How to implement logstash in golang

When it comes to log management, Logstash is a popular tool capable of processing, transforming, and sending log files in real time. However, with the continuous development of modern software architecture, it is increasingly difficult for Logstash to meet complex data processing and storage needs. To this end, the Golang language provides a lightweight and efficient implementation that can be easily integrated into various workflows.

This article will introduce how to use Golang to implement some core functions of Logstash, including log file reading, parsing, filtering and output to the target location. We will also discuss how to use common data storage and transfer tools like ElasticSearch and Kafka in Golang.

1. File reading

The most commonly used input source for Logstash is a file. We first need to write code to read the contents of the file. In Golang, the most commonly used Scanner in the bufio package can efficiently read files line by line.

file, err := os.Open("logfile.log")
if err != nil {
    // Handle error
}

scanner := bufio.NewScanner(file)

for scanner.Scan() {
    line := scanner.Text()
    // Process line
}

if err := scanner.Err(); err != nil {
    // Handle error
}

file.Close()

2. Log analysis

Logstash can parse log files according to different formats, such as JSON, XML, CSV, Apache logs, etc. In Golang, these tasks can be accomplished using the encoding/json, encoding/xml, and encoding/csv packages from the standard library. Take parsing log data in JSON format as an example:

type LogEntry struct {
    Timestamp string `json:"timestamp"`
    Message   string `json:"message"`
}

func parseJSON(line string) (*LogEntry, error) {
    entry := &LogEntry{}
    err := json.Unmarshal([]byte(line), entry)
    if err != nil {
        return nil, err
    }
    return entry, nil
}

3. Data filtering

Another powerful function of Logstash is the ability to filter and modify log data, such as deleting unnecessary fields, Add additional fields, format fields, and more. In Golang, you can use structures and functions to implement these processing logic. For example, we can store and operate log data by defining a structure:

type LogEntry struct {
    Timestamp string `json:"timestamp"`
    Message   string `json:"message"`
}

type FilterConfig struct {
    RemoveFields []string `json:"remove_fields"`
    AddFields    map[string]interface{} `json:"add_fields"`
    DateFormat  string  `json:"date_format,omitempty"`
}

func applyFilter(config *FilterConfig, entry *LogEntry) {
    for _, field := range config.RemoveFields {
        delete(entry, field)
    }

    for key, value := range config.AddFields {
        entry[key] = value
    }

    if config.DateFormat != "" {
        // Convert timestamp to desired format
        // using format string
    }
}

4. Output processing

Logstash can output log data to various target locations. Common methods include output to ElasticSearch, Kafka, Redis, S3 and more. We can use related libraries in Golang to implement these operations. For example, output to ElasticSearch:

import (
    "context"
    "github.com/elastic/go-elasticsearch/v8"
    "github.com/elastic/go-elasticsearch/v8/esapi"
)

type ESOutputConfig struct {
    IndexName string `json:"index_name"`
    BatchSize int    `json:"batch_size"`
}

func createESOutput(config *ESOutputConfig) (*ElasticSearchOutput, error) {
    client, err := elasticsearch.NewDefaultClient()
    if err != nil {
        return nil, err
    }

    return &ElasticSearchOutput{
        client:    client,
        indexName: config.IndexName,
        batchSize: config.BatchSize,
    }, nil
}

func (out *ElasticSearchOutput) Write(entry *LogEntry) error {
    req := esapi.IndexRequest{
        Index:      out.indexName,
        DocumentID: "",
        Body:       strings.NewReader(entry.Message),
        Refresh:    "true",
    }

    res, err := req.Do(context.Background(), out.client)
    if err != nil {
        return err
    }

    defer res.Body.Close()

    if res.IsError() {
        return fmt.Errorf("failed to index log: %s", res.String())
    }
    return nil
}

5. Integrate ElasticSearch and Kafka

Logstash One of the most widely used data storage and transmission tools is ElasticSearch and Kafka. In Golang, you can use related libraries to interact with these services, such as ElasticSearch's go-elasticsearch package and Kafka's sarama package. The following is an example of using these libraries:

import (
    "github.com/Shopify/sarama"
    "github.com/elastic/go-elasticsearch/v8"
)

func main() {
    // Create ElasticSearch client
    esClient, _ := elasticsearch.NewDefaultClient()

    // Create Kafka producer
    kafkaConfig := sarama.NewConfig()
    producer, _ := sarama.NewAsyncProducer([]string{"localhost:9092"}, kafkaConfig)

    // Read log file
    scanner := bufio.NewScanner(file)
    for scanner.Scan() {
        line := scanner.Text()

        // Parse log entry from JSON
        entry, _ := parseJSON(line)

        // Apply filters
        applyFilter(config, entry)

        // Write to ElasticSearch
        createESOutput(config).Write(entry)

        // Write to Kafka
        KafkaOutput(producer, "my_topic").Write(entry)
    }
}

6. Summary

This article introduces how to use Golang to implement the core functions of Logstash, including log file reading, parsing, filtering and output to the target Location. We also discussed how to use common data storage and transfer tools like ElasticSearch and Kafka with Golang. Through these tools, we can easily implement an efficient, flexible and customizable log management process.

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