How Kafka Is Tested

Kevin Feasel

2017-09-15

Hadoop

Colin McCabe walks us through the process of a change in Apache Kafka:

The Kafka community has a culture of deep and extensive code review that tries to proactively find correctness and performance issues. Code review is, of course, a pretty common practice in software engineering but it is often cursory check of style and high-level design. We’ve found a deeper investment of time in code review really pays off.

The failures in distributed systems often have to do with error conditions, often in combinations and states that can be difficult to trigger in a targeted test. There is simply no substitute for a deeply paranoid individual going through new code line-by-line and spending significant time trying to think of everything that could go wrong. This often helps to find the kind of rare problem that can be hard to trigger in a test.

Testing data processing engines is difficult, particularly distributed systems where things like network partitions and transient errors are hard to reproduce in a test environment.

Related Posts

Working With Skewed Data In Pig

Dmitry Tolpeko explains how you can use the Weighted Range Partitioner in Apache Pig to work with highly skewed data: The problem is that there are 3,000 map tasks are launched to read the daily data and there are 250 distinct event types, so the mappers will produce 3,000 * 250 = 750,000 files per day. That’s […]

Read More

Spark Streaming Using DStreams Or DataFrames?

Yaroslav Tkachenko contrasts the two methods for operating on data with Spark Streaming: Spark Streaming went alpha with Spark 0.7.0. It’s based on the idea of discretized streams or DStreams. Each DStream is represented as a sequence of RDDs, so it’s easy to use if you’re coming from low-level RDD-backed batch workloads. DStreams underwent a lot […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930