Track: IT Foundations → Linux operations
Prerequisites: Linux Terminal 01 and 02: paths, permissions, pipes, grep, processes, and SSH
Format: 35–45 minutes teaching + 20-minute local lab
Outcome: distinguish a running process from a working service, collect bounded evidence, and explain a next action without guessing.

1. The checkout button stopped working

Imagine you help operate a small online shop. Someone says, “The website is down.” The home page opens, but the orders page shows an error. One teammate suggests restarting the server. Another suggests changing the firewall. You have enough terminal knowledge to do either. This lesson is about deciding whether either action makes sense.

First, turn the report into an observable statement: “At 14:05 UTC, requesting /orders from the application returns HTTP 503. /health returns HTTP 200.” That sentence gives you a time, a test, and a boundary. It leaves room for the site to be partially working.

The exercise uses a deliberately broken teaching service. Its inventory timeout is simulated; no real shop, customer, or external service is involved. Your task is to explain the evidence. A restart is not part of this lab because the process already answers requests.

Use a simple loop: observe → form one hypothesis → run a discriminating test → record what changed. A useful test distinguishes plausible explanations. Repeating the same command ten times without a question rarely does.

2. Know which machine you are examining

After connecting through SSH, orient yourself before collecting output:

hostname
date -u
whoami
pwd

On a real incident, record the application, environment, machine, and time zone. An accurate command on a staging server does not explain a production failure. Compare timestamps in a common time zone; clock differences can make events appear in the wrong order.

Keep observations separate from conclusions. “One request returned 503” is an observation. “The database is broken” is a hypothesis. “The log names inventory as the failing dependency” is another observation. That distinction helps the next person reproduce your thinking rather than inherit a guess.

Do not copy whole environment dumps, authorization headers, or complete customer logs into a public discussion. Collect the smallest useful excerpt. A request identifier, timestamp, route, status, and redacted error often explain more than a thousand unrelated lines.

3. A process is only one layer

Processes have IDs, owners, parent processes, and states. For a PID you already identified, take a focused snapshot:

ps -p 12345 -o pid,ppid,stat,etime,comm

Replace 12345 with your actual target; the number is illustrative. pid identifies the process, ppid its parent, stat its state, and etime its elapsed lifetime. comm keeps the view focused on the command name rather than full arguments, which sometimes contain secrets. Linux and macOS both support these fields, although details vary. Linux ps documentation

An S state usually means interruptible sleep: a normal service may be waiting for work. An R means running or runnable. A Z means the process has exited but its parent has not yet collected its status; it is not a healthy worker simply because a row remains visible.

One snapshot does not establish sustained resource pressure. Watch over an interval and correlate with request timing. A busy process can be doing useful work; a quiet process can be stalled on a dependency. Avoid diagnosing by one large number.

Signals are actions, not observations. If you own a disposable process, kill -TERM PID requests termination. The lab remembers the PID of the child it created and cleans up that child only. On shared infrastructure, use the service’s operating procedure rather than broad name-based termination.

4. Resources provide context

These read-only checks are common on a Linux host:

uptime
free -h
df -h .

uptime includes load averages over one, five, and fifteen minutes. On Linux, load includes runnable tasks and tasks in uninterruptible sleep, often waiting on I/O. It is not a CPU percentage. Compare load with available CPUs and other evidence. Linux uptime documentation

free -h describes memory. On modern Linux, the available estimate is generally more useful than treating every cached byte as unavailable. df -h . reports filesystem capacity for the current directory’s filesystem, not the size of the current directory. A filesystem can also exhaust inodes; df -i . is a separate useful check when many tiny files are involved.

These are context checks, not requirements for the portable lab. macOS has uptime and df, but not Linux’s free; its Activity Monitor and vm_stat use different presentations. Containers may show host-related values or imposed limits differently. Name the environment in your notes.

5. Read a window of logs

Start small. For a known application log:

tail -n 40 app.log
grep -n 'status=503' app.log
grep -n -C 2 'dependency=inventory' app.log

The exact strings depend on the application. A severity label alone is not proof of user impact. Some applications log handled failures at ERROR; others return failed requests without that label. Correlate route, time, request ID, and status.

On a Linux machine using systemd, the journal provides filters. For an existing service named shop.service:

journalctl -u shop.service --since '15 minutes ago' -n 100 --no-pager

This restricts the unit, time range, and output count. Service names and access rights vary. An empty result might mean the wrong unit, a different log destination, or limited access. It does not automatically mean no errors. macOS does not use systemd, and some Linux containers do not either. systemd journalctl reference

The lab emits an explicit synthetic clue: dependency=inventory reason=timeout. In a real application that would justify investigating the inventory path next. It would not yet prove why inventory timed out. The dependency might be overloaded, unreachable, or responding too slowly for this caller’s deadline.

6. Separate listening, reaching, and succeeding

Think of three questions. Is a socket listening? Can this client reach it? Does the requested operation succeed? Each answer covers a different layer.

On Linux, ss -ltn lists listening TCP sockets with numeric addresses. ss -ltnp also attempts to show process information; visibility depends on permissions. On macOS, an available lsof can inspect listeners with lsof -nP -iTCP -sTCP:LISTEN. These tools have different output; read local help when a flag is unfamiliar. Linux ss reference

Binding to 127.0.0.1 means loopback: clients on that same machine can reach the listener. It is intentional for our local lab. A service listening locally does not prove that traffic from the internet, a load balancer, or another subnet can reach it. That path introduces routing, access controls, name resolution, and sometimes TLS.

For HTTP, make the actual request and preserve its status:

curl -sS --connect-timeout 2 --max-time 5 \
  -o response.txt -w 'HTTP %{http_code}\n' \
  http://127.0.0.1:8080/orders

Use the actual lab port, which is printed when the script starts. Connection timeout bounds the connection phase; maximum time bounds the whole transfer. A timeout and an HTTP 503 describe different evidence. A 503 means you received an HTTP response from something on that request path. curl manual

7. HTTP status and command exit status are different

By default, curl can exit successfully after receiving an HTTP error response: the transfer itself completed. For a health check that should fail on HTTP errors, add --fail. For the lab’s 503, curl then returns exit status 22.

That behavior matters in scripts. A monitor that only checks “did curl run?” may report a broken endpoint as healthy. Conversely, adding --fail without handling its result can make an intentional negative test look like a broken lab. Capture the status immediately, because the next command replaces $?.

HTTP 200 on /health can mean only that the process is alive. A business route might need a database, an inventory service, or configuration that the liveness endpoint never checks. Readiness checks often examine whether traffic should be sent to an instance, but the actual checks depend on the application. Verify what your endpoint measures.

8. Run the complete local exercise

The supplied files require Bash 3.2 or later, Python 3, and curl. No AWS account, administrator access, installation, internet request, or firewall change is needed. The server binds only to loopback and asks the operating system for an available port. Python’s HTTP server is used as a teaching fixture, not a production deployment. Python HTTP server documentation

From this lesson’s directory:

bash lab/run-lab.sh

Read the script first. It checks prerequisites, creates a private temporary directory, launches its own child, requests two routes, inspects the generated log, and tests curl’s error behavior. The port and PID vary, but these lines should appear:

health HTTP: 200
orders HTTP: 503
Count of logged 503 responses: 1
curl --fail exit: 22 (expected HTTP failure)
PASS: process alive, HTTP reachable, orders unavailable; synthetic dependency evidence found.

The log includes dependency=inventory reason=timeout synthetic=true. The count is one at the moment it is printed; the later negative curl test makes a second orders request. That sequencing is intentional. Explain the count before assuming a discrepancy.

The exit handler stops only the owned server and removes only the named files the run created, then removes the empty temporary directory. Ctrl+C also triggers cleanup. If the process listing is restricted by an execution environment, the script prints that limitation while continuing the endpoint tests. A loopback restriction may prevent this lab from running; do not change machine security settings merely to bypass such a restriction.

9. Write an incident note someone can use

Complete this five-line handoff after the run:

1. Symptom: /orders returns 503 in the local teaching service.
2. Scope: /health still returns 200; requests from this local client reach the service.
3. Evidence: the owned process exists; the log associates orders failures with the synthetic inventory timeout.
4. Next test: in a real system, inspect the inventory caller’s timeout and the dependency’s matching time window.
5. Limit: this exercise does not test real DNS, TLS, external routing, or a real inventory service.

That last line strengthens the note. It tells the next engineer where your evidence stops. Avoid “network fine” when you tested only loopback, or “fixed” when you never reran the affected operation.

For a portfolio exercise, save your explanation and a redacted excerpt of actual output. Describe what you tested, what you observed, and the next discriminating check. The learning artifact is the reasoning, not a fictional production incident.

10. Apply the method to the next problem

If the process is missing, investigate startup and supervision. If it is listening only on loopback but needs remote clients, examine the intended binding and network design. If the request connects but returns 503, move up to application evidence. If a change is made, repeat the original user-facing test and compare the same time window.

Next, Linux Terminal 04 turns the same evidence habit into automation. A backup command that exits successfully is only one layer. You will create an archive, restore it into a separate directory, compare the files, and make the script tell the truth about failure.

Sources checked: September 18, 2026. Command examples use portable Bash where stated; systemd, ss, and free sections target Linux. Platform command availability and permissions still depend on the learner’s installation. Additional references: procps free manual, lsof manual.

curl -sS --connect-timeout 2 --max-time 5 \
  -o response.txt -w 'HTTP %{http_code}\n' \
  http://127.0.0.1:8080/orders