tsb — Log Analysis

1 · Parse and bucket access logs

Each row is one HTTP request. Extract the hour and the status class (2xx/4xx/5xx).

TypeScript

import { DataFrame, pivot } from "tsb";

const log = DataFrame.fromColumns({
  ts: [
    "10:01","10:03","10:09","10:14","10:22","10:31","10:47","10:55",
    "11:02","11:04","11:09","11:18","11:25","11:33","11:41","11:50","11:55","11:58",
    "12:01","12:03","12:08","12:14","12:22","12:31","12:38","12:45",
    "13:02","13:09","13:14","13:25","13:31","13:48","13:55",
  ],
  status: [
    200,200,200,404,200,500,200,200,
    200,500,500,500,200,200,404,500,500,500,
    200,200,200,200,200,200,404,200,
    200,500,200,200,200,200,200,
  ],
});

const hour = log.col("ts").map(v => Number(String(v).slice(0, 2)));
const cls  = log.col("status").map(v => {
  const s = Number(v);
  if (s >= 500) return "5xx";
  if (s >= 400) return "4xx";
  return "2xx";
});
const enriched = log.assign({ hour, cls });

// Count requests per (hour, cls)
const counts = enriched.groupby(["hour", "cls"]).size();
const grouped = DataFrame.fromColumns({
  hour: [...counts.index.values].map(k => Number(String(k).split("__SEP__")[0])),
  cls:  [...counts.index.values].map(k => String(String(k).split("__SEP__")[1])),
  n:    [...counts.values].map(Number),
});

const piv = pivot(grouped, { index: "hour", columns: "cls", values: "n" }).fillna(0);
console.log("Requests per hour by status class:");
console.log(piv.toString());

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2 · Error rate per hour & alert threshold

Compute the 5xx error percentage per hour and flag any hour above 30%.

TypeScript

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🪵 Server Access Log Analysis

1 · Parse and bucket access logs

2 · Error rate per hour & alert threshold