Technical articles on stream processing, Apache Flink, architecture, and the internals that power real-time data platforms.
How a one-line connector swap silently poisoned a savepoint, why raising heap memory only bought time, and what it took to cut the stale state out.
Savepoints vs checkpoints in Apache Flink: how they differ in objective, implementation and lifecycle, and when to use a savepoint in your streaming application.
Step by step: how the Flink Kafka source stores partition offsets in checkpoints, how barriers keep operator state consistent, and how offsets are restored after a failure.
How Apache Flink combines checkpoints with a two-phase commit protocol to deliver end-to-end exactly-once semantics with Apache Kafka transactions.
How Apache Flink incremental checkpoints work with RocksDB: only new SST files are uploaded, a shared state registry counts references, and old files are pruned.
Streaming fault-tolerance architectures compared (record acks, micro-batching, transactional updates, distributed snapshots), with Flink vs Storm throughput and latency benchmarks.
