Content Comparison

...

This indicates a gradual performance degradation as the event rate increases, emphasizing the need for optimization in high-load scenarios.

Recommendations:

...

Possible Bottlenecks

• Kafka partitioning or consumer lag affecting event processing.

  • using a single Kafka partition can significantly affect event processing, especially under high load.

...

• • Here’s how it impacts performance:

Impact of Single Kafka Partition on Event Processing:

1. Throughput Bottleneck:

   • Kafka distributes messages across partitions, allowing multiple consumers to process data in parallel.

   • With only one partition, all events are handled by a single consumer, limiting the processing speed.

2. Increased Latency:

   • Since there is no parallelism, events are processed sequentially rather than concurrently.

   • As load increases (e.g., 500 or 1000 AVC events/sec), processing delays may accumulate, causing performance degradation.

3. Consumer Scaling Limitation:

   • Kafka allows multiple consumers within a consumer group to read from different partitions.

   • With a single partition, adding more consumers will not improve performance since only one consumer can read from it at a time.

Recommendations to Improve Performance:

✅ Increase the Number of Partitions

• Use multiple partitions to enable parallel processing and higher throughput.

• A good rule of thumb: Number of partitions = Number of consumers × Desired parallelism level.

✅ Tune Consumer Configuration

• Increase fetch.max.bytes, max.poll.records, and optimize commit.interval.ms for better performance.

Conclusion:

Yes, using a single Kafka partition is likely affecting event processing, especially under high event rates (500 or 1,000 AVC events/sec). Scaling partitions and optimizing consumer settings can help mitigate performance issues. 🚀