DagsterDocs

Source code for dagster_aws.s3.io_manager

import pickle

from dagster import Field, IOManager, StringSource, check, io_manager
from dagster.utils import PICKLE_PROTOCOL


class PickledObjectS3IOManager(IOManager):
    def __init__(
        self,
        s3_bucket,
        s3_session,
        s3_prefix=None,
    ):
        self.bucket = check.str_param(s3_bucket, "s3_bucket")
        self.s3_prefix = check.str_param(s3_prefix, "s3_prefix")
        self.s3 = s3_session
        self.s3.head_bucket(Bucket=self.bucket)

    def _get_path(self, context):
        return "/".join([self.s3_prefix, "storage", *context.get_run_scoped_output_identifier()])

    def _rm_object(self, key):
        check.str_param(key, "key")
        check.param_invariant(len(key) > 0, "key")

        def delete_for_results(store, results):
            store.s3.delete_objects(
                Bucket=store.bucket,
                Delete={"Objects": [{"Key": result["Key"]} for result in results["Contents"]]},
            )

        if self._has_object(key):
            results = self.s3.list_objects_v2(Bucket=self.bucket, Prefix=key)
            delete_for_results(self, results)

            continuation = results["IsTruncated"]
            while continuation:
                continuation_token = results["NextContinuationToken"]
                results = self.s3.list_objects_v2(
                    Bucket=self.bucket, Prefix=key, ContinuationToken=continuation_token
                )
                delete_for_results(self, results)
                continuation = results["IsTruncated"]

    def _has_object(self, key):
        check.str_param(key, "key")
        check.param_invariant(len(key) > 0, "key")

        key_count = self.s3.list_objects_v2(Bucket=self.bucket, Prefix=key)["KeyCount"]
        return bool(key_count > 0)

    def _uri_for_key(self, key):
        check.str_param(key, "key")
        return "s3://" + self.bucket + "/" + "{key}".format(key=key)

    def load_input(self, context):
        key = self._get_path(context.upstream_output)
        context.log.debug(f"Loading S3 object from: {self._uri_for_key(key)}")
        obj = pickle.loads(self.s3.get_object(Bucket=self.bucket, Key=key)["Body"].read())

        return obj

    def handle_output(self, context, obj):
        key = self._get_path(context)
        context.log.debug(f"Writing S3 object at: {self._uri_for_key(key)}")

        if self._has_object(key):
            context.log.warning(f"Removing existing S3 key: {key}")
            self._rm_object(key)

        pickled_obj = pickle.dumps(obj, PICKLE_PROTOCOL)
        self.s3.put_object(Bucket=self.bucket, Key=key, Body=pickled_obj)


[docs]@io_manager( config_schema={ "s3_bucket": Field(StringSource), "s3_prefix": Field(StringSource, is_required=False, default_value="dagster"), }, required_resource_keys={"s3"}, ) def s3_pickle_io_manager(init_context): """Persistent IO manager using S3 for storage. Serializes objects via pickling. Suitable for objects storage for distributed executors, so long as each execution node has network connectivity and credentials for S3 and the backing bucket. Attach this resource definition to a :py:class:`~dagster.ModeDefinition` in order to make it available to your pipeline: .. code-block:: python pipeline_def = PipelineDefinition( mode_defs=[ ModeDefinition( resource_defs={'io_manager': s3_pickle_io_manager, "s3": s3_resource, ...}, ), ... ], ... ) You may configure this storage as follows: .. code-block:: YAML resources: io_manager: config: s3_bucket: my-cool-bucket s3_prefix: good/prefix-for-files- """ s3_session = init_context.resources.s3 s3_bucket = init_context.resource_config["s3_bucket"] s3_prefix = init_context.resource_config.get("s3_prefix") # s3_prefix is optional pickled_io_manager = PickledObjectS3IOManager(s3_bucket, s3_session, s3_prefix=s3_prefix) return pickled_io_manager