Confluent Developer ft. Tim Berglund, Adi Polak & Viktor Gamov

Common Apache Kafka Mistakes to Avoid

Jun 23, 2022
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Episode notes
1
Introduction
00:00 • 2min
2
How to Run Kafka Properly and Gotchas
01:45 • 3min
3
The Tradeoff Between Throughput and Latency With Batching
04:58 • 3min
4
The Importance of LingerMS in Batching
07:56 • 2min
5
How to Monitor Your Kafka Brokers
10:25 • 3min
6
How to Adapt to Kafka Batching
12:58 • 2min
7
The Importance of Compression in Cloud Computing
15:00 • 3min
8
How to Optimize Compression for Deep Performance Tuning
18:29 • 2min
9
The Importance of Schema Validation in the Cloud
20:20 • 3min
10
The Importance of Monitoring Performance
22:51 • 2min
11
How to Use the Kafka Producer to Monitor Your Broker's Performance
24:56 • 5min
12
How to Program Asynchronously to Handle Back Pressure
29:57 • 3min
13
How to Scale a Multi-Threaded Microservice
32:43 • 3min
14
The Importance of Batching in Kafka
35:38 • 2min
15
How to Shrink Partitions and Grow Partition Numbers
37:41 • 2min
16
How to Pick the Right Number First Time
39:20 • 1min
17
How to Guess the Performance of Your Application
40:49 • 2min
18
The Dangers of Overcommitting
42:55 • 3min
19
How to Commit Multiple Offsets at the Same Time
45:55 • 2min
20
How to Tune Your Fetching of Data
47:38 • 4min
21
How to Tune a Multi-Threaded Consumption Model
52:03 • 2min
22
How to Navigate to Kafka Parameters
54:01 • 2min
23
The Importance of Providing a Consumer Rebalance Listener
55:41 • 3min
24
Kubernetes and the Herd Model of Deployment Management
58:16 • 2min
25
How to Survive a Disaster
01:00:17 • 3min
26
How to Scale Kafka for Growth
01:03:11 • 3min
27
How to Scale Your Kafka Cluster
01:06:02 • 3min