Understanding Logs, Their Value, and Distributed Log Operations in Modern Systems

Logs are time‑ordered records that capture events, errors, and system behavior, enabling precise fault location and root‑cause analysis; they are classified by levels such as FATAL, WARNING, NOTICE, DEBUG, and TRACE.

In large‑scale web architectures, logs become a critical component for troubleshooting, performance optimization, user behavior analysis, security monitoring, and audit trails.

Distributed systems introduce challenges: heterogeneous languages and formats, rapid service iteration leading to missing or mis‑leveled logs, and massive instance counts across data centers, making manual log inspection inefficient.

Effective log‑operation platforms must provide centralized collection, real‑time analysis, storage, and alerting, supporting pre‑failure risk analysis, rapid fault detection, and post‑incident review.

APM (Application Performance Management) combines logs, metrics, and tracing (the three pillars of observability) to collect, process, store, and visualize data, addressing issues of program heterogeneity, component diversity, complete traceability, and timely sampling.

Metrics, exemplified by Prometheus, gather aggregatable time‑series data (e.g., CPU usage, request latency) for dashboards and alerts, while logging tools like the ELK stack (Elasticsearch, Logstash, Kibana) enable full‑text search and visualization of discrete events.

Tracing systems (e.g., OpenTracing, SkyWalking) record request‑scoped spans to reconstruct call chains, helping pinpoint performance bottlenecks and failures across microservices.

Combining metrics, logs, and tracing allows aggregated statistics, detailed request information, and comprehensive performance insights, as illustrated by a fault‑diagnosis workflow that moves from alerts to metrics, logs, and traces.

The article also lists common log anti‑patterns (unclear messages, inconsistent formats, insufficient detail, noisy logs, misuse of levels, string concatenation, excessive logging in loops, lack of data masking, improper file rotation, missing trace propagation) and good practices (clear problem location, actionable information, system state awareness, bottleneck detection, iterative improvement, non‑intrusive logging).

A practical case from 文库 demonstrates a log‑operation pipeline using Argus for collection, BNS for instance mapping, MQ and TSDB for processing, and Sia for visualization, along with batch log querying implemented in Go:

package main
import (
    "fmt"
    "log"
    "os/exec"
    "runtime"
    "sync"
)

var wg sync.WaitGroup

func main() {
    runtime.GOMAXPROCS(runtime.NumCPU())
    instancesHost := getInstances()
    wg.Add(len(instancesHost))
    for _, host := range instancesHost {
        go sshCmd(host)
    }
    wg.Wait()
    fmt.Println("over!")
}

func sshCmd(host string) {
    defer wg.Done()
    logPath := "/xx/xx/xx/"
    logShell := "grep 'FATAL' xx.log.20230207"
    cmd := exec.Command("ssh", "PasswordAuthentication=no", "ConnectTimeout=1", host, "-l", "root", "cd", logPath, "&&", logShell)
    out, err := cmd.CombinedOutput()
    fmt.Printf("exec: %s\n", cmd)
    if err != nil {
        fmt.Printf("combined out:\n%s\n", string(out))
        log.Fatalf("cmd.Run() failed with %s\n", err)
    }
    fmt.Printf("combined out:\n%s\n", string(out))
}

func getInstances() []string {
    return []string{"x.x.x.x", "x.x.x.x", "x.x.x.x"}
}

The article concludes that in the cloud‑native era, building an appropriate log‑operation platform that provides searchable, analyzable, and alertable data transforms silent server logs into actionable insights, facilitating diagnosis, system improvement, and overall operational excellence.