How to implement distributed log analysis and mining in PHP microservices

How to implement distributed log analysis and mining in PHP microservices

Introduction:

With the rapid development of Internet technology, more and more Many applications are built using microservices. In the microservice architecture, log analysis and mining are a very important part. It can help us monitor the running status of the system in real time, discover potential problems, and handle them in a timely manner. This article will introduce how to implement distributed log analysis and mining methods in PHP microservices, and provide specific code examples.

1. Build a log collection system

1. Choose the appropriate log collection tool

The first step to implement distributed log analysis and mining in PHP microservices is to choose Suitable log collection tools. Commonly used log collection tools include Logstash, Fluentd, Grafana, etc. These tools have powerful log collection and analysis functions.

2. Add a log collection plug-in to each microservice

Add a log collection plug-in to each microservice project to send the logs generated by the microservice to the log collection tool in real time. Taking Logstash as an example, you can use the Filebeat plug-in for log collection. The specific steps are as follows:

(1) Install the Filebeat plug-in

Run the following command to install the Filebeat plug-in:

$ curl -L -O https://artifacts.elastic.co/downloads/beats/filebeat/filebeat-7.10.2-darwin-x86_64.tar.gz
$ tar xzvf filebeat-7.10.2-darwin-x86_64.tar.gz
$ cd filebeat-7.10.2-darwin-x86_64/

(2)Configure Filebeat

Create a name For the configuration file of filebeat.yml, configure it in the following format:

filebeat.inputs:
- type: log
  paths:
    - /path/to/your/microservice/logs/*.log

output.logstash:
  hosts: ["your_logstash_host:your_logstash_port"]

(3) Run Filebeat

Run the following command to start Filebeat:

$ ./filebeat -e -c filebeat.yml

3. Configuration log Collection tool

Configure the input plug-in in Logstash to receive log data from each microservice. The specific steps are as follows:

(1) Install Logstash

Run the following command to install Logstash:

$ curl -L -O https://artifacts.elastic.co/downloads/logstash/logstash-7.10.2-darwin-x86_64.tar.gz
$ tar xzvf logstash-7.10.2-darwin-x86_64.tar.gz
$ cd logstash-7.10.2-darwin-x86_64/

(2) Configure Logstash

Create a file named logstash .conf configuration file and configure it in the following format:

input {
  beats {
    port => your_logstash_port
  }
}

filter {
  # 编写日志过滤规则
}

output {
  elasticsearch {
    hosts => ["your_elasticsearch_host:your_elasticsearch_port"]
    index => "your_index_name-%{+YYYY.MM.dd}"
  }
}

(3) Run Logstash

Run the following command to start Logstash:

$ ./logstash -f logstash.conf

4. Configure Elasticsearch and Kibana

Elasticsearch and Kibana are the core components for storing and displaying log data. The specific steps are as follows:

(1) Install Elasticsearch and Kibana

Refer to the official documentation to install Elasticsearch and Kibana.

(2) Configure Elasticsearch and Kibana

Modify the configuration files of Elasticsearch and Kibana to ensure that they can be accessed normally.

(3) Configure Logstash output

Modify the hosts configuration of the output part in the Logstash configuration file to ensure that the log data is correctly output to Elasticsearch.

(4) Use Kibana for log analysis and mining

Open Kibana’s web interface, connect to the started Elasticsearch instance, and use KQL query language for log analysis and mining.

2. Log analysis and mining

1. Use Elasticsearch for log analysis

Elasticsearch provides powerful query functions and can analyze log data by writing DSL query statements. . The following is a sample code for using Elasticsearch for log analysis:

$curl -X GET "localhost:9200/your_index_name/_search" -H 'Content-Type: application/json' -d'
{
    "query": {
        "match": {
            "message": "error"
        }
    }
}'

2. Using Kibana for log mining

Kibana provides an intuitive interface and rich chart display functions, which can help us be more convenient perform log mining. The following is a sample code using Kibana for log mining:

GET your_index_name/_search
{
  "query": {
    "match": {
      "message": "error"
    }
  },
  "aggs": {
    "level_count": {
      "terms": {
        "field": "level.keyword"
      }
    }
  }
}

The above code will query the logs containing the "error" keyword, perform aggregate statistics based on the log level, and generate a chart to display the distribution of the log level. .

Conclusion:

By building a log collection system and using Elasticsearch and Kibana for log analysis and mining, we can better monitor and analyze the running status of microservices in real time and discover problems in a timely manner. And handle it accordingly to improve the stability and usability of the application. I hope this article will help you understand how to implement distributed log analysis and mining in PHP microservices.

References:

[1] Elastic. (2021). Elastic Stack - Elasticsearch, Kibana, Beats, and Logstash. Retrieved from https://www.elastic.co/

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