![]() ![]() Scaling ( EXPANSION)Īdding an element to a Bloom filter never fails due to the data structure "filling up". That's where the added latency comes from. The reason for this is the way the checks work: a regular check would first be performed on the top (latest) filter and if a negative answer is returned the next one is checked and so on. ![]() In the cases when a filter consists of multiple sub-filters stacked on top of each other latency for adds stays the same, but the latency for presence checks increases. If you undersize, the filter will fill up and a new one will have to be stacked on top of it (sub-filter stacking). It's important to get the number right because if you oversize - you'll end up wasting memory. This is the number of elements you expect having in your filter in total and is trivial when you have a static set but it becomes more challenging when your set grows over time. For example, for a desired false positive rate of 0.1% (1 in 1000), error_rate should be set to 0.001. The rate is a decimal value between 0 and 1. Package io.redis.examples import import import import public class BloomFilterExample Then add multiple model names and check if they exist. Add one model name and check if it exists. In the example that follows, you'll create a filter with space for a million entries and with a 0.1% error rate. A Bloom filter can be used to detect duplicates.
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