[feat](linkedin dag): add LinkedIn insight dag into airflow

.github/workflows/dockerimage.yml

@@ -1,65 +1,45 @@
 name: Docker Image CI
-
 on:
   push:
     branches: [ master, prod ]
   pull_request:
     branches: [ master, prod ]
 env:
-  RC_NAME: davidtnfsh/pycon_etl
-
+  RC_NAME: asia-east1-docker.pkg.dev/${{ secrets.GCP_PROJECT_ID }}/data-team/pycon-etl
 jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
-    - name: Login to docker hub
-      uses: actions-hub/docker/login@master
-      env:
-        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
-        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
-
+    - uses: actions/checkout@v4
+    - name: Authenticate to Google Cloud
+      uses: google-github-actions/auth@v1
+      with:
+        credentials_json: ${{ secrets.GCP_SERVICE_ACCOUNT_KEY }}
+    - name: Configure docker to use gcloud command-line tool as a credential helper
+      run: |
+        gcloud auth configure-docker asia-east1-docker.pkg.dev
     - name: Pull cache
       run: |
-        docker login -u ${{ secrets.DOCKER_USERNAME }} -p ${{ secrets.DOCKER_PASSWORD }}
-        docker pull ${RC_NAME}:cache
-
+        docker pull ${RC_NAME}:cache || true
     - name: Build the Docker image
-      if: always()
       run: |
-        docker build -t ${RC_NAME}:${GITHUB_SHA} --cache-from ${RC_NAME}:cache .
-        docker tag ${RC_NAME}:${GITHUB_SHA} ${RC_NAME}:cache
+        docker build -t ${RC_NAME}:cache --cache-from ${RC_NAME}:cache .
         docker build -t ${RC_NAME}:test --cache-from ${RC_NAME}:cache -f Dockerfile.test .
-        docker tag ${RC_NAME}:${GITHUB_SHA} ${RC_NAME}:staging
-        docker tag ${RC_NAME}:${GITHUB_SHA} ${RC_NAME}:latest
-
     - name: Run test
       run: |
-        docker run -d --rm -p 8080:8080 --name airflow -v $(pwd)/dags:/usr/local/airflow/dags -v $(pwd)/fixtures:/usr/local/airflow/fixtures ${RC_NAME}:test webserver
+        docker run -d --rm -p 8080:8080 --name airflow -v $(pwd)/dags:/opt/airflow/dags -v $(pwd)/fixtures:/opt/airflow/fixtures ${RC_NAME}:test webserver
         sleep 10
-        docker exec airflow bash -c "airflow test OPENING_CRAWLER_V1 CRAWLER 2020-01-01"
-        docker exec airflow bash -c "airflow test QUESTIONNAIRE_2_BIGQUERY TRANSFORM_data_questionnaire 2020-09-29"
-
-    - name: Push Cache to docker registry
-      uses: actions-hub/docker@master
-      if: always()
-      with:
-        args: push ${RC_NAME}:cache
-
-    - name: Push GITHUB_SHA to docker registry
-      uses: actions-hub/docker@master
-      if: always()
-      with:
-        args: push ${RC_NAME}:${GITHUB_SHA}
-
-    - name: Push staging to docker registry
-      uses: actions-hub/docker@master
-      if: ${{ github.ref == 'refs/heads/master' }} && success()
-      with:
-        args: push ${RC_NAME}:staging
-
-    - name: Push prod version to docker registry
-      uses: actions-hub/docker@master
+    - name: Push cache to Google Container Registry
+      if: success()
+      run: |
+        docker push ${RC_NAME}:cache
+    - name: Push staging to Google Container Registry
+      if: github.ref == 'refs/heads/master' && success()
+      run: |
+        docker tag ${RC_NAME}:cache ${RC_NAME}:staging
+        docker push ${RC_NAME}:staging
+    - name: Push prod version to Google Container Registry
       if: github.ref == 'refs/heads/prod' && success()
-      with:
-        args: push ${RC_NAME}:latest
+      run: |
+        docker tag ${RC_NAME}:cache ${RC_NAME}:latest
+        docker push ${RC_NAME}:latest

dags/ods/linkedin_post_insights/dags.py

@@ -0,0 +1,36 @@
+from datetime import datetime, timedelta
+
+from airflow import DAG
+from airflow.operators.python_operator import PythonOperator
+from ods.linkedin_post_insights import udfs
+
+DEFAULT_ARGS = {
+    "owner": "Angus Yang",
+    "depends_on_past": False,
+    "start_date": datetime(2023, 6, 14, 0),
+    "retries": 2,
+    "retry_delay": timedelta(minutes=5),
+    "on_failure_callback": lambda x: "Need to send notification to Discord!",
+}
+dag = DAG(
+    "LINKEDIN_POST_INSIGHTS_V1",
+    default_args=DEFAULT_ARGS,
+    schedule_interval="5 8 * * *",
+    max_active_runs=1,
+    catchup=False,
+)
+with dag:
+    CREATE_TABLE_IF_NEEDED = PythonOperator(
+        task_id="CREATE_TABLE_IF_NEEDED", python_callable=udfs.create_table_if_needed,
+    )
+
+    SAVE_TWITTER_POSTS_AND_INSIGHTS = PythonOperator(
+        task_id="SAVE_LINKEDIN_POSTS_AND_INSIGHTS",
+        python_callable=udfs.save_posts_and_insights,
+    )
+
+    CREATE_TABLE_IF_NEEDED >> SAVE_TWITTER_POSTS_AND_INSIGHTS
+
+
+if __name__ == "__main__":
+    dag.cli()

dags/ods/linkedin_post_insights/udfs.py

@@ -0,0 +1,226 @@
+import logging
+import os
+from datetime import datetime
+from typing import List, Optional
+
+import requests
+from airflow.models import Variable
+from google.cloud import bigquery
+
+logger = logging.getLogger(__name__)
+
+
+def create_table_if_needed() -> None:
+    client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT"))
+    post_sql = """
+    CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_linkedin_posts` (
+        id STRING,
+        created_at TIMESTAMP,
+        message STRING
+    )
+    """
+    client.query(post_sql)
+    insights_sql = """
+    CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_linkedin_posts_insights` (
+        post_id STRING,
+        query_time TIMESTAMP,
+        period STRING,
+        favorite INTEGER,
+        reply INTEGER,
+        retweet INTEGER,
+        views INTEGER
+    )
+    """
+    client.query(insights_sql)
+
+    # Example output from the Rapid API, not all fields will exists for a specific post
+    #
+    # {
+    #   "text": "For your kids in senior high.",
+    #   "totalReactionCount": 6,
+    #   "likeCount": 6,
+    #   "repostsCount": 1,
+    #   "empathyCount": 1,
+    #   "commentsCount": 20,
+    #   repostsCount:1,
+    #   "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:6940542340960763905/",
+    #   "postedAt": "1yr",
+    #   "postedDate": "2022-06-09 05:57:23.126 +0000 UTC",
+    #   "postedDateTimestamp": 1654754243126,
+    #   "urn": "6940542340960763905",
+    #   "author": {
+    #     "firstName": "Angus",
+    #     "lastName": "Yang",
+    #     "username": "angus-yang-8885279a",
+    #     "url": "https://www.linkedin.com/in/angus-yang-8885279a"
+    #   },
+    #   "company": {},
+    #   "article": {
+    #     "title": "2022 AWS STEM Summer Camp On The Cloud",
+    #     "subtitle": "pages.awscloud.com • 2 min read",
+    #     "link": "https://pages.awscloud.com/tw-2022-aws-stem-summer-camp-on-the-cloud_registration.html"
+    #   }
+    # },
+
+
+def save_posts_and_insights() -> None:
+    posts = request_posts_data()
+
+    last_post = query_last_post()
+    new_posts = (
+        [
+            post
+            for post in posts
+            if post["postedDateTimestamp"] > last_post["created_at"].timestamp()
+        ]
+        if last_post
+        else posts
+    )
+
+    if not dump_posts_to_bigquery(
+        [
+            {
+                "id": post["urn"],
+                "created_at": post["postedDateTimestamp"],
+                "message": post["text"],
+            }
+            for post in new_posts
+        ]
+    ):
+        raise RuntimeError("Failed to dump posts to BigQuery")
+
+    if not dump_posts_insights_to_bigquery(
+        [
+            {
+                "post_id": post["urn"],
+                "query_time": datetime.now().timestamp(),
+                "period": "lifetime",
+                "favorite": post["likeCount"],
+                "reply": post["commentsCount"],
+                "retweet": post["repostsCount"],
+                "views": "0",  # not support by RapidAPI
+            }
+            for post in posts
+        ]
+    ):
+        raise RuntimeError("Failed to dump posts insights to BigQuery")
+
+
+def query_last_post() -> Optional[dict]:
+    client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT"))
+    sql = """
+    SELECT
+        created_at
+    FROM
+        `pycontw-225217.ods.ods_pycontw_linkedin_posts`
+    ORDER BY
+        created_at DESC
+    LIMIT 1
+    """
+    result = client.query(sql)
+    data = list(result)
+    return data[0] if data else None
+
+
+def request_posts_data() -> List[dict]:
+
+    # Define the request options
+    # url = 'https://linkedin-data-api.p.rapidapi.com/get-profile-posts' # for user
+    url = "https://linkedin-data-api.p.rapidapi.com/get-company-posts"
+    querystring = {"username": "pycontw"}
+    headers = {
+        "X-RapidAPI-Key": Variable.get("LINKEDIN_RAPIDAPI_KEY"),
+        "X-RapidAPI-Host": "linkedin-data-api.p.rapidapi.com",
+    }
+
+    response = requests.get(url, headers=headers, params=querystring, timeout=180)
+    if not response.ok:
+        raise RuntimeError(f"Failed to fetch posts data: {response.text}")
+
+    media_insight_list = []
+    media_res_list = response.json()["data"]
+    # format handling, the response may not include the required fields
+    for media_res in media_res_list:
+        media_insight = {}
+        media_insight["urn"] = media_res.get("urn", "0")
+        media_insight["postedDateTimestamp"] = (
+            media_res.get("postedDateTimestamp", "0") / 1000
+        )
+        media_insight["text"] = media_res.get("text", "No Content")
+        media_insight["likeCount"] = media_res.get("totalReactionCount", "0")
+        media_insight["commentsCount"] = media_res.get("commentsCount", "0")
+        media_insight["repostsCount"] = media_res.get("repostsCount", "0")
+        # logger.info(media_insight)
+        media_insight_list.append(media_insight)
+
+    return media_insight_list
+
+
+def dump_posts_to_bigquery(posts: List[dict]) -> bool:
+    if not posts:
+        logger.info("No posts to dump!")
+        return True
+
+    client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT"))
+    job_config = bigquery.LoadJobConfig(
+        schema=[
+            bigquery.SchemaField("id", "STRING", mode="REQUIRED"),
+            bigquery.SchemaField("created_at", "TIMESTAMP", mode="REQUIRED"),
+            bigquery.SchemaField("message", "STRING", mode="REQUIRED"),
+        ],
+        write_disposition="WRITE_APPEND",
+    )
+    try:
+        job = client.load_table_from_json(
+            posts,
+            "pycontw-225217.ods.ods_pycontw_linkedin_posts",
+            job_config=job_config,
+        )
+        job.result()
+        return True
+    except Exception as e:
+        logger.error(f"Failed to dump posts to BigQuery: {e}", exc_info=True)
+        return False
+
+
+def dump_posts_insights_to_bigquery(posts: List[dict]) -> bool:
+    if not posts:
+        logger.info("No post insights to dump!")
+        return True
+
+    client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT"))
+    job_config = bigquery.LoadJobConfig(
+        schema=[
+            bigquery.SchemaField("post_id", "STRING", mode="REQUIRED"),
+            bigquery.SchemaField("query_time", "TIMESTAMP", mode="REQUIRED"),
+            bigquery.SchemaField("period", "STRING", mode="REQUIRED"),
+            bigquery.SchemaField("favorite", "INTEGER", mode="NULLABLE"),
+            bigquery.SchemaField("reply", "INTEGER", mode="NULLABLE"),
+            bigquery.SchemaField("retweet", "INTEGER", mode="NULLABLE"),
+            bigquery.SchemaField("views", "INTEGER", mode="NULLABLE"),
+        ],
+        write_disposition="WRITE_APPEND",
+    )
+    try:
+        job = client.load_table_from_json(
+            posts,
+            "pycontw-225217.ods.ods_pycontw_linkedin_posts_insights",
+            job_config=job_config,
+        )
+        job.result()
+        return True
+    except Exception as e:
+        logger.error(f"Failed to dump posts insights to BigQuery: {e}", exc_info=True)
+        return False
+
+
+def test_main():
+    create_table_if_needed()
+
+    # request_posts_data()
+
+    save_posts_and_insights()
+
+
+if __name__ == "__main__":
+    test_main()