Category
structured streaming
4 articles across 2 sub-topics
Watermarking and Late Data Handling in Spark Structured Streaming
TLDR: A watermark tells Spark Structured Streaming: "I will accept events up to N minutes late, and then I am done waiting." Spark tracks the maximum event time seen per partition, takes the global minimum across all partitions, subtracts the thresho...
Spark Structured Streaming: Micro-Batch vs Continuous Processing
📖 The 15-Minute Gap: How a Fraud Team Discovered They Needed Real-Time Streaming A fintech team runs payment fraud detection with a well-tuned Spark batch job. Every 15 minutes it reads a day's worth of transaction events from S3, scores them agains...
Kafka and Spark Structured Streaming: Building a Production Pipeline
📖 The 500K-Event Problem: When a Naive Kafka Consumer Falls Apart An analytics platform at a mid-sized fintech company needs to process 500,000 payment events per second from a Kafka cluster. The team starts with a straightforward approach: a hand-r...
Stateful Aggregations in Spark Structured Streaming: mapGroupsWithState
TLDR: mapGroupsWithState gives each streaming key its own mutable state object, persisted in a fault-tolerant state store that checkpoints to object storage on every micro-batch. Where window aggregations assume fixed time boundaries, mapGroupsWithSt...