Transform a Spark Dataset in Scala and keep the result reproducible from the same input.
Avoid task races caused by shared mutable state.
Which implementation is most appropriate?
Chain Dataset transformations such as map and filter and avoid shared mutable state or direct concurrent file appends.
Detailed explanation
Concurrent appends can conflict and leave partial or inconsistent output.
Concurrent appends can conflict and leave partial or inconsistent output.
IAM changes permissions and does not change a program's data structures.
IAM changes permissions and does not change a program's data structures.
Immutable transformations reduce side effects and remain safe under distributed retries.
Immutable transformations reduce side effects and remain safe under distributed retries.
Parallel retries can race or double-count, making the result non-reproducible.
Parallel retries can race or double-count, making the result non-reproducible.
Check official documentation
Compare the following points with the official documentation.
Apache Spark Dataset・RDDのtransformations、immutability、lazy evaluationを確認する。
What to confirm
分散処理における不変性・副作用・再試行の関係を説明できる。
Key points
- Scala
- Spark Dataset
- Immutability
- Side effects
Notes
- Environment: AWS公式DEA-C01試験ガイドと各サービスの公式ドキュメントの確認
- Confirm current AWS service behavior in the official DEA-C01 documentation.
Foundation review
Distributed retry
A transformation should produce the same result if a task executes more than once.
Output commit
Write task output to a safe temporary location and commit successful results atomically.