From cc89b00373c3a7bf0fdeb94000f7b41e9fd3a605 Mon Sep 17 00:00:00 2001 From: Luisa Date: Tue, 26 May 2026 17:44:53 -0600 Subject: [PATCH 1/6] add more tests --- .../constructs/instrument_lambdas.py | 60 +- .../pipeline_lambdas/reprocessing_proxy.py | 27 + sds_data_manager/orchestration/dependency.py | 41 +- sds_data_manager/orchestration/imap_job.py | 614 ++++++++++-------- .../orchestration/reprocessing.py | 128 ++++ sds_data_manager/utils/stackbuilder.py | 11 +- .../test_instrument_lambda_construct.py | 2 +- tests/orchestration/test_reprocessing.py | 157 +++++ 8 files changed, 769 insertions(+), 271 deletions(-) create mode 100644 sds_data_manager/lambda_code/SDSCode/pipeline_lambdas/reprocessing_proxy.py create mode 100644 sds_data_manager/orchestration/reprocessing.py create mode 100644 tests/orchestration/test_reprocessing.py diff --git a/sds_data_manager/constructs/instrument_lambdas.py b/sds_data_manager/constructs/instrument_lambdas.py index 1e276d337..f363c7f4c 100644 --- a/sds_data_manager/constructs/instrument_lambdas.py +++ b/sds_data_manager/constructs/instrument_lambdas.py @@ -134,17 +134,73 @@ def __init__( for q in sqs_queues: self.instrument_lambda.add_event_source(SqsEventSource(q)) + # In addition to sending events to batch starter, send them to a sqs that + # dagster can poll from. + # TODO eventually we only want the reprocessing api to send messages to the sqs + # and not to batch starter. Dagster should be fully responsible for + # reprocessing eventually. + # Create a dead letter queue to save messages that could not be processed. + # This DLQ just saves the messages and doesn't do anything with them. + self.dead_letter_queue = sqs.Queue( + self, + "ReprocessingDeadLetterQueue", + queue_name="reprocessing_dead_letter_queue.fifo", + encryption=sqs.QueueEncryption.UNENCRYPTED, + fifo=True, + ) + + self.reprocessing_queue = sqs.Queue( + self, + "ReprocessQueue", + queue_name="ReprocessQueue.fifo", + # This timeout determines how long the queue waits for processing. + visibility_timeout=Duration.seconds(300), + fifo=True, + # Removes messages with identical content. + content_based_deduplication=True, + # The dead letter queue will take messages that failed retry. + dead_letter_queue=sqs.DeadLetterQueue( + max_receive_count=1, queue=self.dead_letter_queue + ), + ) + self.reprocessing_sqs_url = self.reprocessing_queue.queue_url + # Create a lambda that the API can trigger to send messages to the reprocessing + # queue. This is necessary because HTTP API Gateway v2's parameter mapping + # expression language is too limited to forward query string parameters as an + # SQS message body. This lambda acts as a proxy that converts the + # query string parameters to a JSON message and sends it to the queue. + self.reprocessing_proxy_lambda = lambda_.Function( + self, + "reprocessing-proxy", + function_name="reprocessing-proxy", + code=code, + handler="SDSCode.pipeline_lambdas.reprocessing_proxy.lambda_handler", + runtime=lambda_.Runtime.PYTHON_3_12, + environment={ + "QUEUE_URL": self.reprocessing_queue.queue_url, + }, + memory_size=128, + timeout=Duration.seconds(30), + vpc=vpc, + vpc_subnets=subnet, + security_groups=[rds_security_group], + allow_public_subnet=True, + layers=layers, + ) + + # Permission for the lambda to send messages to the reprocessing queue + self.reprocessing_queue.grant_send_messages(self.reprocessing_proxy_lambda) # Add api routes for triggering batch starter with a bulk reprocessing request # Only allow authenticated routes for reprocessing api.add_route( route="/authorized/reprocess", http_method="POST", - lambda_function=self.instrument_lambda, + lambda_function=self.reprocessing_proxy_lambda, ) api.add_route( route="/api-key/reprocess", http_method="POST", - lambda_function=self.instrument_lambda, + lambda_function=self.reprocessing_proxy_lambda, ) # Set up eventBridge rules to trigger batch starter lambda. diff --git a/sds_data_manager/lambda_code/SDSCode/pipeline_lambdas/reprocessing_proxy.py b/sds_data_manager/lambda_code/SDSCode/pipeline_lambdas/reprocessing_proxy.py new file mode 100644 index 000000000..e36b26b95 --- /dev/null +++ b/sds_data_manager/lambda_code/SDSCode/pipeline_lambdas/reprocessing_proxy.py @@ -0,0 +1,27 @@ +import json +import logging +import os + +import boto3 + +sqs_client = boto3.client("sqs") +QUEUE_URL = os.environ["QUEUE_URL"] +# Logger setup +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +def lambda_handler(event, context): + params = event.get("queryStringParameters", {}) + + logger.info( + f"Received event: {json.dumps(event, indent=2)}. Sending to reprocessing queue" + ) + + sqs_client.send_message( + QueueUrl=QUEUE_URL, + MessageBody=json.dumps(params), + MessageGroupId="reprocess", + ) + + return {"statusCode": 200, "body": json.dumps({"message": "Reprocess job queued"})} diff --git a/sds_data_manager/orchestration/dependency.py b/sds_data_manager/orchestration/dependency.py index 564d7c5ae..5026ea971 100644 --- a/sds_data_manager/orchestration/dependency.py +++ b/sds_data_manager/orchestration/dependency.py @@ -209,7 +209,7 @@ def _load_all_dependencies( descriptor=descriptor, inputs=upstream_deps_nodes, outputs=job_outputs_list, - partition=value.get("partition") + partition=value.get("partition"), ) except (ValueError, IndexError) as e: @@ -271,6 +271,43 @@ def recursive_flatten_list(self, nested_list): return flat_list +def get_kickoff_jobs(instrument: str | None = None) -> list[ProcessingJobNode]: + """Return all the jobs that kick off each instrument pipeline. + + These are nodes that are downstream from a node with the data_level equal to + "l0" and the descriptor equal to "raw". + + If instrument is provided, return only the kickoff job for that instruments + pipeline. + + Parameters + ---------- + instrument : str, optional + The instrument for which to get the kickoff job. + + Returns + ------- + list[ProcessingJobNode] + List of ProcessingJobNode that are the root job node of each instrument pipeline. + If instrument is provided, return only the kickoff job for that instrument. + """ + kick_off_jobs = [] + + reader = DependencyConfigReader() + for potential_job in reader.config: + for upstream_node in reader.inputs(potential_job): + if upstream_node.data_type == "l0" and upstream_node.descriptor == "raw": + if instrument and upstream_node.source == instrument: + return [reader.config[potential_job]] + kick_off_jobs.append(reader.config[potential_job]) + + if not kick_off_jobs: + logger.info( + "No kickoff jobs found. Please check the instrument dependency YAML files." + ) + return kick_off_jobs + + def upload_dependency_file(dependency_file_path: Path, serialized_dependencies: str): """Upload a JSON file containing a job's dependencies to S3. @@ -327,4 +364,4 @@ def upload_dependency_file(dependency_file_path: Path, serialized_dependencies: f"Unexpected error during cadence file upload: {e}. " f"Dependency file upload failed and the job did not get kicked off." ) - return None \ No newline at end of file + return None diff --git a/sds_data_manager/orchestration/imap_job.py b/sds_data_manager/orchestration/imap_job.py index 25732486c..b9fd62333 100644 --- a/sds_data_manager/orchestration/imap_job.py +++ b/sds_data_manager/orchestration/imap_job.py @@ -1,37 +1,38 @@ """IMAP job handler for managing dependencies and job submission.""" -import json import datetime +import hashlib +import json +import logging +import os import re import time + import boto3 -import os -import logging -import hashlib +import imap_data_access +from botocore.exceptions import ClientError from dagster import ( AssetExecutionContext, - Failure, - SensorEvaluationContext, - sensor, - EventRecordsFilter, + AssetOut, DagsterEventType, - RunRequest, DefaultSensorStatus, + EventRecordsFilter, + Failure, + RunRequest, + SensorEvaluationContext, + define_asset_job, multi_asset, - AssetOut, - define_asset_job + sensor, ) +from imap_data_access import processing_input +from imap_data_access.io import download +from sqlalchemy import func + from sds_data_manager.lambda_code.SDSCode.database import database as db from sds_data_manager.lambda_code.SDSCode.database import models -from sds_data_manager.orchestration.types import DependencyNode, ProcessingJobNode from sds_data_manager.orchestration import custom_partitions from sds_data_manager.orchestration.dagster_utilities import get_materialization_result -import imap_data_access -from imap_data_access import processing_input -from imap_data_access.io import download -import boto3 -from botocore.exceptions import ClientError -from sqlalchemy import func +from sds_data_manager.orchestration.types import DependencyNode, ProcessingJobNode BATCH_CLIENT = boto3.client("batch", region_name="us-west-2") # Create an ECR client for getting container image digests @@ -55,57 +56,61 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -priority_levels = {'l0':'0', - 'l1a':'-1', - 'l1b':'-2', - 'l1c':'-3', - 'l1d':'-4', - 'l2':'-5', - 'l2a':'-6', - 'l2b':'-7', - 'l2c':'-8', - 'l2d':'-9', - 'l3':'-10', - 'l3a':'-11', - 'l3b':'-12', - 'l3c':'-13', - 'l3d':'-14',} - -_sensor_schedule = {'l0': 300, - 'l1a':300, - 'l1b':300, - 'l1c':300, - 'l1d':300, - 'l2': 300, - 'l2a':300, - 'l2b':300, - 'l2c':300, - 'l2d':300, - 'l3': 300, - 'l3a':300, - 'l3b':300, - 'l3c':300, - 'l3d':300} - -_partition_map = { - "daily": custom_partitions.daily_partitions, - "repoint": custom_partitions.repoint_partitions, - "10d": custom_partitions.idex10_partitions, - # NOTE: Right now, IDEX is the only instrument who uses 1mo cadence job that - # maps to exactly 30 days. If this changes, this logic will need update. - "30d": custom_partitions.idex30_partitions, - # TODO: add cadence custom partition definition and update to use those - # later - "3mo": custom_partitions.idex30_partitions, - "6mo": custom_partitions.idex30_partitions, - "1yr": custom_partitions.whole_mission_partition, - } +priority_levels = { + "l0": "0", + "l1a": "-1", + "l1b": "-2", + "l1c": "-3", + "l1d": "-4", + "l2": "-5", + "l2a": "-6", + "l2b": "-7", + "l2c": "-8", + "l2d": "-9", + "l3": "-10", + "l3a": "-11", + "l3b": "-12", + "l3c": "-13", + "l3d": "-14", +} + +_sensor_schedule = { + "l0": 300, + "l1a": 300, + "l1b": 300, + "l1c": 300, + "l1d": 300, + "l2": 300, + "l2a": 300, + "l2b": 300, + "l2c": 300, + "l2d": 300, + "l3": 300, + "l3a": 300, + "l3b": 300, + "l3c": 300, + "l3d": 300, +} + +partition_map = { + "daily": custom_partitions.daily_partitions, + "repoint": custom_partitions.repoint_partitions, + "10d": custom_partitions.idex10_partitions, + # NOTE: Right now, IDEX is the only instrument who uses 1mo cadence job that + # maps to exactly 30 days. If this changes, this logic will need update. + "30d": custom_partitions.idex30_partitions, + # TODO: add cadence custom partition definition and update to use those + # later + "3mo": custom_partitions.idex30_partitions, + "6mo": custom_partitions.idex30_partitions, + "1yr": custom_partitions.whole_mission_partition, +} + class IMAPJobHandler: """Handle IMAP job dependencies and submission.""" - def __init__(self, - job: ProcessingJobNode): + def __init__(self, job: ProcessingJobNode): """Initialize handler with job node and process dependencies. Parameters @@ -115,18 +120,21 @@ def __init__(self, """ self.job_config = job - self.partitions_def = _partition_map.get(self.job_config.partition) + self.partitions_def = partition_map.get(self.job_config.partition) self.sensor_schedule = _sensor_schedule.get(self.job_config.data_type, 600) - + outputs_for_job = [x.to_dagster_asset() for x in self.job_config.outputs] - self.dagster_job = define_asset_job(name=f"{self.job_config.to_dagster_asset().to_user_string()}_processing_job", - selection=outputs_for_job, - tags={"dagster/priority": priority_levels.get(self.job_config.data_type, '0')}) - + self.dagster_job = define_asset_job( + name=f"{self.job_config.to_dagster_asset().to_user_string()}_processing_job", + selection=outputs_for_job, + tags={ + "dagster/priority": priority_levels.get(self.job_config.data_type, "0") + }, + ) + def build_asset(self): - """ - This builds the Asset for Dagster for a particular job. This function will: + """This builds the Asset for Dagster for a particular job. This function will: 1) Get all dependencies from the dependency tree 2) Check if the job had been submitted before @@ -134,73 +142,85 @@ def build_asset(self): b) If Dagster does know about it, we exit 3) Get the Job version 4) Submit the job - 5) Wait for the output files, and materialize them as we see them in the database. + 5) Wait for the output files, and materialize them as we see them in the database. """ input_keys = [dep.to_dagster_asset() for dep in self.job_config.inputs] output_assets = {} for out in self.job_config.outputs: - output_assets[out.to_dagster_asset().to_user_string()] = AssetOut(is_required=False) + output_assets[out.to_dagster_asset().to_user_string()] = AssetOut( + is_required=False + ) @multi_asset( name=f"{self.job_config.to_dagster_asset().to_user_string()}_multi_asset_op", - deps=input_keys, + deps=input_keys, partitions_def=self.partitions_def, - outs=output_assets + outs=output_assets, ) def _generic_batch_submitter(context: AssetExecutionContext): parts = context.partition_key.split("_") - start_date = datetime.datetime.strptime(parts[-3], "%Y-%m-%dT%H:%M:%S").replace(tzinfo=datetime.timezone.utc).date() + start_date = ( + datetime.datetime.strptime(parts[-3], "%Y-%m-%dT%H:%M:%S") + .replace(tzinfo=datetime.timezone.utc) + .date() + ) if "repoint" in parts[0]: pointing_number = int(parts[0][7:]) else: pointing_number = None - + # 1. Figure out what time window this specific run is responsible for target_partition = context.partition_key target_start, target_end = self._parse_dates_from_key(target_partition) - - # 2. Get Dependencies + + # 2. Get Dependencies dependency_inputs = self.get_dependencies(context, target_start, target_end) if not dependency_inputs: - raise Failure(description="Processing failed: Dependency inputs were missing.") + raise Failure( + description="Processing failed: Dependency inputs were missing." + ) already_processed = False with db.Session() as session: for output in self.job_config.outputs: - previous_file = self.is_duplicate_job(context, - session, - dependency_inputs, - self.job_config.source, - self.job_config.data_type, - output.descriptor, - self.job_config.descriptor, - start_date, - pointing_number) - + previous_file = self.is_duplicate_job( + context, + session, + dependency_inputs, + self.job_config.source, + self.job_config.data_type, + output.descriptor, + self.job_config.descriptor, + start_date, + pointing_number, + ) + if previous_file: already_processed = True - materialization = get_materialization_result(context, - output.to_dagster_asset(), - context.partition_key, - [os.path.basename(previous_file.file_path)], - previous_file.version, - "science", - inputs = dependency_inputs.serialize()) + materialization = get_materialization_result( + context, + output.to_dagster_asset(), + context.partition_key, + [os.path.basename(previous_file.file_path)], + previous_file.version, + "science", + inputs=dependency_inputs.serialize(), + ) if materialization: yield materialization if not already_processed: job_version = self._determine_job_version( - session=session, - instrument=self.job_config.source, - descriptor=self.job_config.descriptor, - start_date=start_date, - data_level=self.job_config.data_type, - current_dependencies=dependency_inputs.serialize(), - ) + session=session, + instrument=self.job_config.source, + descriptor=self.job_config.descriptor, + start_date=start_date, + data_level=self.job_config.data_type, + current_dependencies=dependency_inputs.serialize(), + ) context.log.info(f"Job Version to Use: {job_version}") - - ''' + + """ # SUBMIT JOB HERE!!! job_node = {"data_source":instrument, "data_type":level, @@ -213,49 +233,59 @@ def _generic_batch_submitter(context: AssetExecutionContext): dependency_inputs.serialize(), repoint=int(target_partition), ) - ''' - #if submit_status.status == 'submitted': + """ + # if submit_status.status == 'submitted': for output in self.job_config.outputs: - context.log.info(f"Waiting for data product {output.source}_{output.data_type}_{output.descriptor} to complete") - file = self.wait_for_file(context, - session, - output, - job_version, - repointing=pointing_number, - start_date=start_date.strftime("%Y%m%d"), - inputs = dependency_inputs.serialize()) + context.log.info( + f"Waiting for data product {output.source}_{output.data_type}_{output.descriptor} to complete" + ) + file = self.wait_for_file( + context, + session, + output, + job_version, + repointing=pointing_number, + start_date=start_date.strftime("%Y%m%d"), + inputs=dependency_inputs.serialize(), + ) if file: yield file - # TODO: We should not yield right away. We should collect up anything that this asset has generated, - # yield what we can, and determine if anything required in the output is missing, then yield a failure. + # TODO: We should not yield right away. We should collect up anything that this asset has generated, + # yield what we can, and determine if anything required in the output is missing, then yield a failure. # Return the generated function back to Dagster return _generic_batch_submitter - def wait_for_file(self, - context, - session: db.Session, - output: DependencyNode, - job_version: str, - start_date: str = None, - repointing: int = None, - inputs = {}): + def wait_for_file( + self, + context, + session: db.Session, + output: DependencyNode, + job_version: str, + start_date: str = None, + repointing: int = None, + inputs={}, + ): if start_date is None and repointing is None: - raise ValueError("You must at least provide either start_date or repointing") - + raise ValueError( + "You must at least provide either start_date or repointing" + ) + parsed_start_date = None if start_date is not None: parsed_start_date = datetime.datetime.strptime(start_date, "%Y%m%d") - - timeout = 3 # TODO: Set this for WAY higher once we're actually waiting for files. + + timeout = ( + 3 # TODO: Set this for WAY higher once we're actually waiting for files. + ) timeout_start = time.time() while time.time() < timeout_start + timeout: filters = [ models.ScienceFiles.instrument == output.source, models.ScienceFiles.data_level == output.data_type, models.ScienceFiles.descriptor == output.descriptor, - models.ScienceFiles.version == job_version + models.ScienceFiles.version == job_version, ] if repointing is not None: filters.append(models.ScienceFiles.repointing == int(repointing)) @@ -263,49 +293,62 @@ def wait_for_file(self, filters.append(models.ScienceFiles.start_date == parsed_start_date) created_file = session.query(models.ScienceFiles).filter(*filters).first() if created_file: - context.log.info(f"Found file {os.path.basename(created_file.file_path)}! Creating Asset.") - materialization = get_materialization_result(context, - output.to_dagster_asset(), - context.partition_key, - [os.path.basename(created_file.file_path)], - str(int(job_version[1:])), - "science", - inputs = inputs) + context.log.info( + f"Found file {os.path.basename(created_file.file_path)}! Creating Asset." + ) + materialization = get_materialization_result( + context, + output.to_dagster_asset(), + context.partition_key, + [os.path.basename(created_file.file_path)], + str(int(job_version[1:])), + "science", + inputs=inputs, + ) if materialization: return materialization - time.sleep(1) # TODO: Set this for WAY higher once we're actually waiting for files. - - def _parse_dates_from_key(self, - partition_key: str) -> tuple[datetime.datetime, datetime.datetime]: - """ - Extracts start and end datetimes from a string formatted like: + time.sleep( + 1 + ) # TODO: Set this for WAY higher once we're actually waiting for files. + + def _parse_dates_from_key( + self, partition_key: str + ) -> tuple[datetime.datetime, datetime.datetime]: + """Extracts start and end datetimes from a string formatted like: '{name}_%Y-%m-%dT%H:%M:%S_to_%Y-%m-%dT%H:%M:%S' """ if not partition_key: return None, None - - date_range = partition_key.split('_', 1)[1] + + date_range = partition_key.split("_", 1)[1] if "_to_" in date_range: p_start_str, p_end_str = date_range.split("_to_") - p_start = datetime.datetime.strptime(p_start_str, "%Y-%m-%dT%H:%M:%S").replace(tzinfo=datetime.timezone.utc) - p_end = datetime.datetime.strptime(p_end_str, "%Y-%m-%dT%H:%M:%S").replace(tzinfo=datetime.timezone.utc) - + p_start = datetime.datetime.strptime( + p_start_str, "%Y-%m-%dT%H:%M:%S" + ).replace(tzinfo=datetime.timezone.utc) + p_end = datetime.datetime.strptime(p_end_str, "%Y-%m-%dT%H:%M:%S").replace( + tzinfo=datetime.timezone.utc + ) + return p_start, p_end - def _get_overlapping_target_partitions(self, upstream_partition_key, up_start, up_end, instance): - """ - Evaluates the upstream date range against existing downstream partitions. + def _get_overlapping_target_partitions( + self, upstream_partition_key, up_start, up_end, instance + ): + """Evaluates the upstream date range against existing downstream partitions. Returns a list of downstream partition keys that overlap. """ target_keys = [] # Fetch the currently known dynamic partitions for your target asset - existing_downstream_keys = instance.get_dynamic_partitions(self.partitions_def.name) + existing_downstream_keys = instance.get_dynamic_partitions( + self.partitions_def.name + ) # Check if the upstream and downstream are in the same partition if upstream_partition_key in existing_downstream_keys: - # These are actually in same partition! No need to loop through the other keys. + # These are actually in same partition! No need to loop through the other keys. return [upstream_partition_key] - + for down_key in existing_downstream_keys: down_start, down_end = self._parse_dates_from_key(down_key) if not down_start or not down_end: @@ -318,45 +361,46 @@ def _get_overlapping_target_partitions(self, upstream_partition_key, up_start, u return target_keys def build_sensor(self): - """ - Factory function that returns a Dagster sensor monitoring the dependencies + """Factory function that returns a Dagster sensor monitoring the dependencies and triggering this asset when overlapping data arrives. 1) Checks for all of the latest asset materializations in dagster for all dependencies of this product 2) Loops through the latest materializations 3) Determines what time range those materializations belong to 4) Determines what partition keys of this asset those new materializations belong to - 5) For each affected partition, we determine the dependencies. If we have all dependencies, we are good! + 5) For each affected partition, we determine the dependencies. If we have all dependencies, we are good! 6) Yield a RunRequest for a job to make the asset for the partitions that have all dependencies. """ deps_keys = [x.to_dagster_asset() for x in self.job_config.triggering_deps] - sensor_name = f"{self.job_config.to_dagster_asset().to_user_string()}_kickoff_sensor" + sensor_name = ( + f"{self.job_config.to_dagster_asset().to_user_string()}_kickoff_sensor" + ) + @sensor( name=sensor_name, job=self.dagster_job, minimum_interval_seconds=self.sensor_schedule, - default_status=DefaultSensorStatus.RUNNING + default_status=DefaultSensorStatus.RUNNING, ) def _sensor(context: SensorEvaluationContext): - + # Load the cursor to track which events we have already seen. # The cursor maps `{asset_name: last_processed_storage_id}`. cursors = json.loads(context.cursor) if context.cursor else {} new_cursors = cursors.copy() - + # Iterate through each dependency to find unconsumed materializations for dep_key in deps_keys: - dep_name = dep_key.to_user_string() context.log.info(f"Checking new dependencies for: {dep_name}") # Fetch the last evaluated event ID for this specific dependency last_event_id = cursors.get(dep_name) filter = EventRecordsFilter( - event_type=DagsterEventType.ASSET_MATERIALIZATION, - asset_key=dep_key, - after_cursor=last_event_id, - ) + event_type=DagsterEventType.ASSET_MATERIALIZATION, + asset_key=dep_key, + after_cursor=last_event_id, + ) new_events = context.instance.get_event_records(filter, limit=1) if new_events: record = new_events[0] @@ -365,32 +409,36 @@ def _sensor(context: SensorEvaluationContext): # Update our new cursor marker to the highest storage ID seen new_cursors[dep_name] = record.storage_id - + upstream_partition_key = record.event_log_entry.dagster_event.partition up_start, up_end = self._parse_dates_from_key(upstream_partition_key) - + if not up_start or not up_end: continue - + # Calculate overlap target_partitions = self._get_overlapping_target_partitions( upstream_partition_key, up_start, up_end, context.instance ) - + for target_partition in target_partitions: # Queue the RunRequest - target_start, target_end = self._parse_dates_from_key(target_partition) - dependencies = self.get_dependencies(context, target_start, target_end) + target_start, target_end = self._parse_dates_from_key( + target_partition + ) + dependencies = self.get_dependencies( + context, target_start, target_end + ) if dependencies: - tags={"dependencies": dependencies.serialize()} + tags = {"dependencies": dependencies.serialize()} yield RunRequest( - partition_key=target_partition, - run_key=f"{self.job_config.to_dagster_asset().to_user_string()}_{target_partition}_{self._dependency_hash(dependencies.serialize())}", - tags=tags - ) - break # To keep the sensor short, we'll only analyze one new dependency at a time. If there are still new ones, we'll consume them next time. - # The get_dependencies will still get the latest stuff regardless, so we're never submitting outdated data. - + partition_key=target_partition, + run_key=f"{self.job_config.to_dagster_asset().to_user_string()}_{target_partition}_{self._dependency_hash(dependencies.serialize())}", + tags=tags, + ) + break # To keep the sensor short, we'll only analyze one new dependency at a time. If there are still new ones, we'll consume them next time. + # The get_dependencies will still get the latest stuff regardless, so we're never submitting outdated data. + # 5. Lock in the new cursor state and execute context.update_cursor(json.dumps(new_cursors)) @@ -398,7 +446,6 @@ def _sensor(context: SensorEvaluationContext): def process_job(self, potential_job_node: DependencyNode): """Process the job by resolving dependencies and submitting to batch.""" - if self.dependencies is not None: self._calculate_crid() self._determine_job_version() @@ -422,59 +469,74 @@ def process_job(self, potential_job_node: DependencyNode): # if job_submit_succeed: # self.clean_up() - def get_dependencies(self, - context: AssetExecutionContext, - target_start: datetime.datetime, - target_end: datetime.datetime): + def get_dependencies( + self, + context: AssetExecutionContext, + target_start: datetime.datetime, + target_end: datetime.datetime, + ): """Get the dependencies for the job using the DependencyResolver.""" - - # Iterate through each upstream dependency + # Iterate through each upstream dependency dependency_inputs = processing_input.ProcessingInputCollection() - context.log.info(f"Checking for all dependencies existing between {target_start} and {target_end}") + context.log.info( + f"Checking for all dependencies existing between {target_start} and {target_end}" + ) for input in self.job_config.inputs: dep_name = input.to_user_string() - found_dep=False + found_dep = False context.log.info(f"Checking out {dep_name}") metadata_list = input.get_all_files_in_time_range() for metadata in metadata_list: if "file_names" in metadata: - found_dep = True # We can finally say we have found at least one dependency + found_dep = ( + True # We can finally say we have found at least one dependency + ) # Dagster wraps metadata in a MetadataValue object, so we call .value file_names = metadata["file_names"].value # Handle both single strings and lists of files safely if file_names: - context.log.info(f"The file names of the matching partition: {file_names}") + context.log.info( + f"The file names of the matching partition: {file_names}" + ) input_type = metadata["input_type"].value - if input_type=='science': - dependency_inputs.add(processing_input.ScienceInput(*file_names)) - if input_type=='ancillary': - dependency_inputs.add(processing_input.AncillaryInput(*file_names)) - if input_type=='spin': + if input_type == "science": + dependency_inputs.add( + processing_input.ScienceInput(*file_names) + ) + if input_type == "ancillary": + dependency_inputs.add( + processing_input.AncillaryInput(*file_names) + ) + if input_type == "spin": dependency_inputs.add(processing_input.SpinInput(*file_names)) - if input_type=='repoint': + if input_type == "repoint": dependency_inputs.add( processing_input.RepointInput(file_names[0]) ) - if input_type=='spice': + if input_type == "spice": dependency_inputs.add(processing_input.SPICEInput(*file_names)) if not found_dep: # TODO: Check here if we have a soft dependency. - context.log.info(f"Not enought information to process. Missing {dep_name} in range {str(target_start)} to {str(target_end)}") + context.log.info( + f"Not enought information to process. Missing {dep_name} in range {target_start!s} to {target_end!s}" + ) return return dependency_inputs - def is_duplicate_job(self, - context, - session, - dependency, - instrument, - level, - descriptor, - job_command, - start_date, - repoint): + def is_duplicate_job( + self, + context, + session, + dependency, + instrument, + level, + descriptor, + job_command, + start_date, + repoint, + ): """Determine if the job is a duplicate. Requirements for duplicate job determination: @@ -505,52 +567,74 @@ def is_duplicate_job(self, # ) # 3. If return exists, it's a duplicate job and return True. - ''' - This function will check if the latest file at the SDC has already been processed using + """ + This function will check if the latest file at the SDC has already been processed using the gathered dependencies. To do this, it: 1) Finds the latest file 2) Finds the corresponding Processing Job 3) Read in the .json dependency file from the corresponding Processing Job - 4) Compares the Ancillary and Science files in the dependency file to the current ones + 4) Compares the Ancillary and Science files in the dependency file to the current ones 5) If they all match, the function returns the materialization result of the latest file - Yes, this is somewhat complicated right now. I think the best way going forward would be to - add the dependencies to a particular file either in the ScienceFiles or ProcessingJob tables. - ''' - context.log.info(f"Checking if we have already have a file of this instrument of {instrument}, this level of {level}, and this {descriptor}.") - context.log.info(f"And this start date {start_date}, and this pointing number {repoint}.") + Yes, this is somewhat complicated right now. I think the best way going forward would be to + add the dependencies to a particular file either in the ScienceFiles or ProcessingJob tables. + """ + context.log.info( + f"Checking if we have already have a file of this instrument of {instrument}, this level of {level}, and this {descriptor}." + ) + context.log.info( + f"And this start date {start_date}, and this pointing number {repoint}." + ) if repoint: - latest_file = session.query(models.ScienceFiles).filter( - models.ScienceFiles.instrument == instrument, - models.ScienceFiles.data_level == level, - models.ScienceFiles.descriptor == descriptor, - models.ScienceFiles.repointing == int(repoint) - ).order_by(models.ScienceFiles.version.desc()).first() + latest_file = ( + session.query(models.ScienceFiles) + .filter( + models.ScienceFiles.instrument == instrument, + models.ScienceFiles.data_level == level, + models.ScienceFiles.descriptor == descriptor, + models.ScienceFiles.repointing == int(repoint), + ) + .order_by(models.ScienceFiles.version.desc()) + .first() + ) else: - latest_file = session.query(models.ScienceFiles).filter( - models.ScienceFiles.instrument == instrument, - models.ScienceFiles.data_level == level, - models.ScienceFiles.descriptor == descriptor, - models.ScienceFiles.start_date == start_date - ).order_by(models.ScienceFiles.version.desc()).first() + latest_file = ( + session.query(models.ScienceFiles) + .filter( + models.ScienceFiles.instrument == instrument, + models.ScienceFiles.data_level == level, + models.ScienceFiles.descriptor == descriptor, + models.ScienceFiles.start_date == start_date, + ) + .order_by(models.ScienceFiles.version.desc()) + .first() + ) if latest_file: - context.log.info("Yes we did! Now we need to check the processing jobs to see if the same dependencies were used to create this. ") + context.log.info( + "Yes we did! Now we need to check the processing jobs to see if the same dependencies were used to create this. " + ) max_version_record = ( session.query(models.ProcessingJob) - .filter(models.ProcessingJob.instrument == latest_file.instrument, + .filter( + models.ProcessingJob.instrument == latest_file.instrument, models.ProcessingJob.data_level == latest_file.data_level, models.ProcessingJob.descriptor == job_command, models.ProcessingJob.start_date == latest_file.start_date, models.ProcessingJob.status.in_( - [models.Status.INPROGRESS.value, models.Status.SUCCEEDED.value] - )) + [models.Status.INPROGRESS.value, models.Status.SUCCEEDED.value] + ), + ) .order_by(models.ProcessingJob.version.desc()) .first() ) if max_version_record: - context.log.info("A job does indeed look like this one...lets check out its dependencies file") - match = re.search(r'--dependency\s+(\S+\.json)', max_version_record.container_command) + context.log.info( + "A job does indeed look like this one...lets check out its dependencies file" + ) + match = re.search( + r"--dependency\s+(\S+\.json)", max_version_record.container_command + ) if match: dependency_file = match.group(1) context.log.info(dependency_file) @@ -562,30 +646,36 @@ def is_duplicate_job(self, ssm_response = SSM_CLIENT.get_parameter( Name=ssm_parameter_name, WithDecryption=True ) - imap_data_access.config["API_KEY"] = ssm_response["Parameter"]["Value"] + imap_data_access.config["API_KEY"] = ssm_response["Parameter"][ + "Value" + ] except Exception as e: print(f"Could not retrieve API key from SSM: {e}") dependency_filepath = download(dependency_file) with open(dependency_filepath) as f: old_inputs = json.loads(f.read()) - context.log.info(f"Check it out! These are the dependencies it previously ran with: {json.dumps(old_inputs)}") + context.log.info( + f"Check it out! These are the dependencies it previously ran with: {json.dumps(old_inputs)}" + ) dependencies_match = True old_science_inputs = [] old_ancillary_inputs = [] new_science_inputs = [] new_ancillary_inputs = [] for dep in old_inputs: - if dep['type'] == "science": - old_science_inputs.extend(dep['files']) - if dep['type'] == "ancillary": - old_ancillary_inputs.extend(dep['files']) + if dep["type"] == "science": + old_science_inputs.extend(dep["files"]) + if dep["type"] == "ancillary": + old_ancillary_inputs.extend(dep["files"]) new_inputs = json.loads(dependency.serialize()) - context.log.info(f"Check it out! These are the dependencies that we want to run it with now: {json.dumps(new_inputs)}") + context.log.info( + f"Check it out! These are the dependencies that we want to run it with now: {json.dumps(new_inputs)}" + ) for dep in new_inputs: - if dep['type'] == "science": - new_science_inputs.extend(dep['files']) - if dep['type'] == "ancillary": - new_ancillary_inputs.extend(dep['files']) + if dep["type"] == "science": + new_science_inputs.extend(dep["files"]) + if dep["type"] == "ancillary": + new_ancillary_inputs.extend(dep["files"]) if set(old_science_inputs) != set(new_science_inputs): dependencies_match = False @@ -593,14 +683,14 @@ def is_duplicate_job(self, dependencies_match = False if dependencies_match: - context.log.info(f"It's a match!") - # The latest file does not need to be updated. - # We need to tell dagster that this asset is complete. + context.log.info("It's a match!") + # The latest file does not need to be updated. + # We need to tell dagster that this asset is complete. context.log.info(f"Latest file: {latest_file.file_path}") context.log.info(f"Version: {latest_file.version}") return latest_file else: - context.log.info(f"It's not a match!") + context.log.info("It's not a match!") else: context.log.info("No files have ever been made like this before!") @@ -716,7 +806,7 @@ def _dependency_hash(self, serialized_dependencies): """Generate a hash for the serialized dependencies. Use only the first 8 characters. This is a unique ID for a particular run. Dagster will refused to run a job with - the same dependency_hash. + the same dependency_hash. Parameters ---------- @@ -732,7 +822,7 @@ def _dependency_hash(self, serialized_dependencies): dependencies = json.loads(serialized_dependencies) non_sclk_deps = [] for dep in dependencies: - for file in dep['files']: + for file in dep["files"]: if "imap_sclk" not in file: # We'll get rid of the spacecraft_clock kernel, if it exists non_sclk_deps.append(file) @@ -746,10 +836,10 @@ def _dependency_hash(self, serialized_dependencies): def _get_container_image_digest(self): """Get the container image digest. - The image digest is a sha256 hash of the image manifest and is a unique identifier - for the specific version of the container image used in the batch job. - This is important for tracking which version of the code is being used for each - job. + The image digest is a sha256 hash of the image manifest and is a unique + identifier for the specific version of the container image used in the batch + job. This is important for tracking which version of the code is being used + for each job. Parameters ---------- @@ -777,8 +867,8 @@ def _get_container_image_digest(self): key=lambda definition: definition.get("revision", 0), ) container_image = job_def["containerProperties"]["image"] - # Parse the container image URI to get the registry id, repository name and image - # tag and use those to call describe_images and get the image digest. + # Parse the container image URI to get the registry id, repository name and + # image tag and use those to call describe_images and get the image digest. # Eg. for 123456789012.dkr.ecr.us-west-2.amazonaws.com/swapi-repo:latest, # "123456789012" is the registry id, "swapi-repo" is the repository and # "latest" is the image tag. diff --git a/sds_data_manager/orchestration/reprocessing.py b/sds_data_manager/orchestration/reprocessing.py new file mode 100644 index 000000000..b06697ee5 --- /dev/null +++ b/sds_data_manager/orchestration/reprocessing.py @@ -0,0 +1,128 @@ +"""Reprocessing logic.""" + +import datetime +import json +import os + +import boto3 +from dagster import AssetKey, AssetSelection, SensorEvaluationContext, sensor +from dagster._core.execution.backfill import PartitionBackfill + +from sds_data_manager.orchestration.dagster_utilities import get_affected_partitions +from sds_data_manager.orchestration.dependency import ( + DependencyConfigReader, + get_kickoff_jobs, +) +from sds_data_manager.orchestration.imap_job import partition_map + +SQS_CLIENT = boto3.client("sqs", "us-west-2") + + +def read_sqs_messages(sqs_queue_url=None): + """Read SQS messages from the reprocessing queue.""" + response = SQS_CLIENT.receive_message( + QueueUrl=sqs_queue_url, + MaxNumberOfMessages=10, + WaitTimeSeconds=1, + ) + return response.get("Messages", []) + + +@sensor( + asset_selection=AssetSelection.all(), + minimum_interval_seconds=100, # TODO what do we want here +) +def reprocess_sensor(context: SensorEvaluationContext): + """Sensor that triggers reprocessing backfills.""" + sqs_queue_url = os.getenv("REPROCESSING_SQS_URL") + messages = read_sqs_messages(sqs_queue_url) + reader = DependencyConfigReader() + if not messages: + return None + + for message in messages: + params = json.loads(message["Body"]) + + instrument = params.get("instrument") + data_level = params.get("data_level") + descriptor = params.get("descriptor") + start_date = params.get("start_date") + end_date = params.get("end_date") + + context.log.info( + f"A reprocessing event was triggered with the parameters: {instrument=}, " + f"{data_level=}, {descriptor=}, {start_date=}, {end_date=}" + ) + if not end_date or not start_date: + raise ValueError( + "Start date and end date are required for a reprocessing Event." + ) + if not instrument: + raise ValueError("Instrument must be provided for a reprocessing event.") + + if bool(data_level) != bool(descriptor): + raise ValueError( + "data_level and descriptor must both be provided or both None." + ) + + if not data_level: + # If data_level is not provided, we need to reprocess all levels. + # Get the jobs that kick of each pipeline, to trigger processing + # for all levels. + root_job = get_kickoff_jobs(instrument)[0] + # Create the asset name based on the root job information + asset_name = f"{instrument}_{root_job.data_type}_{root_job.descriptor}" + partition = root_job.partition + else: + # If data_level is provided (and therefore descriptor) construct the + # asset name using the input parameters + asset_name = f"{instrument}_{data_level}_{descriptor}" + # Get the partition type from the dependency config + partition = reader.config[(instrument, data_level, descriptor)].partition + + # Get the partitions definition based on the DependencyNode + partition_def = partition_map.get(partition) + # convert start and end date to datetime + start_date = datetime.datetime.strptime(start_date, "%Y%m%d").replace( + tzinfo=datetime.timezone.utc + ) + end_date = datetime.datetime.strptime(end_date, "%Y%m%d").replace( + tzinfo=datetime.timezone.utc + ) + + # Determine the affected partitions based on the start_date and end_date + partition_keys = get_affected_partitions( + context, partition_def, start_date, end_date + ) + if not partition_keys: + return None + context.log.info( + f"Reprocessing asset {asset_name} across {partition_keys} partitions" + ) + + backfill = PartitionBackfill.from_asset_partitions( + backfill_id=f"reprocess-{instrument}-{int(datetime.datetime.now().timestamp())}", + asset_graph=context.repository_def.asset_graph, + partition_names=partition_keys, + asset_selection=[AssetKey(asset_name)], + backfill_timestamp=datetime.datetime.now(datetime.timezone.utc).timestamp(), + tags={ + "instrument": instrument, + "descriptor": descriptor or "", + "data_level": data_level or "", + }, + dynamic_partitions_store=context.instance, + all_partitions=False, + title=None, + description=None, + run_config=None, + ) + + context.instance.add_backfill(backfill) + + SQS_CLIENT.delete_message( + QueueUrl=sqs_queue_url, + ReceiptHandle=message["ReceiptHandle"], + ) + + return None diff --git a/sds_data_manager/utils/stackbuilder.py b/sds_data_manager/utils/stackbuilder.py index ed5ece0fa..fee5e7a75 100644 --- a/sds_data_manager/utils/stackbuilder.py +++ b/sds_data_manager/utils/stackbuilder.py @@ -289,7 +289,7 @@ def build_sds( instrument_delay_sqs = file_arrive_sqs_construct.delay_queue - instrument_lambdas.BatchStarterLambda( + batch_starter_construct = instrument_lambdas.BatchStarterLambda( scope=sdc_stack, construct_id="BatchStarterLambda", env=env, @@ -461,8 +461,9 @@ def build_sds( secret_name=ialirt_secret_name, account_name=account_name, ) - # Dagster Stack - # Repo root is three levels up from this file (sds_data_manager/utils/stackbuilder.py) + # Dagster Stack + # Repo root is three levels up from this + # file (sds_data_manager/utils/stackbuilder.py) repo_root = str(Path(__file__).parent.parent.parent) dagster_repo_stack = dagster_construct.ElasticContainerRegistryStack( @@ -493,7 +494,7 @@ def build_sds( # db_name # username: dagster # port: 5432 - # + # # Dagster will look for DB credentails through dagster.yaml in # sds_data_manager/orchestration/dagster.yaml. # In there, it's going to read DAGSTER_PG_PASSWORD and DAGSTER_PG_HOST. @@ -508,6 +509,7 @@ def build_sds( "imap_data_access_url", "https://api.dev.imap-mission.com" ), "SSM_API_KEY_PARAMETER": "/imap-sdc/batch-jobs/api-key", + "REPROCESSING_SQS_URL": batch_starter_construct.reprocessing_sqs_url, } dagster_ecs_stack = dagster_construct.DagsterEcsStack( @@ -519,6 +521,7 @@ def build_sds( ) dagster_ecs_stack.node.add_dependency(dagster_image_stack) + def build_backup(scope: App, env: Environment, source_account: str): """Build backup bucket with permissions for replication from source_account. diff --git a/tests/infrastructure/test_instrument_lambda_construct.py b/tests/infrastructure/test_instrument_lambda_construct.py index 74154899a..bd4ce0f9d 100644 --- a/tests/infrastructure/test_instrument_lambda_construct.py +++ b/tests/infrastructure/test_instrument_lambda_construct.py @@ -60,7 +60,7 @@ def template(stack, env, code): def test_batch_starter_resources(template): """Ensure the template has appropriate IAM roles.""" - template.resource_count_is("AWS::IAM::Role", 7) + template.resource_count_is("AWS::IAM::Role", 9) # 2 for RDS stack + 1 for BatchStarterLambda + 1 for API Gateway + 1 for # bucket notifications template.resource_count_is("AWS::Lambda::Function", 5) diff --git a/tests/orchestration/test_reprocessing.py b/tests/orchestration/test_reprocessing.py new file mode 100644 index 000000000..b8d0bb819 --- /dev/null +++ b/tests/orchestration/test_reprocessing.py @@ -0,0 +1,157 @@ +"""Test the dagster reprocessing functionality.""" + +import json +import os +from unittest.mock import Mock, patch + +import pytest +from dagster import AssetKey, DagsterInstance, Definitions, asset, build_sensor_context +from dagster._core.definitions.partitions.subset import DefaultPartitionsSubset + +from sds_data_manager.orchestration import reprocessing +from sds_data_manager.orchestration.custom_partitions import ( + daily_partitions, + repoint_partitions, +) + + +@pytest.fixture +def mock_env_vars(): + """Mock environment variables.""" + with patch.dict( + os.environ, + { + "REPROCESSING_SQS_URL": "https://sqs.us-west-2.amazonaws.com/test/reprocessing_queue" + }, + ): + yield + + +def test_reprocess_one_repoint_partition(mock_env_vars) -> None: + """Test the reprocessing functionality.""" + instance = DagsterInstance.ephemeral() + instance.add_dynamic_partitions( + "repoint_partitions", ["repoint123_2026-01-01T00:00:00_to_2026-01-02T00:00:00"] + ) + + # Set up upstream assets for glows pipeline + @asset(partitions_def=repoint_partitions) + def glows_l1a_all(): + pass + + @asset(partitions_def=repoint_partitions) + def glows_l1b_de(): + pass + + @asset(partitions_def=repoint_partitions) + def glows_l1b_hist(): + pass + + # Create a definition object with all the related assets + defs = Definitions(assets=[glows_l1a_all, glows_l1b_de, glows_l1b_hist]) + + context = build_sensor_context( + instance=instance, + repository_def=defs.get_repository_def(), + ) + + mock_sqs_client = Mock() + + mock_sqs_client.receive_message.return_value = { + "Messages": [ + { + "MessageId": "test-id", + "ReceiptHandle": "test-handle", + "Body": json.dumps( + { + "data_level": "l1a", + "descriptor": "all", + "end_date": "20260101", + "instrument": "glows", + "reprocessing": "True", + "start_date": "20260101", + } + ), + } + ] + } + with ( + patch.object(reprocessing, "SQS_CLIENT", mock_sqs_client), + ): + reprocessing.reprocess_sensor(context) + + # Check that there was a backfill submitted + backfills = instance.get_backfills() + assert len(backfills) == 1 + backfill_subset = backfills[ + 0 + ].asset_backfill_data.target_subset.partitions_subsets_by_asset_key + # There should be only one asset key + assert len(backfill_subset) == 1 + assert backfill_subset[AssetKey("glows_l1a_all")] == DefaultPartitionsSubset( + subset={"repoint123_2026-01-01T00:00:00_to_2026-01-02T00:00:00"} + ) + + +def test_reprocess_all_codice(mock_env_vars) -> None: + """Test the reprocessing functionality for all of codice.""" + instance = DagsterInstance.ephemeral() + instance.add_dynamic_partitions( + "daily_partitions", ["daily_2026-01-01T00:00:00_to_2026-01-02T00:00:00"] + ) + + # Set up upstream assets for the codice pipeline + @asset(partitions_def=daily_partitions) + def codice_l1a_all(): + pass + + @asset(partitions_def=daily_partitions) + def codice_l1b_hi_omni(): + pass + + @asset(partitions_def=daily_partitions) + def codice_l2_hi_omni(): + pass + + # Create a definition object with all the related assets + defs = Definitions(assets=[codice_l1a_all, codice_l1b_hi_omni, codice_l2_hi_omni]) + + context = build_sensor_context( + instance=instance, + repository_def=defs.get_repository_def(), + ) + + mock_sqs_client = Mock() + + mock_sqs_client.receive_message.return_value = { + "Messages": [ + { + "MessageId": "test-id", + "ReceiptHandle": "test-handle", + "Body": json.dumps( + { + "end_date": "20260101", + "instrument": "codice", + "reprocessing": "True", + "start_date": "20260101", + } + ), + } + ] + } + with ( + patch.object(reprocessing, "SQS_CLIENT", mock_sqs_client), + ): + reprocessing.reprocess_sensor(context) + + # Check that there was a backfill submitted + backfills = instance.get_backfills() + assert len(backfills) == 1 + backfill_subset = backfills[ + 0 + ].asset_backfill_data.target_subset.partitions_subsets_by_asset_key + # There should be only one asset key + assert len(backfill_subset) == 1 + assert backfill_subset[AssetKey("codice_l1a_all")] == DefaultPartitionsSubset( + subset={"daily_2026-01-01T00:00:00_to_2026-01-02T00:00:00"} + ) From 500f5c1957600f9dd11713f5295d5ac9f8b6c7b1 Mon Sep 17 00:00:00 2001 From: Luisa Date: Tue, 26 May 2026 17:48:49 -0600 Subject: [PATCH 2/6] revert formatting --- sds_data_manager/orchestration/dependency.py | 2 +- sds_data_manager/orchestration/imap_job.py | 608 ++++++++----------- 2 files changed, 260 insertions(+), 350 deletions(-) diff --git a/sds_data_manager/orchestration/dependency.py b/sds_data_manager/orchestration/dependency.py index 5026ea971..12d4c1823 100644 --- a/sds_data_manager/orchestration/dependency.py +++ b/sds_data_manager/orchestration/dependency.py @@ -209,7 +209,7 @@ def _load_all_dependencies( descriptor=descriptor, inputs=upstream_deps_nodes, outputs=job_outputs_list, - partition=value.get("partition"), + partition=value.get("partition") ) except (ValueError, IndexError) as e: diff --git a/sds_data_manager/orchestration/imap_job.py b/sds_data_manager/orchestration/imap_job.py index b9fd62333..05fba75d3 100644 --- a/sds_data_manager/orchestration/imap_job.py +++ b/sds_data_manager/orchestration/imap_job.py @@ -1,38 +1,37 @@ """IMAP job handler for managing dependencies and job submission.""" -import datetime -import hashlib import json -import logging -import os +import datetime import re import time - import boto3 -import imap_data_access -from botocore.exceptions import ClientError +import os +import logging +import hashlib from dagster import ( AssetExecutionContext, - AssetOut, - DagsterEventType, - DefaultSensorStatus, - EventRecordsFilter, Failure, - RunRequest, SensorEvaluationContext, - define_asset_job, - multi_asset, sensor, + EventRecordsFilter, + DagsterEventType, + RunRequest, + DefaultSensorStatus, + multi_asset, + AssetOut, + define_asset_job ) -from imap_data_access import processing_input -from imap_data_access.io import download -from sqlalchemy import func - from sds_data_manager.lambda_code.SDSCode.database import database as db from sds_data_manager.lambda_code.SDSCode.database import models +from sds_data_manager.orchestration.types import DependencyNode, ProcessingJobNode from sds_data_manager.orchestration import custom_partitions from sds_data_manager.orchestration.dagster_utilities import get_materialization_result -from sds_data_manager.orchestration.types import DependencyNode, ProcessingJobNode +import imap_data_access +from imap_data_access import processing_input +from imap_data_access.io import download +import boto3 +from botocore.exceptions import ClientError +from sqlalchemy import func BATCH_CLIENT = boto3.client("batch", region_name="us-west-2") # Create an ECR client for getting container image digests @@ -56,61 +55,57 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -priority_levels = { - "l0": "0", - "l1a": "-1", - "l1b": "-2", - "l1c": "-3", - "l1d": "-4", - "l2": "-5", - "l2a": "-6", - "l2b": "-7", - "l2c": "-8", - "l2d": "-9", - "l3": "-10", - "l3a": "-11", - "l3b": "-12", - "l3c": "-13", - "l3d": "-14", -} - -_sensor_schedule = { - "l0": 300, - "l1a": 300, - "l1b": 300, - "l1c": 300, - "l1d": 300, - "l2": 300, - "l2a": 300, - "l2b": 300, - "l2c": 300, - "l2d": 300, - "l3": 300, - "l3a": 300, - "l3b": 300, - "l3c": 300, - "l3d": 300, -} +priority_levels = {'l0':'0', + 'l1a':'-1', + 'l1b':'-2', + 'l1c':'-3', + 'l1d':'-4', + 'l2':'-5', + 'l2a':'-6', + 'l2b':'-7', + 'l2c':'-8', + 'l2d':'-9', + 'l3':'-10', + 'l3a':'-11', + 'l3b':'-12', + 'l3c':'-13', + 'l3d':'-14',} + +_sensor_schedule = {'l0': 300, + 'l1a':300, + 'l1b':300, + 'l1c':300, + 'l1d':300, + 'l2': 300, + 'l2a':300, + 'l2b':300, + 'l2c':300, + 'l2d':300, + 'l3': 300, + 'l3a':300, + 'l3b':300, + 'l3c':300, + 'l3d':300} partition_map = { - "daily": custom_partitions.daily_partitions, - "repoint": custom_partitions.repoint_partitions, - "10d": custom_partitions.idex10_partitions, - # NOTE: Right now, IDEX is the only instrument who uses 1mo cadence job that - # maps to exactly 30 days. If this changes, this logic will need update. - "30d": custom_partitions.idex30_partitions, - # TODO: add cadence custom partition definition and update to use those - # later - "3mo": custom_partitions.idex30_partitions, - "6mo": custom_partitions.idex30_partitions, - "1yr": custom_partitions.whole_mission_partition, -} - + "daily": custom_partitions.daily_partitions, + "repoint": custom_partitions.repoint_partitions, + "10d": custom_partitions.idex10_partitions, + # NOTE: Right now, IDEX is the only instrument who uses 1mo cadence job that + # maps to exactly 30 days. If this changes, this logic will need update. + "30d": custom_partitions.idex30_partitions, + # TODO: add cadence custom partition definition and update to use those + # later + "3mo": custom_partitions.idex30_partitions, + "6mo": custom_partitions.idex30_partitions, + "1yr": custom_partitions.whole_mission_partition, + } class IMAPJobHandler: """Handle IMAP job dependencies and submission.""" - def __init__(self, job: ProcessingJobNode): + def __init__(self, + job: ProcessingJobNode): """Initialize handler with job node and process dependencies. Parameters @@ -122,19 +117,16 @@ def __init__(self, job: ProcessingJobNode): self.partitions_def = partition_map.get(self.job_config.partition) self.sensor_schedule = _sensor_schedule.get(self.job_config.data_type, 600) - + outputs_for_job = [x.to_dagster_asset() for x in self.job_config.outputs] - self.dagster_job = define_asset_job( - name=f"{self.job_config.to_dagster_asset().to_user_string()}_processing_job", - selection=outputs_for_job, - tags={ - "dagster/priority": priority_levels.get(self.job_config.data_type, "0") - }, - ) - + self.dagster_job = define_asset_job(name=f"{self.job_config.to_dagster_asset().to_user_string()}_processing_job", + selection=outputs_for_job, + tags={"dagster/priority": priority_levels.get(self.job_config.data_type, '0')}) + def build_asset(self): - """This builds the Asset for Dagster for a particular job. This function will: + """ + This builds the Asset for Dagster for a particular job. This function will: 1) Get all dependencies from the dependency tree 2) Check if the job had been submitted before @@ -142,85 +134,73 @@ def build_asset(self): b) If Dagster does know about it, we exit 3) Get the Job version 4) Submit the job - 5) Wait for the output files, and materialize them as we see them in the database. + 5) Wait for the output files, and materialize them as we see them in the database. """ input_keys = [dep.to_dagster_asset() for dep in self.job_config.inputs] output_assets = {} for out in self.job_config.outputs: - output_assets[out.to_dagster_asset().to_user_string()] = AssetOut( - is_required=False - ) + output_assets[out.to_dagster_asset().to_user_string()] = AssetOut(is_required=False) @multi_asset( name=f"{self.job_config.to_dagster_asset().to_user_string()}_multi_asset_op", - deps=input_keys, + deps=input_keys, partitions_def=self.partitions_def, - outs=output_assets, + outs=output_assets ) def _generic_batch_submitter(context: AssetExecutionContext): parts = context.partition_key.split("_") - start_date = ( - datetime.datetime.strptime(parts[-3], "%Y-%m-%dT%H:%M:%S") - .replace(tzinfo=datetime.timezone.utc) - .date() - ) + start_date = datetime.datetime.strptime(parts[-3], "%Y-%m-%dT%H:%M:%S").replace(tzinfo=datetime.timezone.utc).date() if "repoint" in parts[0]: pointing_number = int(parts[0][7:]) else: pointing_number = None - + # 1. Figure out what time window this specific run is responsible for target_partition = context.partition_key target_start, target_end = self._parse_dates_from_key(target_partition) - - # 2. Get Dependencies + + # 2. Get Dependencies dependency_inputs = self.get_dependencies(context, target_start, target_end) if not dependency_inputs: - raise Failure( - description="Processing failed: Dependency inputs were missing." - ) + raise Failure(description="Processing failed: Dependency inputs were missing.") already_processed = False with db.Session() as session: for output in self.job_config.outputs: - previous_file = self.is_duplicate_job( - context, - session, - dependency_inputs, - self.job_config.source, - self.job_config.data_type, - output.descriptor, - self.job_config.descriptor, - start_date, - pointing_number, - ) - + previous_file = self.is_duplicate_job(context, + session, + dependency_inputs, + self.job_config.source, + self.job_config.data_type, + output.descriptor, + self.job_config.descriptor, + start_date, + pointing_number) + if previous_file: already_processed = True - materialization = get_materialization_result( - context, - output.to_dagster_asset(), - context.partition_key, - [os.path.basename(previous_file.file_path)], - previous_file.version, - "science", - inputs=dependency_inputs.serialize(), - ) + materialization = get_materialization_result(context, + output.to_dagster_asset(), + context.partition_key, + [os.path.basename(previous_file.file_path)], + previous_file.version, + "science", + inputs = dependency_inputs.serialize()) if materialization: yield materialization if not already_processed: job_version = self._determine_job_version( - session=session, - instrument=self.job_config.source, - descriptor=self.job_config.descriptor, - start_date=start_date, - data_level=self.job_config.data_type, - current_dependencies=dependency_inputs.serialize(), - ) + session=session, + instrument=self.job_config.source, + descriptor=self.job_config.descriptor, + start_date=start_date, + data_level=self.job_config.data_type, + current_dependencies=dependency_inputs.serialize(), + ) context.log.info(f"Job Version to Use: {job_version}") - - """ + + ''' # SUBMIT JOB HERE!!! job_node = {"data_source":instrument, "data_type":level, @@ -233,59 +213,49 @@ def _generic_batch_submitter(context: AssetExecutionContext): dependency_inputs.serialize(), repoint=int(target_partition), ) - """ - # if submit_status.status == 'submitted': + ''' + #if submit_status.status == 'submitted': for output in self.job_config.outputs: - context.log.info( - f"Waiting for data product {output.source}_{output.data_type}_{output.descriptor} to complete" - ) - file = self.wait_for_file( - context, - session, - output, - job_version, - repointing=pointing_number, - start_date=start_date.strftime("%Y%m%d"), - inputs=dependency_inputs.serialize(), - ) + context.log.info(f"Waiting for data product {output.source}_{output.data_type}_{output.descriptor} to complete") + file = self.wait_for_file(context, + session, + output, + job_version, + repointing=pointing_number, + start_date=start_date.strftime("%Y%m%d"), + inputs = dependency_inputs.serialize()) if file: yield file - # TODO: We should not yield right away. We should collect up anything that this asset has generated, - # yield what we can, and determine if anything required in the output is missing, then yield a failure. + # TODO: We should not yield right away. We should collect up anything that this asset has generated, + # yield what we can, and determine if anything required in the output is missing, then yield a failure. # Return the generated function back to Dagster return _generic_batch_submitter - def wait_for_file( - self, - context, - session: db.Session, - output: DependencyNode, - job_version: str, - start_date: str = None, - repointing: int = None, - inputs={}, - ): + def wait_for_file(self, + context, + session: db.Session, + output: DependencyNode, + job_version: str, + start_date: str = None, + repointing: int = None, + inputs = {}): if start_date is None and repointing is None: - raise ValueError( - "You must at least provide either start_date or repointing" - ) - + raise ValueError("You must at least provide either start_date or repointing") + parsed_start_date = None if start_date is not None: parsed_start_date = datetime.datetime.strptime(start_date, "%Y%m%d") - - timeout = ( - 3 # TODO: Set this for WAY higher once we're actually waiting for files. - ) + + timeout = 3 # TODO: Set this for WAY higher once we're actually waiting for files. timeout_start = time.time() while time.time() < timeout_start + timeout: filters = [ models.ScienceFiles.instrument == output.source, models.ScienceFiles.data_level == output.data_type, models.ScienceFiles.descriptor == output.descriptor, - models.ScienceFiles.version == job_version, + models.ScienceFiles.version == job_version ] if repointing is not None: filters.append(models.ScienceFiles.repointing == int(repointing)) @@ -293,62 +263,49 @@ def wait_for_file( filters.append(models.ScienceFiles.start_date == parsed_start_date) created_file = session.query(models.ScienceFiles).filter(*filters).first() if created_file: - context.log.info( - f"Found file {os.path.basename(created_file.file_path)}! Creating Asset." - ) - materialization = get_materialization_result( - context, - output.to_dagster_asset(), - context.partition_key, - [os.path.basename(created_file.file_path)], - str(int(job_version[1:])), - "science", - inputs=inputs, - ) + context.log.info(f"Found file {os.path.basename(created_file.file_path)}! Creating Asset.") + materialization = get_materialization_result(context, + output.to_dagster_asset(), + context.partition_key, + [os.path.basename(created_file.file_path)], + str(int(job_version[1:])), + "science", + inputs = inputs) if materialization: return materialization - time.sleep( - 1 - ) # TODO: Set this for WAY higher once we're actually waiting for files. - - def _parse_dates_from_key( - self, partition_key: str - ) -> tuple[datetime.datetime, datetime.datetime]: - """Extracts start and end datetimes from a string formatted like: + time.sleep(1) # TODO: Set this for WAY higher once we're actually waiting for files. + + def _parse_dates_from_key(self, + partition_key: str) -> tuple[datetime.datetime, datetime.datetime]: + """ + Extracts start and end datetimes from a string formatted like: '{name}_%Y-%m-%dT%H:%M:%S_to_%Y-%m-%dT%H:%M:%S' """ if not partition_key: return None, None - - date_range = partition_key.split("_", 1)[1] + + date_range = partition_key.split('_', 1)[1] if "_to_" in date_range: p_start_str, p_end_str = date_range.split("_to_") - p_start = datetime.datetime.strptime( - p_start_str, "%Y-%m-%dT%H:%M:%S" - ).replace(tzinfo=datetime.timezone.utc) - p_end = datetime.datetime.strptime(p_end_str, "%Y-%m-%dT%H:%M:%S").replace( - tzinfo=datetime.timezone.utc - ) - + p_start = datetime.datetime.strptime(p_start_str, "%Y-%m-%dT%H:%M:%S").replace(tzinfo=datetime.timezone.utc) + p_end = datetime.datetime.strptime(p_end_str, "%Y-%m-%dT%H:%M:%S").replace(tzinfo=datetime.timezone.utc) + return p_start, p_end - def _get_overlapping_target_partitions( - self, upstream_partition_key, up_start, up_end, instance - ): - """Evaluates the upstream date range against existing downstream partitions. + def _get_overlapping_target_partitions(self, upstream_partition_key, up_start, up_end, instance): + """ + Evaluates the upstream date range against existing downstream partitions. Returns a list of downstream partition keys that overlap. """ target_keys = [] # Fetch the currently known dynamic partitions for your target asset - existing_downstream_keys = instance.get_dynamic_partitions( - self.partitions_def.name - ) + existing_downstream_keys = instance.get_dynamic_partitions(self.partitions_def.name) # Check if the upstream and downstream are in the same partition if upstream_partition_key in existing_downstream_keys: - # These are actually in same partition! No need to loop through the other keys. + # These are actually in same partition! No need to loop through the other keys. return [upstream_partition_key] - + for down_key in existing_downstream_keys: down_start, down_end = self._parse_dates_from_key(down_key) if not down_start or not down_end: @@ -361,46 +318,45 @@ def _get_overlapping_target_partitions( return target_keys def build_sensor(self): - """Factory function that returns a Dagster sensor monitoring the dependencies + """ + Factory function that returns a Dagster sensor monitoring the dependencies and triggering this asset when overlapping data arrives. 1) Checks for all of the latest asset materializations in dagster for all dependencies of this product 2) Loops through the latest materializations 3) Determines what time range those materializations belong to 4) Determines what partition keys of this asset those new materializations belong to - 5) For each affected partition, we determine the dependencies. If we have all dependencies, we are good! + 5) For each affected partition, we determine the dependencies. If we have all dependencies, we are good! 6) Yield a RunRequest for a job to make the asset for the partitions that have all dependencies. """ deps_keys = [x.to_dagster_asset() for x in self.job_config.triggering_deps] - sensor_name = ( - f"{self.job_config.to_dagster_asset().to_user_string()}_kickoff_sensor" - ) - + sensor_name = f"{self.job_config.to_dagster_asset().to_user_string()}_kickoff_sensor" @sensor( name=sensor_name, job=self.dagster_job, minimum_interval_seconds=self.sensor_schedule, - default_status=DefaultSensorStatus.RUNNING, + default_status=DefaultSensorStatus.RUNNING ) def _sensor(context: SensorEvaluationContext): - + # Load the cursor to track which events we have already seen. # The cursor maps `{asset_name: last_processed_storage_id}`. cursors = json.loads(context.cursor) if context.cursor else {} new_cursors = cursors.copy() - + # Iterate through each dependency to find unconsumed materializations for dep_key in deps_keys: + dep_name = dep_key.to_user_string() context.log.info(f"Checking new dependencies for: {dep_name}") # Fetch the last evaluated event ID for this specific dependency last_event_id = cursors.get(dep_name) filter = EventRecordsFilter( - event_type=DagsterEventType.ASSET_MATERIALIZATION, - asset_key=dep_key, - after_cursor=last_event_id, - ) + event_type=DagsterEventType.ASSET_MATERIALIZATION, + asset_key=dep_key, + after_cursor=last_event_id, + ) new_events = context.instance.get_event_records(filter, limit=1) if new_events: record = new_events[0] @@ -409,36 +365,32 @@ def _sensor(context: SensorEvaluationContext): # Update our new cursor marker to the highest storage ID seen new_cursors[dep_name] = record.storage_id - + upstream_partition_key = record.event_log_entry.dagster_event.partition up_start, up_end = self._parse_dates_from_key(upstream_partition_key) - + if not up_start or not up_end: continue - + # Calculate overlap target_partitions = self._get_overlapping_target_partitions( upstream_partition_key, up_start, up_end, context.instance ) - + for target_partition in target_partitions: # Queue the RunRequest - target_start, target_end = self._parse_dates_from_key( - target_partition - ) - dependencies = self.get_dependencies( - context, target_start, target_end - ) + target_start, target_end = self._parse_dates_from_key(target_partition) + dependencies = self.get_dependencies(context, target_start, target_end) if dependencies: - tags = {"dependencies": dependencies.serialize()} + tags={"dependencies": dependencies.serialize()} yield RunRequest( - partition_key=target_partition, - run_key=f"{self.job_config.to_dagster_asset().to_user_string()}_{target_partition}_{self._dependency_hash(dependencies.serialize())}", - tags=tags, - ) - break # To keep the sensor short, we'll only analyze one new dependency at a time. If there are still new ones, we'll consume them next time. - # The get_dependencies will still get the latest stuff regardless, so we're never submitting outdated data. - + partition_key=target_partition, + run_key=f"{self.job_config.to_dagster_asset().to_user_string()}_{target_partition}_{self._dependency_hash(dependencies.serialize())}", + tags=tags + ) + break # To keep the sensor short, we'll only analyze one new dependency at a time. If there are still new ones, we'll consume them next time. + # The get_dependencies will still get the latest stuff regardless, so we're never submitting outdated data. + # 5. Lock in the new cursor state and execute context.update_cursor(json.dumps(new_cursors)) @@ -446,6 +398,7 @@ def _sensor(context: SensorEvaluationContext): def process_job(self, potential_job_node: DependencyNode): """Process the job by resolving dependencies and submitting to batch.""" + if self.dependencies is not None: self._calculate_crid() self._determine_job_version() @@ -469,74 +422,59 @@ def process_job(self, potential_job_node: DependencyNode): # if job_submit_succeed: # self.clean_up() - def get_dependencies( - self, - context: AssetExecutionContext, - target_start: datetime.datetime, - target_end: datetime.datetime, - ): + def get_dependencies(self, + context: AssetExecutionContext, + target_start: datetime.datetime, + target_end: datetime.datetime): """Get the dependencies for the job using the DependencyResolver.""" - # Iterate through each upstream dependency + + # Iterate through each upstream dependency dependency_inputs = processing_input.ProcessingInputCollection() - context.log.info( - f"Checking for all dependencies existing between {target_start} and {target_end}" - ) + context.log.info(f"Checking for all dependencies existing between {target_start} and {target_end}") for input in self.job_config.inputs: dep_name = input.to_user_string() - found_dep = False + found_dep=False context.log.info(f"Checking out {dep_name}") metadata_list = input.get_all_files_in_time_range() for metadata in metadata_list: if "file_names" in metadata: - found_dep = ( - True # We can finally say we have found at least one dependency - ) + found_dep = True # We can finally say we have found at least one dependency # Dagster wraps metadata in a MetadataValue object, so we call .value file_names = metadata["file_names"].value # Handle both single strings and lists of files safely if file_names: - context.log.info( - f"The file names of the matching partition: {file_names}" - ) + context.log.info(f"The file names of the matching partition: {file_names}") input_type = metadata["input_type"].value - if input_type == "science": - dependency_inputs.add( - processing_input.ScienceInput(*file_names) - ) - if input_type == "ancillary": - dependency_inputs.add( - processing_input.AncillaryInput(*file_names) - ) - if input_type == "spin": + if input_type=='science': + dependency_inputs.add(processing_input.ScienceInput(*file_names)) + if input_type=='ancillary': + dependency_inputs.add(processing_input.AncillaryInput(*file_names)) + if input_type=='spin': dependency_inputs.add(processing_input.SpinInput(*file_names)) - if input_type == "repoint": + if input_type=='repoint': dependency_inputs.add( processing_input.RepointInput(file_names[0]) ) - if input_type == "spice": + if input_type=='spice': dependency_inputs.add(processing_input.SPICEInput(*file_names)) if not found_dep: # TODO: Check here if we have a soft dependency. - context.log.info( - f"Not enought information to process. Missing {dep_name} in range {target_start!s} to {target_end!s}" - ) + context.log.info(f"Not enought information to process. Missing {dep_name} in range {str(target_start)} to {str(target_end)}") return return dependency_inputs - def is_duplicate_job( - self, - context, - session, - dependency, - instrument, - level, - descriptor, - job_command, - start_date, - repoint, - ): + def is_duplicate_job(self, + context, + session, + dependency, + instrument, + level, + descriptor, + job_command, + start_date, + repoint): """Determine if the job is a duplicate. Requirements for duplicate job determination: @@ -567,74 +505,52 @@ def is_duplicate_job( # ) # 3. If return exists, it's a duplicate job and return True. - """ - This function will check if the latest file at the SDC has already been processed using + ''' + This function will check if the latest file at the SDC has already been processed using the gathered dependencies. To do this, it: 1) Finds the latest file 2) Finds the corresponding Processing Job 3) Read in the .json dependency file from the corresponding Processing Job - 4) Compares the Ancillary and Science files in the dependency file to the current ones + 4) Compares the Ancillary and Science files in the dependency file to the current ones 5) If they all match, the function returns the materialization result of the latest file - Yes, this is somewhat complicated right now. I think the best way going forward would be to - add the dependencies to a particular file either in the ScienceFiles or ProcessingJob tables. - """ - context.log.info( - f"Checking if we have already have a file of this instrument of {instrument}, this level of {level}, and this {descriptor}." - ) - context.log.info( - f"And this start date {start_date}, and this pointing number {repoint}." - ) + Yes, this is somewhat complicated right now. I think the best way going forward would be to + add the dependencies to a particular file either in the ScienceFiles or ProcessingJob tables. + ''' + context.log.info(f"Checking if we have already have a file of this instrument of {instrument}, this level of {level}, and this {descriptor}.") + context.log.info(f"And this start date {start_date}, and this pointing number {repoint}.") if repoint: - latest_file = ( - session.query(models.ScienceFiles) - .filter( - models.ScienceFiles.instrument == instrument, - models.ScienceFiles.data_level == level, - models.ScienceFiles.descriptor == descriptor, - models.ScienceFiles.repointing == int(repoint), - ) - .order_by(models.ScienceFiles.version.desc()) - .first() - ) + latest_file = session.query(models.ScienceFiles).filter( + models.ScienceFiles.instrument == instrument, + models.ScienceFiles.data_level == level, + models.ScienceFiles.descriptor == descriptor, + models.ScienceFiles.repointing == int(repoint) + ).order_by(models.ScienceFiles.version.desc()).first() else: - latest_file = ( - session.query(models.ScienceFiles) - .filter( - models.ScienceFiles.instrument == instrument, - models.ScienceFiles.data_level == level, - models.ScienceFiles.descriptor == descriptor, - models.ScienceFiles.start_date == start_date, - ) - .order_by(models.ScienceFiles.version.desc()) - .first() - ) + latest_file = session.query(models.ScienceFiles).filter( + models.ScienceFiles.instrument == instrument, + models.ScienceFiles.data_level == level, + models.ScienceFiles.descriptor == descriptor, + models.ScienceFiles.start_date == start_date + ).order_by(models.ScienceFiles.version.desc()).first() if latest_file: - context.log.info( - "Yes we did! Now we need to check the processing jobs to see if the same dependencies were used to create this. " - ) + context.log.info("Yes we did! Now we need to check the processing jobs to see if the same dependencies were used to create this. ") max_version_record = ( session.query(models.ProcessingJob) - .filter( - models.ProcessingJob.instrument == latest_file.instrument, + .filter(models.ProcessingJob.instrument == latest_file.instrument, models.ProcessingJob.data_level == latest_file.data_level, models.ProcessingJob.descriptor == job_command, models.ProcessingJob.start_date == latest_file.start_date, models.ProcessingJob.status.in_( - [models.Status.INPROGRESS.value, models.Status.SUCCEEDED.value] - ), - ) + [models.Status.INPROGRESS.value, models.Status.SUCCEEDED.value] + )) .order_by(models.ProcessingJob.version.desc()) .first() ) if max_version_record: - context.log.info( - "A job does indeed look like this one...lets check out its dependencies file" - ) - match = re.search( - r"--dependency\s+(\S+\.json)", max_version_record.container_command - ) + context.log.info("A job does indeed look like this one...lets check out its dependencies file") + match = re.search(r'--dependency\s+(\S+\.json)', max_version_record.container_command) if match: dependency_file = match.group(1) context.log.info(dependency_file) @@ -646,36 +562,30 @@ def is_duplicate_job( ssm_response = SSM_CLIENT.get_parameter( Name=ssm_parameter_name, WithDecryption=True ) - imap_data_access.config["API_KEY"] = ssm_response["Parameter"][ - "Value" - ] + imap_data_access.config["API_KEY"] = ssm_response["Parameter"]["Value"] except Exception as e: print(f"Could not retrieve API key from SSM: {e}") dependency_filepath = download(dependency_file) with open(dependency_filepath) as f: old_inputs = json.loads(f.read()) - context.log.info( - f"Check it out! These are the dependencies it previously ran with: {json.dumps(old_inputs)}" - ) + context.log.info(f"Check it out! These are the dependencies it previously ran with: {json.dumps(old_inputs)}") dependencies_match = True old_science_inputs = [] old_ancillary_inputs = [] new_science_inputs = [] new_ancillary_inputs = [] for dep in old_inputs: - if dep["type"] == "science": - old_science_inputs.extend(dep["files"]) - if dep["type"] == "ancillary": - old_ancillary_inputs.extend(dep["files"]) + if dep['type'] == "science": + old_science_inputs.extend(dep['files']) + if dep['type'] == "ancillary": + old_ancillary_inputs.extend(dep['files']) new_inputs = json.loads(dependency.serialize()) - context.log.info( - f"Check it out! These are the dependencies that we want to run it with now: {json.dumps(new_inputs)}" - ) + context.log.info(f"Check it out! These are the dependencies that we want to run it with now: {json.dumps(new_inputs)}") for dep in new_inputs: - if dep["type"] == "science": - new_science_inputs.extend(dep["files"]) - if dep["type"] == "ancillary": - new_ancillary_inputs.extend(dep["files"]) + if dep['type'] == "science": + new_science_inputs.extend(dep['files']) + if dep['type'] == "ancillary": + new_ancillary_inputs.extend(dep['files']) if set(old_science_inputs) != set(new_science_inputs): dependencies_match = False @@ -683,14 +593,14 @@ def is_duplicate_job( dependencies_match = False if dependencies_match: - context.log.info("It's a match!") - # The latest file does not need to be updated. - # We need to tell dagster that this asset is complete. + context.log.info(f"It's a match!") + # The latest file does not need to be updated. + # We need to tell dagster that this asset is complete. context.log.info(f"Latest file: {latest_file.file_path}") context.log.info(f"Version: {latest_file.version}") return latest_file else: - context.log.info("It's not a match!") + context.log.info(f"It's not a match!") else: context.log.info("No files have ever been made like this before!") @@ -806,7 +716,7 @@ def _dependency_hash(self, serialized_dependencies): """Generate a hash for the serialized dependencies. Use only the first 8 characters. This is a unique ID for a particular run. Dagster will refused to run a job with - the same dependency_hash. + the same dependency_hash. Parameters ---------- @@ -822,7 +732,7 @@ def _dependency_hash(self, serialized_dependencies): dependencies = json.loads(serialized_dependencies) non_sclk_deps = [] for dep in dependencies: - for file in dep["files"]: + for file in dep['files']: if "imap_sclk" not in file: # We'll get rid of the spacecraft_clock kernel, if it exists non_sclk_deps.append(file) @@ -836,10 +746,10 @@ def _dependency_hash(self, serialized_dependencies): def _get_container_image_digest(self): """Get the container image digest. - The image digest is a sha256 hash of the image manifest and is a unique - identifier for the specific version of the container image used in the batch - job. This is important for tracking which version of the code is being used - for each job. + The image digest is a sha256 hash of the image manifest and is a unique identifier + for the specific version of the container image used in the batch job. + This is important for tracking which version of the code is being used for each + job. Parameters ---------- @@ -867,8 +777,8 @@ def _get_container_image_digest(self): key=lambda definition: definition.get("revision", 0), ) container_image = job_def["containerProperties"]["image"] - # Parse the container image URI to get the registry id, repository name and - # image tag and use those to call describe_images and get the image digest. + # Parse the container image URI to get the registry id, repository name and image + # tag and use those to call describe_images and get the image digest. # Eg. for 123456789012.dkr.ecr.us-west-2.amazonaws.com/swapi-repo:latest, # "123456789012" is the registry id, "swapi-repo" is the repository and # "latest" is the image tag. From 66b695bdb2dbbbc87529ae1e749deb25c7f1bf3a Mon Sep 17 00:00:00 2001 From: Luisa Date: Tue, 26 May 2026 17:51:25 -0600 Subject: [PATCH 3/6] add reprocessing sensor to deff --- sds_data_manager/orchestration/imap_dagster.py | 4 +++- sds_data_manager/orchestration/reprocessing.py | 3 +++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/sds_data_manager/orchestration/imap_dagster.py b/sds_data_manager/orchestration/imap_dagster.py index 0e5c6c4e9..07a1a3e3e 100644 --- a/sds_data_manager/orchestration/imap_dagster.py +++ b/sds_data_manager/orchestration/imap_dagster.py @@ -1,6 +1,7 @@ from dagster import Definitions from imap_data_access import VALID_DATALEVELS -from sds_data_manager.orchestration import custom_partitions, repoint_file, spice, idex +from sds_data_manager.orchestration import custom_partitions, repoint_file, spice, idex, \ + reprocessing from sds_data_manager.orchestration import spin from sds_data_manager.orchestration.imap_file import IMAPAncillaryFileHandler, IMAPScienceFileHandler from sds_data_manager.orchestration.imap_job import IMAPJobHandler @@ -103,5 +104,6 @@ + custom_partitions.sensors + spice.sensors + sensors + + reprocessing.sensor + idex.L0_sensor, ) \ No newline at end of file diff --git a/sds_data_manager/orchestration/reprocessing.py b/sds_data_manager/orchestration/reprocessing.py index b06697ee5..adade1992 100644 --- a/sds_data_manager/orchestration/reprocessing.py +++ b/sds_data_manager/orchestration/reprocessing.py @@ -126,3 +126,6 @@ def reprocess_sensor(context: SensorEvaluationContext): ) return None + + +sensors = [reprocess_sensor] \ No newline at end of file From 17008b650280fd679ac5cb67b64de8f572345ca5 Mon Sep 17 00:00:00 2001 From: Luisa Date: Tue, 26 May 2026 17:53:08 -0600 Subject: [PATCH 4/6] fix comment --- sds_data_manager/constructs/instrument_lambdas.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/sds_data_manager/constructs/instrument_lambdas.py b/sds_data_manager/constructs/instrument_lambdas.py index f363c7f4c..2c503a65d 100644 --- a/sds_data_manager/constructs/instrument_lambdas.py +++ b/sds_data_manager/constructs/instrument_lambdas.py @@ -134,11 +134,7 @@ def __init__( for q in sqs_queues: self.instrument_lambda.add_event_source(SqsEventSource(q)) - # In addition to sending events to batch starter, send them to a sqs that - # dagster can poll from. - # TODO eventually we only want the reprocessing api to send messages to the sqs - # and not to batch starter. Dagster should be fully responsible for - # reprocessing eventually. + # Send reprocessing events to a sqs that dagster can poll from. # Create a dead letter queue to save messages that could not be processed. # This DLQ just saves the messages and doesn't do anything with them. self.dead_letter_queue = sqs.Queue( From c67a4d5f33322bf45ad2368f5ccd1bfc506dfcca Mon Sep 17 00:00:00 2001 From: Luisa Date: Tue, 26 May 2026 17:55:38 -0600 Subject: [PATCH 5/6] poetry lock --- poetry.lock | 2012 ++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 1913 insertions(+), 99 deletions(-) diff --git a/poetry.lock b/poetry.lock index 55261f825..60a29fcf6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. [[package]] name = "accessible-pygments" @@ -37,14 +37,14 @@ files = [ name = "alembic" version = "1.18.4" description = "A database migration tool for SQLAlchemy." -optional = true +optional = false python-versions = ">=3.10" -groups = ["main"] -markers = "extra == \"tool\"" +groups = ["main", "cdk-install"] files = [ {file = "alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a"}, {file = "alembic-1.18.4.tar.gz", hash = "sha256:cb6e1fd84b6174ab8dbb2329f86d631ba9559dd78df550b57804d607672cedbc"}, ] +markers = {main = "extra == \"tool\""} [package.dependencies] Mako = "*" @@ -54,6 +54,49 @@ typing-extensions = ">=4.12" [package.extras] tz = ["tzdata"] +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +groups = ["cdk-install"] +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "antlr4-python3-runtime" +version = "4.13.2" +description = "ANTLR 4.13.2 runtime for Python 3" +optional = false +python-versions = "*" +groups = ["cdk-install"] +files = [ + {file = "antlr4_python3_runtime-4.13.2-py3-none-any.whl", hash = "sha256:fe3835eb8d33daece0e799090eda89719dbccee7aa39ef94eed3818cafa5a7e8"}, + {file = "antlr4_python3_runtime-4.13.2.tar.gz", hash = "sha256:909b647e1d2fc2b70180ac586df3933e38919c85f98ccc656a96cd3f25ef3916"}, +] + +[[package]] +name = "anyio" +version = "4.13.0" +description = "High-level concurrency and networking framework on top of asyncio or Trio" +optional = false +python-versions = ">=3.10" +groups = ["cdk-install"] +files = [ + {file = "anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708"}, + {file = "anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc"}, +] + +[package.dependencies] +idna = ">=2.8" +typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} + +[package.extras] +trio = ["trio (>=0.32.0)"] + [[package]] name = "astropy" version = "7.2.0" @@ -148,7 +191,7 @@ description = "Atomic file writes." optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["main"] -markers = "extra == \"test\" and sys_platform == \"win32\"" +markers = "sys_platform == \"win32\" and extra == \"test\"" files = [ {file = "atomicwrites-1.4.1.tar.gz", hash = "sha256:81b2c9071a49367a7f770170e5eec8cb66567cfbbc8c73d20ce5ca4a8d71cf11"}, ] @@ -273,6 +316,18 @@ files = [ [package.extras] dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""] +[[package]] +name = "backoff" +version = "2.2.1" +description = "Function decoration for backoff and retry" +optional = false +python-versions = ">=3.7,<4.0" +groups = ["cdk-install"] +files = [ + {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"}, + {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"}, +] + [[package]] name = "beautifulsoup4" version = "4.14.3" @@ -303,7 +358,7 @@ version = "1.42.96" description = "The AWS SDK for Python" optional = false python-versions = ">=3.9" -groups = ["main"] +groups = ["main", "cdk-install"] files = [ {file = "boto3-1.42.96-py3-none-any.whl", hash = "sha256:2f4566da2c209a98bdbfc874d813ef231c84ad24e4f815e9bc91de5f63351a24"}, {file = "boto3-1.42.96.tar.gz", hash = "sha256:b38a9e4a3fbbee9017252576f1379780d0a5814768676c08df2f539d31fcdd68"}, @@ -323,7 +378,7 @@ version = "1.42.96" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.9" -groups = ["main"] +groups = ["main", "cdk-install"] files = [ {file = "botocore-1.42.96-py3-none-any.whl", hash = "sha256:db2c3e2006628be6fde81a24124a6563c363d6982fb92728837cf174bad9d98a"}, {file = "botocore-1.42.96.tar.gz", hash = "sha256:75b3b841ffacaa944f645196655a21ca777591dd8911e732bfb6614545af0250"}, @@ -383,13 +438,32 @@ dev = ["ipython", "matplotlib", "pre-commit"] docs = ["mkdocs", "mkdocs-material", "mkdocstrings[python]"] tests = ["astropy", "h5netcdf", "hypothesis", "netcdf4", "pooch", "pytest (>=3.9)", "pytest-cov", "pytest-remotedata", "xarray"] +[[package]] +name = "cdk-ecr-deployment" +version = "4.2.1" +description = "CDK construct to deploy docker image to Amazon ECR" +optional = false +python-versions = "~=3.9" +groups = ["cdk-install"] +files = [ + {file = "cdk_ecr_deployment-4.2.1-py3-none-any.whl", hash = "sha256:c6ce7a49c8301534cfb97ea59934568cd53816d8236dd4b1d5450eeed9743070"}, + {file = "cdk_ecr_deployment-4.2.1.tar.gz", hash = "sha256:e47c07da1ec9a80f2f6eb35534e5d587ef5f08965f65b0e28f83597b29638bb7"}, +] + +[package.dependencies] +aws-cdk-lib = ">=2.80.0,<3.0.0" +constructs = ">=10.0.5,<11.0.0" +jsii = ">=1.128.0,<2.0.0" +publication = ">=0.0.3" +typeguard = "2.13.3" + [[package]] name = "certifi" version = "2026.4.22" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.7" -groups = ["main", "layer-processing", "layer-spice"] +groups = ["main", "cdk-install", "layer-processing", "layer-spice"] files = [ {file = "certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a"}, {file = "certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580"}, @@ -512,7 +586,7 @@ version = "3.4.7" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." optional = false python-versions = ">=3.7" -groups = ["main", "layer-processing", "layer-spice"] +groups = ["main", "cdk-install", "layer-processing", "layer-spice"] files = [ {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"}, {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"}, @@ -651,7 +725,7 @@ version = "8.3.3" description = "Composable command line interface toolkit" optional = false python-versions = ">=3.10" -groups = ["layer-processing"] +groups = ["cdk-install", "layer-processing"] files = [ {file = "click-8.3.3-py3-none-any.whl", hash = "sha256:a2bf429bb3033c89fa4936ffb35d5cb471e3719e1f3c8a7c3fff0b8314305613"}, {file = "click-8.3.3.tar.gz", hash = "sha256:398329ad4837b2ff7cbe1dd166a4c0f8900c3ca3a218de04466f38f6497f18a2"}, @@ -666,12 +740,30 @@ version = "0.4.6" description = "Cross-platform colored terminal text." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" -groups = ["main", "layer-processing"] +groups = ["main", "cdk-install", "layer-processing"] files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -markers = {main = "(extra == \"doc\" or extra == \"test\") and sys_platform == \"win32\"", layer-processing = "platform_system == \"Windows\""} +markers = {main = "sys_platform == \"win32\" and (extra == \"doc\" or extra == \"test\")", cdk-install = "platform_system == \"Windows\" or sys_platform == \"win32\"", layer-processing = "platform_system == \"Windows\""} + 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python-versions = ">=3.11,<3.13" -content-hash = "af60cee65cdbb64698b1d1305ecd0462def32d8c161a0e01df922bd225847e50" +content-hash = "e45f381aaf07377747ca3bfa450e60ef5df1f24f0330b8bb41320e5c62290028" From e8fea687d159068003501c40247bce9e300e7c71 Mon Sep 17 00:00:00 2001 From: Luisa Date: Tue, 26 May 2026 17:59:00 -0600 Subject: [PATCH 6/6] poetry lock --- poetry.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 60a29fcf6..861bd55ec 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. [[package]] name = "accessible-pygments"