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Uses file upload processing to practice bounded memory, stream ownership, temporary files, and retained buffers.
Covers chunking, bounded concurrency, scoped dependencies, progress, retries, and avoiding full-dataset memory spikes.
Covers queue-backed work, idempotency, retries, visibility timeout, poison messages, progress tracking, and choosing Storage Queues or Service Bus.
Separates process survival, traffic readiness, and slow initialization without turning shared dependency failure into a restart storm.
Creates queryable structured events with trace correlation, stable fields, useful context, and deliberate redaction.
Covers deployment logs, startup failures, runtime stack, app settings, missing files, Key Vault references, and post-deployment diagnosis.
Finds repeated relationship queries and fixes the data shape without introducing a larger query problem.
Moves from user-visible latency to route, dependency, database, executor, JVM, and change evidence before selecting a fix.
Connects one slow Python request to generated SQL, representative parameters, database plans, and measured validation.
Uses Hikari metrics, thread evidence, database activity, transaction scope, and query behavior to separate leaks from slow or excessive work.
Turns LOH theory into an API investigation around large payloads, buffers, serialization, and concurrency.
Separates Python allocation growth from process RSS, native memory, fragmentation, caches, and workload peaks.
Separates server request capacity from application executor capacity and finds blocking, queueing, rejection, or downstream causes.
Gives a practical incident flow for memory growth using metrics, traffic correlation, heap evidence, and safe mitigation.
Uses representative traffic and tail latency to find the real bottleneck and a safe operating range.
Chooses evidence based on whether latency comes from CPU execution, waiting, contention, or an external dependency.
Frames allocation reduction as measurement-driven API work rather than premature micro-optimization.
Explains correlation IDs, distributed tracing, dependency telemetry, message boundaries, and logs across App Service, queues, and downstream APIs.
Uses thread dumps, heap dumps, logger changes, mappings, and configuration evidence through a tightly controlled operator path.
Builds a bounded readiness check for one required dependency while keeping liveness independent and responses non-sensitive.
Implements a small in-memory fixed-window rate limiter with per-key counters.
Processes a large import in chunks with fresh DbContexts, idempotent keys, cancellation, and bounded tracker growth.
Explains how to inspect EF Core generated SQL and connect LINQ shape to database execution behavior.
Chooses between atomic operations and a critical section based on whether one value or a multi-step invariant must change safely.