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#ask-ai
Title
# ask-ai
g

Gabriel M

02/21/2024, 9:12 PM
@Scout Can you provide an example of using the built-in IOManager class to pass a JSON object from memory and store it into an s3 bucket?
s

Scout

02/21/2024, 9:12 PM
Certainly! To store a JSON object in an S3 bucket using Dagster's built-in
IOManager
, you would typically need to create a custom IO manager that handles the serialization of the JSON object to a string and then stores it in S3. However, the documentation provides an example of using the
s3_pickle_io_manager
for storing objects in S3, which uses pickling for serialization. Since JSON is not handled by default, you would need to customize your own IO manager. Here's a conceptual example based on the documentation's guidance on defining an IO manager. Please note that this is not a direct code example from the documentation, but an illustration of how you might extend the
IOManager
to handle JSON objects:
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python from dagster import IOManager, io_manager, Out, op, job from dagster_aws.s3 import S3Resource import boto3 import json  class JsonS3IOManager(IOManager):     def __init__(self, s3_bucket, s3_prefix):         self.s3_bucket = s3_bucket         self.s3_prefix = s3_prefix         self.s3 = boto3.client('s3')      def handle_output(self, context, obj):         key = f"{self.s3_prefix}/{context.step_key}/{context.name}"         self.s3.put_object(Bucket=self.s3_bucket, Key=key, Body=json.dumps(obj))      def load_input(self, context):         key = f"{self.s3_prefix}/{context.upstream_output.step_key}/{context.upstream_output.name}"         obj = self.s3.get_object(Bucket=self.s3_bucket, Key=key)         return json.loads(obj['Body'].read())  @io_manager def json_s3_io_manager(init_context):     s3_bucket = init_context.resource_config['s3_bucket']     s3_prefix = init_context.resource_config['s3_prefix']     return JsonS3IOManager(s3_bucket=s3_bucket, s3_prefix=s3_prefix)  @op(out=Out(io_manager_key="json_s3_io")) def generate_data():     return {"key": "value"}  @job(resource_defs={"json_s3_io": json_s3_io_manager, "s3": S3Resource}) def my_job():     generate_data()  # Job configuration run_config = {     "resources": {         "json_s3_io": {             "config": {                 "s3_bucket": "my-cool-bucket",                 "s3_prefix": "my/prefix"             }         }     } }
In this example,
JsonS3IOManager
is a custom IO manager that serializes and deserializes JSON objects to and from S3. The
generate_data
op outputs a JSON object, which is then handled by the
JsonS3IOManager
. The
my_job
job defines the resources needed, including the custom IO manager and the S3 resource. Please adapt this conceptual example to fit your specific use case and environment. Remember to handle any necessary permissions and authentication for accessing your S3 bucket.
g

Gabriel M

02/21/2024, 11:43 PM
@Scout Can I create the JsonS3IOManager in my io_managers.py without importing S3Resource from dagster_aws.s3, and instantiate it in the resources.py file instead?
s

Scout

02/21/2024, 11:43 PM
Yes, you can define the
JsonS3IOManager
in your
io_managers.py
file without directly importing
S3Resource
from
dagster_aws.s3
. Instead, you can rely on the
S3Resource
being provided to your IO manager at runtime through the resource configuration in your job definition. Here's how you might structure your code: In `io_managers.py`:
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python from dagster import IOManager, io_manager import boto3 import json  class JsonS3IOManager(IOManager):     def __init__(self, s3):         self.s3 = s3      def handle_output(self, context, obj):         s3_bucket = context.resource_config['s3_bucket']         s3_prefix = context.resource_config['s3_prefix']         key = f"{s3_prefix}/{context.step_key}/{context.name}"         self.s3.put_object(Bucket=s3_bucket, Key=key, Body=json.dumps(obj))      def load_input(self, context):         s3_bucket = context.resource_config['s3_bucket']         s3_prefix = context.resource_config['s3_prefix']         key = f"{s3_prefix}/{context.upstream_output.step_key}/{context.upstream_output.name}"         obj = self.s3.get_object(Bucket=s3_bucket, Key=key)         return json.loads(obj['Body'].read())  @io_manager(required_resource_keys={"s3"}) def json_s3_io_manager(init_context):     return JsonS3IOManager(s3=init_context.resources.s3)
In `resources.py`:
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python from dagster_aws.s3 import S3Resource  # Define your S3 resource configuration here s3_resource = S3Resource(...)
In your job definition:
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python from dagster import job from .io_managers import json_s3_io_manager from .resources import s3_resource  @job(resource_defs={"json_s3_io": json_s3_io_manager, "s3": s3_resource}) def my_job():     # Define your job's ops here     pass
In this setup, the
JsonS3IOManager
is defined without directly importing
S3Resource
, and it expects an S3 client to be passed during initialization. The
json_s3_io_manager
factory function is annotated with
@io_manager
and specifies that it requires the
s3
resource key. When you define your job, you provide the
s3_resource
that you defined in
resources.py
, and the
json_s3_io_manager
will use this resource to interact with S3. This approach allows you to keep your IO manager definition separate from the specifics of the S3 resource configuration, which can be beneficial for testing and modularity.
g

Gabriel M

02/21/2024, 11:54 PM
@Scout Since I would no longer be importing S3Resource library, how would the
get_object
and
put_object
methods instantiate?
s

Scout

02/21/2024, 11:54 PM
Apologies for any confusion. Even though you are not directly importing
S3Resource
in your
io_managers.py
, you still need to ensure that the
S3Resource
is provided to your IO manager at runtime. The
S3Resource
is a wrapper around the
boto3
client, and it is responsible for providing the
get_object
and
put_object
methods that interact with AWS S3. Here's how it works: 1. You define an
S3Resource
in your
resources.py
or wherever you configure your resources. This resource is an instance of
S3Resource
from the
dagster_aws.s3
library, which encapsulates the
boto3
S3 client. 2. In your job definition, you include the
S3Resource
in the
resource_defs
mapping. This makes the
S3Resource
available to all ops within the job that declare a resource requirement for
s3
. 3. Your custom IO manager (
JsonS3IOManager
) declares a dependency on the
s3
resource by using the
required_resource_keys
parameter in the
@io_manager
decorator. 4. When the job runs, Dagster will inject the
S3Resource
into your IO manager based on the
required_resource_keys
. Your IO manager can then use this resource to call
get_object
and
put_object
. Here's an example of how you might define the
S3Resource
in your `resources.py`:
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python from dagster_aws.s3 import s3_resource  # You can configure the S3Resource with any required settings, such as region, profile, etc. configured_s3_resource = s3_resource.configured({'region_name': 'us-west-2'})
And in your job definition, you would include this resource:
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python from dagster import job from .io_managers import json_s3_io_manager from .resources import configured_s3_resource  @job(resource_defs={"json_s3_io": json_s3_io_manager, "s3": configured_s3_resource}) def my_job():     # Define your job's ops here     pass
This way, your
JsonS3IOManager
can use the
s3
resource to interact with S3 without directly importing
S3Resource
in the
io_managers.py
file. The
S3Resource
provides the
boto3
client, which has the
get_object
and
put_object
methods needed to interact with S3.
g

Gabriel M

02/22/2024, 12:08 AM
@Scout What if I am not initially configuring a job and am materializing my asset manually via the UI?
s

Scout

02/22/2024, 12:08 AM
When you materialize an asset manually via the Dagster UI, the system uses the configuration provided in the UI to execute the materialization. If you have set up your
JsonS3IOManager
to depend on an
s3
resource, you need to ensure that the
s3
resource is properly configured in the UI when you trigger the materialization. Here's what happens when you materialize an asset manually via the UI: 1. You navigate to the asset in the Dagster UI and click the "Materialize" button. 2. The UI prompts you to provide a run configuration. This is where you specify the configuration for any resources that your asset depends on, including the
s3
resource. 3. You enter the necessary configuration for the
s3
resource, such as the S3 bucket name, AWS access keys, region, and any other required settings. 4. When you submit the materialization request, Dagster uses the provided configuration to instantiate the
s3
resource and passes it to your
JsonS3IOManager
. 5. Your
JsonS3IOManager
uses the
s3
resource to call the
get_object
and
put_object
methods and interact with S3 as needed. It's important to note that the configuration you provide in the UI must match the expectations of your
JsonS3IOManager
. If your IO manager expects certain configuration fields (like
s3_bucket
and
s3_prefix
), you need to provide those fields in the run configuration JSON. Here's an example of what the run configuration might look like in the UI:
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json {   "resources": {     "s3": {       "config": {         "s3_bucket": "my-cool-bucket",         "s3_prefix": "my/prefix",         "region_name": "us-west-2",         "access_key_id": "YOUR_ACCESS_KEY_ID",         "secret_access_key": "YOUR_SECRET_ACCESS_KEY"       }     }   } }
This configuration ensures that when you materialize the asset, the
JsonS3IOManager
has the necessary
s3
resource with the correct settings to perform the S3 operations.
g

Gabriel M

02/22/2024, 12:35 AM
@Scout Can I pass the s3_bucket, s3_prefix, and s3_session through my resources.py instead of the job?
s

Scout

02/22/2024, 12:35 AM
Yes, you can configure your
s3
resource with the necessary settings such as
s3_bucket
,
s3_prefix
, and the S3 session (client) in your
resources.py
file. This way, you define the configuration for the S3 resource in one place, and you can use this resource across multiple jobs or assets without having to specify the configuration each time. Here's an example of how you might define the
s3
resource in your `resources.py`:
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python from dagster_aws.s3 import S3Resource import boto3  def create_s3_session():     # Create your boto3 session or client here, potentially with specific credentials     return boto3.client(         's3',         aws_access_key_id='YOUR_ACCESS_KEY_ID',         aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',         region_name='YOUR_REGION'     )  s3_resource = S3Resource(     s3_session=create_s3_session(),     s3_bucket='my-cool-bucket',     s3_prefix='my/prefix' )
In this example,
create_s3_session
is a function that creates and returns a boto3 S3 client with the specified credentials and region. The
s3_resource
is then an instance of
S3Resource
that includes the S3 session, bucket, and prefix. When you define your job, you can reference this `s3_resource`:
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python from dagster import job from .io_managers import json_s3_io_manager from .resources import s3_resource  @job(resource_defs={"json_s3_io": json_s3_io_manager, "s3": s3_resource}) def my_job():     # Define your job's ops here     pass
By doing this, you don't need to specify the
s3_bucket
,
s3_prefix
, or S3 session in the job's run configuration. Instead, the
s3
resource is pre-configured in
resources.py
, and you can use it directly in your IO manager or anywhere else in your job where the
s3
resource is required. Keep in mind that if you need to override these settings for specific jobs or environments, you can still do so in the job's run configuration. The approach shown here is for when you have a common configuration that you want to apply across multiple jobs or assets.
g

Gabriel M

02/22/2024, 12:40 AM
@Scout Can you pull it all together into one refreshed example for the io_manager.py, resources.py, and jobs.py?
s

Scout

02/22/2024, 12:40 AM
Certainly! Below is an example of how you might structure your
io_manager.py
,
resources.py
, and
jobs.py
files to use a custom
JsonS3IOManager
with an S3 resource that is configured in
resources.py
. `io_manager.py`:
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python
from dagster import IOManager, io_manager, ResourceDefinition
import json

class JsonS3IOManager(IOManager):
    def __init__(self, s3_session, s3_bucket, s3_prefix):
        self.s3_session = s3_session
        self.s3_bucket = s3_bucket
        self.s3_prefix = s3_prefix

    def handle_output(self, context, obj):
        key = f"{self.s3_prefix}/{context.step_key}/{context.name}"
        self.s3_session.put_object(Bucket=self.s3_bucket, Key=key, Body=json.dumps(obj))

    def load_input(self, context):
        key = f"{self.s3_prefix}/{context.upstream_output.step_key}/{context.upstream_output.name}"
        obj = self.s3_session.get_object(Bucket=self.s3_bucket, Key=key)
        return json.loads(obj['Body'].read())

@io_manager(required_resource_keys={'s3'})
def json_s3_io_manager(init_context):
    s3_session = init_context.resources.s3.s3_session
    s3_bucket = init_context.resources.s3.s3_bucket
    s3_prefix = init_context.resources.s3.s3_prefix
    return JsonS3IOManager(s3_session=s3_session, s3_bucket=s3_bucket, s3_prefix=s3_prefix)
`resources.py`:
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python
from dagster_aws.s3 import S3Resource
import boto3

def create_s3_session():
    # Create your boto3 session or client here, potentially with specific credentials
    return boto3.client(
        's3',
        aws_access_key_id='YOUR_ACCESS_KEY_ID',
        aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',
        region_name='YOUR_REGION'
    )

# Define your S3 resource with the session, bucket, and prefix
s3_resource = S3Resource(
    s3_session=create_s3_session(),
    s3_bucket='my-cool-bucket',
    s3_prefix='my/prefix'
)
`jobs.py`:
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python
from dagster import job, op
from .io_managers import json_s3_io_manager
from .resources import s3_resource

@op
def generate_data():
    # Replace with your logic to generate data
    return {"key": "value"}

@job(resource_defs={"json_s3_io": json_s3_io_manager, "s3": s3_resource})
def my_job():
    generate_data()
In this setup, the
JsonS3IOManager
is defined in
io_manager.py
and requires the
s3
resource key. The
s3
resource is configured in
resources.py
with the necessary S3 session, bucket, and prefix. Finally, in
jobs.py
, the
my_job
job uses the
json_s3_io_manager
and the
s3
resource to handle the output of the
generate_data
op. This structure allows you to define your S3 configuration in one place (
resources.py
) and use it across multiple jobs or assets. It also keeps your IO manager definition separate from the specifics of the S3 resource configuration, which can be
beneficial for testing and modularity.
To use your custom
S3ClientResource
class as a boto3 wrapper in
resources.py
, you would define the resource by subclassing
ConfigurableResource
and providing the necessary attributes for the AWS region and profile name. Then, you would implement a method to create the S3 client. Here's how you might update your
resources.py
to include the `S3ClientResource`:
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python from dagster import ResourceDefinition, Field from typing import Optional import boto3  class S3ClientResource:     def __init__(self, region_name: str, aws_profile_name: Optional[str] = None):         self.region_name = region_name         self.aws_profile_name = aws_profile_name      def create_s3_client(self):         session = boto3.Session(profile_name=self.aws_profile_name) if self.aws_profile_name else boto3.Session()         return session.client('s3', region_name=self.region_name)  def s3_client_resource(config):     return S3ClientResource(         region_name=config['region_name'],         aws_profile_name=config.get('aws_profile_name')     )  s3_client = ResourceDefinition(     resource_fn=s3_client_resource,     config_schema={         'region_name': Field(str, is_required=True, description='AWS region name'),         'aws_profile_name': Field(str, is_required=False, description='AWS profile name')     } )
In this example,
S3ClientResource
is a simple class that takes a region name and an optional AWS profile name. The
create_s3_client
method creates a boto3 client using the provided configuration. The
s3_client_resource
function is a factory function that creates an instance of
S3ClientResource
based on the provided config. Finally,
s3_client
is a
ResourceDefinition
that specifies the configuration schema and the factory function. You would then use this
s3_client
resource in your job definitions, similar to how you would use any other Dagster resource. Here's an example of how you might use it in a job:
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python from dagster import job from .io_managers import json_s3_io_manager from .resources import s3_client  @job(resource_defs={"json_s3_io": json_s3_io_manager, "s3_client": s3_client}) def my_job():     # Define your job's ops here     pass
In this job definition, the
s3_client
resource is provided under the key
"s3_client"
, which you would then reference in your IO manager or ops as needed.