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Migration goal Consider a small Structured Streaming job that reads from Kafka and writes to a relational sink. The plan is to move it to a newer Spark 3.x release, possibly with a revised aggregation, without pausing the pipeline. The open decision is whether the existing checkpoint directory can carry the query across that boundary, or whether cutover need
Goal: configure a Spark Structured Streaming job so that retrying a failed micro-batch does not produce duplicate rows in the downstream sink. The job uses foreachBatch to write to a table, and the checkpoint directory retains committed offsets. If a task fails after the sink acknowledges part of a batch but before the checkpoint advances, the next attempt m
Spark 4.0 changes the Scala binary compatibility boundary compared with Spark 3.5. Spark 3.5 ships Scala 2.12 and 2.13 builds, while Spark 4.0 is documented as Scala 2.13 only and requires Java 17 at runtime. The goal is to determine the impact on existing codebases compiled against Spark 3.5 with Scala 2.12 and Java 8/11 toolchains. The constraints include
Evaluating Structured Streaming state management and checkpointing for fault tolerance in a test integration without production credentials. The goal is to understand how checkpoint metadata and state snapshots behave when a stateful aggregation is exercised against a local file system checkpoint location rather than a production distributed file system. The
Goal Maintain exactly‑once semantics across all Structured Streaming sinks during retries, avoiding duplicate writes when a task restarts. Current Landscape Apache Spark relies on checkpointing and write‑ahead logs to recover from failures. Exactly‑once guarantees are only available for sinks that implement idempotent writes (Kafka, Delta Lake, HDFS/Parquet)
Spark driver UI access: localhost-only binding versus reverse-proxy exposure The goal is to let distributed team members monitor Spark applications through the driver UI without exposing job details, stage metrics, or environment information to unintended network peers. Spark historically binds its web UI to all interfaces unless the bind host is restricted,
Apache Spark's Adaptive Query Execution (AQE) optimizes query plans at runtime by utilizing statistics from completed shuffle stages. This allows the engine to dynamically coalesce shuffle partitions and switch join strategies, such as converting a SortMergeJoin to a BroadcastHashJoin when actual relation sizes fall below the threshold. However, the integrat
Goal We want to confirm that data recovered from a Delta Lake backup remains accurate after a subsequent OPTIMIZE compaction, while reads are performed concurrently. Constraints The Delta transaction log guarantees ACID semantics, but the compaction commit is appended asynchronously. When a reader starts after the compaction trigger but before the commit is