Comprehensive Metrics Summary Pipeline

How do we build a complete telemetry summary computing record volume, unique users, payload MBs, and latency bounds?

Intermediate⏱ 3 Minutes⭐ 450 XP

🤔 THE QUESTION

How do we build a complete telemetry summary computing record volume, unique users, payload MBs, and latency bounds?

💡 WHAT IS IT?

A multi-metric aggregation pipeline computing row counts, distinct users, megabytes processed, and latency bounds.

🎯 WHAT IS IT USED FOR?

Production data pipeline health dashboards, telemetry reporting, and cluster SLA audits.

💻 EXAMPLE

from pyspark.sql.functions import col, count, countDistinct, max, min, round, sum

summary_df = df.select(
    count("*").alias("total_records"),
    countDistinct(col("user_id")).alias("unique_users"),
    round(sum(col("payload_bytes")) / 1024 / 1024, 2).alias("total_mb"),
    min(col("latency_ms")).alias("min_latency"),
    max(col("latency_ms")).alias("max_latency")
)
summary_df.show()

🎯 Mission Objectives

Practice typing production-grade PySpark code for Comprehensive Metrics Summary Pipeline.

✓ Combine multiple statistical aggregate functions
✓ Convert bytes to megabytes with rounding
✓ Generate complete telemetry summary

🚀 Start Mission

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