Parallelism and Indexed Work

completions sets total successful work units and parallelism sets concurrency. Indexed Jobs expose a stable completion index useful for sharding. Work queues often use unknown completion counts and external coordination. Concurrency must respect downstream limits such as database pools and rate caps.

Analogy: Kubernetes is a thermostat, not a remote control. You declare the temperature; independent controllers measure reality and keep acting. Debugging means finding which sensor, rule or actuator prevents convergence.

Read the object as evidence

kubectl get RESOURCE NAME -n NAMESPACE -o yaml
kubectl describe RESOURCE NAME -n NAMESPACE
kubectl get events -n NAMESPACE --sort-by=.metadata.creationTimestamp
kubectl explain RESOURCE.spec

Do not memorize these as a ritual. The first command exposes desired and observed state, the second connects conditions and events, the third supplies a timeline, and the fourth checks the server's schema. Compare what the controller was asked to do with what it reports doing. Then test the narrowest hypothesis.

Scenario: Scaling a migration Job to 100 parallel Pods exhausts PgBouncer and slows completion. More Pods are not automatically more throughput.

Bound parallel work

completions is required successful units; parallelism is simultaneous Pods. Indexed Jobs give each Pod a stable completion index for deterministic shards, but the application must map indexes to non-overlapping work. Account for database connections, API rate limits and cluster capacity before raising parallelism. Watch succeeded, failed and active counts rather than only Pod phase.

Scenario: Parallelism rises from ten to one hundred and total throughput falls because every worker contends on one database lock. Kubernetes delivered concurrency correctly; the dependency was the bottleneck.
Tip: Start below the downstream concurrency budget, measure queue latency and error rate, then increase gradually. Pod count is a control input, not a throughput guarantee.

Production reasoning

Ask four questions: Who owns this object? What dependency must become ready next? Which controller reports the blocking condition? What evidence would disprove my current theory? This prevents symptom-driven changes. Record the context, namespace, object generation, image digest and recent rollout before mutation; a recreated Pod may erase the evidence you needed.

Warning: Running is not the same as ready, healthy, durable or correct. Kubernetes status is layered. Confirm the application-level outcome as well as the object state.
Goal: Put this model into practice in the Kubernetes lab init-pgbouncer-postgres. Open /labs/kubernetes, choose init-pgbouncer-postgres, predict the failure path before changing anything, then use the simulator's check command to validate the finished state.

Deliberate practice

Before the lab, write the expected object relationship and the first three commands you will run. Afterward, explain why the fix converged and name one tempting change that would only mask the symptom. Repeat using an explicit namespace and a structured output format. This prediction-observation-explanation loop is what turns command familiarity into production judgment.

45-minute investigation

  1. Map (5 min): draw the owner-to-child chain and mark every namespace, selector, identity and dependency involved.
  2. Predict (5 min): write one expected status condition, one likely event and one log or metric signal before opening the lab.
  3. Observe (10 min): collect YAML, describe output and ordered events. Do not mutate state. Record which observation disproves your first theory.
  4. Repair (15 min): make the smallest declarative correction, watch the responsible controller converge, and verify the user-facing path rather than stopping at Running.
  5. Stress (10 min): change one relevant constraint - replica count, label, readiness, resource value or placement rule - predict the outcome, observe it, then restore the known-good declaration.
Tip: Keep a short incident note with symptom, evidence, hypothesis, change, result. Across four sections this produces a reusable runbook instead of a pile of remembered commands.