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"""
This is an example dag for managing twitter data.
"""

from __future__ import annotations

import os
from datetime import date, datetime, timedelta

from airflow import DAG
from airflow.decorators import task
from airflow.operators.bash import BashOperator
from airflow.providers.apache.hive.operators.hive import HiveOperator

# --------------------------------------------------------------------------------
# Caveat: This Dag will not run because of missing scripts.
# The purpose of this is to give you a sample of a real world example DAG!
# --------------------------------------------------------------------------------

# --------------------------------------------------------------------------------
# Load The Dependencies
# --------------------------------------------------------------------------------


ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "example_twitter_dag"


@task
def fetch_tweets():
    """
    This task should call Twitter API and retrieve tweets from yesterday from and to for the four twitter
    users (Twitter_A,..,Twitter_D) There should be eight csv output files generated by this task and naming
    convention is direction(from or to)_twitterHandle_date.csv
    """


@task
def clean_tweets():
    """
    This is a placeholder to clean the eight files. In this step you can get rid of or cherry pick columns
    and different parts of the text.
    """


@task
def analyze_tweets():
    """
    This is a placeholder to analyze the twitter data. Could simply be a sentiment analysis through algorithms
    like bag of words or something more complicated. You can also take a look at Web Services to do such
    tasks.
    """


@task
def transfer_to_db():
    """
    This is a placeholder to extract summary from Hive data and store it to MySQL.
    """


with DAG(
    dag_id=DAG_ID,
    default_args={
        "owner": "Ekhtiar",
        "retries": 1,
    },
    schedule="@daily",
    start_date=datetime(2021, 1, 1),
    tags=["example"],
    catchup=False,
) as dag:
    fetch = fetch_tweets()
    clean = clean_tweets()
    analyze = analyze_tweets()
    hive_to_mysql = transfer_to_db()

    fetch >> clean >> analyze

    # --------------------------------------------------------------------------------
    # The following tasks are generated using for loop. The first task puts the eight
    # csv files to HDFS. The second task loads these files from HDFS to respected Hive
    # tables. These two for loops could be combined into one loop. However, in most cases,
    # you will be running different analysis on your incoming and outgoing tweets,
    # and hence they are kept separated in this example.
    # --------------------------------------------------------------------------------

    from_channels = ["fromTwitter_A", "fromTwitter_B", "fromTwitter_C", "fromTwitter_D"]
    to_channels = ["toTwitter_A", "toTwitter_B", "toTwitter_C", "toTwitter_D"]
    yesterday = date.today() - timedelta(days=1)
    dt = yesterday.strftime("%Y-%m-%d")
    # define where you want to store the tweets csv file in your local directory
    local_dir = "/tmp/"
    # define the location where you want to store in HDFS
    hdfs_dir = " /tmp/"

    for channel in to_channels:
        file_name = f"to_{channel}_{dt}.csv"

        load_to_hdfs = BashOperator(
            task_id=f"put_{channel}_to_hdfs",
            bash_command=(
                f"HADOOP_USER_NAME=hdfs hadoop fs -put -f {local_dir}{file_name}{hdfs_dir}{channel}/"
            ),
        )

        # [START create_hive]
        load_to_hive = HiveOperator(
            task_id=f"load_{channel}_to_hive",
            hql=(
                f"LOAD DATA INPATH '{hdfs_dir}{channel}/{file_name}'"
                f"INTO TABLE {channel}"
                f"PARTITION(dt='{dt}')"
            ),
        )
        # [END create_hive]

        analyze >> load_to_hdfs >> load_to_hive >> hive_to_mysql

    for channel in from_channels:
        file_name = f"from_{channel}_{dt}.csv"
        load_to_hdfs = BashOperator(
            task_id=f"put_{channel}_to_hdfs",
            bash_command=(
                f"HADOOP_USER_NAME=hdfs hadoop fs -put -f {local_dir}{file_name}{hdfs_dir}{channel}/"
            ),
        )

        load_to_hive = HiveOperator(
            task_id=f"load_{channel}_to_hive",
            hql=(
                f"LOAD DATA INPATH '{hdfs_dir}{channel}/{file_name}' "
                f"INTO TABLE {channel} "
                f"PARTITION(dt='{dt}')"
            ),
        )

        analyze >> load_to_hdfs >> load_to_hive >> hive_to_mysql

#     from tests.system.utils.watcher import watcher
#
#     # This test needs watcher in order to properly mark success/failure
#     # when "tearDown" task with trigger rule is part of the DAG
#     list(dag.tasks) >> watcher()
#
# from tests.system.utils import get_test_run  # noqa: E402
#
# # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
# test_run = get_test_run(dag)