Sparkling Water is a scalable system for detecting, merging, and clustering similar server processes based on interaction logs. Using Apache Spark, MinHash, LSH, and time-series hashing (SSH, BSeSH), it efficiently identifies behavior patterns in large server infrastructures for performance optimization, anomaly detection, and system analysis. A detailed decription can be found in the report

• Similarity detection between server processes (name + timing)
• Time-series analysis using SSH and BSeSH
• Merging of equivalent processes
• Clustering using k-means++
• Scalable, distributed log processing via Apache Spark

Ensure you have Python 3.7+ and Apache Spark installed.

Install the required Python packages:

pip install -r requirements.txt

The project has two main parts. Each has its own script:

Run:

python pipeline_part1.py

• Input: res/output.txt
• Outputs:
  • res/part1Observations.txt – similarity analysis
  • res/part1Output.txt – merged process candidates

Run:

python pipeline_part2.py

• Input: res/part1Output.txt
• Output:
  • res/part2Observations.txt – final clustering results

• Liva van der Velden — Utrecht University
• Robin Kollmann — Utrecht University
• Simon Menke — Utrecht University