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<@U0667DNC02Y> When I use the ```meta: dagster: ...
# ask-ai
i
@Scout When I use the
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meta:
  dagster:
    asset_key: [ "asset_fidc_idtrust_reports" ]
to make a dependency between a asset and a dbt asset on sources.yml it doesn't work. The dependency is not created
s
Based on the documentation, to define a dbt source as an upstream dependency for a dbt model and subsequently manage it as a Dagster asset, you need to declare the source in the 
sources.yml
file of your dbt project and use the 
meta
configuration to specify the 
asset_key
. Here's how you can do it: 1. Declare the source in your 
sources.yml
file and provide the asset key using the 
meta
configuration:
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yaml sources:   - name: your_source_name     tables:       - name: your_table_name         meta:           dagster:             asset_key: ["asset_fidc_idtrust_reports"]
2. In your dbt model, you would then select from this source data to create the dependency relationship:
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sql select *   from {{ source("your_source_name", "your_table_name") }}  where condition = value
This setup should create a dependency relationship between your upstream asset and the dbt model. If you've followed these steps and the dependency is not being created, there are a few things you can check: - Ensure that the 
asset_key
specified in the 
meta
configuration matches the asset key you expect to see in Dagster. - Verify that the source and table names in the 
sources.yml
file match those used in your dbt model. - Make sure that you are using the correct syntax and that there are no typos in your 
sources.yml
and dbt model files. - Check if there are any errors in the dbt run logs that might indicate a problem with the source or model configuration. If you've checked all of the above and the issue persists, it could be a more specific problem that might require looking into the details of your dbt and Dagster setup. In such cases, it's often helpful to refer to the error messages or logs for more context on why the dependency is not being recognized. If necessary, you can also seek further assistance by creating an issue in the Dagster GitHub repository or asking for help in the Dagster community channels.
i
@Scout even using correctly the meta propertie, the asset seems to be searching for the name I gave to the table instead of the name of the asset I've specified
s
If you've correctly used the 
meta
property in your 
sources.yml
file to specify the 
asset_key
for a dbt source and the asset is still searching for the table name instead of the specified asset key, there might be a few reasons for this behavior: 1. Caching Issues: Sometimes, changes in the configuration files might not be picked up immediately due to caching. Make sure to clear any caches and rerun your dbt and Dagster jobs to ensure that the latest configurations are being used. 2. Configuration Overlook: Double-check that the 
meta
property is correctly nested under the appropriate source and table in the 
sources.yml
file. It's easy to misplace configuration blocks, especially in YAML files where indentation is significant. 3. Version Compatibility: Ensure that the versions of dbt and Dagster you are using are compatible and that you are using the correct syntax for the 
meta
property as per the version's documentation. 4. Asset Materialization: Verify that the asset is being materialized correctly in Dagster. If the asset is not materialized, it might not show up as expected in the asset graph or the asset catalog. 5. Custom Asset Key Generation: If you are using a custom asset key generation method, such as a custom 
DagsterDbtTranslator
, ensure that it is implemented correctly and that it is being used by Dagster to generate the asset keys. 6. Dagster System Configuration: Check your Dagster system configuration to ensure that there are no overrides or settings that might be affecting the asset key resolution. If you've gone through these checks and the issue still persists, it may be helpful to look at the logs or error messages provided by Dagster during the asset materialization process for more clues. If the problem is not apparent from the logs, consider reaching out to the Dagster community for support, providing details about your setup, the versions of Dagster and dbt you are using, and any relevant logs or error messages. This information can help others in the community to better understand the issue and provide more targeted assistance.
i
@Scout may you help me here? I have a use-case where one multi-asset yields two assetkeys and they are partitioned assets by multipartition key. The problem is that the dbt assets that are executed after these two materialize only do that if all the two partitions are materialized for the previous assetkeys. And that doesn't make sense, because I only needed one materialization from the previoues assets to keep going
s
In Dagster, when dealing with multi-assets and dependencies between them, especially when they are partitioned, it's important to understand how the partition mappings and materialization policies are set up. If your dbt assets are only materializing when all partitions of the upstream assets are materialized, it sounds like there might be an issue with the partition mapping or the materialization policy you have configured. Here are some steps you can take to troubleshoot and potentially resolve this issue: 1. Review Partition Mappings: Ensure that your partition mappings are set up correctly. Partition mappings define the relationship between the partitions of upstream and downstream assets. If you have a one-to-one mapping, then each partition of the downstream asset expects a corresponding partition from the upstream asset to be materialized. If you want to allow downstream assets to materialize with only one upstream partition, you might need to adjust your partition mappings to reflect that. 2. Check Materialization Policies: Review the 
AutoMaterializePolicy
you have set for your assets. If you are using a policy like 
AutoMaterializePolicy.all_parents_updated()
, it will wait for all upstream partitions to be materialized before materializing the downstream asset. You might want to use a different policy that allows for more flexibility, such as 
AutoMaterializePolicy.any_parent_updated()
. 3. Examine Asset Dependencies: Look at how dependencies are defined between your assets. If you have strict dependencies that require all upstream assets to be materialized, consider relaxing these dependencies if your use case allows for it. 4. Use Dynamic Partitioning: If your use case involves complex partitioning logic, consider using dynamic partitioning, which allows you to create partitions based on runtime information. This can give you more control over which partitions are materialized and when. 5. Debugging with Logs: Utilize Dagster's logging capabilities to gain insight into the materialization process. Logs can provide valuable information about which partitions are being considered for materialization and what might be preventing the downstream assets from materializing. 6. Consult the Documentation: Review the relevant sections of the Dagster documentation on partitioning, materialization policies, and multi-assets to ensure that you are using these features as intended. 7. Community and Support: If you're still facing issues, consider reaching out to the Dagster community for support. You can provide details about your partitioning setup, the materialization policies you're using, and any relevant code snippets. The community or the Dagster team may be able to provide specific guidance for your use case
. Remember that in complex scenarios involving partitioned assets, it's crucial to have a clear understanding of how your assets are interconnected and how the materialization of one asset affects the others. By carefully configuring your partition mappings and materialization policies, you can control the behavior of your asset graph to match your desired workflow.
i
@Scout you talk a lot about partition mapping, but can you give me a single example of defining this partition mapping for a one to one asset relationship?
s
Certainly! In Dagster, you can define a one-to-one relationship between partitions of upstream and downstream assets using the 
IdentityPartitionMapping
. This mapping ensures that each partition of the downstream asset corresponds directly to a partition of the upstream asset with the same partition key. Here's an example of how you might define this in your code:
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python from dagster import asset, DailyPartitionsDefinition, AssetIn, IdentityPartitionMapping  # Define the partitions for both upstream and downstream assets partitions_def = DailyPartitionsDefinition(start_date="2023-01-01")  @asset(partitions_def=partitions_def) def upstream_asset():     # Logic for upstream asset     ...  @asset(     partitions_def=partitions_def,     ins={"upstream_asset": AssetIn(partition_mapping=IdentityPartitionMapping())} ) def downstream_asset(upstream_asset):     # Logic for downstream asset that depends on the upstream_asset     ...
In this example, both 
upstream_asset
and 
downstream_asset
are defined with the same 
DailyPartitionsDefinition
, which means they have the same set of partitions based on dates. The 
IdentityPartitionMapping
is used to specify that for any given partition key (e.g., a specific date), the downstream asset's partition should depend on the upstream asset's partition with the same key. When you materialize 
downstream_asset
for a specific partition, Dagster will automatically infer that it should use the corresponding partition of 
upstream_asset
as its input, thanks to the 
IdentityPartitionMapping
. This is a simple and common way to set up a one-to-one partition relationship between assets in Dagster.
i
@Scout let's grab my example, please
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job_fidc_idtrust_reports_partitions_def = MultiPartitionsDefinition(
        {
            "entity": StaticPartitionsDefinition(ID_TRUST_ENTITIES),
            "date": DailyPartitionsDefinition(start_date='2023-12-01', timezone='America/Fortaleza')
        }
    )
I have this definition for the first asset, suppose I need two partitions 2024-02-02|DOG and 2024-02-02|CAT and I want to generate one asset key for every partition and then I have my dbt_assets which are going to depend on the two assetkey generated before how would you configure the entire schema considering your suggestion about partition mapping?
s
To configure the entire schema with partition mapping for your use case, you would first define the 
MultiPartitionsDefinition
for your upstream asset, which you've already done. Then, you would define your dbt assets to depend on the partitions of the upstream asset using an appropriate partition mapping. Here's how you can set up the schema: 1. Define the upstream asset with `MultiPartitionsDefinition`:
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python
from dagster import asset, MultiPartitionsDefinition, StaticPartitionsDefinition, DailyPartitionsDefinition

ID_TRUST_ENTITIES = ["DOG", "CAT"]  # Assuming this is defined somewhere

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

@asset(partitions_def=job_fidc_idtrust_reports_partitions_def)
def upstream_asset():
    # Logic for the upstream asset
    ...
2. Define the dbt assets with dependencies on the upstream asset. Since dbt assets are typically defined using the 
@dbt_asset
decorator, you would use the 
AssetIn
object to specify the dependency and the 
IdentityPartitionMapping
to map the partitions one-to-one:
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python
from dagster import dbt_asset, AssetIn, IdentityPartitionMapping

@dbt_asset(
    ins={"upstream_asset": AssetIn(partition_mapping=IdentityPartitionMapping())}
)
def dbt_model_a(upstream_asset):
    # Logic for dbt model A that depends on upstream_asset
    ...

@dbt_asset(
    ins={"upstream_asset": AssetIn(partition_mapping=IdentityPartitionMapping())}
)
def dbt_model_b(upstream_asset):
    # Logic for dbt model B that depends on upstream_asset
    ...
In this setup, 
dbt_model_a
and 
dbt_model_b
will each depend on the corresponding partition of 
upstream_asset
. For example, if you materialize 
dbt_model_a
for the partition 
2024-02-02|DOG
, it will automatically use the 
2024-02-02|DOG
partition of 
upstream_asset
. Please note that the 
@dbt_asset
decorator is a hypothetical example, as the actual implementation may vary based on how you integrate dbt with Dagster. Dagster's dbt integration typically involves using the 
dbt_cli_resource
or similar resources to run dbt commands within a Dagster pipeline. The actual dependency configuration would depend on how you've set up your dbt project and Dagster resources. If you need to define more complex partition mappings or if the dbt assets should depend on multiple partitions of the upstream asset, you would need to use a custom partition mapping class that defines the desired behavior. Remember to ensure that your dbt project is configured to recognize these dependencies and that the corresponding Dagster resources are set up to handle dbt runs within the Dagste
r asset framework.
i
@Scout but does the dbt_asset accepts the ins param? I don't know, can you validate?
s
I apologize for the confusion earlier. The 
@dbt_asset
decorator I mentioned does not exist in Dagster. Instead, Dagster integrates with dbt through the 
dbt_cli_resource
or similar resources, and dbt models are typically represented as assets using the 
@asset
decorator or by loading them from a dbt project manifest. To define dependencies between dbt models and other assets in Dagster, you would typically use the 
@asset
decorator for non-dbt assets and load dbt models as assets using Dagster's dbt integration functions. The 
ins
parameter is used with the 
@asset
decorator to specify dependencies for non-dbt assets. For dbt models, dependencies are usually inferred from the dbt project itself. When you load dbt models as assets into Dagster, the dependencies between dbt models and sources are automatically inferred based on the dbt project configuration. If you want to create dependencies between dbt models and other assets in Dagster, you would need to ensure that the dbt models are loaded as assets and then specify the dependencies using the 
AssetIn
object with the appropriate partition mapping. Here's an example of how you might define a non-dbt asset that depends on a dbt model asset:
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python from dagster import asset, AssetIn, AssetKey  @asset(     ins={"dbt_model_asset": AssetIn(asset_key=AssetKey("dbt_model_asset"))} ) def non_dbt_asset(dbt_model_asset):     # Logic for the non-dbt asset that depends on the dbt_model_asset     ...
In this example, 
non_dbt_asset
depends on an asset that represents a dbt model (
dbt_model_asset
). The 
AssetKey
for the dbt model asset should match the asset key that is generated when loading the dbt project into Dagster. To load dbt models as assets and infer their dependencies, you would use Dagster's dbt integration functions, such as 
load_assets_from_dbt_project
or 
load_assets_from_dbt_manifest
, depending on your setup. Please refer to the Dagster documentation for the most accurate and up-to-date information on integrating dbt with Dagster and defining dependencies between dbt models and other assets. If you have a specific setup or integration in mind, please provide more details, and I can give you more targeted advice.
i
@Scout you were not wrong, the dbt_asset does exist, I was only asking if the "ins" param exist whithin the dbt_asset decorator
s
I apologize for the confusion. The 
@dbt_assets
decorator in Dagster is used to load a set of dbt models from a dbt project into Dagster assets. According to the documentation, the 
@dbt_assets
decorator does not directly accept an 
ins
parameter like the 
@asset
decorator does for specifying dependencies. The dependencies between dbt assets and other assets in Dagster are typically inferred from the dbt project's structure. For example, if a dbt model depends on a source or another model, this relationship is defined within the dbt project files (e.g., 
models/
, 
sources/
, etc.), and Dagster will recognize these relationships when the dbt assets are loaded. If you need to define dependencies between dbt assets and other non-dbt assets in Dagster, you would typically do this by representing the non-dbt assets as dbt sources or by using the dbt project's ref and source functions within your dbt models to create dependencies on these assets. For more complex scenarios where you need to customize how dbt models are translated into Dagster assets, including setting dependencies, you might use a custom 
DagsterDbtTranslator
. This allows you to override methods that determine how dbt nodes map to asset keys, groups, and other properties. If you have a specific use case or configuration that requires setting dependencies directly on dbt assets using the 
@dbt_assets
decorator, please refer to the latest Dagster documentation or reach out to the Dagster community for the most current and detailed guidance.
i
@Scout give me an example of two assets using this MultiToSingleDimensionPartitionMapping
s
The 
MultiToSingleDimensionPartitionMapping
is used when you have a multi-dimensional partitioned asset and you want to create a dependency on a single-dimensional partitioned asset. This mapping allows you to specify that a single-dimensional partition depends on a slice of the multi-dimensional partitions that match on one dimension. Here's an example of how you might use 
MultiToSingleDimensionPartitionMapping
to define a dependency from a single-dimensional asset to a multi-dimensional asset:
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python from dagster import asset, MultiPartitionsDefinition, StaticPartitionsDefinition, DailyPartitionsDefinition, MultiToSingleDimensionPartitionMapping  # Define the multi-dimensional partitions multi_partitions_def = MultiPartitionsDefinition({     "entity": StaticPartitionsDefinition(["DOG", "CAT"]),     "date": DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza") })  # Define the single-dimensional partitions single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")  # Define the multi-dimensional asset @asset(partitions_def=multi_partitions_def) def multi_dimensional_asset():     # Logic for the multi-dimensional asset     ...  # Define the single-dimensional asset with a dependency on the multi-dimensional asset @asset(     partitions_def=single_partitions_def,     ins={"multi_dimensional_asset": AssetIn(partition_mapping=MultiToSingleDimensionPartitionMapping("date"))} ) def single_dimensional_asset(multi_dimensional_asset):     # Logic for the single-dimensional asset that depends on the multi-dimensional asset     ...
In this example, 
single_dimensional_asset
is a single-dimensional asset partitioned by date. It depends on 
multi_dimensional_asset
, which is a multi-dimensional asset partitioned by both 
entity
and 
date
. The 
MultiToSingleDimensionPartitionMapping("date")
specifies that each partition of 
single_dimensional_asset
should depend on the corresponding 
date
partition in 
multi_dimensional_asset
, regardless of the 
entity
. When you materialize 
single_dimensional_asset
for a specific date partition, Dagster will automatically infer that it should use all partitions of 
multi_dimensional_asset
that have the same date in their partition key. Please note that the actual usage of 
MultiToSingleDimensionPartitionMapping
may vary based on the version of Dagster you are using and the specific details of your use case. Always refer to the latest Dagster documentation for the most accurate and up-to-date information.
i
This should also work with multi_asset?
@Scout This should also work with multi_asset?
s
As of my knowledge cutoff date in early 2023, the 
MultiToSingleDimensionPartitionMapping
is designed to work with assets, including those defined using the 
@multi_asset
decorator. The 
@multi_asset
decorator allows you to define a computation that produces multiple assets, and you can specify different partitioning schemes for each of the output assets. Here's an example of how you might use 
MultiToSingleDimensionPartitionMapping
with a `@multi_asset`:
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python
from dagster import (
    multi_asset,
    AssetOut,
    MultiPartitionsDefinition,
    StaticPartitionsDefinition,
    DailyPartitionsDefinition,
    MultiToSingleDimensionPartitionMapping,
)

# Define the multi-dimensional partitions
multi_partitions_def = MultiPartitionsDefinition({
    "entity": StaticPartitionsDefinition(["DOG", "CAT"]),
    "date": DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")
})

# Define the single-dimensional partitions
single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")

# Define the multi-asset with multi-dimensional partitions
@multi_asset(
    outs={
        "multi_asset_dog": AssetOut(partitions_def=multi_partitions_def),
        "multi_asset_cat": AssetOut(partitions_def=multi_partitions_def),
    },
    partitions_def=multi_partitions_def,
)
def multi_dimensional_assets():
    # Logic for producing the contents of the multi-assets
    yield "data_for_dog", "multi_asset_dog"
    yield "data_for_cat", "multi_asset_cat"

# Define the single-dimensional asset with a dependency on the multi-dimensional asset
@asset(
    partitions_def=single_partitions_def,
    ins={"multi_asset_dog": AssetIn(partition_mapping=MultiToSingleDimensionPartitionMapping("date"))}
)
def single_dimensional_asset(multi_asset_dog):
    # Logic for the single-dimensional asset that depends on the multi_asset_dog
    ...
In this example, 
multi_dimensional_assets
is a 
@multi_asset
that produces two assets, 
multi_asset_dog
and 
multi_asset_cat
, each with multi-dimensional partitions. The 
single_dimensional_asset
is a single-dimensional asset partitioned by date and depends on the 
multi_asset_dog
asset. The 
MultiToSingleDimensionPartitionMapping("date")
specifies that each partition of 
single_dimensional_asset
should depend on the corresponding 
date
partition in 
multi_asset_dog
. When you materialize 
single_dimensional_asset
for a specific date partition, Dagster will automatically infer that it should use all partitions of 
multi_asset_dog
that have the same date in their partition key. Please note that the actual usage of 
MultiToSingleDimensionPartitionMapping
with 
@multi_asset
may vary based on the version of Dagster you are using and the specific details of your us
e case. Always refer to the latest Dagster documentation for the most accurate and up-to-date information.
i
@Scout I mean, the multi_asset being the asset that uses the MultiToSingleDimensionPartitionMapping
s
I apologize for the confusion. The 
MultiToSingleDimensionPartitionMapping
is indeed intended to define dependencies between assets with different partitioning schemes. However, the 
@multi_asset
decorator is used to define a computation that produces multiple assets, which may or may not have the same partitioning scheme. Here's an example of how you might use 
MultiToSingleDimensionPartitionMapping
with a 
@multi_asset
where the 
@multi_asset
is the one using the 
MultiToSingleDimensionPartitionMapping
to depend on a single-dimensional partitioned asset:
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python
from dagster import (
    asset,
    multi_asset,
    AssetOut,
    MultiPartitionsDefinition,
    StaticPartitionsDefinition,
    DailyPartitionsDefinition,
    MultiToSingleDimensionPartitionMapping,
    AssetIn,
)

# Define the single-dimensional partitions
single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")

# Define the single-dimensional asset
@asset(partitions_def=single_partitions_def)
def single_dimensional_asset():
    # Logic for the single-dimensional asset
    ...

# Define the multi-dimensional partitions
multi_partitions_def = MultiPartitionsDefinition({
    "entity": StaticPartitionsDefinition(["DOG", "CAT"]),
    "date": single_partitions_def,
})

# Define the multi-asset with a dependency on the single-dimensional asset
@multi_asset(
    outs={
        "multi_asset_dog": AssetOut(partitions_def=multi_partitions_def),
        "multi_asset_cat": AssetOut(partitions_def=multi_partitions_def),
    },
    ins={
        "single_dimensional_asset": AssetIn(partition_mapping=MultiToSingleDimensionPartitionMapping("date"))
    }
)
def multi_dimensional_assets(single_dimensional_asset):
    # Logic for producing the contents of the multi-assets
    # This logic would use the single_dimensional_asset data to produce the multi-assets
    yield "data_for_dog", "multi_asset_dog"
    yield "data_for_cat", "multi_asset_cat"
In this example, 
single_dimensional_asset
is a single-dimensional asset partitioned by date. The 
multi_dimensional_assets
is a 
@multi_asset
that produces two assets, 
multi_asset_dog
and 
multi_asset_cat
, each with multi-dimensional partitions. The 
MultiToSingleDimensionPartitionMapping("date")
is used to specify that the multi-assets depend on the 
single_dimensional_asset
by matching the 
date
dimension. When you materialize 
multi_asset_dog
or 
multi_asset_cat
for a specific partition, Dagster will infer that it should use the corresponding 
date
partition from 
single_dimensional_asset
. Please ensure that you refer to the latest Dagster documentation for the most accurate and up-to-date information, as the actual usage and capabilities of these features may have evolved since my k
nowledge cutoff date.
i
@Scout now that I made the configuration, the downstream assets are searching randomly in the upstream assets Example, materialization from day 2024-02-02/CAT instead of 2024-02-02/DOG
s
If your downstream assets are incorrectly selecting partitions from the upstream assets (e.g., selecting 
2024-02-02/CAT
instead of 
2024-02-02/DOG
), it suggests there might be an issue with how the partition mapping is being applied or interpreted. Here are some steps you can take to troubleshoot this issue: 1. Review Partition Mapping Configuration: Ensure that the 
MultiToSingleDimensionPartitionMapping
is correctly configured to map the desired dimension. If you're mapping based on the "date" dimension, verify that the mapping is set up to correctly identify the partition keys based on the date and not the entity. 2. Check Asset Definitions: Verify that the asset definitions for both the upstream and downstream assets are correctly specifying the partition keys and that the partition keys are being generated as expected. 3. Examine Materialization Logic: Look at the logic within the 
@multi_asset
computation to ensure that it's correctly handling the partition keys from the upstream asset and that it's yielding the correct asset keys for the downstream assets. 4. Debugging: Utilize Dagster's logging capabilities to log the partition keys being used during the materialization process. This can help you identify where the incorrect partition key is being selected. 5. Dagster Version: Check if there are any known issues with the version of Dagster you are using that might affect partition mappings. If necessary, consider upgrading to the latest version. 6. Consult Documentation: Review the latest Dagster documentation on partition mappings and multi-assets to ensure that you are using these features as intended. 7. Community and Support: If you're still facing issues after these checks, consider reaching out to the Dagster community for support. Provide details about your partition mapping setup, the asset definitions, and any relevant logs or error messages. Here's a simplified example of how you might define a 
@multi_asset
with a `MultiToSingleDimensionPartitionMapping`: ```python from dagster import ( asset, multi_asset, AssetOut, MultiPartitionsDefinition, StaticPartitionsDefinition, DailyPartitionsDefinition, MultiToSingleDimensionPartitionMapping, AssetIn, ) # Define the single-dimensional partitions single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza") # Define the single-dimensional asset @asset(partitions_def=single_partitions_def) def single_dimensional_asset(): # Logic for the single-dimensional asset ... # Define the multi-dimensional partitions multi_partitions_def = MultiPartitionsDefinition({ "entity": StaticPartitionsDefinition(["DOG", "CAT"]), "date": single_partitions_def, }) # Define th
e multi-asset with a dependency on the single-dimensional asset @multi_asset( outs={ "multi_asset_dog": AssetOut(partitions_def=multi_partitions_def), "multi_asset_cat": AssetOut(partitions_def=multi_partitions_def), }, ins={ "single_dimensional_asset": AssetIn(partition_mapping=MultiToSingleDimensionPartitionMapping("date")) } ) def multi_dimensional_assets(single_dimensional_asset): # Logic for producing the contents of the multi-assets # This logic would use the single_dimensional_asset data to produce the multi-assets yield "data_for_dog", "multi_asset_dog" yield "data_for_cat", "multi_asset_cat" ``` In this example, the 
multi_dimensional_assets
should correctly depend on the 
single_dimensional_asset
by matching the "date" dimension. If the downstream assets are not selecting the correct partitions, the issue may lie in the partition mapping logic or the way the partitions are being specified in the asset definitions.
i
@Scout my assets are not what you just said. They're like this: my_asset_1 -> multi dimensional my_multi_asset_2 -> Single dimensional dbt_assets -> single dimensional where the singe dimension is date
s
I see, you have a multi-dimensional asset (
my_asset_1
), a single-dimensional 
@multi_asset
(
my_multi_asset_2
), and single-dimensional dbt assets, all partitioned by date. If you're experiencing issues with the downstream dbt assets incorrectly selecting partitions from the upstream assets, you'll want to ensure that the partition mappings are set up correctly to reflect the dependencies based on the date dimension. Here's a conceptual example of how you might set up these dependencies using `MultiToSingleDimensionPartitionMapping`:
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python
from dagster import (
    asset,
    multi_asset,
    AssetOut,
    MultiPartitionsDefinition,
    StaticPartitionsDefinition,
    DailyPartitionsDefinition,
    MultiToSingleDimensionPartitionMapping,
    AssetIn,
)

# Define the multi-dimensional partitions for my_asset_1
multi_partitions_def = MultiPartitionsDefinition({
    "entity": StaticPartitionsDefinition(["DOG", "CAT"]),
    "date": DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")
})

# Define the single-dimensional partitions for my_multi_asset_2 and dbt_assets
single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")

# Define the multi-dimensional asset (my_asset_1)
@asset(partitions_def=multi_partitions_def)
def my_asset_1():
    # Logic for the multi-dimensional asset
    ...

# Define the single-dimensional @multi_asset (my_multi_asset_2) with a dependency on my_asset_1
@multi_asset(
    outs={"my_multi_asset_2": AssetOut(partitions_def=single_partitions_def)},
    ins={"my_asset_1": AssetIn(partition_mapping=MultiToSingleDimensionPartitionMapping("date"))}
)
def my_multi_asset_2(my_asset_1):
    # Logic for the single-dimensional @multi_asset that depends on my_asset_1
    yield "data_for_date", "my_multi_asset_2"

# Define the dbt assets with a dependency on my_multi_asset_2
# Assuming dbt_assets are loaded and represented as Dagster assets
@asset(
    partitions_def=single_partitions_def,
    ins={"my_multi_asset_2": AssetIn()}
)
def dbt_asset(my_multi_asset_2):
    # Logic for the dbt asset that depends on my_multi_asset_2
    ...
In this setup, 
my_multi_asset_2
is a single-dimensional 
@multi_asset
that depends on the 
date
dimension of the multi-dimensional asset 
my_asset_1
. The 
dbt_asset
then depends on 
my_multi_asset_2
, which is also single-dimensional and partitioned by date. If the dbt assets are incorrectly selecting partitions from 
my_multi_asset_2
, you'll want to check the following: 1. Ensure that the 
MultiToSingleDimensionPartitionMapping
is correctly configured in 
my_multi_asset_2
to map the 
date
dimension from 
my_asset_1
. 2. Verify that the partition keys for 
my_multi_asset_2
are being generated correctly and match th
e expected date format. 3. Confirm that the dbt assets are correctly identifying their dependencies based on the partition keys from 
my_multi_asset_2
. If you continue to experience issues, you may need to debug the partition mappings and asset materialization logic to ensure that the correct partitions are being selected. Additionally, consult the latest Dagster documentation for any updates or changes to partition mappings and multi-assets that may affect your setup. If necessary, reach out to the Dagster community for further assistance.
i
@Scout you made a mistake, AssetOut has no atribute partitions_def, this is used on the multi_asset decorator
s
You are correct, and I apologize for the oversight. The 
partitions_def
should be specified on the 
@multi_asset
decorator, not on the individual 
AssetOut
objects. The 
AssetOut
objects are used to specify the output assets produced by a 
@multi_asset
. Here's a corrected example that defines a 
@multi_asset
with a single-dimensional partition scheme that depends on a multi-dimensional asset:
Copy code
python from dagster import (     asset,     multi_asset,     AssetOut,     MultiPartitionsDefinition,     StaticPartitionsDefinition,     DailyPartitionsDefinition,     MultiToSingleDimensionPartitionMapping,     AssetIn, )  # Define the multi-dimensional partitions for my_asset_1 multi_partitions_def = MultiPartitionsDefinition({     "entity": StaticPartitionsDefinition(["DOG", "CAT"]),     "date": DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza") })  # Define the single-dimensional partitions for my_multi_asset_2 and dbt_assets single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")  # Define the multi-dimensional asset (my_asset_1) @asset(partitions_def=multi_partitions_def) def my_asset_1():     # Logic for the multi-dimensional asset     ...  # Define the single-dimensional @multi_asset (my_multi_asset_2) with a dependency on my_asset_1 @multi_asset(     outs={"my_multi_asset_2": AssetOut()},     ins={"my_asset_1": AssetIn(partition_mapping=MultiToSingleDimensionPartitionMapping("date"))},     partitions_def=single_partitions_def ) def my_multi_asset_2(my_asset_1):     # Logic for the single-dimensional @multi_asset that depends on my_asset_1     yield "data_for_date", "my_multi_asset_2"  # Define the dbt assets with a dependency on my_multi_asset_2 # Assuming dbt_assets are loaded and represented as Dagster assets @asset(     partitions_def=single_partitions_def,     ins={"my_multi_asset_2": AssetIn()} ) def dbt_asset(my_multi_asset_2):     # Logic for the dbt asset that depends on my_multi_asset_2     ...
In this corrected example, 
my_multi_asset_2
is defined with the 
partitions_def
attribute on the 
@multi_asset
decorator, indicating that it is single-dimensionally partitioned by date. The 
my_multi_asset_2
depends on 
my_asset_1
through the 
MultiToSingleDimensionPartitionMapping
, which maps the 
date
dimension from the multi-dimensional asset to the single-dimensional 
@multi_asset
. The 
dbt_asset
then depends on 
my_multi_asset_2
and is also partitioned by date. The dependencies between these assets should now be correctly configured to respect the single date dimension across all assets.
i
@Scout so, now considering this code, why the my_multi_asset_2 is searching for a my_asset_1/2024-02-02/CAT instead of just searching the date?
s
If 
my_multi_asset_2
is incorrectly searching for 
my_asset_1/2024-02-02/CAT
instead of just the date, it suggests that the 
MultiToSingleDimensionPartitionMapping
may not be functioning as expected. The purpose of 
MultiToSingleDimensionPartitionMapping
is to map a single dimension from a multi-dimensional partitioned asset to a single-dimensional partitioned asset. Here are a few things to check and consider: 1. Correct Usage of 
MultiToSingleDimensionPartitionMapping
: Ensure that the 
MultiToSingleDimensionPartitionMapping
is being used correctly. The mapping should be set up to map the "date" dimension from the multi-dimensional asset to the single-dimensional asset. 2. Partition Keys: Verify that the partition keys for both the multi-dimensional and single-dimensional assets are being generated correctly. The partition keys should reflect the correct date format and should match between the two assets for the mapping to work correctly. 3. Asset Definitions: Double-check the asset definitions to ensure that the 
partitions_def
is correctly specified for both the multi-dimensional and single-dimensional assets and that the 
outs
of the 
@multi_asset
are correctly defined. 4. Materialization Logic: Review the materialization logic within the 
@multi_asset
to ensure that it's correctly handling the partition keys from the upstream asset and that it's yielding the correct asset keys for the downstream assets. 5. Dagster Version: Ensure that you are using a version of Dagster that supports the 
MultiToSingleDimensionPartitionMapping
as expected. If there have been updates or bug fixes related to partition mappings, you may need to update your Dagster installation. 6. Debugging: Utilize Dagster's logging capabilities to log the partition keys being used during the materialization process. This can help you identify where the incorrect partition key is being selected. If after checking these items the issue persists, it may be a bug or an unexpected behavior in the Dagster framework. In such cases, it would be advisable to reach out to the Dagster community or create an issue on the Dagster GitHub repository with detailed information about your setup, the versions you are using, and the behavior you are observing. The maintainers or other community members may be able to provide further assistance or a workaround.
i
@Scout the multi_asset expects the upstream asset to materialize a key with the name of the asset, but this does not happen because it's a multipartitioned asset, so it materializes with the name of the asset, date and entity. What to do now?
s
If your 
@multi_asset
is expecting the upstream asset to materialize a key with just the name of the asset, but the upstream asset is actually materializing keys that include both the date and entity due to its multi-partitioned nature, you will need to adjust the partition mapping to correctly handle this situation. The 
MultiToSingleDimensionPartitionMapping
is designed to map a single dimension from a multi-dimensional partitioned asset to a single-dimensional partitioned asset. However, if the upstream asset is materializing keys that include more than just the date (e.g., 
asset_name/date/entity
), you need to ensure that the downstream asset is looking for the correct partition keys. Here's what you can do: 1. Review the Partition Mapping: Make sure that the 
MultiToSingleDimensionPartitionMapping
is set up correctly to map only the "date" dimension from the upstream asset to the downstream asset. This mapping should ignore the "entity" dimension and only focus on the "date" dimension. 2. Adjust the 
@multi_asset
Definition: If necessary, adjust the 
@multi_asset
definition to correctly reference the partition keys that include both the date and entity. You may need to use a custom partition mapping if the standard mappings do not fit your use case. 3. Custom Partition Mapping: If the standard partition mappings provided by Dagster do not support your use case, you might need to implement a custom partition mapping. This would involve creating a subclass of 
PartitionMapping
and implementing the 
get_upstream_partition_keys
method to return the correct set of partition keys based on your specific logic. 4. Debugging: Use Dagster's logging capabilities to log the partition keys at various points in your pipeline to ensure that the correct keys are being used and generated. 5. Consult the Documentation: Review the latest Dagster documentation on partition mappings to ensure that you are using these features as intended and to check if there are any updates or additional options that could help in your situation. 6. Community and Support: If you're still facing issues after these checks, consider reaching out to the Dagster community for support. Provide details about your partition mapping setup, the asset definitions, and any relevant logs or error messages. Remember that the partition keys used for materialization should match the expectations of the downstream assets. If the standard partition mappings do not work for your use case, a custom partition mapping may be necessary to ensure that the correct dependencies are established between your assets.
i
@Scout hi, about my problem yesterday, this could help me? MultiPartitionsMapping if so, how would I use it?
s
Yes, 
MultiPartitionsMapping
could potentially help with your problem. It is designed to handle complex partition mapping scenarios, including mapping between multi-dimensional and single-dimensional partitioned assets. 
MultiPartitionsMapping
allows you to define a mapping for each dimension of your multi-dimensional asset to the corresponding dimension of your downstream assets. For dimensions that are not explicitly defined in the mapping, Dagster will assume an 
AllPartitionsMapping
, meaning that all upstream partitions in those dimensions will be mapped to all downstream partitions in those dimensions. Here's a conceptual example of how you might use 
MultiPartitionsMapping
in your scenario:
Copy code
python
from dagster import (
    MultiPartitionsDefinition,
    StaticPartitionsDefinition,
    DailyPartitionsDefinition,
    MultiPartitionsMapping,
    DimensionPartitionMapping,
    StaticPartitionMapping,
    TimeWindowPartitionMapping,
)

# Define your multi-dimensional partitions for the upstream asset
multi_partitions_def = MultiPartitionsDefinition({
    "entity": StaticPartitionsDefinition(["DOG", "CAT"]),
    "date": DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")
})

# Define your single-dimensional partitions for the downstream asset
single_partitions_def = DailyPartitionsDefinition(start_date="2023-12-01", timezone="America/Fortaleza")

# Define the MultiPartitionsMapping
multi_partitions_mapping = MultiPartitionsMapping({
    "date": DimensionPartitionMapping(
        dimension_name="date",
        partition_mapping=TimeWindowPartitionMapping(),
    ),
    # Assuming "entity" is the dimension you want to ignore for the downstream asset
    "entity": DimensionPartitionMapping(
        dimension_name="entity",
        partition_mapping=StaticPartitionMapping({"DOG": None, "CAT": None}),  # Maps to no partition
    ),
})

# Use the MultiPartitionsMapping in your downstream asset definition
@asset(
    partitions_def=single_partitions_def,
    ins={"upstream_asset": AssetIn(partition_mapping=multi_partitions_mapping)}
)
def downstream_asset(upstream_asset):
    # Logic for the downstream asset that depends on the upstream_asset
    ...
In this example, the 
multi_partitions_mapping
is used to map the "date" dimension of the upstream asset to the "date" dimension of the downstream asset using a 
TimeWindowPartitionMapping
. For the "entity" dimension, which you want to ignore, you use a 
StaticPartitionMapping
that maps both "DOG" and "CAT" to 
None
, effectively ignoring this dimension for the downstream asset. Please note that this is a conceptual example, and the actual implementation may vary based on your specific use case and the version of Dagster you are using. Always refer to the latest Da
gster documentation for the most accurate and up-to-date information on using 
MultiPartitionsMapping
and other partition mapping classes. If you encounter any issues or have further questions, the Dagster community is a valuable resource for support and guidance.
i
@Scout but this alone will make my downstream asset uses his single_dimension partition without errors?
s
The 
MultiPartitionsMapping
is designed to define the correspondence between two 
MultiPartitionsDefinitions
or between a 
MultiPartitionsDefinition
and a single-dimensional partitions definition. It should allow you to specify how partitions from a multi-dimensional upstream asset correspond to partitions of a single-dimensional downstream asset. However, whether this alone will ensure that your downstream asset uses its single-dimension partition without errors depends on several factors: 1. Correct Configuration: The 
MultiPartitionsMapping
must be correctly configured to map the relevant dimension(s) from the upstream asset to the downstream asset. If the mapping is not set up correctly, you may still encounter errors. 2. Partition Keys: The partition keys for both the upstream and downstream assets must be correctly defined and generated. If there is a mismatch in partition keys, the downstream asset may not be able to resolve the correct upstream partitions. 3. Dagster Version: Ensure that you are using a version of Dagster that supports the 
MultiPartitionsMapping
as expected. If there have been updates or bug fixes related to partition mappings, you may need to update your Dagster installation. 4. Asset Materialization: The logic within your assets must correctly handle the partitioned data. This includes any logic for loading inputs, processing data, and producing outputs based on the partition keys. 5. Execution Environment: The execution environment, including any I/O managers or other resources, must be set up to support partitioned assets and their dependencies. If you have configured everything correctly and are still encountering errors, it may be necessary to debug the issue further. This could involve checking logs, examining the generated partition keys, and ensuring that the partition mapping logic aligns with the actual data and dependencies. If you are unsure or need further assistance, you can reach out to the Dagster community with specific details about your setup, the errors you are encountering, and the behavior you expect. The community or the Dagster team may be able to provide additional guidance or identify any potential issues with your configuration.
i
@Scout I got the following error cannot import name 'MultiPartitionsMapping' from 'dagster'
s
The error message you're seeing indicates that 
MultiPartitionsMapping
cannot be imported from the 
dagster
module. This could be due to several reasons: 1. Non-Existent Import: The class or function you're trying to import does not exist in the module you're importing from. This could be due to a typo in the name, or the class/function might not be available in the version of Dagster you are using. 2. Incorrect Module: The class or function might exist in a different module or subpackage within Dagster, and you might need to adjust your import statement accordingly. 3. Version Mismatch: If 
MultiPartitionsMapping
is a feature added in a newer version of Dagster, and you are using an older version, you will not be able to import it. Make sure you are using a version of Dagster that includes 
MultiPartitionsMapping
. 4. Deprecated Feature: If 
MultiPartitionsMapping
has been deprecated or removed in the version of Dagster you are using, you will also encounter this error. Check the Dagster release notes or documentation for any notes on deprecation or removal. To resolve this issue, you should: - Verify that you are using the correct name for the import. - Check the Dagster documentation for the correct module path for 
MultiPartitionsMapping
. - Ensure that you are using the correct version of Dagster where 
MultiPartitionsMapping
is available. - If 
MultiPartitionsMapping
has been deprecated or removed, look for alternative approaches or updated patterns recommended by the Dagster documentation. If 
MultiPartitionsMapping
is not part of the public API or has been removed, you may need to find an alternative way to achieve your goal. You can consult the Dagster documentation or reach out to the Dagster community for guidance on how to handle partition mappings in the version of Dagster you are using.
i
@Scout so, the StaticPartitionMapping does not accept NoneType, what can I do?
s
If you're encountering an issue where 
StaticPartitionMapping
does not accept 
NoneType
, it suggests that you're trying to map a partition to 
None
, which is not allowed. 
StaticPartitionMapping
is used to map a set of upstream partition keys to a set of downstream partition keys, and it expects that every upstream partition key maps to a valid downstream partition key. Here are some steps you can take to resolve this issue: 1. Review Your Mapping: Ensure that all upstream partition keys are being mapped to valid downstream partition keys. If you intended to map to 
None
because there is no corresponding downstream partition, you will need to adjust your approach. 2. Filter Out Unnecessary Partitions: If there are upstream partitions that should not be mapped to any downstream partitions (i.e., they should be ignored), you might need to filter these out before defining the 
StaticPartitionMapping
. Only include the mappings for partitions that have a corresponding downstream partition. 3. Use Custom Logic: If your partition mapping logic is more complex and cannot be handled by 
StaticPartitionMapping
, you may need to implement custom logic to handle the partition mapping. This could involve creating a custom subclass of 
PartitionMapping
and implementing the 
get_upstream_partition_keys
method to return the correct set of partition keys based on your specific logic. 4. Consult the Documentation: Review the latest Dagster documentation on partition mappings to ensure that you are using these features as intended and to check if there are any updates or additional options that could help in your situation. 5. Community and Support: If you're still facing issues after these checks, consider reaching out to the Dagster community for support. Provide details about your partition mapping setup, the asset definitions, and any relevant logs or error messages. Remember that the partition mappings in Dagster are designed to establish a clear relationship between upstream and downstream partitions. If you need to represent a scenario where some upstream partitions do not correspond to any downstream partitions, you will need to handle this case explicitly, either by filtering out those partitions or by implementing custom partition mapping logic.
i
@Scout consider the example you gave me, there you used a mapping of {"CAT": None, "DOG": None}, that's what I'm talking about. Your example is wrong, can you fix it?
s
I apologize for the confusion earlier. You are correct that using 
None
in a 
StaticPartitionMapping
is not valid, as each upstream partition key must map to a valid downstream partition key. If you want to map a multi-dimensional upstream asset to a single-dimensional downstream asset and effectively ignore one of the dimensions (e.g., "entity"), you should not use 
StaticPartitionMapping
with 
None
values. Instead, you should use a partition mapping that only considers the relevant dimension ("date" in your case) for the downstream asset. Since 
MultiPartitionsMapping
is not available and 
StaticPartitionMapping
cannot be used with 
None
, you might need to create a custom partition mapping. Here's an example of how you might create a custom partition mapping that only considers the "date" dimension:
Copy code
python from dagster import PartitionMapping, PartitionKeyRange, PartitionKey  class DateOnlyPartitionMapping(PartitionMapping):     def get_upstream_partition_keys(self, downstream_partition_key_range: PartitionKeyRange):         # Assuming downstream_partition_key_range is a range of date partitions         # This function should return the corresponding upstream partition keys         # that match the date range, ignoring the "entity" dimension.                  # Example logic (you will need to adjust this based on your actual partition format):         date_str = downstream_partition_key_range.start         return [PartitionKey(date_str)]  # Return a list of partition keys that only include the date  # Use the custom partition mapping in your downstream asset definition @asset(     partitions_def=single_partitions_def,     ins={"upstream_asset": AssetIn(partition_mapping=DateOnlyPartitionMapping())} ) def downstream_asset(upstream_asset):     # Logic for the downstream asset that depends on the upstream_asset     ...
In this custom partition mapping, 
get_upstream_partition_keys
is implemented to return the upstream partition keys that correspond to the downstream partition's date, ignoring the "entity" dimension. Please note that this is a simplified example, and you will need to adjust the logic in 
get_upstream_partition_keys
to match your specific partition key format and requirements. If you are unsure how to implement this or need further assistance, I recommend reaching out to the Dagster community for support. They can provide guidance on creating custom partition mappings that fit your use case.
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