You optimized the consumption of streaming quotes. What was the problem and what did you do?

The problem was related to excessive resource consumption when processing streaming quotes: for example, frequent updates led to a large amount of unnecessary computations and network traffic. To optimize, I implemented data filtering and aggregation on the client side to process only significant changes (for example, updates with a price change exceeding a certain threshold). I also applied buffering and batch processing of messages to reduce system load.

An example in Go using channels and timers for aggregating updates:

func processQuotes(input <-chan Quote) {
    buffer := make([]Quote, 0)
    ticker := time.NewTicker(500 * time.Millisecond)
    defer ticker.Stop()

    for {
        select {
        case q := <-input:
            // Add to buffer only if the change is significant
            if isSignificantChange(q) {
                buffer = append(buffer, q)
            }
        case <-ticker.C:
            if len(buffer) > 0 {
                // Process batch of updates
                handleBatch(buffer)
                buffer = buffer[:0]
            }
        }
    }
}