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In practical applications, coalescing filters often employ algorithms that identify patterns or duplicates within incoming data streams. These algorithms are designed to recognize similarities quickly and determine when to merge data points. For example, in a financial transaction system, multiple transactions from the same user in a short timeframe might be coalesced into a single entry detailing the total amount transacted, rather than recording each transaction separately.


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