@Scout i have a partritioned asset and i want to use one specific partition_key in an downstream asset. how to this? This is what it looks like: @asset(compute_kind="python", partitions_def=teamtailor_partition_defintion_by_project_id, description="Get Canonical URLs for Jobs", ins={"job_url_list": AssetIn(key=AssetKey("job_url_list"))}) def get_canonical_urls(context:AssetExecutionContext, job_url_list: set) -> set: job_url_list = list(job_url_list) # Crawling each URL and extracting canonical tag canonical_tags = [] for url in job_url_list: canonical_tags.append(get_canonical_tag(url)) # Creating a DataFrame df = pd.DataFrame({'url': job_url_list, 'canonical_tag': canonical_tags}) df_only_canonicals = df[df['canonical_tag'].notna()]["canonical_tag"] nan_urls = df[df['canonical_tag'].isna()]['url'] #here we add urls where canonical is found with urls where no cannical is set, so we get the best outcome set_nan_urls = set(df[df['canonical_tag'].isna()]['url']) set_canonical_urls = set(df[df['canonical_tag'].notna()]['canonical_tag']) final_set_with_correct_urls = set_nan_urls.union(set_canonical_urls) context.add_output_metadata(metadata={ "nr_of_jobs_total": len(df), "nr_of_jobs_without_canonicals": len(nan_urls), "nr_of_canonical_urls": len(df_only_canonicals), "final_set_for_downstream_usage": len(final_set_with_correct_urls), "preview_final_set": MetadataValue.json(list(final_set_with_correct_urls)) }) return final_set_with_correct_urls

To use a specific partition key from a partitioned asset in a downstream asset, you can utilize the context within the asset function to access partition information. The documentation provides examples of how to work with partition keys within asset functions. Here's a step-by-step guide based on the documentation: 1. Define your partitioned asset with a

partitions_def

that specifies how the asset is partitioned. 2. In the downstream asset function, you can access the partition key for the current run using

context.partition_key

. This will give you the specific partition key that is being processed in the current run. 3. Use the obtained partition key to filter or select the data that corresponds to that partition in your downstream logic. Here's an example of how you might modify your asset function to use a specific partition key:

python from dagster import asset, AssetExecutionContext, AssetIn, AssetKey  @asset(     compute_kind="python",     partitions_def=teamtailor_partition_defintion_by_project_id,     description="Get Canonical URLs for Jobs",     ins={"job_url_list": AssetIn(key=AssetKey("job_url_list"))} ) def get_canonical_urls(context: AssetExecutionContext, job_url_list: set) -> set:     # Access the specific partition key for the current run     partition_key = context.partition_key      # Use the partition_key to filter or select the data for this partition     # (The following is a placeholder for your partition-specific logic)     # job_url_list = filter_data_for_partition(job_url_list, partition_key)      # Your existing logic to process the job_url_list     canonical_tags = []     for url in job_url_list:         canonical_tags.append(get_canonical_tag(url))      # ... rest of your function ...      return final_set_with_correct_urls

In this example,

filter_data_for_partition

would be a function you define to filter or select the job URLs that correspond to the specific

partition_key

. The actual implementation of this function would depend on how your data is structured and how you want to use the partition key to filter the data. Remember to replace the placeholder

filter_data_for_partition

with your actual logic for handling partitioned data. The

context.partition_key

will provide the partition key for the current run, which you can use as needed in your asset function.

No i mean: In the downstream asset im going to use get_canonical_urls as input function. but i only want "final_set_with_correct_urls" for a specific partition_key, which i define manually @Scout

To use a specific partition key from an upstream partitioned asset in a downstream asset, you can define a partition mapping that specifies the correspondence between the upstream partition keys and the downstream partition keys. The documentation describes several types of partition mappings, such as

SpecificPartitionsPartitionMapping

, which allows you to map to a specific subset of partitions in the upstream asset. Here's an example based on the documentation that shows how you might define a downstream asset that depends on a specific partition key from an upstream asset:

python from dagster import asset, AssetIn, SpecificPartitionsPartitionMapping  @asset(     # ... other parameters ...     ins={         "get_canonical_urls": AssetIn(             key=AssetKey("get_canonical_urls"),             partition_mapping=SpecificPartitionsPartitionMapping(["specific_partition_key"])         )     } ) def downstream_asset(context, get_canonical_urls):     # The `get_canonical_urls` input will only include data from the "specific_partition_key" partition.     # Your downstream logic here...     pass

In this example, replace

"specific_partition_key"

with the actual partition key you want to use from the upstream asset. The

SpecificPartitionsPartitionMapping

is used to define an explicit correspondence between the upstream partition keys and the downstream asset. This means that the downstream asset will only depend on the data from the specified partition key of the upstream asset. Please note that the actual implementation details may vary based on your specific use case and the structure of your data. The example provided is a general guide based on the documentation's description of partition mappings.

@Scout for some reason the downstream asset throws an error because it excepts an tuple. i provide an tuple. but downstream asset only gets the set. whats the issue here? def get_canonical_urls(context:AssetExecutionContext, job_url_list: set) -> tuple: job_url_list = list(job_url_list) # Crawling each URL and extracting canonical tag canonical_tags = [] for url in job_url_list: canonical_tags.append(get_canonical_tag(url)) # Creating a DataFrame df = pd.DataFrame({'url': job_url_list, 'canonical_tag': canonical_tags}) df_only_canonicals = df[df['canonical_tag'].notna()]["canonical_tag"] nan_urls = df[df['canonical_tag'].isna()]['url'] #here we add urls where canonical is found with urls where no cannical is set, so we get the best outcome set_nan_urls = set(df[df['canonical_tag'].isna()]['url']) set_canonical_urls = set(df[df['canonical_tag'].notna()]['canonical_tag']) final_set_with_correct_urls = set_nan_urls.union(set_canonical_urls) context.add_output_metadata(metadata={ "nr_of_jobs_total": len(df), "nr_of_jobs_without_canonicals": len(nan_urls), "nr_of_canonical_urls": len(df_only_canonicals), "final_set_for_downstream_usage": len(final_set_with_correct_urls), "preview_final_set": MetadataValue.json(list(final_set_with_correct_urls)) }) tuple_for_return = (final_set_with_correct_urls, df) return tuple_for_return

If your downstream asset is expecting a tuple but is only receiving a set, it's possible that the issue lies in how the asset is being defined or how the output is being handled. In Dagster, each asset function should return its output directly, and if there are multiple outputs, you should use multiple assets or a tuple as the output type and yield each output separately. Here's an example of how you might define an asset that yields multiple outputs using a tuple:

python from dagster import asset, OutputDefinition, Output  @asset(     # ... other parameters ...     out={         "final_set_with_correct_urls": OutputDefinition(name="final_set_with_correct_urls"),         "df": OutputDefinition(name="df")     } ) def get_canonical_urls(context, job_url_list: set):     # ... your existing logic ...      # Yield each output separately     yield Output(final_set_with_correct_urls, "final_set_with_correct_urls")     yield Output(df, "df")

In this example, the

get_canonical_urls

asset function yields two separate outputs, each with its own name. The downstream asset should then be defined to accept these outputs as inputs. If you want to keep using a single return statement with a tuple, ensure that the downstream asset is correctly unpacking the tuple. Here's an example of how you might define the downstream asset to accept a tuple from the upstream asset:

python from dagster import asset, AssetIn  @asset(     # ... other parameters ...     ins={         "canonical_data": AssetIn(key=AssetKey("get_canonical_urls"))     } ) def downstream_asset(canonical_data):     # Unpack the tuple received from the upstream asset     final_set_with_correct_urls, df = canonical_data     # Your downstream logic here...

In this example, the

downstream_asset

function expects

canonical_data

to be a tuple containing both the set and the DataFrame. It then unpacks this tuple into two separate variables that can be used in the downstream logic. If you're still encountering issues, please ensure that the upstream asset is correctly returning a tuple and that the downstream asset is correctly defined to accept and unpack this tuple. If the problem persists, it may be helpful to provide the exact error message you're receiving, as this can offer more context for troubleshooting the issue.