@Scout my dbt assets did not start after my main assets. Why? In my main asset I have a Multidimensional partition with a date and a list of entities in my dbt assets I configured with a daily partitions. Do I need to use the same partitions in both assets definitions?

The behavior you're experiencing with your dbt assets not starting after your main assets could be related to how you've set up your partitioning and dependencies between assets. In Dagster, partitioning schemes and dependencies between assets need to be carefully defined to ensure that assets are materialized in the correct order. If your main asset has a multidimensional partition with a date and a list of entities, and your dbt assets are configured with daily partitions, there are a few things to consider: 1. Partition Mapping: The documentation describes various partition mappings, such as

IdentityPartitionMapping

,

AllPartitionMapping

, and

LastPartitionMapping

. These mappings define how partitions in downstream assets relate to partitions in upstream assets. If your dbt assets depend on the output of the main asset, you need to ensure that the partition mapping is set up correctly so that the dbt assets understand which partitions of the main asset they depend on. 2. Dependencies: Dependencies between assets are defined using the

ins

parameter in the

@asset

decorator. If your dbt assets depend on the main asset, you need to specify this dependency explicitly. 3. Partition Alignment: While it's not strictly necessary for all assets to use the same partitioning scheme, the dependencies between them need to be clearly defined. If your dbt assets are meant to run after the main asset, the partition mapping and dependencies should reflect that relationship. 4. Scheduling and Materialization Policies: The timing of asset materialization can also be influenced by scheduling and materialization policies. For instance,

AutoMaterializePolicy

can be used to define rules for when assets should be materialized. Without more specific details about your asset definitions and how you've configured the dependencies and partition mappings, it's challenging to pinpoint the exact reason why your dbt assets did not start after your main assets. However, ensuring that your partition mappings and dependencies are correctly set up is a critical step. If you continue to face issues, you might want to review the partitioning and dependency configurations in your asset definitions, and ensure that the partition mappings align with the intended execution order of your assets. If necessary, you can also consult the documentation or community discussions for examples and guidance on setting up complex partitioning schemes and dependencies between assets.

@Scout I can't understand partition mapping without examples

Partition mappings in Dagster define the relationship between the partitions of upstream and downstream assets. They are crucial when you have assets that depend on each other and are partitioned differently. Here are some examples of partition mappings based on the documentation: 1. IdentityPartitionMapping: This is the simplest form of partition mapping where each partition in the downstream asset corresponds directly to a partition in the upstream asset with the same partition key. 2. TimeWindowPartitionMapping: This mapping is useful when your downstream asset's partition depends on a range of partitions from the upstream asset. For example, a weekly partition in the downstream asset might depend on daily partitions from the upstream asset for the entire week. 3. AllPartitionMapping: This mapping specifies that a partition in the downstream asset depends on all partitions of the upstream asset. 4. Custom PartitionMapping: You can define a custom partition mapping if the built-in mappings do not fit your use case. This involves subclassing

PartitionMapping

and implementing the

get_downstream_partitions_for_partition_range

and

get_upstream_partitions_for_partition_range

methods to define the relationship between partitions. Here's a hypothetical example to illustrate how you might set up a custom partition mapping:

python
from dagster import PartitionMapping, PartitionKeyRange

class CustomPartitionMapping(PartitionMapping):
    def get_downstream_partitions_for_partition_range(
        self,
        upstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def: Optional[PartitionsDefinition],
        upstream_partitions_def: PartitionsDefinition,
    ) -> PartitionKeyRange:
        # Custom logic to map upstream partitions to downstream partitions
        pass

    def get_upstream_partitions_for_partition_range(
        self,
        downstream_partition_key_range: Optional[PartitionKeyRange],
        downstream_partitions_def: Optional[PartitionsDefinition],
        upstream_partitions_def: PartitionsDefinition,
    ) -> PartitionKeyRange:
        # Custom logic to map downstream partitions to upstream partitions
        pass

In this example,

CustomPartitionMapping

is a subclass of

PartitionMapping

where you would implement the logic to map between upstream and downstream partitions based on your specific requirements. Please note that the above code is a template to show where you would insert your custom logic. The actual implementation would depend on how you want to map your partitions. For your specific case, where you have a multidimensional partition with a date and a list of entities in your main asset, and daily partitions in your dbt assets, you would need to define a parti

@Scout give me a example of how to configure this partition mapping in a multiasset

As of my knowledge cutoff date in early 2023, Dagster's asset model does not directly support the concept of a "multiasset." However, you can define multiple assets within a single Python file or module, and these assets can have dependencies on one another. If you are referring to defining multiple assets with partition mappings, I can provide an example based on the documentation. Let's say you have an upstream asset that is partitioned by date and color (a multidimensional partition), and you want to define a downstream asset that is partitioned daily. You would need to create a custom partition mapping to map the multidimensional partition keys to the daily partition keys. Here's an example of how you might define such a partition mapping and use it in your asset definitions:

python
from dagster import asset, DailyPartitionsDefinition, MultiPartitionsDefinition, StaticPartitionsDefinition, PartitionMapping, PartitionKeyRange

# Define your custom partition mapping
class MyCustomPartitionMapping(PartitionMapping):
    def get_downstream_partitions_for_partition_range(
        self,
        upstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def,
        upstream_partitions_def,
    ):
        # Implement logic to map from upstream to downstream partitions
        # For example, you might extract the date from the multidimensional key
        # and map it to the corresponding daily partition in the downstream asset
        pass

    def get_upstream_partitions_for_partition_range(
        self,
        downstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def,
        upstream_partitions_def,
    ):
        # Implement logic to map from downstream to upstream partitions
        pass

# Define your upstream asset with multidimensional partitions
@asset(partitions_def=MultiPartitionsDefinition({
    "date": DailyPartitionsDefinition(start_date="2022-01-01"),
    "color": StaticPartitionsDefinition(["red", "yellow", "blue"]),
}))
def upstream_asset():
    # Your computation logic here
    pass

# Define your downstream asset with daily partitions
@asset(
    partitions_def=DailyPartitionsDefinition(start_date="2022-01-01"),
    ins={"upstream_asset": AssetIn(partition_mapping=MyCustomPartitionMapping())}
)
def downstream_asset(upstream_asset):
    # Your computation logic here, which depends on the upstream_asset
    pass

In this example,

MyCustomPartitionMapping

is a subclass of

PartitionMapping

that you would need to implement to define the logic for mapping between the multidimensional partitions of the

upstream_asset

and the daily partitions of the

downstream_asset

. The

ins

parameter in the

@asset

decorator for

downstream_asset

specifies that it depends on `upstream_ass

@Scout generate a custom partition mapping that takes this partition definition

job_fidc_idtrust_reports_partitions_def = MultiPartitionsDefinition(
        {
            "entity": StaticPartitionsDefinition(ID_TRUST_ENTITIES),
            "date": DailyPartitionsDefinition(start_date='2023-12-01', timezone='America/Fortaleza')
        }
    )

And then extracts only the DailyPartitionsDefinition from it

To create a custom partition mapping that extracts only the

DailyPartitionsDefinition

from a

MultiPartitionsDefinition

, you can subclass

PartitionMapping

and implement the logic to map the multidimensional partition keys to the daily partition keys. Here's an example of how you might define such a custom partition mapping:

python
from dagster import (
    PartitionMapping,
    PartitionKeyRange,
    MultiPartitionsDefinition,
    StaticPartitionsDefinition,
    DailyPartitionsDefinition,
)

# Assuming ID_TRUST_ENTITIES is defined somewhere
ID_TRUST_ENTITIES = ["entity1", "entity2", "entity3"]  # Example entities

# Define the MultiPartitionsDefinition
job_fidc_idtrust_reports_partitions_def = MultiPartitionsDefinition(
    {
        "entity": StaticPartitionsDefinition(ID_TRUST_ENTITIES),
        "date": DailyPartitionsDefinition(start_date='2023-12-01', timezone='America/Fortaleza')
    }
)

# Custom partition mapping to extract only the DailyPartitionsDefinition
class ExtractDailyPartitionMapping(PartitionMapping):
    def get_downstream_partitions_for_partition_range(
        self,
        upstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def,
        upstream_partitions_def: MultiPartitionsDefinition,
    ):
        # Assuming that the downstream asset is partitioned daily
        # Extract the date component from the upstream partition keys
        date_partition_keys = [
            key["date"] for key in upstream_partition_key_range.get_partition_keys()
        ]
        return PartitionKeyRange(date_partition_keys)

    def get_upstream_partitions_for_partition_range(
        self,
        downstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def,
        upstream_partitions_def: MultiPartitionsDefinition,
    ):
        # Map each downstream daily partition key to a range of upstream partition keys
        # that have the same date but different entities
        upstream_partition_keys = []
        for downstream_key in downstream_partition_key_range.get_partition_keys():
            for entity in ID_TRUST_ENTITIES:
                upstream_partition_keys.append(
                    {"entity": entity, "date": downstream_key}
                )
        return PartitionKeyRange(upstream_partition_keys)

# Use the custom partition mapping in your asset definitions

In this example,

ExtractDailyPartitionMapping

is a custom partition mapping that you would use to map between the multidimensional partition keys of an upstream asset and the daily partition keys of a downstream asset. The

get_downstream_partitions_for_partition_range

method extracts the date component from the upstream partition keys, while the

get_upstream_partitions_for_partition_range

method maps each dow

@Scout so with this, if I have a asset that has that MultiPartition definition and another wich has only daily definition, It sould work just fine, right?

Yes, if you have an upstream asset with a

MultiPartitionsDefinition

that includes both an entity and a date, and a downstream asset with only a

DailyPartitionsDefinition

, the custom

PartitionMapping

I described should allow you to define the relationship between these assets. The mapping ensures that the downstream asset's daily partitions correspond to the appropriate date partitions of the upstream asset, regardless of the entity. Here's how you might use the

ExtractDailyPartitionMapping

in your asset definitions:

python from dagster import asset, repository, AssetIn  # Assuming the custom ExtractDailyPartitionMapping is defined as shown previously  # Define your upstream asset with the MultiPartitionsDefinition @asset(partitions_def=job_fidc_idtrust_reports_partitions_def) def upstream_asset():     # Your computation logic here     pass  # Define your downstream asset with DailyPartitionsDefinition @asset(     partitions_def=DailyPartitionsDefinition(start_date='2023-12-01', timezone='America/Fortaleza'),     ins={"upstream_asset": AssetIn(partition_mapping=ExtractDailyPartitionMapping())} ) def downstream_asset(upstream_asset):     # Your computation logic here, which depends on the upstream_asset     pass  # Collect assets into a repository @repository def my_repository():     return [upstream_asset, downstream_asset]

In this setup,

downstream_asset

is defined to depend on

upstream_asset

through the

ins

parameter, which specifies the custom

ExtractDailyPartitionMapping

. This mapping tells Dagster how to resolve the dependencies between the partitions of the two assets. When you materialize

downstream_asset

, Dagster will use the

ExtractDailyPartitionMapping

to determine which partitions of

upstream_asset

are required. The

get_downstream_partitions_for_partition_range

method will be used to map a range of upstream partition keys to the corresponding downstream partition keys, and vice versa for

get_upstream_partitions_for_partition_range

. This should work fine as long as the partition mapping is implemented correctly and aligns with the actual partitioning logic of your assets. If you encounter any issues, you may need to adjust the partition mapping logic to match the specific requirements of your data and dependencies.

@Scout in your code the upstream_partition_key_range does not have the method get_partition_keys()

Apologies for the confusion. The

PartitionKeyRange

object does not have a

get_partition_keys()

method. Instead, it has

start

and

end

attributes that represent the range of partition keys. To correct the example, you would need to use these attributes to determine the appropriate partition keys for the downstream asset. Here's a revised example of how you might define the `ExtractDailyPartitionMapping`:

python
from dagster import PartitionMapping, PartitionKeyRange

class ExtractDailyPartitionMapping(PartitionMapping):
    def get_downstream_partitions_for_partition_range(
        self,
        upstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def,
        upstream_partitions_def,
    ):
        # Assuming that the downstream asset is partitioned daily
        # Extract the date component from the upstream partition keys
        # Here we assume that the upstream_partition_key_range.start and .end are tuples
        # where the first element is the entity and the second element is the date
        start_date = upstream_partition_key_range.start[1]
        end_date = upstream_partition_key_range.end[1]
        return PartitionKeyRange(start_date, end_date)

    def get_upstream_partitions_for_partition_range(
        self,
        downstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def,
        upstream_partitions_def,
    ):
        # Map each downstream daily partition key to a range of upstream partition keys
        # that have the same date but different entities
        # Here we assume that the downstream_partition_key_range.start and .end are strings representing dates
        start_date = downstream_partition_key_range.start
        end_date = downstream_partition_key_range.end
        # Assuming ID_TRUST_ENTITIES is a list of entities
        upstream_partition_keys = [
            (entity, date)
            for entity in ID_TRUST_ENTITIES
            for date in [start_date, end_date]  # Replace with a range if necessary
        ]
        return PartitionKeyRange(upstream_partition_keys[0], upstream_partition_keys[-1])

In this revised example, the

get_downstream_partitions_for_partition_range

method extracts the date component from the start and end of the

upstream_partition_key_range

and creates a new

PartitionKeyRange

for the downstream partitions. The

get_upstream_partitions_for_partition_range

method does the opposite: it takes the start and end dates from the

downstream_partition_key_range

and creates a range of upstream partition keys that include all entities for those dates. Please note that this example assumes that the partition keys are tuples where the first element is the entity and the second element is the date for the upstream asset, and st

@Scout in your get_upstream_partitions_for_partition_range i got the following error on the return Expected type 'str', got 'tuple[Any, str]' instead

I apologize for the oversight. The

PartitionKeyRange

object expects

start

and

end

to be strings, not tuples. If you're working with a

MultiPartitionsDefinition

where the partition keys are tuples, you'll need to convert these tuples into a string representation that uniquely identifies each partition. Here's how you might adjust the

get_upstream_partitions_for_partition_range

method to return a range of string partition keys:

python from dagster import PartitionMapping, PartitionKeyRange  class ExtractDailyPartitionMapping(PartitionMapping):     # ... (other methods remain the same)      def get_upstream_partitions_for_partition_range(         self,         downstream_partition_key_range: PartitionKeyRange,         downstream_partitions_def,         upstream_partitions_def,     ):         # Map each downstream daily partition key to a range of upstream partition keys         # that have the same date but different entities         start_date = downstream_partition_key_range.start         end_date = downstream_partition_key_range.end          # Generate a list of partition keys for each entity and date         upstream_partition_keys = [             f"{entity}-{date}"  # This format assumes entity and date are strings             for entity in ID_TRUST_ENTITIES             for date in [start_date, end_date]  # Replace with a range if necessary         ]          # Return a PartitionKeyRange with the start and end keys as strings         return PartitionKeyRange(             start=upstream_partition_keys[0],             end=upstream_partition_keys[-1]         )

In this example, the

get_upstream_partitions_for_partition_range

method generates a list of partition keys as strings by concatenating the entity and date with a delimiter (in this case, a hyphen). It then creates a

PartitionKeyRange

with the first and last keys in this list. Please ensure that the format used to generate the string partition keys matches the format expected by your upstream asset's partitioning scheme. If your upstream asset expects a different format or delimiter, you should adjust the string concatenation accordingly.

@Scout I got the following error TypeError: Can't instantiate abstract class ExtractDailyPartitionMapping with abstract methods description, get_downstream_partitions_for_partitions, get_upstream_mapped_partitions_result_for_partitions

The error message you're encountering indicates that the

ExtractDailyPartitionMapping

class is missing implementations for required abstract methods from the

PartitionMapping

base class. To resolve this error, you need to implement all the abstract methods defined in the

PartitionMapping

class. As of my knowledge cutoff date, the

PartitionMapping

class in Dagster requires you to implement the following methods: -

get_downstream_partitions_for_partitions

-

get_upstream_partitions_for_partitions

-

get_upstream_mapped_partitions_result_for_partitions

-

description

Here's an example of how you might implement these methods in your

ExtractDailyPartitionMapping

class:

python
from dagster import PartitionMapping, PartitionsDefinition, MappedPartition

class ExtractDailyPartitionMapping(PartitionMapping):
    def get_downstream_partitions_for_partitions(self, upstream_partitions, downstream_partitions_def):
        # Implement logic to map from upstream to downstream partitions
        # This should return a list of partition keys for the downstream asset
        return [upstream_partition.split('-')[1] for upstream_partition in upstream_partitions]

    def get_upstream_partitions_for_partitions(self, downstream_partitions, upstream_partitions_def):
        # Implement logic to map from downstream to upstream partitions
        # This should return a list of partition keys for the upstream asset
        return [f"{entity}-{downstream_partition}" for entity in ID_TRUST_ENTITIES for downstream_partition in downstream_partitions]

    def get_upstream_mapped_partitions_result_for_partitions(self, downstream_partitions, upstream_partitions_def):
        # Implement logic to map from downstream to upstream partitions
        # This should return a list of MappedPartition objects
        return [
            MappedPartition(
                upstream_partition_key=f"{entity}-{downstream_partition}",
                downstream_partition_key=downstream_partition
            )
            for entity in ID_TRUST_ENTITIES
            for downstream_partition in downstream_partitions
        ]

    def description(self):
        # Return a string description of the partition mapping
        return "Extracts the daily partition from a multidimensional partition."

In this example, the

get_downstream_partitions_for_partitions

method assumes that the upstream partition keys are strings in the format "entity-date" and extracts the date part to create the downstream partition keys. The

get_upstream_partitions_for_partitions

method does the opposite, combining each entity with the downstream date partition to create the upstream partition keys. The

get_upstream_mapped_partitions_result_for_partitions

method returns a list of `MappedPartitio