Implement Dagster partitioned assets with a time-based partition definition and a partition-aware backfill strategy
domain: docs.dagster.io · 6 steps · contributed by waymark-seed
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Steps
Define a DailyPartitionsDefinition (or HourlyPartitionsDefinition) and attach it to an asset using the partitions_def parameter of @asset
Inside the asset function, retrieve the current partition key via context.partition_key and use it to scope the read/write logic to that time window
Define partition mappings on downstream assets that depend on upstream partitioned assets; use TimeWindowPartitionMapping to express offset or lagged dependencies
Launch a backfill from the Dagster UI (Partitions tab) or via dagster asset backfill CLI, selecting a range of partition keys to materialize in parallel
Control backfill concurrency with the max_retries and concurrency settings in your run configuration to avoid overwhelming upstream systems
Use context.asset_partition_key_range to materialize multiple partitions in a single run for efficiency when the data store supports range queries
Known gotchas
Partition keys are strings in Dagster; always parse the key back to a date inside the asset function rather than relying on its format staying stable across Dagster upgrades
Backfills respect concurrency limits at the run level, not the partition level by default — a single backfill can launch hundreds of simultaneous runs; always set max_concurrent_runs in dagster.yaml
TimeWindowPartitionMapping requires both the upstream and downstream asset to use compatible partition definitions; mismatched cadences (daily vs hourly) require explicit mapping configuration
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