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#ask-ai
Title
# ask-ai
i

Ismael Rodrigues

02/20/2024, 4:59 PM
Is there a SnowflakePandasIOManager for Redshift?
s

Scout

02/20/2024, 4:59 PM
No, there is not a
SnowflakePandasIOManager
for Redshift. The
SnowflakePandasIOManager
is specifically designed for use with Snowflake, as indicated by its name and the configuration options provided in the documentation. For Redshift, you would need an I/O manager that is designed to work with the Redshift database service. As of the last update from the documentation, there is a mention of an issue discussing the addition of an I/O manager for Amazon Redshift, but it does not indicate the existence of a Redshift-specific I/O manager equivalent to
SnowflakePandasIOManager
.
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Ismael Rodrigues

02/20/2024, 5:00 PM
@Scout let me reprhase it. Is there any SnowflakePandasIOManager equivalent for using Redshift instead of SnowFlake? Something like a RedshiftPandasIOManager. That's what I meant
s

Scout

02/20/2024, 5:00 PM
As of the last update from the documentation, there is no direct equivalent to
SnowflakePandasIOManager
specifically for Amazon Redshift, such as a
RedshiftPandasIOManager
. The
SnowflakePandasIOManager
is tailored for Snowflake's ecosystem. For Redshift, you would typically use other tools or libraries that can interface with Redshift to handle data loading and unloading tasks. If you need to work with Pandas DataFrames and Redshift in Dagster, you may need to create a custom I/O manager or use existing resources and libraries that can interact with Redshift, such as
psycopg2
or
sqlalchemy-redshift
, to build your data loading and unloading logic. This would involve writing code to manage the connection to the Redshift database, execute queries, and handle the conversion between Pandas DataFrames and the data stored in Redshift.
i

Ismael Rodrigues

02/20/2024, 5:02 PM
@Scout Could you generate a snippet of code that creates me a RedshiftPandasIOManager? Or maybe a S3PandasIOManager?
s

Scout

02/20/2024, 5:02 PM
I'm sorry, but I cannot provide a code snippet for a
RedshiftPandasIOManager
or
S3PandasIOManager
that is not explicitly supported by the documentation. However, I can guide you on how you might approach creating a custom I/O manager for Redshift or S3. For Redshift, you would typically use a library like
psycopg2
or
sqlalchemy-redshift
to interact with the database. Your custom I/O manager would need to implement methods for loading data from a Redshift table into a Pandas DataFrame and for writing a Pandas DataFrame to a Redshift table. For S3, you would use the
boto3
library to interact with S3. An
S3PandasIOManager
would need to implement methods for reading a file from S3 into a Pandas DataFrame and for writing a Pandas DataFrame to a file in S3. Here's a high-level outline of what a custom I/O manager might look like:
Copy code
python from dagster import IOManager, io_manager import pandas as pd import boto3  @io_manager class S3PandasIOManager(IOManager):     def __init__(self, s3_bucket, s3_prefix):         self.s3_bucket = s3_bucket         self.s3_prefix = s3_prefix         self.s3_client = boto3.client('s3')      def handle_output(self, context, obj):         key = f"{self.s3_prefix}/{context.asset_key.path[-1]}.csv"         with self.s3_client.open(f"s3://{self.s3_bucket}/{key}", 'w') as f:             obj.to_csv(f)      def load_input(self, context):         key = f"{self.s3_prefix}/{context.upstream_output.asset_key.path[-1]}.csv"         with self.s3_client.open(f"s3://{self.s3_bucket}/{key}", 'r') as f:             return pd.read_csv(f)
Please note that this is a simplified example and does not include error handling, configuration, or other important aspects of a production-ready I/O manager. You would need to adapt this outline to fit your specific use case and ensure that it handles all necessary details, such as managing S3 credentials, handling different file formats, and so on. For a complete and working implementation, you would need to refer to the Dagster documentation and the APIs of the libraries you choose to use for interacting with Redshift and S3.
i

Ismael Rodrigues

02/20/2024, 5:08 PM
@Scout is there any IOManager for S3 in Dagster?
s

Scout

02/20/2024, 5:08 PM
Yes, Dagster provides an I/O manager for S3. The
S3PickleIOManager
is a persistent I/O manager that uses S3 for storage and serializes objects via pickling. It is suitable for object storage for distributed executors, as long as each execution node has network connectivity and credentials for S3 and the backing bucket. Here is an example usage of the
S3PickleIOManager
from the documentation:
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python from dagster_aws.s3 import S3PickleIOManager, S3Resource from dagster import asset, Definitions  @asset def asset1():     # create df ...     return df  @asset def asset2(asset1):     return asset1[:5]  defs = Definitions(     assets=[asset1, asset2],     resources={         "io_manager": S3PickleIOManager(             s3_resource=S3Resource(),             s3_bucket="my-cool-bucket",             s3_prefix="my-cool-prefix",         )     } )
This I/O manager assigns each op output to a unique filepath containing run ID, step key, and output name. It also assigns each asset to a single filesystem path, at
<base_dir>/<asset_key>
. Subsequent materializations of an asset will overwrite previous materializations of that asset. When configuring your job to use the
S3PickleIOManager
, you will need to provide the S3 bucket and optionally a prefix for the S3 bucket in your job's config. The resource uses
boto3
under the hood, and if you're accessing private buckets, you'll need to provide the appropriate AWS credentials. Please refer to the Dagster documentation for more details on how to configure and use the
S3PickleIOManager
in your Dagster pipelines.
i

Ismael Rodrigues

02/20/2024, 5:47 PM
@Scout write a ConfigurableResource whichs calls a get API and returns its data
s

Scout

02/20/2024, 5:47 PM
Creating a
ConfigurableResource
in Dagster that calls a GET API and returns its data involves defining a Python class that inherits from
ConfigurableResource
. This class will need to define a method that performs the GET request and returns the data, typically using a library like
requests
. Here's an example of how you might define such a resource:
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python from dagster import ConfigurableResource, InitResourceContext, Field import requests  class HttpApiResource(ConfigurableResource):     # Define the configuration schema for the resource     base_url: str = Field(         str,         description="The base URL of the API endpoint"     )     timeout: int = Field(         30,         description="The timeout for the API request, in seconds"     )      def __init__(self, base_url: str, timeout: int):         self.base_url = base_url         self.timeout = timeout      def get_data(self, endpoint: str):         """         Perform a GET request to the specified endpoint and return the response data.                  :param endpoint: The API endpoint to append to the base URL for the GET request.         :return: The data returned by the API.         """         response = requests.get(f"{self.base_url}/{endpoint}", timeout=self.timeout)         response.raise_for_status()  # Raise an exception for HTTP error responses         return response.json()  # Example usage in an asset @asset(required_resource_keys={"http_api"}) def fetch_data_from_api(context):     api_data = context.resources.http_api.get_data("data_endpoint")     return api_data  # Example configuration for the resource resource_defs = {     "http_api": HttpApiResource.configured({"base_url": "<https://my-api.com>", "timeout": 60}) }
In this example,
HttpApiResource
is a
ConfigurableResource
that has two configuration fields:
base_url
and
timeout
. The
get_data
method performs the GET request using the
requests
library and returns the JSON response data. The
fetch_data_from_api
asset uses this resource to fetch data from the API. The
resource_defs
dictionary shows how you might configure the
HttpApiResource
with a specific base URL and timeout. When using this resource in a Dagster job or asset, you would provide the necessary configuration in your Dagster repository configuration file or when launching the job. Please note that this is a simplified example and does not include error handling or authentication logic that might be necessary for interacting with real-world APIs. You would need to extend this example to handle such cases as needed.
i

Ismael Rodrigues

02/21/2024, 2:08 AM
@Scout
Copy code
@asset(
    compute_kind="api",
    partitions_def=daily_partitions,
    metadata={"partition_expr": "created_at"},
    backfill_policy=BackfillPolicy.single_run()
)
def users(context, api: RawDataAPI):
    """A table containing all users data"""
    # during a backfill the partition range will span multiple hours
    # during a single run the partition range will be for a single hour
    first_partition, last_partition = context.asset_partitions_time_window_for_output()
    partition_seq = _daily_partition_seq(first_partition, last_partition)
    all_users = []
    for partition in partition_seq:
        resp = api.get_users(partition)
        users = pd.read_json(resp.json())
        all_users.append(users)

    return pd.concat(all_users)
This code could be simplified using OPs instead of a single asset, am I right? I mean, for every partition key you would have a DynamicOutput and process things in parallel would be faster than with this for loop
s

Scout

02/21/2024, 2:08 AM
Yes, you are correct that the code could potentially be simplified and made more efficient by using ops with
DynamicOutput
in Dagster. By doing so, you could parallelize the processing of each partition, which might lead to performance improvements, especially if the API calls and subsequent data processing are independent and can be executed concurrently. Here's a conceptual example of how you might refactor the code to use ops with `DynamicOutput`:
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python from dagster import op, Out, DynamicOut, DynamicOutput, job, AssetIn, AssetOut import pandas as pd  @op(out=DynamicOut()) def partition_keys_op(context):     first_partition, last_partition = context.asset_partitions_time_window_for_output()     partition_seq = _daily_partition_seq(first_partition, last_partition)     for partition in partition_seq:         yield DynamicOutput(partition, mapping_key=partition.strftime("%Y%m%d"))  @op(ins={"partition": AssetIn()}, out=Out()) def fetch_users_for_partition(context, partition, api: RawDataAPI):     resp = api.get_users(partition)     users = pd.read_json(resp.json())     return users  @job def fetch_users_job():     partition_seq = partition_keys_op()     users = partition_seq.map(fetch_users_for_partition)     # You can define how to combine the users DataFrames here if needed
In this refactored code: - The
partition_keys_op
op generates a sequence of partition keys and yields each one as a
DynamicOutput
. Each partition key is associated with a unique
mapping_key
that is used to parallelize the downstream processing. - The
fetch_users_for_partition
op takes a single partition key as input and fetches the corresponding users data for that partition. This op is mapped over the dynamic output of
partition_keys_op
, allowing it to run in parallel for each partition key. - The
fetch_users_job
job orchestrates the execution of these ops. By using this approach, each partition's data fetch and processing can happen in parallel, which could lead to faster overall execution compared to a single for loop that processes each partition sequentially. Please note that this is a high-level example and assumes that the
api.get_users
method and
_daily_partition_seq
function are defined elsewhere in your code. You would also need to handle combining the individual DataFrames into a single DataFrame if that is required for your use case.
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