luigi

Luigi enables developers to define workflows as Python classes where tasks declare their dependencies through method signatures and class attributes. The framework automatically builds a directed acyclic graph (DAG) by introspecting task definitions, resolving dependencies at runtime without requiring explicit graph construction code. This approach uses Python's object-oriented patterns to represent tasks as first-class objects with built-in dependency tracking through parameter passing and task output references.

Luigi implements smart task caching by tracking task outputs (typically files or database records) and only re-executing tasks when their inputs have changed or outputs are missing. The framework uses a Target abstraction (file paths, S3 objects, database tables) to determine task completion status without re-running successful tasks. This enables efficient re-runs of large pipelines where only downstream tasks affected by changes are re-executed.

Luigi provides a pluggable scheduler architecture that supports multiple execution backends: local single-threaded execution, multi-process execution on a single machine, and distributed execution via a central scheduler service. The framework abstracts task execution through a Worker interface, allowing tasks to run locally, on remote machines, or in containerized environments. The central scheduler (luigi.server) coordinates distributed workers, tracks task state, and manages resource allocation across a cluster.

Luigi provides a parameter system where task inputs are declared as typed class attributes (IntParameter, DateParameter, PathParameter, etc.) that are automatically validated and coerced from command-line arguments or programmatic task invocation. The framework validates parameter types at task instantiation time, rejecting invalid inputs before task execution begins. This enables type-safe task composition and prevents runtime errors from malformed inputs.

Luigi abstracts task outputs through a Target interface that supports multiple storage backends (local filesystem, S3, HDFS, databases, HTTP) without requiring task code changes. Tasks declare their outputs as Target objects, and the framework handles reading/writing through the appropriate backend. This enables seamless migration between storage systems and supports heterogeneous pipelines where different tasks write to different backends.

Luigi provides a web-based dashboard (luigi.server) that visualizes task dependency graphs, displays real-time execution status, and tracks task completion metrics. The dashboard shows which tasks are running, queued, completed, or failed, with drill-down capability to view task logs and error messages. This enables operators to monitor pipeline health without parsing log files or querying external systems.

Luigi implements task retry logic with configurable retry counts, delays, and backoff strategies. Tasks can be configured to automatically retry on failure with exponential backoff, and the framework tracks retry attempts to prevent infinite loops. Custom failure handlers can be implemented to perform cleanup or logging on task failure, enabling graceful degradation and recovery strategies.

Luigi enables task code reuse through Python class inheritance, allowing developers to create base task classes with common logic and parameters that are inherited by concrete task implementations. This pattern reduces boilerplate and enables consistent behavior across related tasks. Mixin classes can be used to add cross-cutting concerns (logging, metrics, caching) to multiple task types without code duplication.