Question 1

How do I use a custom hash function with Bloomex?

Accepted Answer

Install a hash function package like Murmur, then pass the function reference when creating the bloom filter, as shown in the README example with &Murmur.hash_x86_128/1. This allows flexibility but requires managing additional dependencies.

Question 2

Is Bloomex better than other Elixir bloom filter libraries?

Accepted Answer

Bloomex excels in scalability and configurability with no external dependencies, but alternatives like 'bloom' might offer different trade-offs in performance or features. Compare based on your specific needs for error rates, memory usage, and integration ease.

Question 3

What are the performance benchmarks for Bloomex in production?

Accepted Answer

The README doesn't provide benchmarks, so performance depends on factors like dataset size and hash function choice. For high-performance needs, test in your environment, as pure Elixir implementation may have overheads versus native code.

Question 4

How to handle false positives when using Bloomex?

Accepted Answer

False positives are inherent to bloom filters; tune parameters like error rate and capacity in Bloomex.scalable() to minimize them, and design your application to handle occasional false positives gracefully, such as with fallback checks.

Question 5

Can I save a bloom filter from Bloomex to disk?

Accepted Answer

Bloomex doesn't include built-in serialization; you'd need to implement custom serialization using Elixir's serialization tools, which adds complexity for persistence and sharing between processes or systems.

Question 6

Best practices for tuning Bloomex parameters?

Accepted Answer

Start with conservative error rates and scaling factors based on expected dataset growth; use the configurable options in Bloomex.scalable() to adjust memory vs. accuracy, and monitor false positive rates in testing to refine settings.

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