Design a PySpark application to perform real-time log analysis

Define concrete log-analysis outputs and their latency targets, such as a five-minute error rate by service and route. Decide which requests qualify, which status codes count as errors, how bots/retries are treated, and which time zone and identity rules apply to any daily-user metric.

Collect logs through agents into a durable ingestion layer such as Kafka, with bounded local buffering, access control and a retention policy. Remove credentials and unnecessary personal data before broad distribution. Kafka durability depends on producer acknowledgement, replication and operational settings; it cannot guarantee loss-free capture by an arbitrary log agent. Kafka producer configuration

Use Structured Streaming to parse a declared schema, normalize event times, quarantine malformed records and compute the selected metrics. Keep event time and ingestion time separate. Use event-time windows and an agreed late-data policy for aggregates. Exact distinct-user counts need a supported design, for example batch recomputation from retained events; do not assume every batch distinct aggregate is supported in a streaming query. Unsupported streaming operations

Store a replayable raw layer and keyed aggregate results. A dashboard can read the aggregates while a separate alerting service applies thresholds, deduplication and routing. Avoid directly sending non-idempotent notifications from tasks that can be retried. Give each query a durable checkpoint and verify the destination's recovery semantics.

Monitor lag, batch duration, state size, parsing failures and destination availability. Test restart, bursts, skewed routes, duplicate logs and delayed events. Define how provisional windows are corrected and how a failed sink is retried. The result is a measurable near-real-time pipeline with explicit completeness limits, rather than an assertion that adding Kafka or more nodes makes every failure harmless.