mms-pip 0.8.4.7
A custom MMS Analytics module for Python3 by the Touchpoint Analytics & Data Discovery

# Doku - mms-pip

Public Python module from the Omnichannel-Analytics Team of MediaMarktSaturn Technology.

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

```bash
pip install mms-pip --upgrade
```

Topics:

1. gcp_logger
2. datastore_handler
3. bq_handler
4. redis_handler
5. gcp_bearer_token
6. mms_fifa
7. gcp_secret_manager
8. pubsub_handler
9. kubeflow_handler

## 1. gcp_logger

Log Module for standardized log purposes on GCP.

### 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

```python
# e.g. for using the cloud run logger

from mms.logger.cloud_run_logger import CloudRunLogger
```

### 1.2 Initalize the logger:

```python
# 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:

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

Cloud Run logger 
-> you have the possibility to add custom attributes to the log entry just add them to the function call
```python
logger.info('your message', custom_attribute="test")
logger.warning('your message', custom_attribute=test)
logger.error('your message', custom_attribute=test)
logger.critical('your message', custom_attribute=test)
logger.debug('your message', custom_attribute=test)
```

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'

### 1.4 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 module for easy interacting with Google Cloud Datastore (DS)

### How to use:

### 2.1 Initialize the datastore handler:

```python
from mms.datastore_handler import Datastore

ds_handler = Datastore("your-projectid")
```

### 2.2 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:

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

**kind** and **prop_df** is required, **name** and **exclude_from_indexes** is optional (Google generates an ID for you, by default all fields are indexed)

Getting and deleting Entity: 

```python
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: 

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

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: 

```python
result = ds_handler.get_all_of_a_kind('kind_name')
```

Query Datastore:

- With only one Filter:

    ```python
    with_one_filter = ["property1", "=", 1]
    result = ds_handler.query(kind="TestKind1", filter=with_one_filter, limit=None, extract_results=True)
    ```

- With two or more Filter:

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

**limit** (int, how many entities you want to receive) and **extract_results** (bool, true: list as result, false: generator object result) are optional. Default values: limit=None,  extract_results=True. 

## 3. bq_handler

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

### 3.1 Initialize the BigQuery handler:

There are two Options: 

- Without default values:

    ```python
    from mms.bq_handler import BQ

    bq_handler = BQ("your-projectid")
    ```

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

    ```python
    from mms.bq_handler import BQ

    bq_handler = BQ("your-projectid", 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 **cred_file_path** (credential file path) if you don't want to use your default credentials from your gcloud sdk. 

### 3.2 Use the BigQuery handler:

Creating a new Dataset: 

```python
# 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): 

```python
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: See source code.):

```python
# 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')

# if check is True -> table exists
```

Getting a schema of an existing table:

```python
# 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):

```python
# 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
errors = bq_handler.streaming_insert_single_json(data)

# Without defaults:
errors = 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:

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

## 4. redis_handler

Python module for easy interacting with a redis instance. 

### How to use:

### 4.1 Initialize the redis handler:

```python
from mms.redis_handler import Redis

redis_client = Redis(host, port, password, exp_s)
```

### 4.2 Use the redis handler:

There are only three methods available. 

```python
redis_client.set_redis(set_key, set_value)
redis_client.get_redis(get_key)
redis_client.delete_key(key_to_delete)
```

## 5. gcp_bearer_token

We support 5 different ways to create a bearer token: 

1. "gcloud-sdk": from local machine via gcloud sdk: 

    ```python
    from mms.gcp_bearer_token import GCPBearerToken
    token_handler = GCPBearerToken("gcloud-sdk")
    token = token_handler.get_token()
    ```

2. "service-account-file": from a local service-account json file:

    ```python
    from mms.gcp_bearer_token import GCPBearerToken

    token_handler = GCPBearerToken("service-account-file",
                                    target_url="https://example-cloud-run-service-q25crkrupq-ew.a.run.app",
                                    path_to_service_account_file="./sa.json")
    token = token_handler.get_token()
    ```

3. "service-account-dictionary": from a parsed service-account json file:

    ```python
    from mms.gcp_bearer_token import GCPBearerToken

    sa_dict = {
          "type": "service_account",
          "project_id": "...",
          "...": "..."
     }

    token_handler = GCPBearerToken("service-account-dictionary",
                                    target_url="https://example-cloud-run-service-q25crkrupq-ew.a.run.app",
                                    service_account_dictionary=sa_dict)
    token = token_handler.get_token()
    ```

4. "cloudrun-to-cloudrun": This works only from a GCP CloudRun Service to another one. (https://cloud.google.com/run/docs/authenticating/service-to-service)

    ```python
    from mms.gcp_bearer_token import GCPBearerToken

    token_handler = GCPBearerToken("cloudrun-to-cloudrun",
                                    target_url="https://example-cloud-run-service-q25crkrupq-ew.a.run.app")
    token = token_handler.get_token()
    ```

5. "compute-default":  App Engine, Cloud Run, ‘Compute Engine‘, or has application default credentials set via GOOGLE_APPLICATION_CREDENTIALS (os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "./sa.json") environment variable

    ```python
    from mms.gcp_bearer_token import GCPBearerToken

    token_handler = GCPBearerToken("compute-default",
                                    target_url="https://example-cloud-run-service-q25crkrupq-ew.a.run.app")
    token = token_handler.get_token()
    ```

The GCPBearerToken class saves the token and the expiration of the token as instance variables.
The ```get_token()``` checks at every call, if there was a token already generated and if the token is still valid. Only if not, it generates a new one.

## 6. mms_fifa

Python module for easy interacting with the mms fifa bouncer. 

### How to use:

### 6.1 Initialize the mms fifa handler:

```python
from mms.mms_fifa import Fifa

fifa_handler = Fifa()

# Optional paramaters (with default values):
#   - jwk_url="https://auth.mediamarktsaturn.com/.well-known/jwks.json",
#   - fifa_token_url="https://auth.mediamarktsaturn.com/oauth/token",
#   - max_num_retries=5
```

### 6.2 Use the fifa handler:

 Get fifa jwt token:

```python
error, token = fifa_handler.get_jwt_token(client_id="...", client_secret="...")
# (If error is None -> everything is ok)
```

Verify fifa jwt token:

```python
error, payload = fifa_handler.verify_jwt_token(jwt_token=token, audience="..")
# (If error is None -> everything is ok)
```

**audience** can be a string or a tuple of strings. 

## 7. gcp_secret_manager

Python module for easy interacting with the GCP Secret Manager. 

### How to use:

First, enable the secret manager API  on GCP console

### 7.1 Initialize the secret manager handler:

```python
from mms.gcp_secret_manager import SecretManager

# Init secretmanager object with your the default credentials from your environment:
secretmanager_handler = SecretManager("v135-5683-playground-goppold")
# Init secretmanager object with your a service account json file - specify the path to your file:
secretmanager_handler = SecretManager("v135-5683-playground-goppold", "test.json")
```

### 7.2 Use the secret manager handler:

Create Secret Value: 

```python
# Create a secret value:
secret_name = secretmanager_handler.create_secret("my-secret")
print(secret_name)
# Create a secret with value
secret_version_name = secretmanager_handler.create_secret("my-secret-with-value", "This is my secret value")
print(secret_version_name)
```

By default: regions=["europe-west4"] and  labels=None. Please specify this params of you need to.

Add a secret value to a existing secret: 

```python
# Add a secret value to a existing secret:
secret_version_name_1 = secretmanager_handler.add_secret_version("my-secret", "This is another secret value")
print(secret_version_name_1)
secret_version_name_2 = secretmanager_handler.add_secret_version("my-secret", "This is a new verison of my secret value")
print(secret_version_name_2)
```

Accessing value of a secret version: 

!!!WARNING: Do not print the secret in a production environment!!!

```python
value_1 = secretmanager_handler.access_secret_version("my-secret", 1)
print(value_1)
value_2 = secretmanager_handler.access_secret_version("my-secret", 2)
print(value_2)
value_3 = secretmanager_handler.access_secret_version("my-secret-with-value", 1)
print(value_3)
```

List secrets and secret versions: 

```python
# List all secrets in my project:
secrets = secretmanager_handler.list_secrets()
print(secrets)

# List all version of a specified secret:
secret_verions = secretmanager_handler.list_secret_versions("my-secret")
print(secret_verions)
```

Get metadata of a specific secret:

```python
# Get metadata of a specific secret:
metadata = secretmanager_handler.get_secret("my-secret")
print(metadata.name)
```

Delete secrets: 

```python
# Delete a secret with the given name and all of its versions:
secretmanager_handler.delete_secret("my-secret")
secretmanager_handler.delete_secret("my-secret-with-value")
```

## 8. pubsub_handler

Python module for easy interacting with the GCP Pubsub. 

### How to use:

First, enable the pubsub API  on GCP console

### 8.1 Initialize the pubsub handler:

```python
from mms.pubsub_handler import Pubsub

pubsub_handler = Pubsub()
'''
Optional default args in Pubsub(): 
project_id: str, default project_id
publish_batch_max_messages=100
publish_batch_max_baytes=1024 (in kB)
publish_batch_max_latency=10 (in ms)
'''
```

### 8.2 Use the pubsub handler:

Create Pubsub Topic: 

```python
# Create Pubsub Topic in default project (only if specified):
topic = pubsub_handler.create_topic("test-topic")
print(topic)

# Create Pubsub Topic in other project:
topic = pubsub_handler.create_topic("test-topic", "spielwiese-tobias")
print(topic)
```

Create pull subscription: 

```python
# Create pull Subscription in default project (only if specified):
subscription = pubsub_handler.create_pull_subscription(subscription_name="test-subs",
                                                       topic_path="projects/playground-josef/topics/temp-test",
                                                       ack_deadline_seconds=100)
print(subscription)

# Create pull Subscription in other project:
subscription = pubsub_handler.create_pull_subscription(subscription_name="test-subs",
                                                       topic_path="projects/spielwiese-tobias/topics/test-topic",
																											 project_id="spielwiese-tobias",
                                                       ack_deadline_seconds=30)
print(subscription)
```

Publish messages: 

```python
# Publish messages - batch (if one bundle to publish) - optional: encoding (default="utf-8"), wait_until_all_published (default=True):
data = [{"column1": i, "column2": "value {}".format(i)} for i in range(10000)]
pubsub_handler.publish_messages(data=data,
                                topic_path="projects/playground-josef/topics/temp-test")

# Publish messages - batch (if more than one bundle to publish) - optional: encoding (default="utf-8"):
data = [{"column1": i, "column2": "value {}".format(i)} for i in range(10000)]
for i in range(10):
		pubsub_handler.publish_messages(data=data,
                                    topic_path="projects/playground-josef/topics/temp-test",
                                    wait_until_all_published=False)
pubsub_handler.wait_for_publishing()

# Publish singe message - waiting until message is published - optional: encoding (default="utf-8")
data = {"column1": 1, "column2": "value 1"}
message_id = pubsub_handler.publish_message(data=data, topic_path="projects/playground-josef/topics/temp-test")
print(message_id)
```


## 9. kubeflow_handler

Can be used to load kubelflow pipeline components from a file placed in a specific GCS Bucket 

### 9.1 init handler 

kf = KubeFlowHandler()

### 9.2 load component from file 

*Returns components from kfp.v2* 

component = kf.load_kfp_component(filename: str, bucket_id: str = 'tadd-kubeflow-components')



## TODOs:

- complete and improve README
- Add Docstrings to Python scripts

## CHANGELOG

see [changelog.txt](https://github.com/MediaMarktSaturn/mms-pip/blob/master/changelog.txt)

---

Josef Goppold, Tobias Hoke  - 2021-07-20