Slackbot
02/21/2024, 11:24 PMScout
02/21/2024, 11:25 PMimf_metadata
. Specifically, the error is a DagsterExecutionStepExecutionError
, which is a type of error that occurs when there is a problem executing an op within a Dagster pipeline. The root cause of the error seems to be a TypeError
that was raised because an unexpected keyword argument name
was passed to the __init__
method of some object. This is indicated by the line in the stack trace: TypeError: __init__() got an unexpected keyword argument 'name'
This error occurred within the fetch_imf_metadata
function, which is called by the imf_metadata
op. The fetch_imf_metadata
function appears to be attempting to return an Output
object, but it seems that the Output
constructor was called with an incorrect argument. To resolve this issue, you should check the definition of the Output
object being returned by the fetch_imf_metadata
function. Ensure that you are only using the expected arguments when constructing the Output
object. If name
is not a valid argument for the Output
constructor, you should remove it or replace it with the correct argument. If you need further assistance with debugging, you can refer to the documentation for the Output
class to understand the expected arguments, or you can provide more context or code snippets for a more detailed analysis.Peter Mburu
02/21/2024, 11:27 PMScout
02/21/2024, 11:27 PMOutput
constructor being called with an unexpected keyword argument name
. In Dagster, the Output
object does not accept a name
argument directly in its constructor. Instead, the name of an output is typically specified by the out
parameter in the @op
decorator or the outs
parameter in the @multi_asset
decorator.
In your fetch_imf_metadata
function, you are trying to return an Output
object with a name
argument, which is causing the error. To fix this, you should remove the name
argument from the Output
constructor.
Here's how you can modify the fetch_imf_metadata
function to fix the error:
python
def fetch_imf_metadata(config: IMFConfig, group_indicator_code: str = "CL_INDICATOR_CPI") -> Output[pd.DataFrame]:
# ... existing code ...
code_to_asset_name_map = {
"CL_INDICATOR_CPI": "cpi",
"CL_INDICATOR_IFS": "gpd_labor",
"CL_INDICATOR_PCPS": "price"
}
asset_name = f"{code_to_asset_name_map.get(group_indicator_code)}_metadata"
return Output(
df,
metadata={
"num_of_metadata": len(df),
"preview": MetadataValue.md(df.head(config.preview_rows).to_markdown()),
}
)
Notice that I've removed the name
argument from the Output
constructor and instead created a variable asset_name
that you can use elsewhere if needed.
For the imf_metadata
function, you are using the @multi_asset
decorator to define multiple outputs. If you want to yield outputs with specific names, you should yield them with the corresponding output name that you have defined in the outs
parameter of the @multi_asset
decorator.
Here's an example of how you might yield named outputs in the imf_metadata
function:
python
@multi_asset(outs={
"cpi_metadata": AssetOut(is_required=False),
"price_metadata": AssetOut(is_required=False),
"gpd_labor_metadata": AssetOut(is_required=False),
},
can_subset=True,)
def imf_metadata(config: IMFConfig):
# ... existing code ...
for code in ["CL_INDICATOR_CPI", "CL_INDICATOR_IFS", "CL_INDICATOR_PCPS"]:
output_df = fetch_imf_metadata(config, code).value
asset_name = f"{code_to_asset_name_map.get(code)}_metadata"
yield Output(output_df, output_name=asset_name)
In this example, output_name
should match one of the keys defined in the outs
parameter of the @multi_asset
decorator. Make sure that the output_name
you yield corresponds to the names you've defined for your assets. Pushing REST-API data to Google Sheets w... button Using Dagster with Google BigQuery | Dag... button Inter group asset dependencies not worki... button Dagster Docs button Thank you for your feedback!Peter Mburu
02/21/2024, 11:35 PMScout
02/21/2024, 11:35 PMremote_imf_data
asset function, we can follow a few steps to improve the readability and maintainability of the code. We'll break down the large function into smaller, more focused functions, and we'll also remove any unnecessary global state or side effects to make the code more functional in style.
Here's a refactored version of the code:
```python
from typing import Optional
import datetime
import pandas as pd
import requests
from dagster import asset, Output, MetadataValue
# Assuming the existence of the following functions and variables:
# - bulk_limiter
# - logger
# - http
# - cpi_indicator_names_remap
# - Labor_indicator_names_map
# - GDP_indicator_names_map
# - GetBaseUrl()
def filter_documents_by_type(
documents: QuerySet, data_series: QuerySet, document_type: str
) -> Tuple[QuerySet, QuerySet]:
if document_type in ["PriceIndex", "MarketProduct"]:
documents = documents.filter(document_type=document_type)
data_series = data_series.select_related("_country").filter(indicator=document_type)
elif document_type in ["Labor Statistics", "Economic Statistics"]:
documents = documents.filter(document_type__name=document_type)
indicators = (
GDP_indicator_names_map.keys()
if document_type == "Economic Statistics"
else Labor_indicator_names_map.keys()
)
data_series = data_series.select_related("_country").filter(indicator__in=indicators)
return documents, data_series
def get_url(
series: QuerySet, imf_metadata: pd.DataFrame, last_collection_date: Optional[datetime.date], document_type: str, domain: str, frequency: str
) -> Optional[str]:
# ... existing get_url function logic ...
pass
def fetch_data(url: str) -> requests.Response:
# ... existing fetch_data function logic ...
pass
def extract_data(
result: requests.Response, series: QuerySet, datasourcedocument: QuerySet, document_type: str
) -> pd.DataFrame:
# ... existing extract_data function logic ...
pass
def fetch_remote_data(
get_imf_data_series: QuerySet, imf_metadata: pd.DataFrame, get_imf_datasource_documents: QuerySet, document_type: str, domain: str, frequency: str
) -> pd.DataFrame:
# ... existing fetch_remote_data function logic ...
pass
@asset
def remote_imf_data(
config: IMFConfig, imf_metadata, get_imf_datasource_documents, get_imf_data_series
) -> Output[pd.DataFrame]:
"""
Get a list of all data matching metadata fetched in FetchMetadata task.
"""
document_type = config.document_type
domain = config.domain
frequency = config.frequency
get_imf_datasource_documents, get_imf_data_series = filter_documents_by_type(
get_imf_datasource_documents, get_imf_data_series, document_typeScout
02/21/2024, 11:36 PMfilter_documents_by_type
. 2. The get_url
, fetch_data
, extract_data
, and fetch_remote_data
functions have been defined outside of the remote_imf_data
function to separate concerns and improve testability. 3. We've removed any global state or side effects from within the functions, making them pure functions that depend only on their input arguments. 4. The remote_imf_data
function now focuses on orchestrating the workflow by calling the helper functions with the appropriate arguments. Please note that this refactoring assumes that the QuerySet
type and other domain-specific functions and variables (like bulk_limiter
, logger
, http
, etc.) are defined elsewhere in your codebase. You'll need to ensure that these are correctly imported or defined for the refactored code to work. Additionally, the get_url
and extract_data
functions will need to be updated with the appropriate logic from the original code, as indicated by the placeholder comments.Peter Mburu
02/21/2024, 11:40 PMScout
02/21/2024, 11:40 PMOutput
object from an op or asset, you can attach metadata to it, which can then be accessed in different ways depending on your needs.
Here's an example of how you might attach metadata to an `Output`:
python
from dagster import op, Output, MetadataValue
@op
def my_op(context):
# ... perform some computation ...
result = compute_something()
yield Output(
result,
metadata={
"my_key": "my_value",
"dashboard_url": MetadataValue.url("<http://mycoolsite.com/my_dashboard>"),
"num_rows": len(result),
},
)
In this example, my_key
, dashboard_url
, and num_rows
are metadata entries attached to the output of my_op
.
To access this metadata downstream, you can use the context
object within an op or asset. However, as of my knowledge cutoff date in early 2023, there is no direct way to access the metadata of an upstream op's output from within the body of a downstream op. Metadata is typically used for display purposes in tools like Dagit, Dagster's web interface, where it can be viewed as part of the run logs or asset catalog.
If you need to pass information from one op to another, you would typically do so by passing the data itself as an output of the upstream op and as an input to the downstream op. If you need to pass metadata-like information, you would include it as part of the data structure being passed between ops.
For more advanced use cases, such as accessing metadata for asset materializations or events, you might need to interact with Dagster's instance APIs to query the event log or asset catalog. This would typically be done outside of the execution of a pipeline, such as in a monitoring script or a custom tool built on top of Dagster.
Please note that Dagster is an actively developed project, and new features or APIs may have been introduced after my knowledge cutoff date. For the most up-to-date information, you should consult the latest Dagster documentation or reach out to the Dagster community for assistance. Request: HTML metadata type for events. button Improving visibility into data operation... button Output metadata access and consolidation button Make runtime output metadata available t... button Thank you for your feedback!Peter Mburu
02/22/2024, 12:24 AMScout
02/22/2024, 12:24 AM@multi_asset
decorator, you can specify dependencies on other assets using the internal_asset_deps
parameter. This parameter maps each output asset to a set of `AssetKey`s that it depends on. When you execute the function decorated with @multi_asset
, Dagster ensures that the upstream assets specified in internal_asset_deps
are available before executing the function.
To access the output of upstream assets within the body of a @multi_asset
function, you should define parameters in the function that correspond to the names of the upstream assets. Dagster will automatically pass the values of these upstream assets into the function when it is executed.
Here's an example of how you might define a @multi_asset
function that depends on and accesses the outputs of upstream assets:
python
from dagster import multi_asset, AssetOut, AssetKey
@multi_asset(
outs={
"cpi_remote_data": AssetOut(is_required=False),
"price_remote_data": AssetOut(is_required=False),
"gpd_remote_data": AssetOut(is_required=False),
"labor_remote_data": AssetOut(is_required=False),
},
internal_asset_deps={
"cpi_remote_data": {AssetKey("cpi_metadata")},
"price_remote_data": {AssetKey("price_metadata")},
"gpd_remote_data": {AssetKey("gpd_labor_metadata")},
"labor_remote_data": {AssetKey("gpd_labor_metadata")},
},
can_subset=True,
)
def remote_imf_data(config: IMFConfig, cpi_metadata, price_metadata, gpd_labor_metadata):
# Here you can access the outputs of the upstream assets
# cpi_metadata, price_metadata, and gpd_labor_metadata are the outputs of the upstream assets
# You can now use these outputs to perform further computations or transformations
# Perform some computation with the upstream data
cpi_data = process_cpi_data(cpi_metadata)
price_data = process_price_data(price_metadata)
gpd_data = process_gpd_data(gpd_labor_metadata)
labor_data = process_labor_data(gpd_labor_metadata)
# Yield the results as outputs of this multi_asset
yield cpi_data, "cpi_remote_data"
yield price_data, "price_remote_data"
yield gpd_data, "gpd_remote_data"
yield labor_data, "labor_remote_data"
In this example, cpi_metadata
, price_metadata
, and gpd_labor_metadata
are the parameters that correspond to the upstream assets. The function remote_imf_data
can use these parameters to access the data produced by the upstream assets. The yield
statements then output the processed data, associating each output with the corresponding asset name defined in the outs
parameter.
Please note that the actual processing functions (process_cpi_data
, process_price_data
, etc.) are not defined in this example and would needScout
02/22/2024, 12:25 AM