🐍 Advanced Python Programming — Thread Life CycleThread State → State-driven Implementation → Practical Thread Application

VLAB THREAD CELL

PYTHON CONCURRENCY

Observe the State. Control the Thread. Apply the Pattern.

Three progressive missions using the KASA Python Runtime.

🖥 Run the Thread

Run the supplied code, then replay the conceptual lifecycle animation.

Ready.

Thread Life Cycle · Conceptual View

Python's threading API does not expose a Java-style lifecycle enum. This visualization maps observable behavior to useful conceptual states.

NEW

→

RUNNING

→

WAITING

→

RUNNING

→

TERMINATED

Run the code to animate the lifecycle.

Key idea: create Thread → start() → work executes → thread may wait/block → resumes → run() returns → terminated. join() makes the caller wait for completion.

Mission · Report Worker

A report worker starts, reaches a gate because data is not ready, waits for a signal, resumes, then finishes.

① NEW — create ReportWorker

② RUNNING — call start()

③ WAITING — worker calls gate.wait()

④ RESUMED — Main calls gate.set()

⑤ TERMINATED — worker completes; Main joins it

Target order: REPORT: RUNNING → REPORT: WAITING → MAIN: SIGNAL → REPORT: RESUMED → REPORT: TERMINATED → MAIN: COMPLETE

🖥 Implement by State

Complete the TODOs so behavior follows the scenario.

Ready.

Real Case · Build a Dashboard Concurrently

A dashboard needs PROFILE, ORDERS and PAYMENT data. These are independent I/O-like tasks, so launch three threads and wait until all results are ready.

PROFILE
350 ms

ORDERS
200 ms

PAYMENT
280 ms

Thread per source — independent tasks overlap

Lock shared result — protect mutation of results

Join all — dashboard becomes READY only after all workers finish

Preview the concurrent flow, then implement it.

🖥 Practical Thread Challenge

Complete the thread creation, start and join phases.

Expected final state: DASHBOARD READY + 3 OK results.