
data generated when runprepare.py
. Each file is inparquet format
, and each log entry follow belowspark
schema:
root
|-- log_timestamp: timestamp (nullable = true)
|-- log_level: string (nullable = true)
|-- log_message: string (nullable = true)
|-- log_length: integer (nullable = true)
|-- log_year: integer (nullable = true)
|-- log_month: integer (nullable = true)
|-- log_day: integer (nullable = true)
|-- log_hour: integer (nullable = true)
|-- log_minute: integer (nullable = true)
|-- log_second: integer (nullable = true)
|-- log_message_length: integer (nullable = true)
-
Check summary report generated when run
summarize.py
. -
Check word frequency analysis report generated when run
count_words.py
. -
Check keywords extraction analysis report generated when run
keywords.py
.
It will be nice, if you open an issue first so that we can know what is going on, then, fork this repo and push in your ideas. Do not forget to add a bit of test(s) of what value you adding.
[email protected], or open a GitHub issue
The MIT License (MIT)
Copyright (c) lykmapipo & Contributors
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