mloda-plugins

name	mloda-plugins
description	mloda plugin development guides. Use when creating FeatureGroups, ComputeFrameworks, Extenders, building plugins, sharing plugins, or understanding the mloda plugin system.

mloda Plugin Development Guides

Location: docs/guides/

Top-Level Guides (01-11)

#	Guide	Description
01	01-use-existing-plugin.md	Install and use a community plugin
02	02-discover-plugins.md	Find available plugins and explore installed ones
03	03-create-plugin-in-project.md	Add feature groups inline without separate package
04	04-create-plugin-package.md	Create standalone installable plugin from template
05	05-share-with-team.md	Distribute via private git repo
06	06-publish-to-community.md	Submit to mloda-registry
07	07-contribute-to-official.md	Improve existing plugins
08	08-become-official.md	Get plugin merged into registry
09	09-create-feature-group.md	Decision tree for choosing the right feature group pattern
10	10-create-compute-framework.md	Decision tree for creating a compute framework plugin
11	11-create-extender.md	Guide for creating extender plugins (logging, tracing, metrics)
--	index.md	Your Plugin Journey: step-by-step progression overview

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Feature Group Decision Tree (Guide 09)

Q1: Does it load external data (file, DB, external API)?
    YES → Pattern 1: Root Feature (if data passed at runtime, also see Q18)
    NO  → Continue

Q2: How many input features?
    0 → Pattern 1: Root Feature (or DataCreator, see 01-root-features)
    1+ → Pattern 2: Derived (see Q3)

Q3: Should it be reusable via naming pattern (input__operation)?
    YES → Add Pattern 3: FeatureChainParserMixin (if 2+ inputs, use Pattern 4)
    NO  → Continue

Q4: How many output columns?
    1 → Standard
    N → Pattern 5: Multi-output (feature~0, feature~1)

Q5: Does it need fitted/trained state between runs?
    YES → Add Pattern 6: Artifact

Q6: Does it need time ordering or group-by?
    YES → Add Pattern 7: Index

Q7: Does it join data from multiple feature groups?
    YES → Add Pattern 8: Links/Joins

Q8: Framework-specific API needed?
    YES → Pattern 9: Framework-specific

Q9: Does the feature name come from configuration vs naming pattern?
    Configuration → Pattern 3 with PROPERTY_MAPPING

Q10: Does it need custom matching logic beyond class name?
    YES → See 14-feature-matching

Q11: Does it need to filter data (time ranges, categories)?
    YES → See 15-filter-concepts

Q12: Does it need input/output validation?
    YES → See 16-validators

Q13: Does it need to match against a data connection?
    YES → See 17-data-connection-matching

Q14: Does it need explicit data type declaration?
    YES → See 18-datatypes

Q15: Could multiple FeatureGroups handle the same feature name?
    YES → See 19-domain

Q16: Do you need to track changes or ensure reproducibility?
    YES → See 20-versioning

Q17: Need to understand calculate_feature() and runtime context?
    YES → See 12-calculate-feature

Q18: Does it receive data programmatically at runtime (e.g. via API call)?
    YES → See ApiInputData docs: https://mloda-ai.github.io/mloda/in_depth/api-input-data/

Q19: Need to create feature groups dynamically or simplify complex input sources?
    YES → See 21-experimental-shortcuts

Q20: Need to define features via JSON config (e.g., for AI agents or config-driven pipelines)?
    YES → See 22-feature-config

Q21: Need to consume results incrementally as each feature group completes?
    YES → See 23-streaming

Q22: Need to build the execution plan once at startup and reuse it for repeated calls with fresh data?
    YES → See 24-realtime

Q23: Need both plan reuse and incremental per-group delivery?
    YES → Use session.stream_run(), see 23-streaming and 24-realtime

Q24: Need conditional aggregation that nulls out non-matching values instead of removing rows?
    YES → See 25-masking

Q25: Several readers under one root group, or a non-file (HTTP/API) source?
    YES → See 27-input-data-readers

Feature Group Pattern Guides

Location: docs/guides/feature-group-patterns/

#	Guide	Description
01	01-root-features.md	Data sources (CSV, DB, API, synthetic)
02	02-derived-features.md	Transform inputs with static output name
03	03-chained-features.md	Reusable transforms via input__op naming
04	04-multi-input-features.md	Combine inputs using & separator
05	05-multi-output-features.md	Multiple outputs using ~ separator
06	06-artifact-features.md	Save/load fitted state (scalers, models)
07	07-index-features.md	Ordering, grouping, joining columns
08	08-links-joins.md	Join data from multiple feature groups
09	09-framework-specific.md	Restrict to certain frameworks
10	10-testing-guide.md	Unit, framework, integration testing
11	11-options.md	Group vs context configuration
12	12-calculate-feature.md	Core computation method
13	13-feature-naming.md	Define feature names
14	14-feature-matching.md	Resolve name to FeatureGroup
15	15-filter-concepts.md	Filter data by conditions
16	16-validators.md	Input/output validation
17	17-data-connection-matching.md	Match against data sources
18	18-datatypes.md	Arrow-based type system
19	19-domain.md	Disambiguate matching
20	20-versioning.md	Version tracking
21	21-experimental-shortcuts.md	Dynamic creation helpers
22	22-feature-config.md	JSON-based feature definition
23	23-streaming.md	Incremental results with stream_all
24	24-realtime.md	Reuse execution plans with prepare + run
25	25-masking.md	Conditional aggregation via FilterMask
26	26-input-feature-forwarding.md	Consume another group's root feature; option forwarding
27	27-input-data-readers.md	Sibling reader selection; non-file / HTTP readers

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Compute Framework Decision Tree (Guide 10)

Q1: Does your framework require a connection/session object?
    YES → Q2
    NO  → Q3

Q2: Is it a data lake table format (Iceberg, Delta, Hudi)?
    YES → Category 5: Data Lake
    NO  → Category 3: Stateful Connection

Q3: Does your framework use lazy evaluation?
    YES → Category 2: Stateless Lazy
    NO  → Q4

Q4: Does your framework have external dependencies?
    YES → Category 1: Stateless Eager
    NO  → Category 4: Zero Dependency

Q5: Do you need cross-framework conversion?
    YES → See 08-framework-transformer

Q6: Does your library have built-in PyArrow conversion?
    YES → Simplifies transformer (use .to_arrow()/.from_arrow())
    NO  → Manual conversion in 08-framework-transformer

Q7: Need to understand merge/join operations?
    YES → See 06-merge-engine

Q8: Do you need multi-column index support for joins?
    YES → See 06-merge-engine (Index with tuple)

Q9: Need to understand filter operations?
    YES → See 07-filter-engine

Q10: Should connections be auto-created or user-provided?
    AUTO  → Add fallback in set_framework_connection_object()
    USER  → Require via data_connections parameter

Q11: Ready to test your implementation?
    YES → See 09-testing-guide

Compute Framework Pattern Guides

Location: docs/guides/compute-framework-patterns/

#	Guide	Description
01	01-stateless-eager.md	In-memory immediate (Pandas, PyArrow)
02	02-stateless-lazy.md	Deferred execution (Polars Lazy, Ibis)
03	03-stateful-connection.md	Connection required (DuckDB, Spark)
04	04-zero-dependency.md	Pure Python (List[Dict])
05	05-data-lake.md	Catalog-based (Iceberg, Delta, Hudi)
06	06-merge-engine.md	JOIN and UNION operations
07	07-filter-engine.md	Filter operations
08	08-framework-transformer.md	Cross-framework conversion
09	09-testing-guide.md	Testing your implementation

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Extender Decision Tree (Guide 11)

Q1: What do you want to wrap?
    Feature calculation → FEATURE_GROUP_CALCULATE_FEATURE
    Input validation   → VALIDATE_INPUT_FEATURE
    Output validation  → VALIDATE_OUTPUT_FEATURE

Q2: Need execution order control?
    YES → Set custom priority (lower runs first, default 100)

Q3: Need state with ParallelizationMode.MULTIPROCESSING?
    YES → Use class-level storage (pickle-safe)

Full guide: docs/guides/11-create-extender.md