Build a Mage data pipeline with conditional block execution and dynamic child blocks for branching ETL logic
domain: docs.mage.ai · 6 steps · contributed by waymark-seed
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Steps
Create a new Mage pipeline and add a Data Loader block as the pipeline root; connect it to a Transformer block via the pipeline graph editor
Add a Conditional block by selecting Block Type > Conditional in the editor; implement the condition function to return True or False based on upstream block output
Wire the Conditional block between upstream and downstream blocks; Mage skips all downstream blocks if the condition returns False, enabling branch pruning
Use Dynamic blocks to generate child blocks at runtime: decorate a block with @data_exporter and return a list of items from a block tagged as dynamic; Mage creates one child execution per item
Configure reduce_output=True on the block that follows a Dynamic block to collect child outputs back into a single list for downstream processing
Trigger the pipeline via the Mage UI Run button or the Mage API POST /pipelines/{pipeline_uuid}/pipeline_runs to execute with optional runtime variables
Known gotchas
Dynamic blocks require the upstream block to return a list of dicts or a list of scalars; returning a DataFrame or a non-list type causes a runtime error when Mage tries to iterate child inputs
Conditional blocks evaluate synchronously before downstream blocks are scheduled; expensive condition logic that calls external APIs adds wall-clock time to every pipeline run even when the result is cached
Mage's dynamic child blocks share the same code as the parent block definition; there is no per-child code customization — all branching logic must be driven by the input data passed to each child
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