[KafkaIO] Only retrieve the shared caches once during setup in ReadFromKafkaDoFn

[sjvanrossum]

Repeatedly calling MemoizingPerInstantiationSerializableSupplier#get() comes with some overhead from volatile reads which can be avoided by fetching the shared caches once in setup and reusing those references through non-volatile fields.

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Summary of Changes

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This pull request optimizes the ReadFromKafkaDoFn class by changing how shared caches are accessed. Instead of repeatedly invoking get() on SerializableSupplier instances, which incurs volatile read overhead, the caches are now retrieved once during the setup phase and stored in transient fields. This refactoring aims to enhance performance by streamlining cache access throughout the DoFn's lifecycle.

Highlights

• Performance Optimization: Reduces overhead in ReadFromKafkaDoFn by fetching shared caches only once during the setup phase, avoiding repeated volatile reads.
• Cache Access Refactoring: Replaces multiple calls to MemoizingPerInstantiationSerializableSupplier#get() with direct access to transient fields that store the cache instances.
• Nullness Annotations: Introduces Checker Framework annotations (@EnsuresNonNull, @RequiresNonNull, @MonotonicNonNull) to improve code correctness and null safety for cache fields.

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[Abacn]

Repeatedly calling MemoizingPerInstantiationSerializableSupplier#get() comes with some overhead from volatile reads which can be avoided by fetching the shared caches once in setup and reusing those references through non-volatile fields.

The change itself looks good, however it may not be relevant to the observed OOM was observed since Beam 2.64.0. I understand the caching was introduced in #34331, in Beam 2.65.0.

In general I would expect overhead of volatile would still be small as each processElement call involves consumer.poll (API call, would be magnitude slower than in memory ops).

[sjvanrossum]

This change was motivated by the CPU overhead incurred by MemoizingPerInstantiationSerializableSupplier#get(), not the recent OoMs. :)

Here's the 50th percentile of CPU time over a 10 minute window of a recent pipeline (500 partitions, 10 vCPUs) I ran:

I'll note that this was a test pipeline, so this finding may not be representative of real workloads.