### INTRO

# This is an example flow to showcase data testing.

# There are several ways you can use this:
# 1. If you simply want to copy paste useful recipes... the flow is yours.
# 2. If you want to learn, I would advice:
#     - Skim through the whole script.
#     - Now read the flow block carefully and refer to other parts of the
#       script when needed.
#     - Try to run the flow as-is. It should succeed.
#     - Try to intentionally break the data test by changing the data or the
#       expectations.

### HOW TO RUN
# The flow is packed with comments to guide you through what's happening.
# The flow is also runnable. To run it:
# - Make a virtual environment with the requirements.txt that live in the same
#   folder as this script.
# - Start a shell, activate the venv, login to AWS and turn on your Mercadão
#   VPN.
# - In the shell, run the command: prefect run -p docs/gallery/data_testing/data_testing_flow.py
#
# Note: this flow is designed to run in your laptop. It won't work in the
# prefect server. Don't bother uploading it.

# The flow connects to DW and makes a silly check on a silly query. You can use
# it as a reference on how to set up a data test in your serious flows.


### IMPORTS

import datetime

from prefect import Flow, task, case
from prefect.run_configs import KubernetesRun

# ↑↑↑ Standard prefect stuff for the flow.

from great_expectations.core.expectation_configuration import ExpectationConfiguration

# ↑↑↑ ExpectationConfiguration is the class that allows us to define a single
# expectation. We use it once for every expectation we define.

from lolafect.lolaconfig import build_lolaconfig

# ↑↑↑ Usual lolaconfig import to get all the env data.
from lolafect.connections import (
    open_ssh_tunnel_with_s3_pkey,  # ←←← We connect through an SSH tunnel
    close_ssh_tunnel,  # ←←← Which we will have to close
    connect_to_mysql,  # ←←← For quarantine purposes
    close_mysql_connection,  # ←←← To close the previous connection
)
from lolafect.slack import SendSlackMessageTask

# ↑↑↑ The task class to send slack messages.
from lolafect.data_testing import (
    run_data_test_on_mysql,
)  # ←←← The task to run a data test

### PREP

LOLACONFIG = build_lolaconfig(flow_name="lolafect-gallery-data-testing-demo")
# ↑↑↑ Get env from S3 and prepare everything related to it


DATA_TEST_NAME = "gallery-example-test"
# ↑↑↑ Our data test must have a name. We will need this if we want to look for
# its logs in S3.

DATA_TEST_QUERY = """
    SELECT "hi" AS some_string,
    1 AS some_number,
    NULL as some_null
"""
# ↑↑↑ Our query defines what data do we want to test. This is a silly select
# with hardcoded values because this is a demo, but in a real environment, you
# most probably will want to have a common SELECT [...] FROM [...] query that
# fetches the data your want to test.

DATA_TEST_EXPECTATIONS = [
    ExpectationConfiguration(
        expectation_type="expect_column_values_to_match_like_pattern",
        kwargs={"column": "some_string", "like_pattern": "%hi%"},
    ),
    ExpectationConfiguration(
        expectation_type="expect_column_values_to_be_between",
        kwargs={"column": "some_number", "min_value": 1, "max_value": 1},
    ),
    ExpectationConfiguration(
        expectation_type="expect_column_values_to_be_null",
        kwargs={"column": "some_null"},
    ),
]

# ↑↑↑ Our expectations define what data should be like. Each expectation is
# defined with a call to ExpectationConfiguration. You can check a reference
# of available expectations and how to call them here:
# https://legacy.docs.greatexpectations.io/en/latest/reference/glossary_of_expectations.html


@task
def fetch_tunnel_host_and_port(ssh_tunnel):
    host = ssh_tunnel.local_bind_address[0]
    port = ssh_tunnel.local_bind_address[1]

    return host, port


# ↑↑↑ A small helper function to get the host and the port where the SSH
# tunnel is listening inside this host.


@task
def fail(exception, message):
    raise exception(message)


# ↑↑↑ A small helper function to cause a task failure with a custom message.


@task
def quarantine_failed_data(connection, query_to_get_quarantine_data):
    cursor = connection.cursor()

    cursor.execute(
        f"""
        CREATE TABLE quarantine.{LOLACONFIG.FLOW_NAME_UDCS}_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")} AS 
        {query_to_get_quarantine_data}
        """
    )
    # ↑↑↑ This query will store the faulty data in a quarantine schema in DW
    # It creates a new table on each run, and uses the flow name and the current time
    # to give the table a unique and informative name.


send_slack_message = SendSlackMessageTask()
# ↑↑↑ Simply making an instance of the task class. send_slack_message will be
# the task we use in the flow.

### FLOW

with Flow(
    LOLACONFIG.FLOW_NAME_UDCS,
    storage=LOLACONFIG.STORAGE,
    run_config=KubernetesRun(
        labels=LOLACONFIG.KUBERNETES_LABELS,
        image=LOLACONFIG.KUBERNETES_IMAGE,
    ),
) as flow:

    ssh_tunnel = open_ssh_tunnel_with_s3_pkey(
        s3_bucket_name=LOLACONFIG.S3_BUCKET_NAME,
        ssh_tunnel_credentials=LOLACONFIG.SSH_TUNNEL_CREDENTIALS,
        remote_target_host=LOLACONFIG.DW_CREDENTIALS["host"],
        remote_target_port=LOLACONFIG.DW_CREDENTIALS["port"],
    )
    # ↑↑↑ We open an SSH tunnel pointing to DW

    # ↓↓↓ This is where we actually run the data test. The result of the test
    # gets stored in data_test_result.
    data_test_result = run_data_test_on_mysql(
        name=DATA_TEST_NAME,  # ←←← The name we set earlier
        # ↓↓↓ The credentials to the MySQL where the data lives. We pass the
        # ssh tunnel host and port instead of the true MySQL because we want
        # to pass through the tunnel. If it was a direct connection, we would
        # simply use the MySQL true host and port.
        mysql_credentials={
            "host": fetch_tunnel_host_and_port(ssh_tunnel)[0],
            "port": fetch_tunnel_host_and_port(ssh_tunnel)[1],
            "user": LOLACONFIG.DW_CREDENTIALS["user"],
            "password": LOLACONFIG.DW_CREDENTIALS["password"],
            "db": "sandbox",  # ←←← We always need to pass a default db, but it
            # is recommended to always specify your schemas
        },  # in the queries regardless.
        query=DATA_TEST_QUERY,  # ←←← The query we set earlier
        expectation_configurations=DATA_TEST_EXPECTATIONS,  # ←←← Idem
        upstream_tasks=[ssh_tunnel],  # ←←← We must wait for the tunnel to be ready
    )

    # ↑↑↑ will take care of everything: connecting to S3 and DW, generate all
    # the necessary configurations, run the actual test and store results both
    # in memory and in S3.
    #
    # What to do from here is up to you. You can easily check if the test
    # passed or not by accessing data_test_result["success"]. If it equals
    # True, the test passed. If it equals False, at least one expectation
    # failed.
    #
    # The following snippets are optional. You should judge if you want to do
    # something similar or not in your flow based on your needs.

    ### RAISING AN EXCEPTION
    # When a test with run_data_test_on_mysql fails, it's important that you
    # keep in mind that this will not cause a failure, in the sense of a
    # prefect task failing. This is intentional: we didn't want to assume that
    # a failing data test always translates into a failed flow.
    #
    # Nevertheless, it might be the case that you want your flow to fail if
    # the data test didn't pass. To do so, you can use a simple helper task and
    # a case, just like this:

    with case(data_test_result["success"], False):
        fail(ValueError, "Woops, the test didn't pass.")

    ### SENDING A SLACK WARNING
    # You might also want to send a slack message to a channel if the test
    # does not pass. You can do so like this:

    with case(data_test_result["success"], False):
        send_slack_message(
            LOLACONFIG.SLACK_WEBHOOKS["data-team-alerts-testing"],  # ←←← A webhook URL
            "Uh oh, the demo flow failed.",  # ←←← Your warning message
        )

    ### QUARANTINE THE TESTED DATA
    # Another common practice is to store the data that doesn't pass a test.
    # This provides a lot of benefits that are discussed in our best practices
    # docs in Confluence. Here is an example of you can quarantine the data
    # that made your test fail:

    dw_connection = connect_to_mysql(
        mysql_credentials=LOLACONFIG.DW_CREDENTIALS,
        overriding_host_and_port=fetch_tunnel_host_and_port(ssh_tunnel),
        upstream_tasks=[data_test_result],
    )
    # ↑↑↑ We connect to DW, and since we are using the SSH tunnel, we
    # override DWs host and port and instead use the listening ones from
    # the tunnel.

    with case(data_test_result["success"], False):
        quarantined = quarantine_failed_data(dw_connection, DATA_TEST_QUERY)
        # ↑↑↑ We call the quarantine_failed_data task. You can review the
        # actions of this task in the definition that appears earlier in this
        # file.

    mysql_closed = close_mysql_connection(dw_connection)
    tunnel_closed = close_ssh_tunnel(ssh_tunnel, upstream_tasks=[mysql_closed])