System Design Fundamentals

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Introduction to Bloom Filters

Imagine needing to check if something is in a huge collection without storing the entire collection in memory. Bloom filters offer a clever solution to this problem. A Bloom filter is a space-efficient probabilistic data structure for set membership tests – it can quickly tell you if an item is definitely not in a set or if it is probably in the set. In other words, false negatives are impossible (it never misses an item that was added), but it can sometimes return a false positive (claim an item is in the set when it isn’t, due to its probabilistic nature)

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kusuma k s

kusuma k s

· 2 years ago

Please add some resources which can explain the Bloom filter concept with an example. Any practical use case example with a structured SQL based example will be ideal.

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Pankaj Wakchaure

· 2 years ago

Please add more explanation with clear diagrams. Possibly give a detailed example with diagrams.

Nathan Frankel

Nathan Frankel

· 9 months ago

Is this ever relevant for a high level system design interview? I've never heard of this in over 10 years in software engineering. All other topics I have at least heard of at some level. I'm sure this has it's use cases, but questioning if this is a 'system design basic'. In what scenario would one bring this up in a one hour interview context?

Md Nuruzzaman Mithun

Md Nuruzzaman Mithun

· 4 months ago

  • Bloom Filters shouldn't be in the topic of System Design Basic.
  • Need to add some use cases with clear sample examples.
  • Explanation is not quite clear to understand.
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anmoldeep1509

· 3 months ago

A good example of Bloom filters applications in system design is the use case of username selection. When you select a username which has to be unique in the system. Bloom filters can be used to check if the given username already exists or not. In this use case small number of false positives can be trade-off for the latency.

Piyush Kuhikar

Piyush Kuhikar

· 14 days ago

Bloom filters are used when you need to quickly test whether an element might be in a set, with a low memory footprint, and when false positives are acceptable but false negatives are not.

Where Bloom filters are used

  • Database/query engines (membership pre-checks): Avoid expensive lookups (e.g., don’t hit a disk/page if it’s definitely not there).
  • Caching / key-set existence checks: Quickly decide whether to check a cache backend.
  • Search systems / inverted index helpers: Prune candidate documents/segments before doing heavier work.
  • Network routing / content discovery: Reduce unnecessary queries (e.g., “maybe you have this item?”).
  • Distributed systems / deduplication pipelines:
Ayush Sur

Ayush Sur

· 3 months ago

Optimal Bit Array Size (m) If you know how many elements n you have and want a specific false positive probability p, the formula is:

m = - n (lnp) / (ln2)^2

As a rule of thumb, for a 1% false positive rate, you need about 9.6 bits per element. Optimal Number of Hash Functions (k) To minimize false positives for a given m and n, the number of hash functions should be:

k = m/n * ln2

Catherine Higgins

Catherine Higgins

· a month ago

How are these typically implemented in memory ? And how do we determine the appropriate size to initialize the array ?