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s
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s
The choice between using 
context.partition_key
and 
context.partition_time_window
depends on the specific needs of your asset computation within Dagster. 
context.partition_key
provides a string representation of the partition key for the current run. This is useful when you need to know exactly which partition is being processed, especially when each run is associated with a single partition. For example, if you are processing data on a daily basis, and each partition corresponds to a single day, you might use 
context.partition_key
to fetch or reference the data for that specific day. On the other hand, 
context.partition_time_window
provides a 
TimeWindow
object representing the time range for the current partition. This is particularly useful when your computation needs to know the start and end times of the time window associated with the partition. For instance, if you are aggregating data over a period, such as a week or a month, you might use 
context.partition_time_window
to determine the time range to query your data source. The documentation provides examples of using both 
context.partition_key
and 
context.partition_time_window
. Here is an example using `context.partition_time_window`:
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python @asset(partitions_def=DailyPartitionsDefinition("2023-08-20")) def my_asset(context: AssetExecutionContext):     <http://context.log.info|context.log.info>(context.partition_time_window)     # materializing the 2023-08-21 partition of this asset will log:     #   TimeWindow("2023-08-21", "2023-08-22")
And here is an example using 
context.partition_keys
to get all the partitions being materialized by a backfill:
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python @asset(partitions_def=DailyPartitionsDefinition("2023-08-20")) def an_asset(context: AssetExecutionContext):     <http://context.log.info|context.log.info>(context.partition_keys)     # running a backfill of the 2023-08-21 through 2023-08-25 partitions of this asset will log:     #   ["2023-08-21", "2023-08-22", "2023-08-23", "2023-08-24", "2023-08-25"]
In summary, neither 
context.partition_key
nor 
context.partition_time_window
is inherently better; they serve different purposes. You should choose the one that aligns with the requirements of your asset computation. If you need to handle multiple partitions in a single run, such as during a backfill, you would use methods like 
context.partition_keys
or 
context.partition_time_window
to work with the range of partitions being processed.
g
@Scout What kind of information does context.log.info(context.partition_keys) offer?
s
The 
<http://context.log.info|context.log.info>(context.partition_keys)
call is used to log information about the partition keys being processed in the current run. When you invoke this within an asset computation, it will output a message to the log that includes the list of partition keys. For example, if you have an asset that is partitioned daily and you are running a backfill that processes multiple days at once, 
context.partition_keys
would return a list of the partition keys for the date range being processed. The 
<http://context.log.info|context.log.info>
call would then log this list, which could look something like 
["2023-08-21", "2023-08-22", "2023-08-23", "2023-08-24", "2023-08-25"]
if you were backfilling data for the dates August 21st through August 25th, 2023. This information can be useful for debugging and monitoring purposes, as it provides visibility into which partitions are being processed during a particular run. It can help you verify that the correct partitions are being targeted and give you a record of the operation for future reference.
g
@Scout Do I just implement it like this?
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def incidents(context: AssetExecutionContext) -> pd.DataFrame:
    <http://context.log.info|context.log.info>(context.partition_keys)

    # Convert the partition key string to a datetime object to use in the API call
    incident_date = datetime.strptime(context.partition_key, '%Y-%m-%d')

    try:
        # Fetch data for each day represented by incident_date
        incidents = function.get_incident_records(incident_date, incident_date)
        return pd.DataFrame(incidents)
    except Exception as e:
        context.log.error(f"An error occurred: {e}")
        return pd.DataFrame()
s
The code snippet you've provided seems to be attempting to log the partition keys and then fetch incident records for a given date. However, there are a few issues with the snippet that need to be addressed: 1. The 
<http://context.log.info|context.log.info>
syntax is incorrect. It should not include the URL-like syntax 
<http://context.log.info|context.log.info>
. Instead, it should just be 
<http://context.log.info|context.log.info>
. 2. The 
context.partition_keys
property is used when you expect to process multiple partitions in a single run, such as during a backfill. If you're only processing a single partition, you should use 
context.partition_key
. 3. The 
datetime.strptime
function is used to convert a string to a datetime object, but you need to ensure that the string format matches the expected format of the partition key. Here's a revised version of your code snippet:
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python from datetime import datetime import pandas as pd from dagster import asset  @asset def incidents(context) -> pd.DataFrame:     # Log the partition keys if there are multiple; otherwise, log the single partition key     if hasattr(context, 'partition_keys'):         <http://context.log.info|context.log.info>(context.partition_keys)     else:         <http://context.log.info|context.log.info>(context.partition_key)      # Convert the partition key string to a datetime object to use in the API call     # Assuming the partition key is in the format 'YYYY-MM-DD'     incident_date = datetime.strptime(context.partition_key, '%Y-%m-%d')      try:         # Fetch data for the day represented by incident_date         incidents = function.get_incident_records(incident_date, incident_date)         return pd.DataFrame(incidents)     except Exception as e:         context.log.error(f"An error occurred: {e}")         return pd.DataFrame()
Please make sure to replace 
function.get_incident_records
with the actual function you're using to fetch the incident records. Also, ensure that the function expects 
datetime
objects for the date parameters. Remember to import the necessary modules at the beginning of your script, and adjust the 
@asset
decorator as needed based on your asset's partitioning scheme and other configurations.
g
@Scout Would you recommend the following if statement for handling partitioned files? if run.partition_key: partition_key = run.partition_key elif run.partition_key_range: partition_key = run.partition_key_range else: raise DagsterInvariantViolationError("Run is not partitioned.")
s
There was an error processing your request. Please try again.
g
@Scout Would you recommend the following if statement for handling partitioned files? if run.partition_key: partition_key = run.partition_key elif run.partition_key_range: partition_key = run.partition_key_range else: raise DagsterInvariantViolationError("Run is not partitioned.")
s
The if statement you've provided is a good starting point for handling partitioned runs in Dagster, but it's important to note that the context object, rather than the run object, should be used within an asset or op to access partition information. The context object provides methods and properties to access partition-related information. Here's a revised version of the if statement that uses the context object:
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python from dagster import DagsterInvariantViolationError  # Assuming 'context' is an instance of AssetExecutionContext or OpExecutionContext if context.has_partition_key:     partition_key = context.partition_key elif context.has_partition_key_range:     partition_key_range = context.partition_key_range else:     raise DagsterInvariantViolationError("Run is not partitioned.")
In this revised version: - 
context.has_partition_key
checks if there is a single partition key associated with the run. - 
context.partition_key
provides the partition key if there is one. - 
context.has_partition_key_range
checks if there is a range of partition keys associated with the run. - 
context.partition_key_range
provides the partition key range if there is one. This approach allows you to handle both cases where a run is associated with a single partition and cases where a run is associated with a range of partitions. If neither condition is met, it raises a 
DagsterInvariantViolationError
, indicating that the run is not partitioned. Remember to adjust the logic based on the specific requirements of your asset or op, and ensure that you handle both single partition keys and partition key ranges appropriately within your computation.
g
@Scout Would it make sense to put that logic in an io manager, or in the asset itself?
s
In Dagster, the decision to place logic in an IOManager or directly in the asset depends on the responsibilities and concerns you're addressing. IOManager: An IOManager in Dagster is responsible for handling the storage and retrieval of data. It abstracts away the details of how and where data is stored, allowing assets to focus on computation rather than data management. If your logic pertains to how data should be read or written based on partition information, then it would make sense to encapsulate this logic within a custom IOManager. For example, if you have an IOManager that writes to different file paths or databases depending on the partition key, the IOManager would be the appropriate place to include the logic for determining the target location based on the partition key or range. Asset: On the other hand, if the logic is about the computation or transformation of data within the asset and is directly influenced by the partition key (such as filtering data for a specific date range), then it should reside within the asset itself. The asset would use the partition key to perform its computation, and the IOManager would simply handle the storage of the asset's output. For instance, if you're generating a report based on data from a specific partition, the asset would use the partition key to query the relevant data, generate the report, and then hand off the report to the IOManager for storage. Conclusion: - Place the logic in an IOManager if it relates to the storage and retrieval of data based on partition information. - Place the logic in the asset if it relates to the computation or processing of data that is influenced by the partition key. In practice, you would typically not need to manually check for partition keys within an IOManager, as Dagster's execution context provides this information when loading or storing an asset's data. However, if you have complex logic that determines how data is stored based on partitions, a custom IOManager would be the right place for that logic.
g
@Scout Can you integrate your revised statement into the existing code? @asset( group_name="group_name_placeholder", compute_kind="swiftly", partitions_def=DailyPartitionsDefinition( start_date="2020-09-12" ), # Inception date of API: 2020-09-12 backfill_policy=BackfillPolicy.multi_run( max_partitions_per_run=5 ), # API has a rate limit of 5 calls per minute io_manager_key="partitioned_csv_io_manager", key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame: # Log the partition keys if there are multiple; otherwise, log the single partition key if hasattr(context, "partition_keys"): context.log.info(context.partition_keys) else: context.log.info(context.partition_key) # Convert the partition key string to a datetime object incident_date = datetime.strptime(context.partition_key, "%Y-%m-%d") # Fetches incident records for a specific date and returns DataFrame try: incidents = api_connector.get_incident_records(incident_date, incident_date) return pd.DataFrame(incidents) except Exception as e: context.log.error(f"An error occurred: {e}") return pd.DataFrame()
s
Certainly! Let's integrate the revised statement into the existing code. I'll correct the logging syntax and ensure that the partition key logic is properly implemented within the asset function. Here's the updated code:
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python from datetime import datetime import pandas as pd from dagster import asset, DailyPartitionsDefinition, BackfillPolicy, AssetExecutionContext  @asset(     group_name="group_name_placeholder",     compute_kind="swiftly",     partitions_def=DailyPartitionsDefinition(         start_date="2020-09-12"     ),  # Inception date of API: 2020-09-12     backfill_policy=BackfillPolicy.multi_run(         max_partitions_per_run=5     ),  # API has a rate limit of 5 calls per minute     io_manager_key="partitioned_csv_io_manager",     key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame:     # Log the partition keys if there are multiple; otherwise, log the single partition key     if hasattr(context, "partition_keys"):         <http://context.log.info|context.log.info>(context.partition_keys)     else:         <http://context.log.info|context.log.info>(context.partition_key)      # Convert the partition key string to a datetime object     incident_date = datetime.strptime(context.partition_key, "%Y-%m-%d")      # Fetches incident records for a specific date and returns DataFrame     try:         incidents = api_connector.get_incident_records(incident_date, incident_date)         return pd.DataFrame(incidents)     except Exception as e:         context.log.error(f"An error occurred: {e}")         return pd.DataFrame()
In this updated code, I've removed the incorrect 
<http://context.log.info|context.log.info>
syntax and replaced it with the correct 
<http://context.log.info|context.log.info>
syntax. Additionally, I've ensured that the 
hasattr(context, "partition_keys")
check is used to determine whether to log multiple partition keys or a single partition key. This code assumes that 
api_connector.get_incident_records
is a function that fetches incident records for the given date and that the 
api_connector
is defined elsewhere in your codebase.
g
@Scout I meant, would you integrate the code snippet you provided as follows, into the function I submitted?
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from dagster import DagsterInvariantViolationError

# Assuming 'context' is an instance of AssetExecutionContext or OpExecutionContext
if context.has_partition_key:
    partition_key = context.partition_key
elif context.has_partition_key_range:
    partition_key_range = context.partition_key_range
else:
    raise DagsterInvariantViolationError("Run is not partitioned.")
s
Certainly! I'll integrate the provided code snippet into the 
incidents
asset function you submitted. The updated function will include the logic to handle partition keys and partition key ranges, and it will raise an error if the run is not partitioned. Here's the updated 
incidents
function with the integrated partition key logic:
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python from datetime import datetime import pandas as pd from dagster import asset, DailyPartitionsDefinition, BackfillPolicy, AssetExecutionContext, DagsterInvariantViolationError  @asset(     group_name="group_name_placeholder",     compute_kind="swiftly",     partitions_def=DailyPartitionsDefinition(         start_date="2020-09-12"     ),  # Inception date of API: 2020-09-12     backfill_policy=BackfillPolicy.multi_run(         max_partitions_per_run=5     ),  # API has a rate limit of 5 calls per minute     io_manager_key="partitioned_csv_io_manager",     key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame:     # Determine the partition key or range for the current run     if context.has_partition_key:         partition_key = context.partition_key         <http://context.log.info|context.log.info>(f"Processing partition: {partition_key}")     elif context.has_partition_key_range:         partition_key_range = context.partition_key_range         <http://context.log.info|context.log.info>(f"Processing partition range: {partition_key_range}")     else:         raise DagsterInvariantViolationError("Run is not partitioned.")      # Convert the partition key string to a datetime object     incident_date = datetime.strptime(partition_key, "%Y-%m-%d")      # Fetches incident records for a specific date and returns DataFrame     try:         incidents = api_connector.get_incident_records(incident_date, incident_date)         return pd.DataFrame(incidents)     except Exception as e:         context.log.error(f"An error occurred: {e}")         return pd.DataFrame()
In this updated function: - The 
if
statement checks if there is a single partition key (
context.has_partition_key
) or a range of partition keys (
context.has_partition_key_range
) associated with the run. - The 
partition_key
or 
partition_key_range
is logged using 
<http://context.log.info|context.log.info>
. - If neither a single partition key nor a partition key range is present, a 
DagsterInvariantViolationError
is raised, indicating that the run is not partitioned. - The 
incident_date
is derived from the 
partition_key
, assuming that the run is associated with a single partition. If you need to handle a range of partitions, you would need to adjust the logic accordingly. Please ensure that 
api_connector.get_incident_records
is defined and that it can handle the 
incident_date
parameter as expected.
g
@Scout Can you generate a docstring for that function?
s
Certainly! Here's a docstring for the 
incidents
asset function:
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python @asset(     group_name="group_name_placeholder",     compute_kind="swiftly",     partitions_def=DailyPartitionsDefinition(         start_date="2020-09-12"     ),     backfill_policy=BackfillPolicy.multi_run(         max_partitions_per_run=5     ),     io_manager_key="partitioned_csv_io_manager",     key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame:     """     Fetches incident records for a specific partition date and returns them as a DataFrame.      This asset is designed to process data that is partitioned daily. It supports backfilling     with a maximum of 5 partitions per run due to API rate limits. The asset will log the     partition key or range it is processing and will raise an error if the run is not partitioned.      Args:         context (AssetExecutionContext): The execution context for the asset, which provides                                          access to partition information and logging.      Returns:         pd.DataFrame: A DataFrame containing the incident records for the specified partition date.      Raises:         DagsterInvariantViolationError: If the run is not associated with a partition key or range.      The function assumes that the `api_connector.get_incident_records` method is defined elsewhere     and is capable of fetching incident records for the given date. The partition key is expected     to be in the format 'YYYY-MM-DD'.     """     # Determine the partition key or range for the current run     if context.has_partition_key:         partition_key = context.partition_key         <http://context.log.info|context.log.info>(f"Processing partition: {partition_key}")     elif context.has_partition_key_range:         partition_key_range = context.partition_key_range         <http://context.log.info|context.log.info>(f"Processing partition range: {partition_key_range}")     else:         raise DagsterInvariantViolationError("Run is not partitioned.")      # Convert the partition key string to a datetime object     incident_date = datetime.strptime(partition_key, "%Y-%m-%d")      # Fetches incident records for a specific date and returns DataFrame     try:         incidents = api_connector.get_incident_records(incident_date, incident_date)         return pd.DataFrame(incidents)     except Exception as e:         context.log.error(f"An error occurred: {e}")         return pd.DataFrame()
This docstring provides a clear description of the asset's purpose, its parameters, the expected return type, and any exceptions that might be raised. It also includes additional context about the asset's behavior and assumptions.
g
@Scout that code block is returning the following error: error: "AssetExecutionContext" has no attribute "has_partition_key_range"; maybe "asset_partition_key_range", "partition_key_range", or "has_partition_key"? [attr-defined] Found 1 error in 1 file (checked 1 source file)
s
The error message indicates that the 
AssetExecutionContext
object does not have an attribute named 
has_partition_key_range
. This is likely because the correct attribute to check for the presence of a partition key range is 
has_partition_key
, and to access the range, you should use 
partition_key_range
. Let's correct the code to use the appropriate attributes: ```python @asset( group_name="group_name_placeholder", compute_kind="swiftly", partitions_def=DailyPartitionsDefinition( start_date="2020-09-12" ), backfill_policy=BackfillPolicy.multi_run( max_partitions_per_run=5 ), io_manager_key="partitioned_csv_io_manager", key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame: """ Fetches incident records for a specific partition date and returns them as a DataFrame. This asset is designed to process data that is partitioned daily. It supports backfilling with a maximum of 5 partitions per run due to API rate limits. The asset will log the partition key or range it is processing and will raise an error if the run is not partitioned. Args: context (AssetExecutionContext): The execution context for the asset, which provides access to partition information and logging. Returns: pd.DataFrame: A DataFrame containing the incident records for the specified partition date. Raises: DagsterInvariantViolationError: If the run is not associated with a partition key or range. The function assumes that the 
api_connector.get_incident_records
method is defined elsewhere and is capable of fetching incident records for the given date. The partition key is expected to be in the format 'YYYY-MM-DD'. """ # Determine the partition key or range for the current run if context.has_partition_key: partition_key = context.partition_key context.log.info(f"Processing partition: {partition_key}") # Convert the partition key string to a datetime object incident_date = datetime.strptime(partition_key, "%Y-%m-%d") elif context.partition_key_range: partition_key_range = context.partition_key_range context.log.info(f"Processing partition range: {partition_key_range}") # Handle the partition key range case as needed # For example, you might need to iterate over the range or adjust the logic to fetch data for the range # This example will need to be adjusted based on how your 
api_connector.get_incident_records
works with ranges raise NotImplementedError("Partition key range processing is not yet implemented.") else: raise DagsterInvariantViolationError("Run is not partition
ed.") # Fetches incident records for a specific date and returns DataFrame try: incidents = api_connector.get_incident_records(incident_date, incident_date) return pd.DataFrame(incidents) except Exception as e: context.log.error(f"An error occurred: {e}") return pd.DataFrame() ``` In this corrected code, we use 
context.has_partition_key
to check for a single partition key and 
context.partition_key_range
to access the partition key range. If a range is present, you would need to implement the logic to handle it, which might involve iterating over the range or adjusting the API call to fetch data for the entire range. Since the handling of partition key ranges can vary based on your specific use case and the capabilities of your 
api_connector.get_incident_records
function, I've added a 
NotImplementedError
as a placeholder for you to fill in the appropriate logic.
g
@Scout Is there a reason the incident_date = datetime.strptime(partition_key, "%Y-%m-%d") line was moved?
s
The line 
incident_date = datetime.strptime(partition_key, "%Y-%m-%d")
was moved inside the 
if context.has_partition_key:
block to ensure that it is only executed when a single partition key is present. This is because the 
partition_key
variable is only defined within the scope of that 
if
block, and attempting to parse it as a date outside of this block would result in an error if 
partition_key
is not set. When dealing with partition key ranges (as would be the case in the 
elif context.partition_key_range:
block), the logic for handling dates would likely be different, as you would be dealing with a range of dates rather than a single date. Therefore, the date parsing and subsequent logic for fetching incident records would need to be adapted to handle multiple dates. To clarify, here's the reasoning for the placement of the line: - If there is a single partition key, we parse it into a date and proceed with fetching incident records for that specific date. - If there is a partition key range, the logic for handling the range would be different, and we would not use the single date parsing line. Instead, we might iterate over the range or develop a different approach to fetch incident records for the entire range. By moving the line inside the 
if
block, we ensure that it is only executed in the appropriate context, which is when a single partition key is available.
g
@Scout How could we ensure the dates in the 
partition_key_range
are in the same date format?
s
There was an error processing your request. Please try again.
g
@Scout How could we ensure the dates in the 
partition_key_range
are in the same date format?
s
Ensuring that the dates in the 
partition_key_range
are in the same date format typically involves establishing a consistent partitioning scheme when defining your partitions. In Dagster, when you define a partitioned asset, you specify a 
PartitionsDefinition
that dictates how the partition keys are formatted and what time range they represent. Here's how you can ensure consistency: 1. Define a Standard Format: When you create your 
PartitionsDefinition
, you should define a standard date format that will be used for all partition keys. For example, if you're using 
DailyPartitionsDefinition
, you might define your partition keys to always be in the format 
YYYY-MM-DD
. 2. Use a Consistent Parsing Function: When processing the 
partition_key_range
, use a consistent parsing function that can handle the predefined format. This function should be used whenever you need to convert partition keys into datetime objects or when performing any operations that require understanding the date. 3. Validation: If there's any possibility that partition keys could be in an unexpected format (e.g., if they are provided by an external source), you should implement validation logic to check the format before attempting to parse them. This could be as simple as a regular expression check or a try-except block around the parsing function. Here's an example of how you might handle a `partition_key_range`:
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python from datetime import datetime from dagster import PartitionKeyRange  def parse_partition_key(key: str, expected_format: str = "%Y-%m-%d") -> datetime:     try:         return datetime.strptime(key, expected_format)     except ValueError as e:         raise ValueError(f"Partition key '{key}' does not match the expected format '{expected_format}'") from e  def process_partition_key_range(partition_key_range: PartitionKeyRange):     start_date = parse_partition_key(partition_key_range.start)     end_date = parse_partition_key(partition_key_range.end)      # Now that we have the start and end dates as datetime objects, we can ensure they are in the same format     # and perform the necessary operations on them.     # ...
In this example, 
parse_partition_key
is a utility function that ensures partition keys are in the expected format and converts them to datetime objects. The 
process_partition_key_range
function then uses this utility to parse the start and end of the range. By following these steps, you can ensure that all dates within a 
partition_key_range
are consistently formatted and processed correctly within your Dagster assets.
g
Does context.partition_key require a string data type?
@Scout Does context.partition_key require a string data type?
s
Yes, in Dagster, 
context.partition_key
is expected to be a string data type. The partition key represents a specific partition for an asset, and it is typically a string that uniquely identifies that partition. For example, if you have daily partitions, the partition key might be a date in the format "YYYY-MM-DD". The documentation mentions that 
context.partition_key
is a string, and this is consistent with how partition keys are generally used to reference specific slices of data within a partitioned dataset. If you're working with a 
MultiPartitionsDefinition
, the partition key might be a more complex string that represents multiple dimensions, but it is still fundamentally a string. If you need to work with the partition key as a date or another data type, you would typically convert it from the string representation to the desired type within your asset computation. For example, you might use 
datetime.strptime(context.partition_key, '%Y-%m-%d')
to convert a date-formatted string to a 
datetime
object.
g
@Scout would that work the same for partition_key_range?
s
No, 
partition_key_range
does not work the same way as 
context.partition_key
. While 
context.partition_key
is a string that represents a single partition, 
partition_key_range
represents a range of partitions and is not a simple string. In Dagster, a 
PartitionKeyRange
typically has two attributes: 
start
and 
end
, which are both strings that represent the beginning and the end of the range, respectively. These strings follow the same format as individual partition keys. When you access 
partition_key_range
, you are dealing with an object that encapsulates this range, rather than a single string value. Here's an example of how you might work with `partition_key_range`:
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python if context.partition_key_range:     start_key = context.partition_key_range.start     end_key = context.partition_key_range.end     # Do something with start_key and end_key, which are strings
In this example, 
start_key
and 
end_key
are both strings that you can then convert to dates or other types as needed, similar to how you would handle a single 
partition_key
. The key difference is that with 
partition_key_range
, you are typically considering multiple partitions at once, rather than a single partition.
g
@Scout How would I convert those to dates?
s
To convert the 
start
and 
end
strings from a 
PartitionKeyRange
to dates, you would use the 
datetime.strptime
function from the 
datetime
module in Python. This function parses a string into a 
datetime
object based on the specified format. Assuming that your partition keys are in the format "YYYY-MM-DD", here's how you could convert them:
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python from datetime import datetime  # Assuming 'context.partition_key_range' is available and contains the start and end keys start_key = context.partition_key_range.start end_key = context.partition_key_range.end  # Convert the start and end partition key strings to datetime objects start_date = datetime.strptime(start_key, "%Y-%m-%d") end_date = datetime.strptime(end_key, "%Y-%m-%d")  # Now 'start_date' and 'end_date' are datetime objects that you can use in your computations
In this example, 
start_date
and 
end_date
are 
datetime
objects representing the start and end of the partition key range. You can now use these dates to filter data, make API calls, or perform any other date-based operations required by your asset computation.
g
@Scout could you apply that logic in the following code?
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# Determine the partition key or range for the current run
    if context.has_partition_key:
        partition_key = context.partition_key
        <http://context.log.info|context.log.info>(f"Processing partition: {partition_key}")
        # Convert the partition key string to a datetime object
        incident_date = datetime.strptime(partition_key, "%Y-%m-%d")
    elif context.partition_key_range:
        partition_key_range = context.partition_key_range
        <http://context.log.info|context.log.info>(f"Processing partition range: {partition_key_range}")
        # Handle the partition key range case as needed
    else:
        raise DagsterInvariantViolationError("Run is not partitioned.")
s
Certainly! I'll apply the logic to convert the 
start
and 
end
strings from a 
PartitionKeyRange
to 
datetime
objects in the provided code snippet. Additionally, I'll correct the logging syntax. Here's the updated code:
Copy code
python from datetime import datetime from dagster import DagsterInvariantViolationError  # ... (other necessary imports and code)  # Determine the partition key or range for the current run if context.has_partition_key:     partition_key = context.partition_key     <http://context.log.info|context.log.info>(f"Processing partition: {partition_key}")     # Convert the partition key string to a datetime object     incident_date = datetime.strptime(partition_key, "%Y-%m-%d")     # Fetches incident records for a specific date and returns DataFrame     # ... (logic to fetch and return data for the single incident_date) elif context.partition_key_range:     partition_key_range = context.partition_key_range     <http://context.log.info|context.log.info>(f"Processing partition range: {partition_key_range}")     # Convert the start and end partition key strings to datetime objects     start_date = datetime.strptime(partition_key_range.start, "%Y-%m-%d")     end_date = datetime.strptime(partition_key_range.end, "%Y-%m-%d")     # Handle the partition key range case as needed     # ... (logic to fetch and return data for the range of dates between start_date and end_date) else:     raise DagsterInvariantViolationError("Run is not partitioned.")
In this updated code: - When 
context.has_partition_key
is 
True
, it processes a single partition key by converting it to a 
datetime
object named 
incident_date
. - When 
context.partition_key_range
is available, it extracts the 
start
and 
end
partition keys from the range and converts them to 
datetime
objects named 
start_date
and 
end_date
. - The logging syntax has been corrected to use 
<http://context.log.info|context.log.info>
directly. - You would need to implement the logic to handle fetching data for the range of dates between 
start_date
and 
end_date
based on your application's requirements. Please ensure that the rest of your code (not shown here) handles the 
incident_date
, 
start_date
, and 
end_date
appropriately for fetching and processing your incident records.
g
@Scout Would the following logic not work for both partition scenarios? # Fetches incident records for a specific date and returns DataFrame try: incidents = api_connector.get_incident_records(incident_date, incident_date) return pd.DataFrame(incidents) except Exception as e: context.log.error(f"An error occurred: {e}") return pd.DataFrame()
s
The logic you've provided for fetching incident records:
Copy code
python try:     incidents = api_connector.get_incident_records(incident_date, incident_date)     return pd.DataFrame(incidents) except Exception as e:     context.log.error(f"An error occurred: {e}")     return pd.DataFrame()
This logic assumes that 
incident_date
is a single 
datetime
object representing a specific date. It works well when you have a single partition key and you want to fetch records for that specific date. However, if you have a 
partition_key_range
, which represents a range of dates, you will need to adjust the logic to handle multiple dates. The 
api_connector.get_incident_records
function, as called above, would not work for a range because it expects a start and end date for fetching records. If you have a range of dates, you would typically need to iterate over each date in the range or modify the function to accept a range and return records for all dates within that range. Here's an example of how you might adjust the logic to handle both scenarios:
Copy code
python # ... (previous code to determine if you have a single date or a range of dates)  # Fetches incident records for a specific date or range of dates and returns DataFrame try:     if context.has_partition_key:         # Single date scenario         incidents = api_connector.get_incident_records(incident_date, incident_date)     elif context.partition_key_range:         # Range of dates scenario         # Adjust this logic based on how your api_connector.get_incident_records function can handle date ranges         incidents = api_connector.get_incident_records(start_date, end_date)     else:         raise DagsterInvariantViolationError("Run is not partitioned.")          return pd.DataFrame(incidents) except Exception as e:     context.log.error(f"An error occurred: {e}")     return pd.DataFrame()
In this adjusted logic: - If there is a single partition key, it fetches records for that date. - If there is a partition key range, it fetches records for the entire range from 
start_date
to 
end_date
. - The 
api_connector.get_incident_records
function needs to be capable of handling both a single date and a range of dates. If it doesn't currently support a range, you'll need to modify it accordingly or implement a loop to fetch records for each date within the range. Make sure to replace 
api_connector.get_incident_records
with the actual logic or function call that is appropriate for your application.
g
@Scout can you provide the full code block?
s
Certainly! Below is the full code block that includes the logic for handling both single partition keys and partition key ranges when fetching incident records. Please note that you will need to adapt the 
api_connector.get_incident_records
function to handle a range of dates if it doesn't already do so. ```python from datetime import datetime import pandas as pd from dagster import asset, DailyPartitionsDefinition, BackfillPolicy, AssetExecutionContext, DagsterInvariantViolationError @asset( group_name="group_name_placeholder", compute_kind="swiftly", partitions_def=DailyPartitionsDefinition( start_date="2020-09-12" ), backfill_policy=BackfillPolicy.multi_run( max_partitions_per_run=5 ), io_manager_key="partitioned_csv_io_manager", key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame: """ Fetches incident records for a specific partition date or range of dates and returns them as a DataFrame. This asset is designed to process data that is partitioned daily. It supports backfilling with a maximum of 5 partitions per run due to API rate limits. The asset will log the partition key or range it is processing and will raise an error if the run is not partitioned. Args: context (AssetExecutionContext): The execution context for the asset, which provides access to partition information and logging. Returns: pd.DataFrame: A DataFrame containing the incident records for the specified partition date or range. Raises: DagsterInvariantViolationError: If the run is not associated with a partition key or range. """ # Determine the partition key or range for the current run if context.has_partition_key: partition_key = context.partition_key context.log.info(f"Processing partition: {partition_key}") # Convert the partition key string to a datetime object incident_date = datetime.strptime(partition_key, "%Y-%m-%d") elif context.partition_key_range: partition_key_range = context.partition_key_range context.log.info(f"Processing partition range: {partition_key_range}") # Convert the start and end partition key strings to datetime objects start_date = datetime.strptime(partition_key_range.start, "%Y-%m-%d") end_date = datetime.strptime(partition_key_range.end, "%Y-%m-%d") else: raise DagsterInvariantViolationError("Run is not partitioned.") # Fetches incident records for a specific date or range of dates and returns DataFrame try: if context.has_partition_key: # Single date scenario incidents = api_connector.get_incident_records(incident_
date, incident_date) elif context.partition_key_range: # Range of dates scenario # Adjust this logic based on how your api_connector.get_incident_records function can handle date ranges incidents = api_connector.get_incident_records(start_date, end_date) else: raise DagsterInvariantViolationError("Run is not partitioned.") return pd.DataFrame(incidents) except Exception as e: context.log.error(f"An error occurred: {e}") return pd.DataFrame() ``` In this code block: - The 
@asset
decorator defines the asset with its partitioning scheme and other configurations. - The 
incidents
function checks if the run has a single partition key or a range of partition keys. - It then fetches incident records for the appropriate date or date range. - The 
try...except
block handles any exceptions that occur during data fetching and logs an error message. Please ensure that 
api_connector.get_incident_records
is defined in your codebase and that it is capable of handling both single dates and date ranges as needed for your application.
g
@Scout How come 
incidents = fraudshare.get_incident_records(incident_date, incident_date)
can be the same variable while 
incidents = fraudshare.get_incident_records(start_date, end_date)
is seperate?
s
The variable 
incidents
in both cases is being used to store the result of the 
fraudshare.get_incident_records
function call. It's common in programming to reuse a variable name for similar purposes within the same scope, especially if the previous value is no longer needed after being reassigned. In the provided code snippet, 
incidents
is being used to hold the DataFrame returned by the 
fraudshare.get_incident_records
function, regardless of whether it's fetching records for a single date or a range of dates. The logic is structured in a way that only one of the two function calls will be executed, depending on whether there's a single partition key or a partition key range. Here's a breakdown of the logic: - If there is a single partition key (
context.has_partition_key
is 
True
), the code fetches records for that specific date by calling 
fraudshare.get_incident_records(incident_date, incident_date)
. The start and end dates are the same because you're only interested in incidents from one day. - If there is a partition key range (
context.partition_key_range
is available), the code fetches records for the entire range by calling 
fraudshare.get_incident_records(start_date, end_date)
. Here, 
start_date
and 
end_date
represent the beginning and end of the range, respectively. In both cases, the result is stored in the 
incidents
variable, which is then used to create a DataFrame. Since the 
incidents
variable is not used between these two assignments, it's perfectly fine to reuse the variable name. This is a common practice to avoid creating unnecessary variables when they serve a similar purpose within the same block of logic. The key point is that the 
incidents
variable will contain the relevant data needed for the next step, which is to return a DataFrame constructed from the fetched incident records. The actual function call to 
fraudshare.get_incident_records
differs based on the context (single date vs. date range), but the resulting data structure that 
incidents
refers to is conceptually the same—a collection of incident records.
g
@Scout should I group the string to date conversion lines together?
s
Grouping the string-to-date conversion lines together can improve the readability and organization of your code, especially if the conversion logic is similar for both single partition keys and partition key ranges. By placing similar logic together, you make the code easier to understand and maintain. Here's how you might refactor the code to group the conversion logic: ```python from datetime import datetime import pandas as pd from dagster import asset, DailyPartitionsDefinition, BackfillPolicy, AssetExecutionContext, DagsterInvariantViolationError @asset( group_name="group_name_placeholder", compute_kind="swiftly", partitions_def=DailyPartitionsDefinition( start_date="2020-09-12" ), backfill_policy=BackfillPolicy.multi_run( max_partitions_per_run=5 ), io_manager_key="partitioned_csv_io_manager", key_prefix="prefix_placeholder", ) def incidents(context: AssetExecutionContext) -> pd.DataFrame: """ Fetches incident records for a specific partition date or range of dates and returns them as a DataFrame. This asset is designed to process data that is partitioned daily. It supports backfilling with a maximum of 5 partitions per run due to API rate limits. The asset will log the partition key or range it is processing and will raise an error if the run is not partitioned. Args: context (AssetExecutionContext): The execution context for the asset, which provides access to partition information and logging. Returns: pd.DataFrame: A DataFrame containing the incident records for the specified partition date or range. Raises: DagsterInvariantViolationError: If the run is not associated with a partition key or range. """ # Initialize variables for date range start_date = None end_date = None # Determine the partition key or range for the current run and convert to datetime objects if context.has_partition_key: partition_key = context.partition_key context.log.info(f"Processing partition: {partition_key}") start_date = end_date = datetime.strptime(partition_key, "%Y-%m-%d") elif context.partition_key_range: partition_key_range = context.partition_key_range context.log.info(f"Processing partition range: {partition_key_range}") start_date = datetime.strptime(partition_key_range.start, "%Y-%m-%d") end_date = datetime.strptime(partition_key_range.end, "%Y-%m-%d") else: raise DagsterInvariantViolationError("Run is not partitioned.") # Fetches incident records for a specific date or range of dates and returns DataFrame try: incidents = api_connector.get_incident_records(start_date, end_date) retur
n pd.DataFrame(incidents) except Exception as e: context.log.error(f"An error occurred: {e}") return pd.DataFrame() ``` In this refactored code: - The variables 
start_date
and 
end_date
are initialized at the beginning of the function. - The string-to-date conversion logic is grouped together within the 
if
and 
elif
blocks. - The 
try
block that fetches the incident records and returns a DataFrame now uses 
start_date
and 
end_date
directly, which have been set appropriately for both single dates and date ranges. This approach ensures that the conversion from string to date is handled in one place, making the code cleaner and the conversion logic more consistent.
g
@Scout With this revision, could I not perform the string to date conversion in the 
incidents = fraudshare.get_incident_records(start_date, end_date)
line of the try block?
s
Performing the string-to-date conversion directly within the line that calls 
fraudshare.get_incident_records(start_date, end_date)
is technically possible, but it is generally not recommended for a couple of reasons: 1. Readability: Inline conversions can make the code harder to read and understand, especially for someone else who might be working on the code later. It's usually clearer to perform such conversions in a separate step. 2. Reusability: If you need to use the converted dates for any other purpose in your function, having them already converted into separate variables (
start_date
and 
end_date
) is helpful. 3. Error Handling: By separating the conversion from the API call, you can more easily handle errors that might occur during the conversion process. This separation allows you to provide more specific error messages and debugging information. 4. Consistency: Keeping the conversion logic separate and before the API call ensures that the dates are processed consistently, regardless of how the 
fraudshare.get_incident_records
function is used later in the code. Here's how you might keep the conversion separate from the API call:
Copy code
python # ... (previous code to determine if you have a single date or a range of dates)  # Fetches incident records for a specific date or range of dates and returns DataFrame try:     # The start_date and end_date have already been converted to datetime objects above     incidents = fraudshare.get_incident_records(start_date, end_date)     return pd.DataFrame(incidents) except Exception as e:     context.log.error(f"An error occurred: {e}")     return pd.DataFrame()
In this example, 
start_date
and 
end_date
are already datetime objects by the time they are passed to 
fraudshare.get_incident_records
, thanks to the earlier conversion logic. This keeps the API call clean and focused on its primary responsibility: fetching the incident records.
g
@Scout Getting the following error:
Copy code
dagster._core.errors.DagsterExecutionHandleOutputError: Error occurred while handling output "result" of step "fraudshare__incidents":

  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step
    for step_event in check.generator(step_events):
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/execute_step.py", line 536, in core_dagster_event_sequence_for_step
    for evt in _type_check_and_store_output(step_context, user_event):
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/execute_step.py", line 587, in _type_check_and_store_output
    for evt in _store_output(step_context, step_output_handle, output):
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/execute_step.py", line 807, in _store_output
    for elt in iterate_with_context(
  File "/usr/local/lib/python3.9/site-packages/dagster/_utils/__init__.py", line 469, in iterate_with_context
    return
  File "/usr/local/lib/python3.9/contextlib.py", line 137, in __exit__
    self.gen.throw(typ, value, traceback)
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/utils.py", line 84, in op_execution_error_boundary
    raise error_cls(

The above exception was caused by the following exception:
dagster._check.CheckError: Failure condition: Tried to access partition key for output 'result' of step 'fraudshare__incidents', but the step output has a partition range: '2024-03-08' to '2024-03-09'.

  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/utils.py", line 54, in op_execution_error_boundary
    yield
  File "/usr/local/lib/python3.9/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context
    next_output = next(iterator)
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/plan/execute_step.py", line 797, in _gen_fn
    gen_output = output_manager.handle_output(output_context, output.value)
  File "/opt/orca/orca_home/orca/io_managers.py", line 32, in handle_output
    path = self._get_path(context)
  File "/opt/orca/orca_home/orca/io_managers.py", line 65, in _get_path
    f"/{context.asset_partition_key}.{self.file_type}",
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/context/output.py", line 427, in asset_partition_key
    return self.step_context.asset_partition_key_for_output(self.name)
  File "/usr/local/lib/python3.9/site-packages/dagster/_core/execution/context/system.py", line 1214, in asset_partition_key_for_output
    check.failed(
  File "/usr/local/lib/python3.9/site-packages/dagster/_check/__init__.py", line 1627, in failed
    raise CheckError(f"Failure condition: {desc}")
with the following io manager:
Copy code
class PartitionedIOManager(ConfigurableIOManager):
    """
    This IOManager will take in a pandas dataframe and store as the specified file
    type at the specified path.
    It stores outputs for different partitions in different filepaths.
    Downstream ops can either load this dataframe by simply retrieving a path
    to where the data is stored.
    """

    file_type: str = "parquet"

    @property
    def _base_path(self) -> UPath:
        raise NotImplementedError()

    def handle_output(self, context: OutputContext, obj: pd.DataFrame) -> None:
        path = self._get_path(context)
        if isinstance(obj, pd.DataFrame):
            row_count = len(obj)
            if self.file_type == "parquet":
                <http://obj.to|obj.to>_parquet(path=path, index=False)
            elif self.file_type == "csv":
                <http://obj.to|obj.to>_csv(path_or_buf=path, index=False)
            else:
                raise TypeError(f"File types of {self.file_type} are not yet supported")
        else:
            raise Exception(f"Outputs of type {type(obj)} not supported.")

        context.add_output_metadata({"row_count": row_count, "path": path})

    def load_input(self, context: InputContext) -> pd.DataFrame:
        path = self._get_path(context)
        if context.dagster_type.typing_type != pd.DataFrame:
            check.failed(
                f"Inputs of type {context.dagster_type} not supported. Please specify "
                "a valid type for this input either on the argument of the "
                "@asset-decorated function."
            )
        if self.file_type == "parquet":
            return pd.read_parquet(path=path)
        if self.file_type == "csv":
            return pd.read_csv(filepath_or_buffer=path)
        raise TypeError(f"File types of {self.file_type} are not yet supported")

    def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:
        if context.has_asset_partitions:
            out = UPath.joinpath(  # type:ignore
                self._base_path,
                f"{context.<http://asset_key.to|asset_key.to>_user_string()}"
                f"/{context.asset_partition_key}.{self.file_type}",
            )
        else:
            out = UPath.joinpath(  # type:ignore
                self._base_path,
                f"{context.<http://asset_key.to|asset_key.to>_user_string()}.{self.file_type}",
            )
        <http://context.log.info|context.log.info>(out)
        return out  # type:ignore


class S3PartitionedIOManager(PartitionedIOManager):
    s3_bucket: str
    file_type: str = "parquet"

    @property
    def _base_path(self) -> UPath:
        return UPath("s3://" + self.s3_bucket)
s
The error message you're encountering indicates that there is a mismatch between the expected partition information for the output and what is being provided. Specifically, the error states that the output has a partition range, but the IOManager is trying to access a single partition key. The issue seems to be in the 
_get_path
method of your 
PartitionedIOManager
. When 
context.has_asset_partitions
is 
True
, the code attempts to construct a path using 
context.asset_partition_key
, which is expected to be a single partition key. However, in the case of a partition range, there is no single 
asset_partition_key
to use. To resolve this issue, you need to modify the 
_get_path
method to handle partition ranges properly. You might need to decide on a naming convention for files that represent a range of partitions. For example, you could use the start and end partition keys to create a directory or file name that encapsulates the range. Here's a possible modification to the 
_get_path
method to handle partition ranges:
Copy code
python def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:     if context.has_asset_partitions:         if context.has_partition_key:             # Handle single partition key             partition_suffix = f"{context.asset_partition_key}.{self.file_type}"         elif context.partition_key_range:             # Handle partition range             start_key = context.partition_key_range.start             end_key = context.partition_key_range.end             partition_suffix = f"range_{start_key}_to_{end_key}.{self.file_type}"         else:             raise DagsterInvariantViolationError("Asset partition information is not available.")                  out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}",             partition_suffix,         )     else:         out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}.{self.file_type}",         )     <http://context.log.info|context.log.info>(out)     return out
In this modified method: - If there is a single partition key, it constructs the file path with that key. - If there is a partition key range, it constructs a file path that includes both the start and end keys in the name. - If neither condition is met, it raises an error. Please note that this is just an example, and you will need to adjust the logic to fit the specific requirements of your application and how you want to handle files representing a range of partitions. Additionally, ensure that the rest of your IOManager and asset code is compatible with this approach to handling partition ranges.
g
Item "InputContext" of "Union[InputContext, OutputContext]" has no attribute "partition_key_range" [union-attr]
@Scout Item "InputContext" of "Union[InputContext, OutputContext]" has no attribute "partition_key_range" [union-attr]
s
The error message you're encountering suggests that the 
InputContext
object does not have an attribute named 
partition_key_range
. This attribute is specific to the 
OutputContext
when dealing with partitioned outputs. The 
InputContext
is used when loading inputs for an asset, and it may not have the same partition-related attributes as the 
OutputContext
. To handle this situation, you need to differentiate the logic for 
InputContext
and 
OutputContext
within the 
_get_path
method of your IOManager. You should only attempt to access 
partition_key_range
if you are dealing with an 
OutputContext
. Here's an example of how you might adjust the 
_get_path
method to account for this:
Copy code
python def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:     if isinstance(context, OutputContext) and context.has_asset_partitions:         if context.has_partition_key:             # Handle single partition key for OutputContext             partition_suffix = f"{context.asset_partition_key}.{self.file_type}"         elif context.partition_key_range:             # Handle partition key range for OutputContext             start_key = context.partition_key_range.start             end_key = context.partition_key_range.end             partition_suffix = f"range_{start_key}_to_{end_key}.{self.file_type}"         else:             raise DagsterInvariantViolationError("Output asset partition information is not available.")                  out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}",             partition_suffix,         )     elif isinstance(context, InputContext):         # Handle InputContext, which does not have partition_key_range         # You may need to adjust this logic based on how you want to handle input paths         out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}.{self.file_type}",         )     else:         raise DagsterInvariantViolationError("Context type is not supported for path generation.")          <http://context.log.info|context.log.info>(out)     return out
In this adjusted method: - The logic checks if the context is an instance of 
OutputContext
and has asset partitions before attempting to access partition-related attributes. - For 
InputContext
, it handles the path generation without attempting to access partition-related attributes, which are not available in 
InputContext
. - An error is raised if the context type is not supported for path generation. This approach ensures that you are only accessing attributes that are appropriate for the type of context you are dealing with. Make sure to adjust the logic for handling input paths based on your application's requirements.
g
@Scout "OutputContext" has no attribute "partition_key_range"; maybe "asset_partition_key_range", "partition_key", or "_partition_key"? [attr-defined]
s
The error message indicates that 
OutputContext
does not have an attribute named 
partition_key_range
. According to the documentation, the correct attribute to use for accessing the partition key range in an 
OutputContext
is 
asset_partition_key_range_for_output
. Here's how you might adjust the 
_get_path
method in your 
PartitionedIOManager
to use the correct attribute:
Copy code
python def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:     if isinstance(context, OutputContext) and context.has_asset_partitions:         if context.has_partition_key:             # Handle single partition key for OutputContext             partition_suffix = f"{context.asset_partition_key}.{self.file_type}"         elif context.asset_partition_key_range_for_output:             # Handle partition key range for OutputContext             partition_key_range = context.asset_partition_key_range_for_output             start_key = partition_key_range.start             end_key = partition_key_range.end             partition_suffix = f"range_{start_key}_to_{end_key}.{self.file_type}"         else:             raise DagsterInvariantViolationError("Output asset partition information is not available.")                  out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}",             partition_suffix,         )     elif isinstance(context, InputContext):         # Handle InputContext, which does not have partition_key_range         # You may need to adjust this logic based on how you want to handle input paths         out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}.{self.file_type}",         )     else:         raise DagsterInvariantViolationError("Context type is not supported for path generation.")          <http://context.log.info|context.log.info>(out)     return out
In this adjusted method: - The logic checks if the context is an instance of 
OutputContext
and has asset partitions before attempting to access partition-related attributes. - For 
OutputContext
, it uses 
asset_partition_key_range_for_output
to access the partition key range. - For 
InputContext
, it handles the path generation without attempting to access partition-related attributes, which are not available in 
InputContext
. - An error is raised if the context type is not supported for path generation. Please ensure that the rest of your IOManager and asset code is compatible with this approach to handling partition ranges.
g
@Scout error: "OutputContext" has no attribute "asset_partition_key_range_for_output"; maybe "asset_partition_key_range"?
s
The error message suggests that the attribute 
asset_partition_key_range_for_output
does not exist on the 
OutputContext
object. Based on the documentation, the correct attribute to access the partition key range for an output in an 
OutputContext
is 
asset_partition_key_range
. Here's how you can adjust the 
_get_path
method in your 
PartitionedIOManager
to use the correct attribute:
Copy code
python def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:     if isinstance(context, OutputContext) and context.has_asset_partitions:         if context.has_partition_key:             # Handle single partition key for OutputContext             partition_suffix = f"{context.asset_partition_key}.{self.file_type}"         elif context.asset_partition_key_range:             # Handle partition key range for OutputContext             partition_key_range = context.asset_partition_key_range             start_key = partition_key_range.start             end_key = partition_key_range.end             partition_suffix = f"range_{start_key}_to_{end_key}.{self.file_type}"         else:             raise DagsterInvariantViolationError("Output asset partition information is not available.")                  out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}",             partition_suffix,         )     elif isinstance(context, InputContext):         # Handle InputContext, which does not have partition_key_range         # You may need to adjust this logic based on how you want to handle input paths         out = UPath.joinpath(             self._base_path,             f"{context.asset_key.to_user_string()}.{self.file_type}",         )     else:         raise DagsterInvariantViolationError("Context type is not supported for path generation.")          <http://context.log.info|context.log.info>(out)     return out
In this adjusted method: - The logic checks if the context is an instance of 
OutputContext
and has asset partitions before attempting to access partition-related attributes. - For 
OutputContext
, it uses 
asset_partition_key_range
to access the partition key range. - For 
InputContext
, it handles the path generation without attempting to access partition-related attributes, which are not available in 
InputContext
. - An error is raised if the context type is not supported for path generation. Please ensure that the rest of your IOManager and asset code is compatible with this approach to handling partition ranges.
g
@Scout I would like this to have the contextual file name to use the key_prefix from the asset instead of "range" in the following code from the io manager:
Copy code
def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:
        if isinstance(context, OutputContext) and context.has_asset_partitions:
            if context.has_partition_key:
                # Handle single partition key for OutputContext
                partition_suffix = f"{context.asset_partition_key}.{self.file_type}"
            elif context.asset_partition_key_range:
                # Handle partition key range for OutputContext
                partition_key_range = context.asset_partition_key_range
                start_key = partition_key_range.start
                end_key = partition_key_range.end
                partition_suffix = f"range_{start_key}_to_{end_key}.{self.file_type}"
            else:
                raise DagsterInvariantViolationError(
                    "Output asset partition information is not available."
                )

            out = UPath.joinpath(  # type:ignore
                self._base_path,
                f"{context.asset_key.to_user_string()}",
                partition_suffix,
            )
        elif isinstance(context, InputContext):
            # Handle InputContext, which does not have partition_key_range
            out = UPath.joinpath(  # type:ignore
                self._base_path,
                f"{context.asset_key.to_user_string()}.{self.file_type}",
            )
        else:
            raise DagsterInvariantViolationError(
                "Context type is not supported for path generation."
            )

        <http://context.log.info|context.log.info>(out)
        return out  # type:ignore
s
To include the 
key_prefix
from the asset in the file name instead of using a generic "range" prefix, you can modify the 
_get_path
method to construct the file name based on the 
asset_key
of the context. The 
asset_key
includes the 
key_prefix
that you define in your asset. Here's how you can adjust the 
_get_path
method:
Copy code
python def _get_path(self, context: Union[InputContext, OutputContext]) -> UPath:     if isinstance(context, OutputContext) and context.has_asset_partitions:         if context.has_partition_key:             # Handle single partition key for OutputContext             partition_suffix = f"{context.asset_partition_key}.{self.file_type}"         elif context.asset_partition_key_range:             # Handle partition key range for OutputContext             partition_key_range = context.asset_partition_key_range             start_key = partition_key_range.start             end_key = partition_key_range.end             # Use the asset key prefix as part of the file name             key_prefix = "_".join(context.asset_key.path)  # Assuming the key prefix is a list of strings             partition_suffix = f"{key_prefix}_{start_key}_to_{end_key}.{self.file_type}"         else:             raise DagsterInvariantViolationError(                 "Output asset partition information is not available."             )          out = UPath.joinpath(  # type:ignore             self._base_path,             f"{context.asset_key.to_user_string()}",             partition_suffix,         )     elif isinstance(context, InputContext):         # Handle InputContext, which does not have partition_key_range         out = UPath.joinpath(  # type:ignore             self._base_path,             f"{context.asset_key.to_user_string()}.{self.file_type}",         )     else:         raise DagsterInvariantViolationError(             "Context type is not supported for path generation."         )      <http://context.log.info|context.log.info>(out)     return out  # type:ignore
In this adjusted method: - When handling a partition key range for an 
OutputContext
, the 
key_prefix
is extracted from 
context.asset_key.path
, which is a list of strings that includes the 
key_prefix
and the asset name. - The 
key_prefix
is then used to construct the file name for the partition range, ensuring that the file name reflects the asset's 
key_prefix
. - The 
join
method is used to concatenate the elements of the 
key_prefix
with underscores, but you can adjust the separator as needed for your naming convention. Please ensure that the 
key_prefix
is defined correctly in your asset and that the 
asset_key.path
is structured in a way that allows you to extract the 
key_prefix
as shown in the example.
g
@Scout We store our secrets in an yml file under steps>inputs>secretValue. What is the recommended way to pass that as a variable in my asset as third argument of my function? 
incidents = api_source.get_incident_records(start_date, end_date)
s
In Dagster, to pass secrets or sensitive information to an asset, you should use resources or configuration to avoid hardcoding secrets into your codebase. Resources in Dagster can be configured separately from the rest of your application, allowing you to define them once and reuse them as needed. This approach also helps keep secrets out of your code and version control. Here's a step-by-step guide on how to pass a secret value from a YAML configuration file to an asset in Dagster: 1. Define a Resource: Create a resource that will hold the secret value. This resource will be responsible for fetching the secret from the YAML file or environment variable.
Copy code
python from dagster import resource, Field, StringSource  @resource({     "secret_value": Field(StringSource, description="The secret value needed for the function.") }) def secret_resource(context):     return context.resource_config["secret_value"]
2. Configure the Resource: In your YAML configuration file, specify the secret value under the resource configuration.
Copy code
yaml resources:   secret_resource:     config:       secret_value: ${SECRET_ENV_VAR}  # Assuming the secret is stored in an environment variable
3. Attach the Resource to your Asset: When defining your asset, specify the required resource keys to include the 
secret_resource
.
Copy code
python from dagster import asset  @asset(required_resource_keys={"secret_resource"}) def incidents(context, start_date, end_date):     secret_value = context.resources.secret_resource     # Now you can use secret_value as the third argument in your function     incidents = api_source.get_incident_records(start_date, end_date, secret_value)     # ... rest of your asset logic
4. Run your Asset with Configuration: When you execute your asset, Dagster will use the configuration provided in the YAML file to supply the secret value to the asset via the resource. By following these steps, you can pass a secret value to your asset in a secure and configurable way. The secret value is not hardcoded in your asset code and can be managed through configuration and environment variables, which is a best practice for handling sensitive information.
g
@Scout In the resources.py file, would I add this to the 
RESOURCES = {}
section?
s
In Dagster, resources are typically defined in a Python module and then included in a 
Definitions
object or directly within an asset's 
resource_defs
argument. If you have a 
resources.py
file where you define all your resources, you would define your new resource in that file and then include it in the 
RESOURCES
dictionary, which is then used to construct a 
Definitions
object or passed to assets. Here's an example of how you might define a new resource for a secret value in your 
resources.py
file:
Copy code
python from dagster import resource, Field, StringSource  # Define your secret resource @resource({     "secret_value": Field(StringSource, description="The secret value needed for the function.") }) def secret_resource(context):     return context.resource_config["secret_value"]  # Add your secret resource to the RESOURCES dictionary RESOURCES = {     # ... (other resources)     "secret_resource": secret_resource, }
Then, when defining your 
Definitions
object or your asset, you would reference the 
RESOURCES
dictionary to include the `secret_resource`:
Copy code
python from dagster import Definitions from .resources import RESOURCES  # If using a Definitions object defs = Definitions(     resources=RESOURCES,     # ... (other definitions like assets, jobs, schedules, etc.) )  # Or, if directly attaching resources to an asset @asset(resource_defs=RESOURCES) def my_asset(context):     # Your asset logic here
By including the 
secret_resource
in the 
RESOURCES
dictionary, you make it available for use in your assets or other parts of your Dagster application. Remember to configure the resource with the actual secret value in your YAML configuration file or through environment variables, as shown in the previous example.
g
@Scout If I want to use the DailyPartitionsDefinition class to get data from multiple days, but store each day in its own partition instead of storing a range of days in a single file, can that be accomplished natively?
s
Yes, Dagster natively supports storing data from multiple days, each in its own partition, using the 
DailyPartitionsDefinition
class. When you define an asset with 
DailyPartitionsDefinition
, Dagster expects that each partition corresponds to a single day, and it will manage the storage of each day's data separately. Here's an example of how you might define an asset with daily partitions:
Copy code
python from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Fetch data for the day represented by the partition key     partition_date = context.partition_key     # Logic to fetch and process data for the given partition_date     # ...     # Return the processed data for this day
In this example, 
my_daily_asset
is an asset that is partitioned daily, starting from January 1st, 2023. Each partition represents data for a single day, and when you materialize this asset, Dagster will invoke the asset computation once for each partition, allowing you to fetch and process data for each day separately. If you have an IOManager configured for your asset, it will handle storing the output of each partition in a separate location based on the partition key. This is the default behavior for IOManagers in Dagster when working with partitioned assets. When you materialize the asset for a range of dates, Dagster will execute the asset once for each day within the range, and each execution will handle data for its respective day. This way, each day's data is stored in its own partition, as you desire.
g
@Scout If I select a range of partitions from the timeline, each partition representing a day, but do not make use of the 
partition_key_range
property in my asset, multiple files for each day in that range will be generated?
s
Yes, if you select a range of partitions from the timeline in Dagit (Dagster's UI) and each partition represents a day, Dagster will execute the asset once for each selected partition. If you do not use the 
partition_key_range
property in your asset and instead rely on the 
partition_key
property, Dagster will treat each execution independently, corresponding to a single day's partition. For each execution of the asset, Dagster will pass the 
partition_key
for that day to the asset's execution context. Your asset can then use this 
partition_key
to fetch and process data for that specific day. If your asset is configured to output data to a file and you have an IOManager that uses the partition key to determine the file path, then each execution will result in a separate file for each day's data. Here's an example of how your asset might look:
Copy code
python from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Fetch data for the day represented by the partition key     partition_date = context.partition_key     # Logic to fetch and process data for the given partition_date     data = fetch_data_for_date(partition_date)     # ...     # Return the processed data for this day     return data
When you materialize this asset for a range of dates, Dagster will invoke 
my_daily_asset
once for each date in the selected range, and each invocation will handle data for its respective day. The IOManager will store the output of each invocation in a separate file, named or located according to the partition key, resulting in multiple files—one for each day in the range.
g
@Scout Will each partition be its separate call to the API to get the data?
s
Yes, when you materialize an asset that is defined with 
DailyPartitionsDefinition
and you select a range of partitions to materialize, Dagster will make a separate call to the API for each partition if your asset's logic is set up to do so. Each partition corresponds to a single day, and Dagster will execute the asset once for each day within the selected range, passing the 
partition_key
for that day to the asset's execution context. Here's a simplified example of how your asset might be set up to make a separate API call for each day:
Copy code
python from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Fetch data for the day represented by the partition key     partition_date = context.partition_key     # Make an API call to fetch data for the given partition_date     data = api_source.get_data_for_date(partition_date)     # ...     # Return the fetched data for this day     return data
In this example, 
api_source.get_data_for_date(partition_date)
represents the function that makes the API call to fetch data for the specified date. When you materialize 
my_daily_asset
for a range of dates, the function 
get_data_for_date
will be called once for each date in the range, resulting in separate API calls for each day's data. Each call will use the 
partition_key
as the date parameter for the API call.
g
@Scout if my api connection requires a start and end date as a parameter, even for a single day, would I need to do anything different in my asset than the example you provided? Below is an example of my api request. https://vendor-api.net/recprds?FromDate=2024-03-06&amp;ToDate=2024-03-11
s
If your API connection requires both a start date and an end date as parameters, even for fetching data for a single day, you will need to provide the same date for both the 
FromDate
and 
ToDate
parameters to fetch data for that specific day. Here's how you can modify the asset to accommodate this requirement:
Copy code
python from datetime import datetime, timedelta from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Fetch data for the day represented by the partition key     partition_date_str = context.partition_key     partition_date = datetime.strptime(partition_date_str, "%Y-%m-%d")      # Since the API requires a range, we'll use the same date for FromDate and ToDate     # For ToDate, add one day to the partition_date and format it as a string     next_day = partition_date + timedelta(days=1)     next_day_str = next_day.strftime("%Y-%m-%d")      # Make an API call to fetch data for the given date range     data = api_source.get_incident_records(partition_date_str, next_day_str)     # ...     # Return the fetched data for this day     return data
In this example, 
partition_date
is the date corresponding to the 
partition_key
, and 
next_day
is the date immediately following 
partition_date
. We format both dates as strings in the "YYYY-MM-DD" format to match the API's expected parameters. Since we want to fetch data for a single day, we use 
partition_date_str
for 
FromDate
and 
next_day_str
for 
ToDate
. This effectively creates a one-day range that includes only the data for 
partition_date
. When you materialize this asset for a range of dates, Dagster will execute the asset once for each date in the range, and the 
api_source.get_incident_records
function will be called with the appropriate one-day range for each execution.
g
@Scout does 
context.partition_key
return strings or datetime types?
s
In Dagster, 
context.partition_key
returns a string that represents the partition key for the current run. This string is typically formatted according to the partition scheme you've defined for your asset. For example, if you're using 
DailyPartitionsDefinition
, the 
partition_key
would be a string representing a date, such as "2023-01-01". If you need to work with the partition key as a 
datetime
object, you would convert it from the string representation to a 
datetime
object using Python's 
datetime.strptime
method or a similar function. Here's an example of how you might do this:
Copy code
python from datetime import datetime from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Convert the partition key string to a datetime object     partition_date = datetime.strptime(context.partition_key, "%Y-%m-%d")     # Now you can use partition_date as a datetime object in your asset computation     # ...
In this example, 
partition_date
is a 
datetime
object derived from the 
partition_key
string using the 
strptime
method with the appropriate format string "%Y-%m-%d".
g
@Scout The 
partition_key
portion of 
partition_date_str = context.partition_key
is not instantiating.
s
If you are encountering an issue where 
context.partition_key
is not providing the expected string value for the partition key, there are a few things you can check: 1. Ensure the Asset is Partitioned: Verify that your asset is correctly configured with a 
PartitionsDefinition
, such as 
DailyPartitionsDefinition
, and that the asset is being materialized for a specific partition or range of partitions. 2. Check the Materialization Context: When you materialize the asset, ensure that you are selecting a specific partition or range of partitions. The 
partition_key
is only available when the asset is being run in the context of a partitioned materialization. 3. Review the Asset Definition: Double-check the asset definition to ensure that the 
partitions_def
argument is correctly specified and that the asset is indeed partitioned. 4. Check for Errors: Look for any errors in the logs or console output that might indicate why the 
partition_key
is not being instantiated as expected. 5. Dagster Version: Ensure that you are using a version of Dagster that supports the features you are using. If you are using deprecated or outdated features, consider updating your code to use the latest recommended practices. If you have verified all of the above and are still facing issues, it could be helpful to see more of your code to provide further assistance. Specifically, it would be useful to see how you are defining and materializing your asset, as well as the context in which you are trying to access 
context.partition_key
.
g
@Scout When I run the above guidance, I encounter the following error: dagster._core.errors.DagsterInvariantViolationError: Cannot access partition_key for a partitioned run with a range of partitions. Call partition_key_range instead.
s
The error message you're encountering indicates that you are attempting to access 
context.partition_key
in a context where a range of partitions is being processed, rather than a single partition. When you materialize an asset over a range of partitions, Dagster expects you to use 
context.partition_key_range
to access the start and end keys of the range. However, if your intention is to process each day's data separately and store each day in its own partition, even when materializing over a range of dates, you should not need to use 
context.partition_key_range
. Instead, Dagster will call your asset's compute function once for each partition in the range, providing the 
context.partition_key
for each individual partition. Here's what you should do: 1. Ensure your asset is defined with daily partitions using 
DailyPartitionsDefinition
. 2. Materialize your asset for a range of dates through Dagit or a Dagster job. Dagster will automatically handle each partition separately. 3. In your asset's compute function, use 
context.partition_key
to access the partition key for the current run. Since Dagster calls the compute function once per partition, 
context.partition_key
will always refer to a single day's partition. Here's an example asset definition:
Copy code
python from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Access the partition key for the current run     partition_key = context.partition_key     # Convert the partition key string to a datetime object     partition_date = datetime.strptime(partition_key, "%Y-%m-%d")          # Since the API requires a range, use the same date for FromDate and ToDate     data = api_source.get_incident_records(partition_date, partition_date)          # Return the fetched data for this day     return data
When you run this asset, ensure that you are not trying to access 
context.partition_key_range
if you are processing individual partitions. If you follow this pattern, each partition (day) will be processed in a separate run, and you should not encounter the error about accessing 
partition_key
for a range of partitions.
g
@Scout I would like to avoid ranges, as this would put several days into a single out. Any alternatives in mind?
s
If you want to avoid using ranges and ensure that each day's data is processed and stored in its own partition, you don't need to do anything different from the standard approach of using 
DailyPartitionsDefinition
. Dagster will handle each partition separately by default. Here's what you need to ensure: 1. Define your asset with 
DailyPartitionsDefinition
: This will create a partition for each day. 2. Use 
context.partition_key
in your asset: This gives you the specific date for the partition being processed. 3. Materialize your asset over a range of dates: When you select a range of dates to materialize, Dagster will execute the asset once for each date within that range, treating each execution independently. Here's an example of how your asset should be defined:
Copy code
python from dagster import asset, DailyPartitionsDefinition from datetime import datetime  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Access the partition key for the current run, which is a string representing a single day     partition_key = context.partition_key     # Convert the partition key string to a datetime object     partition_date = datetime.strptime(partition_key, "%Y-%m-%d")          # Make an API call to fetch data for the given day     # Since the API requires a start and end date, use the same date for both     data = api_source.get_incident_records(partition_date, partition_date)          # Return the fetched data for this day     return data
When you materialize this asset for a range of dates, Dagster will invoke 
my_daily_asset
once for each date in the selected range. Each invocation will process data for its respective day, and if you have an IOManager configured, it will store the output for each day separately. By following this approach, you avoid using ranges, and each day's data is processed and stored in its own partition, as desired.
g
@Scout using the same partition date will not return anything from the API.
s
If using the same date for both 
FromDate
and 
ToDate
in your API request does not return any data, it's likely that the API expects the 
ToDate
to be exclusive, meaning the end date is not included in the data returned. In this case, you should set the 
ToDate
to be one day after the 
FromDate
to cover the full day's data. Here's how you can adjust your asset to accommodate this:
Copy code
python from datetime import datetime, timedelta from dagster import asset, DailyPartitionsDefinition  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_daily_asset(context):     # Access the partition key for the current run, which is a string representing a single day     partition_key = context.partition_key     # Convert the partition key string to a datetime object     partition_date = datetime.strptime(partition_key, "%Y-%m-%d")          # Since the API requires a range and might expect the end date to be exclusive,     # set the ToDate to one day after the FromDate     from_date_str = partition_date.strftime("%Y-%m-%d")     to_date_str = (partition_date + timedelta(days=1)).strftime("%Y-%m-%d")          # Make an API call to fetch data for the given day     data = api_source.get_incident_records(from_date_str, to_date_str)          # Return the fetched data for this day     return data
In this example, 
from_date_str
is the date of the partition, and 
to_date_str
is the next day. This setup should ensure that you get all the data for the entire day specified by 
partition_key
. When you materialize this asset for a range of dates, Dagster will invoke 
my_daily_asset
once for each date in the selected range, and each invocation will fetch data for its respective day.
g
@Scout Could this function be improved with logging or any other enhancement?
Copy code
def incidents(context) -> pd.DataFrame:
    """

    partition_date = context.partition_key

    from_date = datetime.strptime(partition_date, "%Y-%m-%d")
    to_date = from_date + timedelta(days=1)

    data = api_connector.get_incident_records(from_date, to_date)

    return pd.DataFrame(data)
s
Adding logging to your function can be very helpful for monitoring and debugging purposes. It allows you to track the asset's execution and understand the flow of data, especially when dealing with API calls that might fail or return unexpected results. You can also add metadata to your asset to provide more context in the Dagster UI. Here's an enhanced version of your function with logging and a docstring:
Copy code
python from datetime import datetime, timedelta from dagster import asset, DailyPartitionsDefinition import pandas as pd  @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def incidents(context) -> pd.DataFrame:     """     Fetches incident records for a given partition date and returns them as a DataFrame.      The function makes an API call to fetch incident records for the date specified by the     partition key. It expects the API to require a date range, so it sets the `to_date` as     one day after the `from_date` to ensure all incidents for the partition date are included.      Args:         context: The execution context for the asset, which provides access to the partition key.      Returns:         A DataFrame containing the incident records for the specified partition date.     """     partition_date = context.partition_key     <http://context.log.info|context.log.info>(f"Fetching incidents for partition date: {partition_date}")      from_date = datetime.strptime(partition_date, "%Y-%m-%d")     to_date = from_date + timedelta(days=1)      try:         data = api_connector.get_incident_records(from_date, to_date)         df = pd.DataFrame(data)         <http://context.log.info|context.log.info>(f"Fetched {len(df)} incidents for partition date: {partition_date}")         return df     except Exception as e:         context.log.error(f"An error occurred while fetching incidents: {e}")         raise
In this enhanced version: - A docstring has been added to explain the purpose of the function, its arguments, and its return value. - Logging statements have been added to log the start of the data fetching process and the number of incidents fetched. - A try-except block has been added to handle potential exceptions from the API call. If an exception occurs, it logs an error message and re-raises the exception to be handled by Dagster's execution engine. - The function now explicitly converts the API response to a 
pd.DataFrame
and logs the number of records fetched. These enhancements will make your asset more robust and easier to monitor and debug.
g
@Scout The API I need to connect to has a rate limit of 5. If I am connecting for one selected partition, how many backfills should the 
max_partitions_per_run
be set to?
s
If the API you are connecting to has a rate limit of 5 requests per minute and you are connecting for one selected partition, you should set the 
max_partitions_per_run
to 
1
. This ensures that each run of your asset will only process a single partition, thereby making only one API call per run. By doing this, you can control the rate at which you make API calls and avoid hitting the rate limit. Here's how you can set the 
max_partitions_per_run
in your asset definition:
Copy code
python
from dagster import asset, DailyPartitionsDefinition, BackfillPolicy

@asset(
    partitions_def=DailyPartitionsDefinition(start_date="2023-01-01"),
    backfill_policy=BackfillPolicy(max_partitions_per_run=1)
)
def incidents(context) -> pd.DataFrame:
    # Your asset logic here
    pass
In this example, the 
backfill_policy
is set to 
BackfillPolicy(max_partitions_per_run=1)
, which tells Dagster to process only one partition per run when performing a backfill. This way, you can schedule your asset runs in a manner that respects the API's rate limit. If you need to backfill multiple days, you can either manually trigger each run with a delay that respects the rate limit or set up a more sophisticated scheduling mechanism that automatically enforces the delay between runs. dynamic partition backfill range-selecti... button Preferred pattern for high-volume backfi... button How do I limit the concurrency for an as... button RFC: per-asset "backfill strategy" that ... button Thank you for your feedback!
g
@Scout Wouldn't 4 or 5 work?
s
If the API has a rate limit of 5 requests per minute and you are confident that your asset executions will not exceed this rate, you could potentially set 
max_partitions_per_run
to 4 or 5. This would allow you to process 4 or 5 partitions (days) in a single run, making the most of the rate limit without exceeding it. However, you should consider a few factors before deciding on the value: 1. Execution Time: If the time taken to process each partition is short and the asset executions for all partitions will complete within a minute, you could set 
max_partitions_per_run
closer to the rate limit. However, if processing takes longer, you risk hitting the rate limit as subsequent runs start. 2. Concurrency: If you have multiple assets or jobs running concurrently that also make API calls, you need to account for their API usage as well when setting 
max_partitions_per_run
. 3. Safety Margin: It's often wise to set a lower 
max_partitions_per_run
than the maximum allowed by the rate limit to provide a safety margin. This helps prevent accidental rate limit breaches due to unexpected delays or overlaps in processing. 4. API Policy: Some APIs have strict policies and penalties for exceeding rate limits. It's important to understand the API's policy and whether it allows for bursts of traffic up to the rate limit or expects the calls to be evenly distributed over time. Given these considerations, if you are confident that your asset processing will stay within the API's rate limit, you could set 
max_partitions_per_run
to 4 or 5. Here's an example with 
max_partitions_per_run
set to 4:
Copy code
python
from dagster import asset, DailyPartitionsDefinition, BackfillPolicy

@asset(
    partitions_def=DailyPartitionsDefinition(start_date="2023-01-01"),
    backfill_policy=BackfillPolicy(max_partitions_per_run=4)
)
def incidents(context) -> pd.DataFrame:
    # Your asset logic here
    pass
Remember to monitor your API usage closely when running your asset to ensure that you do not exceed the rate limit. If you encounter any issues, you may need to adjust 
max_partitions_per_run
accordingly. Thank you for your feedback!
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