Configure Spark Structured Streaming trigger modes (processingTime, availableNow, continuous)

Steps

1. ProcessingTime trigger: .trigger(Trigger.ProcessingTime('30 seconds')) fires micro-batches at fixed intervals; if a batch takes longer than the interval, the next batch starts immediately.
2. AvailableNow trigger: .trigger(Trigger.AvailableNow()) processes all available data at job start in one or more micro-batches then stops the query; useful for scheduled batch-style streaming runs.
3. Continuous trigger: .trigger(Trigger.Continuous('1 second')) enables experimental low-latency mode with asynchronous checkpointing at the specified interval; latency can drop to milliseconds but feature support is limited (verify supported operations in current Spark docs).
4. Default (no trigger call) behaves like ProcessingTime with as-fast-as-possible batches, immediately starting the next batch when the previous finishes.
5. Choose trigger mode based on latency requirements: ProcessingTime for standard micro-batch, AvailableNow for scheduled incremental loads, Continuous for sub-second latency (with caveats).