CSV至今和浮动

Question

I'm currently writing a small program which converts CSV-files into structs to be used for further prosessing. The csv lines look like this

20140102,09:30,38.88,38.88,38.82,38.85,67004

I have 500 files, each about 20-30 MB. My code works just fine, but I can't help wondering if there isn't a better way to convert these files than what I'm doing now. First reading the file and converting to csv records (pseudo code)

    data, err := ioutil.ReadFile(path)
    if err != nil {
        ... 
    }
    r := csv.NewReader(bytes.NewReader(data))
    records, err := r.ReadAll()
    if err != nil {
        ... 
    }

Then looping over all the records and doing

    parsedTime, err := time.Parse("2006010215:04", record[0]+record[1])
    if err != nil {
        return model.ZorroT6{}, time.Time{}, err
    }

    t6.Date = ConvertToOle(parsedTime)
    if open, err := strconv.ParseFloat(record[2], 32); err == nil {
        t6.Open = float32(open)
    }
    if high, err := strconv.ParseFloat(record[3], 32); err == nil {
        t6.High = float32(high)
    }
    if low, err := strconv.ParseFloat(record[4], 32); err == nil {
        t6.Low = float32(low)
    }
    if close, err := strconv.ParseFloat(record[5], 32); err == nil {
        t6.Close = float32(close)
    }
    if vol, err := strconv.ParseInt(record[6], 10,32); err == nil {
        t6.Vol = int32(vol)
    }

For example I have to go through []byte -> string -> float64 -> float32 to get my float values. What could I do to improve this code?

EDIT: Just to be clear I don't really need to improve the performance, I'm just better trying to understand Go and what performance optimization that could be applied to a problem like this. For example it seems like a lot of overhead to create loads of strings and float64 when I have a byte slice and want a float32.

Answer 1

There is only one problem I see that needs fix:

Do not use ioutil.ReadFile together with bytes.NewReader. It reads all the contents into the memory, which is inefficient when the file is large.

Instead, use os.Open(file), it perfectly provides a io.Reader that csv.NewReader can utilize. Do not forget to close the file and handle errors.

If you still want to improve performance:

1. Since your csv file is of fixed format, it is possible to using raw bytes instead provided by bufio instead of csv.

2. You can copy and paste the underlying code in strconv and time to avoid general code that is not of your need.

But I think they are not worth the trouble.