Unit
- Synthesis operation: A sequence of steps that transform input data into output through a defined pipeline. Each operation is idempotent and composable, allowing complex workflows to be built from simple, reusable units.
- Pipeline architecture: Stages are connected serially or in parallel with clear data contracts between them. This modular design enables independent testing, scaling, and replacement of individual stages without affecting the whole.
- Data contract: A formal specification of the data format, schema, and constraints expected at each pipeline interface. Contracts prevent silent failures and make dependencies explicit.
- Idempotency: Running the same synthesis operation multiple times produces the same result as a single execution. This property is critical for retries and exactly-once processing guarantees.
- Composability: Small operations can be combined into larger ones using standard combinators like sequence, fork-join, and iterate. This encourages reuse and reduces duplication.
- Determinism: Given the same input and state, a synthesis operation always produces the same output. Deterministic operations simplify debugging and testing by removing nondeterministic failures.
- Observability: Operations emit structured logs, metrics, and traces that allow operators to understand internal behavior without instrumentation changes. This is built into the operation runtime, not added later.
- Fault isolation: Each stage in a pipeline runs in its own execution context with bounded resources. A failure in one stage does not cascade to others, and the system can recover gracefully.
- Backpressure: A mechanism that propagates flow control signals from downstream stages to upstream sources when processing capacity is exceeded. Stages signal their readiness, and the pipeline adapts by throttling or buffering. This prevents resource exhaustion and ensures stable throughput under variable load.
- Data contract versioning: Contracts are versioned so that changes to schemas or constraints can be introduced incrementally. A pipeline can run multiple contract versions simultaneously, allowing gradual migration. This avoids breaking changes when updating stages and supports backwards compatibility across deployments.
- Error handling strategy: Each stage defines a policy for nonfatal errors such as invalid records or transient failures. Common strategies include skipping the record with a logged warning, retrying with exponential backoff, or routing to a dead letter queue. Clear error handling prevents silent data loss and supports auditability.
- Immutable data flow: Data passing between stages is treated as immutable; stages never modify input data in place. Any transformation produces a new data object. This eliminates side effects, simplifies reasoning about state changes, and makes replay and debugging straightforward since intermediate snapshots are preserved.
Design Principle
Principle: Single Responsibility for Pipeline Stages
Definition: Each stage in a synthesis pipeline should perform exactly one transformation or check. This keeps stages small, testable, and replaceable. A stage that both transforms data and validates it violates this principle and should be split.
Application: In a data ingestion pipeline, separate the parsing stage (e.g., convert CSV to JSON) from the validation stage (e.g., check required fields). This allows the parsing logic to be reused across different sources while validation can be enhanced independently without touching parsing code.
Quick Revision
- Synthesis operations are composable, idempotent sequences that transform data through pipelines.
- Pipeline architecture uses modular stages connected by explicit data contracts.
- Idempotency and determinism ensure reliable retries and predictable behavior.
- Observability and fault isolation are built-in, not afterthoughts.
- The single responsibility principle keeps stages focused and maintainable.