What techniques are used to increase throughput and data volume in database management systems?

Several techniques are used to increase throughput and data volume in database management systems (DBMS):

• Sharding — horizontal partitioning of data across different servers, allowing parallel query processing.
• Indexing — creating indexes to speed up data search and retrieval.
• Caching — storing frequently requested data in memory for quick access.
• Query optimization — rewriting queries to reduce load and execution time.
• Parallel processing — using goroutines in Go for concurrent handling of multiple requests.
• Replication — creating copies of the database to distribute read load.

Example of using goroutines for parallel request processing in Go:

func fetchData(id int, ch chan<- string) {
    // simulate database request
    time.Sleep(time.Millisecond * 100)
    ch <- fmt.Sprintf("Data for ID %d", id)
}

func main() {
    ch := make(chan string)
    ids := []int{1, 2, 3, 4, 5}

    for _, id := range ids {
        go fetchData(id, ch)
    }

    for range ids {
        fmt.Println(<-ch)
    }
}

This approach allows efficient resource utilization and increases system throughput.