Summary
Overview
This session is a technical course segment focused on monitoring and analyzing a serverless game data pipeline using Kafka topics, KSQL, and observability tools. The instructor guides participants through inspecting real-time message flows from user game events, examining Kafka topic metrics, and preparing to deploy Grafana for system-level monitoring. The content centers on understanding producer-consumer dynamics, topic configuration, and stream processing with KSQL for aggregating user statistics.
Topic (Timeline)
1. Initial Check-in and System Setup Context [00:00:34 - 00:01:28]
The session begins with a brief check-in to confirm participants have no outstanding questions. The instructor then references prior work fixing a cluster issue related to a web service (WS) unable to accept requests. The next step involves accessing a pinned link to a live game dashboard to observe real-time data flow, followed by planned steps to install and configure Grafana for local monitoring.
2. Live Dashboard Inspection and Data Flow Analysis [00:01:39 - 00:02:48]
The instructor demonstrates the live game dashboard, noting that user events (score, lives, weight, level) are emitted every 20 seconds. The data originates from a serverless architecture where each game session triggers an AWS Lambda function. Each Lambda invocation acts as a single producer, sending one message to the Kafka topic “user game” asynchronously. The instructor emphasizes the one-to-one relationship between Lambda invocations and message production.
3. Kafka Topic Metrics and Configuration [00:02:48 - 00:04:26]
The instructor details the Kafka topic “user game,” highlighting key metrics: number of partitions, bytes in/out, messages in/out, retention time (set to 1 hour), and retention size (infinite). The architecture is explained as having 3–49 concurrent producers, each corresponding to a unique Lambda-triggered game session. The topic’s design supports high-throughput, ephemeral event ingestion with configurable storage policies.
4. KSQL Stream Processing and User Statistics [00:04:26 - 00:04:33]
Two KSQL queries are introduced to process the stream:
stats per user: Aggregates the highest score achieved by each individual user.summary stats: Computes a global summary of all users who have played, capturing overall engagement metrics.
The session ends with the intent to use these aggregations for monitoring and analysis, prior to deploying Grafana for visualization.
Appendix
Key Principles
- Serverless event generation: Each game session triggers a single Lambda, producing one Kafka message.
- Topic design: High partition count and infinite retention support scalable, long-term data capture.
- Stream processing: KSQL enables real-time aggregation without batch processing.
Tools Used
- AWS Lambda (event producer)
- Kafka (message broker with “user game” topic)
- KSQL (stream processing for user stats)
- Grafana (planned for visualization)
Common Pitfalls
- Misunderstanding producer count: 3–49 producers represent concurrent sessions, not persistent services.
- Retention misconfiguration: Infinite size may lead to unbounded storage; should be aligned with business needs.
Next Steps
- Install and configure Grafana for dashboarding Kafka and KSQL metrics.
- Validate data ingestion and aggregation accuracy via the live game interface.