mms-pip 0.8.2.1

A custom MMS Thor module for python by Data Access

mms-pip

Public Python module from the Data-Access Team of MediaMarktSaturn Technology.

!!!!!!!! ATTENTION: BETA VERSION !!!!!!!!!!

pip install mms-pip --upgrade

Topics:

1. gcp_logger
2. redis_handler
3. bq_handler
4. redis_handler
5. auth_handler
6. kms_handler

---

1. gcp_logger

Log Module for standardized log purposes.

How to use:

1.1 Import module:

Import the module your app is running on GCP later on - at the moment the following products are supported:

• app_engine_logger
• cloud_function_logger
• cloud_run_logger
• composer_logger
• compute_engine_logger
• dataproc_logger
• kubernetes_logger

# e.g. for using the cloud run logger

from mms.logger.cloud_run_logger import CloudRunLogger

1.2 Initalize the logger:

# Cloud Run (Serverless):
logger = CloudRunLogger(project_id, local_run)
logger = CloudRunLogger(project_id='my-project', local_run=True/False)

**IMPORTANT**

In every API route the following methods need to be called during initialization - currently only Flask is supported for this logger: 
    from flask import Flask, request

    logger.update_request_header(api_request=request)
    logger.update_trace_id(str(uuid.uuid4()))




# Cloud Function Logging:
logger = CloudFunctionLogger(service_name, trace_id, project_id, function_name)
logger = CloudFunctionLogger(service_name='my-service', trace_id='lksjdf2', project_id='my-project-id', function_name='ppx-price-updates-de-gcs-bq')

# App Engine Logging:
logger = AppEngineLogger(service_name, trace_id, project_id, module_id, version_id)
logger = AppEngineLogger(service_name='my-service', trace_id='lksjdfl98', project_id='v135-5683-alice-ksk-explore', module_id='app-flex-sample-service', version_id='v0.0.1')

# Compute Engine:
logger = ComputeEngineLogger(service_name, trace_id, project_id)
logger = ComputeEngineLogger(service_name='my-service', trace_id='lksjdfl98', project_id='v135-5683-alice-ksk-explore')

# Kubernetes Engine: 
logger = KubernetesLogger(service_name, trace_id, project_id, cluster_name, container_name, location, namespace_name)
logger = KubernetesLogger(service_name='my-service', trace_id='id12345', project_id='v135-5683-alice-ksk-explore', cluster_name='jg-k8-testcluster', container_name=CONTAINER_NAME, location=ZONE, namespace_name='default')

# Dataproc:
logger = DataprocLogger(service_name, trace_id, project_id, cluster_name, location)
logger = DataprocLogger(service_name='my-service', trace_id='lksjdfl98', project_id='v135-5683-alice-ksk-explore', cluster_name='my-cluster', location='europe-west4')

# Cloud Composer:
logger = ComposerLogger(service_name, trace_id, project_id, environment_name, location)
logger = ComposerLogger(service_name='my-service', trace_id='lksjdfl98', project_id='v135-5683-alice-ksk-explore', environment_name='my-composer-environment', location='europe-west4')

1.3 Use the logger:

logger.info('your message')
logger.warning('your message')
logger.error('your message')
logger.critical('your message')
logger.debug('your message')

The logs are visible in Stackdriver Logging via:

• GAE Application -> Module_id -> Version_id for App Engine.
• Or under Cloudfunctions -> Function_id
• Or under GCE VM Instance -> Instance_id
• Or under Kubernetes Container -> cluster_name -> namespace_name -> container_name
• Or under Cloud Run Revision -> service_name -> revision_name
• Or under Global

Important

This log tool only works in App Engine Standard/Flexible, Cloud Function, Compute Engine and Kubernetes, Cloud Run (Serverless) environment.

For local testing please set the boolean flag 'local_run' during initialization to 'True'

How we log

We initialize the logger only in the "app.py" file. From there every log entry will be written - Modules used within app.py need to return the exceptions to the caller so error etc. get logged at one central point within app.py.

---

2. datastore_handler

Python script for easy interacting with Google Cloud Datastore (DS)

How to use:

2.1 Import Datastore class:

from mms.datastore_handler import Datastore

2.2 Initalize the datastore handler:

ds_handler = Datastore()

2.3 Use the Datastore handler:

You can set/get an id or a name in Datastore. For this package if you specify the parameter name as a string, than it's a name in Datastore. If you specify the parameter name as an integer, than it's an id in Datastore.

• Creating a new Entity:

ds_handler.put_new_entity(kind='kind_name', prop_df={'property1': 1, 'property2'='Test String'}, name='The Name/ID of the Entity')

kind and prop_df is required, name is optional (Google generates an ID for you)

• Getting and deleting Entity:

result_entitiy_dict = ds_handler.get_entity(kind='kind_name', name='The Name/ID of the Entity')
ds_handler.delete_entity(kind='kind_name', name='The Name/ID of the Entity')

• Updating Entity:

ds_handler.update_entity(kind='kind_name', name='The Name/ID of the Entity', prop_df={'property1': 1, 'property2'='Test String'})

You cannot update specific properties of an entity. The Updates must include all properties that should be persisted. Whenever you need to do an update, you need to first retrieve the existing entity as a whole, then update one or more properties by setting new values on prop_df and update the entity.

• Get all Entities of a specific kind:

result = ds_handler.get_all_of_a_kind('kind_name')

• Query Datastore: With only one Filter:

with_one_filter = ["property1", "=", 1]
result1 = ds_handler.query(kind="TestKind1", filter=with_one_filter)

With two or more Filter:

with_more_filter = [["property1", "=", 1], ["property3", "<=", 3.33]]
result2 = ds_handler.query(kind="TestKind1", filter=with_more_filter)

---

3. bq_handler

Python script for easy interacting with Google Cloud BigQuery (BQ)

How to use:

3.1 Import BigQuery class:

from mms.bq_handler import BQ

3.2 Initalize the BigQuery handler:

There are two Options:

i) Without default values:

bq_handler = BQ()

ii) With default values (i. e. if you only work with one dataset or table):

bq_handler = BQ(dataset_id='default_dataset_id', table_id='default_table_id')

Every time a dataset_id and/or a table_id is not specified in a specific method of the BQ class, the method will use the default dataset_id and/or table_id of the bq_handler.

You can also specify a project_id and/or a cred_file_path (credential file path) if you need it.

3.3 Use the BigQuery handler:

• Creating a new Dataset:

# With default values from bq_handler
bq_handler.create_dataset()

# Without defaults:
bq_handler.create_dataset('test_dataset_id')

• Creating a new Table (dataset have to exists):

from google.cloud import bigquery
SCHEMA = [bigquery.SchemaField('full_name', 'STRING', mode='required', description="Visitor's Name"),
          bigquery.SchemaField('visit_time', 'TIMESTAMP', mode='required', description="Visit Time"),
          bigquery.SchemaField('visit_length', 'INT64', mode='required', description="Length of Visit in Seconds"),
          bigquery.SchemaField('sentiment', 'FLOAT64', mode='required', description="Calculated Happiness Score")]

# With default values from bq_handler
bq_handler.create_table(SCHEMA)

# Without defaults:
bq_handler.create_table(SCHEMA, dataset_id='test_dataset_id', table_id='test_table_id')

# You can also create a table with day partitioning and/or with clustering:
bq_handler.create_table(SCHEMA, dataset_id='test_dataset_id', table_id='test_table_id', partitioning_field='_PARTITIONTIME', require_partition_filter=True, clustering_fields=['visit_time'])
# _PARTITIONTIME is the default partitioning of BigQuery

• Checking if a table exists or not (ATTENTION: No guarantees! See source code.):

# With default values from bq_handler
check = bq_handler.check_if_table_exists()

# Without defaults:
check = bq_handler.check_if_table_exists(dataset_id='test_dataset_id', table_id='test_table_id')

• Getting a schema of an existing table:

# With default values from bq_handler
schema = bq_handler.get_schema()

# Without defaults:
schema = bq_handler.get_schema(dataset_id='test_dataset_id', table_id='test_table_id')

• Streaming a row into an existing BQ table (append to table):

# Create Dictionary:  
data = {'full_name': 'Max Mustermann', 'visit_time': '2019-07-23 13:45:07.372826 UTC', 'visit_length': 100, 'sentiment': 1.111}

# With default values from bq_handler
bq_handler.streaming_insert_single_json(data)

# Without defaults:
bq_handler.streaming_insert_single_json(data, dataset_id='test_dataset_id', table_id='test_table_id')

• Running a query and get results as a dataframe:

query = '''
SELECT * FROM `v135-5683-playground-goppold.test_dataset_id.test_table_id`
'''
df = bq_handler.run_query(query)

---

4. redis_handler

Python script for easy usage of the Python redis class.

How to use:

4.1 Import Redis class:

from mms.redis_handler import Redis

4.2 Initalize the redis handler:

redis-client = Redis(host, port, password)

4.3 Use the redis handler:

There are only three methods available:

• set (there is a default expiration of 60*15 set as default)
• get
• delete

Reddis key

redis_client.set_redis(set_key, set_value, exp)

redis_client.get_redis(get_key)

redis_client.delete_key(key_to_delete)

---

5. auth_handler.py

https://cloud.google.com/run/docs/authenticating/service-to-service

First, you'll need to configure the receiving service to accept requests from the calling service: Grant the Cloud Run Invoker (roles/run.invoker) role to the calling service identity on the receiving service.

TODO

6. KMS handler

There is a module available to easily use KMS:

6.1 Import KMS class:

from mms.kms_handler import KmsService

6.2 Use the KMS client:

decrpyted = KmsService.decrypt(project_id, location_id, key_ring_id, crypto_key_id, ciphertext)

encrypted = KmsService.encrypt(project_id, location_id, key_ring_id, crypto_key_id, plaintext)

---

TODOs

• Complete README
• Add Docstrings to Python scripts

---

CHANGELOG:

0.5.3:

• Local Run option was introduced -> initialization flag can be set

0.5.4:

• Added 'service_name' to Logger class -> needs to be given in the arguments

0.5.5:

• Change the logname for each log entry to the specific service_name
• Added Cloud Run (Serverless as new source)

0.5.6:

• Logname bugfix

0.5.7:

• Cloud run logging code refinement

0.5.9:

• Change default values from None to empty string

0.6.0:

• Added Dataproc as new log resource 'cloud_dataproc_cluster'

0.6.1:

• Added Cloud Composer as new log resource 'cloud_composer_environment'

0.7.0:

• Added Datastore Handler for GCP for easy interaction with Google Cloud Datastore

0.8.0:

• Added BigQuery and GCS Handler for GCP for easy interaction with Google Cloud BigQuery and Storage

0.8.1:

• Added auth_handler for service to service jwt tokens (Google Cloud Run)

0.8.1.1 and 0.8.1.2:

• Changed Readme

0.8.1.3

• Refactored Logger (TH)
• Introduced KMS client (TH)

0.8.1.4

• Refactor Bug KMS handler (TH)
• Refactor logger (introduced do_log function) (TH)

0.8.1.5

• Refactor Bug KMS handler (TH)

0.8.1.6

• Updated required packages (TH)

0.8.1.7

• Updated Readme (TH)

0.8.1.8

• Bugfix logger (TH)

0.8.1.9

• Bugfix BQ handler (TH)

0.8.2.0

• Equiped Datastore handler with project_id feature during initialization (TH)

0.8.2.1

• New Cloudrun Logger implemented -> necessary because of high request Cloud Run APIs: Stackdriver Logging API Quota: 60.000 calls / minute

  New log uses stdout for log gathering independed from Stackdriver Logging API (TH)

---

Tobias Hoke - Josef Goppold - 22.01.2020