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feat: update lambda state machine to accommodate tenantId #367

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Jun 30, 2021
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feat: update lambda state machine to accommodate tenantId #367

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ea6f51c
feat: update lambda state machine to accomandate tenantId
Jun 25, 2021
d3460a5
use tenant specific S3 path
Jun 28, 2021
c2b6228
update comment
Jun 29, 2021
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15 changes: 14 additions & 1 deletion bulkExport/glueScripts/export-script.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,9 @@
groupId = None
if ('--{}'.format('groupId') in sys.argv):
groupId = getResolvedOptions(sys.argv, ['groupId'])['groupId']
tenantId = None
if ('--{}'.format('tenantId') in sys.argv):
tenantId = getResolvedOptions(sys.argv, ['tenantId'])['tenantId']

job_id = args['jobId']
export_type = args['exportType']
Expand Down Expand Up @@ -60,12 +63,21 @@
}
)

print('Start filtering by tenantId')
# Filter by resource listed in Type and with correct STATUS
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if (tenantId is None):
filtered_tenant_id_frame = original_data_source_dyn_frame
else:
filtered_tenant_id_frame = Filter.apply(frame = original_data_source_dyn_frame,
f = lambda x:
x['_tenantId'] == tenantId)
Comment on lines +71 to +73
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instead of doing glue side filtering would it be better to have a secondary index on the tenantId? This will become an expensive operation if we have to scan across all tenants

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Emm that's a great question. In the design doc, it specified the filtering is to be done as part of the Glue job, and secondary index was not introduced for any tables. @carvantes Any thoughts?

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The glue job always scans the entire DDB table no matter what, there's no way to use a query. This is a limitation on the current AWS Glue + DDB integration.

There are existing scenarios where this is far from ideal. e.g. exporting a single FHIR resource type or exporting the resources modified in the last hour will both scan the entire table.

There is room for improvement on the bulk export solution, but we are not changing the fundamentals here.


print('Start filtering by transactionTime and Since')
# Filter by transactionTime and Since
datetime_since = datetime.strptime(since, "%Y-%m-%dT%H:%M:%S.%fZ")
datetime_transaction_time = datetime.strptime(transaction_time, "%Y-%m-%dT%H:%M:%S.%fZ")

filtered_dates_dyn_frame = Filter.apply(frame = original_data_source_dyn_frame,
filtered_dates_dyn_frame = Filter.apply(frame = filtered_tenant_id_frame,
f = lambda x:
datetime.strptime(x["meta"]["lastUpdated"], "%Y-%m-%dT%H:%M:%S.%fZ") > datetime_since and
datetime.strptime(x["meta"]["lastUpdated"], "%Y-%m-%dT%H:%M:%S.%fZ") <= datetime_transaction_time
Expand All @@ -81,6 +93,7 @@
else x["documentStatus"] in valid_document_state_to_be_read_from and x["resourceType"] in type_list
)


# Drop fields that are not needed
print('Dropping fields that are not needed')
data_source_cleaned_dyn_frame = DropFields.apply(frame = filtered_dates_resource_dyn_frame, paths = ['documentStatus', 'lockEndTs', 'vid', '_references'])
Expand Down
6 changes: 3 additions & 3 deletions bulkExport/state-machine-definition.yaml
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Expand Up @@ -27,21 +27,21 @@ definition:
updateStatusToFailed:
Type: Task
Resource: !GetAtt updateStatus.Arn
Parameters: {"jobId.$":"$.jobId", "status": "failed"}
Parameters: {"globalParams.$":"$", "status": "failed"}
Retry:
- ErrorEquals: [ "States.ALL" ]
End: true
updateStatusToCanceled:
Type: Task
Resource: !GetAtt updateStatus.Arn
Parameters: {"jobId.$":"$.jobId", "status": "canceled"}
Parameters: {"globalParams.$":"$", "status": "canceled"}
Retry:
- ErrorEquals: [ "States.ALL" ]
End: true
updateStatusToCompleted:
Type: Task
Resource: !GetAtt updateStatus.Arn
Parameters: {"jobId.$":"$.jobId", "status": "completed"}
Parameters: {"globalParams.$":"$", "status": "completed"}
Retry:
- ErrorEquals: [ "States.ALL" ]
End: true
Expand Down