Senior LevelSystem Design & Architecture
How does the HyperLogLog (HLL) algorithm estimate distinct count cardinality in $O(1)$ memory?
❓ Interview Prompt: Explain HyperLogLog (HLL) algorithm, estimating unique element counts (distinct count), 1.5KB memory usage, leading zeros counting, and Redis `PFADD`.
Calculating exact 'COUNT(DISTINCT user_id)' across 1 billion items requires gigabytes of RAM to store set keys. HyperLogLog (HLL) estimates unique cardinality with 98.1% accuracy using only 1.5KB of memory:
How HyperLogLog Works:
1
Hashes input items into 64-bit binary strings.
2
Counts the maximum number of leading zeros in the binary hashes.
3
The probability of seeing $k$ consecutive leading zeros is $2^{-k}$. HLL averages estimates across sub-buckets to compute total unique cardinality!
Code Implementation Example
bash
# Executing HyperLogLog distinct count operations in Redis:
PFADD daily_active_users "user_1001" "user_1002" "user_1003"
PFCOUNT daily_active_users # Returns estimated distinct count in 1.5KB RAM!💡Estimating unique element counts in Redis using HyperLogLog.
🎯 Interview Key Takeaway
Use HyperLogLog to estimate distinct count cardinality in $O(1)$ memory when 98%+ accuracy is acceptable.
#System Design#HyperLogLog#Redis#Probabilistic#Senior
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