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When dealing with sizeable files containing structured data, such as text, JSON, or CSV, memory constraints can pose challenges. This article explores various approaches for reading such files efficiently while minimizing RAM usage.
There are two primary approaches to file parsing: document parsing and stream parsing.
Document parsing creates a complete in-memory representation of the file, allowing for efficient queries but requiring considerable memory.
Stream parsing, on the other hand, processes data one element or line at a time, consuming minimal memory. This approach is suitable for situations where the entire file doesn't need to be loaded into memory.
Go provides built-in libraries for handling common file formats, such as CSV. These libraries enable stream parsing, reducing the memory footprint:
<code class="go">package main import ( "encoding/csv" "fmt" "io" "log" "os" ) func main() { file, err := os.Open("test.csv") if err != nil { log.Fatal(err) } parser := csv.NewReader(file) for { record, err := parser.Read() if err == io.EOF { break } if err != nil { log.Fatal(err) } fmt.Println(record) } }</code>
For more complex scenarios, concurrency can further enhance efficiency. Creating a channel to feed data to a goroutine allows for parallel processing:
<code class="go">package main import ( "encoding/csv" "fmt" "log" "os" "io" "sync" ) func main() { file, err := os.Open("test.csv") if err != nil { log.Fatal(err) } parser := csv.NewReader(file) records := make(chan []string) wg := sync.WaitGroup{} wg.Add(1) go func() { defer close(records) for { record, err := parser.Read() if err == io.EOF { break } if err != nil { log.Fatal(err) } records <- record } wg.Done() }() processRecords(records) wg.Wait() } func processRecords(records chan []string) { for record := range records { // Process the record concurrently } }</code>
Conclusion: By utilizing stream parsing techniques and embracing concurrency, developers can effectively read large files with small RAM in Go, optimizing file processing performance.
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