Python: Fetching nested dictionary values GCP Recommendations - python-3.x

Question

I am trying to illicit certain key values out of an API JSON output from GCP Recommendations API using Python, and am newer to using Python. Most of the values that I am trying to illicit I can fetch without an issue, however, when I try to illicit certain values within a more deeply nested code block in the JSON, it fails with the error: TypeError: 'OperationGroup' object is not subscriptable
The full output of the JSON response is here (some of the values are changed to protect company information):
name: "projects/12345678910/locations/us-central1-a/recommenders/google.compute.instance.MachineTypeRecommender/recommendations/abcd-efg-hijk-lmnop-qrstuv-123456"
description: "Save cost by changing machine type from e2-medium to e2-small."
last_refresh_time {
seconds: 1623222401
}
primary_impact {
category: COST
cost_projection {
cost {
currency_code: "USD"
units: -12
nanos: -98539964
}
duration {
seconds: 2592000
}
}
}
content {
operation_groups {
operations {
action: "test"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/xyz/zones/us-central1-a/instances/abcname123"
path: "/machineType"
value_matcher {
matches_pattern: ".*zones/us-central1-a/machineTypes/e2-medium"
}
}
operations {
action: "replace"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/xyz/zones/us-central1-a/instances/abcname123"
path: "/machineType"
value {
string_value: "zones/us-central1-a/machineTypes/e2-small"
}
}
}
}
state_info {
state: ACTIVE
}
etag: "\"abc-123-def-456\""
recommender_subtype: "CHANGE_MACHINE_TYPE"
name: "projects/12345678910/locations/us-central1-a/recommenders/google.compute.instance.MachineTypeRecommender/recommendations/abcdefg-hijklmnop-123-456"
description: "Save cost by changing machine type from e2-medium to e2-small."
last_refresh_time {
seconds: 1623222401
}
primary_impact {
category: COST
cost_projection {
cost {
currency_code: "USD"
units: -12
nanos: -99648292
}
duration {
seconds: 2592000
}
}
}
content {
operation_groups {
operations {
action: "test"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/xyz/zones/us-central1-a/instances/instance-example1"
path: "/machineType"
value_matcher {
matches_pattern: ".*zones/us-central1-a/machineTypes/e2-medium"
}
}
operations {
action: "replace"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/xyz/zones/us-central1-a/instances/instance-example1"
path: "/machineType"
value {
string_value: "zones/us-central1-a/machineTypes/e2-small"
}
}
}
}
state_info {
state: ACTIVE
}
etag: "\"abcdefg12345\""
recommender_subtype: "CHANGE_MACHINE_TYPE"
name: "projects/12345678910/locations/us-central1-a/recommenders/google.compute.instance.MachineTypeRecommender/recommendations/abcd1234"
description: "Save cost by changing machine type from e2-medium to e2-small."
last_refresh_time {
seconds: 1623222401
}
primary_impact {
category: COST
cost_projection {
cost {
currency_code: "USD"
units: -11
nanos: -568971875
}
duration {
seconds: 2592000
}
}
}
content {
operation_groups {
operations {
action: "test"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/abcd/zones/us-central1-a/instances/instance-example2"
path: "/machineType"
value_matcher {
matches_pattern: ".*zones/us-central1-a/machineTypes/e2-medium"
}
}
operations {
action: "replace"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/abcd/zones/us-central1-a/instances/instance-example2"
path: "/machineType"
value {
string_value: "zones/us-central1-a/machineTypes/e2-small"
}
}
}
}
state_info {
state: ACTIVE
}
etag: "\"abcd1234\""
recommender_subtype: "CHANGE_MACHINE_TYPE"
Here is my code in Python:
from google.cloud import recommender
import os
client = recommender.RecommenderClient()
def main():
name = client.list_recommendations(parent='projects/xyzproject/locations/us-central1-a/recommenders/google.compute.instance.MachineTypeRecommender')
for element in name:
# print(element)
print(element.description)
print(element.primary_impact.category)
print(element.primary_impact.cost_projection.cost.currency_code)
print(element.primary_impact.cost_projection.cost.units)
print(element.state_info.state)
print(element.content.operation_groups)
for item in element.content.operation_groups:
print(item['resource_type'])
main()
The following portions of the above work:
print(element.description)
print(element.primary_impact.category)
print(element.primary_impact.cost_projection.cost.currency_code)
print(element.primary_impact.cost_projection.cost.units)
print(element.state_info.state)
print(element.content.operation_groups)
but the one that I'm having the error and trouble with is:
for item in element.content.operation_groups:
print(item['resource_type'])
Whenever I try to use that portion of the python script it fails with the error:
TypeError: 'OperationGroup' object is not subscriptable
So, can someone help me understand how I can properly tap into the JSON response and illicit the information within the below block (e.g. the 'resource_type')?
content {
operation_groups {
operations {
action: "test"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/abcd/zones/us-central1-a/instances/instance-example2"
path: "/machineType"
value_matcher {
matches_pattern: ".*zones/us-central1-a/machineTypes/e2-medium"
}
}
operations {
action: "replace"
resource_type: "compute.googleapis.com/Instance"
resource: "//compute.googleapis.com/projects/abcd/zones/us-central1-a/instances/instance-example2"
path: "/machineType"
value {
string_value: "zones/us-central1-a/machineTypes/e2-small"
}
}
}
}

Answer 1

I took the code in your question and rewrote it to match the Python Recommender API. The primary issue is that you are accessing internal member and field names. I have changed the code to use public interfaces.
Review the type definitions for the API:
Types for Recommender API Client
GitHub Gist: Python example demonstrating the Google Cloud Compute Recommender list recommendations api
# pip install google-cloud-recommender
from google.cloud import recommender
import os
# Enter values for your Project ID and Zone
PROJECT=
LOCATION=
RECOMMENDER = 'google.compute.instance.MachineTypeRecommender'
def print_recommendations():
client = recommender.RecommenderClient()
parent = client.recommender_path(PROJECT, LOCATION, RECOMMENDER)
recommendations = client.list_recommendations(parent=parent)
for recommendation in recommendations:
print('Recommendation:')
print(' Description: ', recommendation.description)
print(' Impact Category: ', recommendation.primary_impact.category)
print(' Currency Code: ', recommendation.primary_impact.cost_projection.cost.currency_code)
print(' Units: ', recommendation.primary_impact.cost_projection.cost.units)
print(' State: ', recommendation.state_info.state)
print('')
for op_group in recommendation.content._pb.operation_groups:
for operation in op_group.operations:
print(' Operation:')
print(' Action: ', operation.action)
print(' Resource Type: ', operation.resource_type)
print(' Path: ', operation.path)
print(' Value: ', operation.value.string_value)
print('')
print_recommendations()
Example output:
Recommendation:
Description: Save cost by changing machine type from e2-standard-4 to e2-highmem-2.
Impact Category: Category.COST
Currency Code: USD
Units: -31
State: State.ACTIVE
Operation:
Action: test
Resource Type: compute.googleapis.com/Instance
Path: /machineType
Value:
Operation:
Action: replace
Resource Type: compute.googleapis.com/Instance
Path: /machineType
Value: zones/us-east1-b/machineTypes/e2-highmem-2