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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
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import os import logging from airflow import DAG from airflow.utils.dates import days_ago from airflow.operators.bash import BashOperator from airflow.operators.python import PythonOperator from google.cloud import storage from airflow.providers.google.cloud.operators.bigquery import BigQueryCreateExternalTableOperator import pyarrow.csv as pv import pyarrow.parquet as pq PROJECT_ID = os.environ.get("GCP_PROJECT_ID") BUCKET = os.environ.get("GCP_GCS_BUCKET") dataset_file = "yellow_tripdata_2021-01.csv" dataset_url = f"https://s3.amazonaws.com/nyc-tlc/trip+data/{dataset_file}" path_to_local_home = os.environ.get("AIRFLOW_HOME", "/opt/airflow/") parquet_file = dataset_file.replace('.csv', '.parquet') BIGQUERY_DATASET = os.environ.get("BIGQUERY_DATASET", 'trips_data_all') def format_to_parquet(src_file): if not src_file.endswith('.csv'): logging.error("Can only accept source files in CSV format, for the moment") return table = pv.read_csv(src_file) pq.write_table(table, src_file.replace('.csv', '.parquet')) # NOTE: takes 20 mins, at an upload speed of 800kbps. Faster if your internet has a better upload speed def upload_to_gcs(bucket, object_name, local_file): """ Ref: https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python :param bucket: GCS bucket name :param object_name: target path & file-name :param local_file: source path & file-name :return: """ # WORKAROUND to prevent timeout for files > 6 MB on 800 kbps upload speed. # (Ref: https://github.com/googleapis/python-storage/issues/74) storage.blob._MAX_MULTIPART_SIZE = 5 * 1024 * 1024 # 5 MB storage.blob._DEFAULT_CHUNKSIZE = 5 * 1024 * 1024 # 5 MB # End of Workaround client = storage.Client() bucket = client.bucket(bucket) blob = bucket.blob(object_name) blob.upload_from_filename(local_file) default_args = { "owner": "airflow", "start_date": days_ago(1), "depends_on_past": False, "retries": 1, } # NOTE: DAG declaration - using a Context Manager (an implicit way) with DAG( dag_id="data_ingestion_gcs_dag", schedule_interval="@daily", default_args=default_args, catchup=False, max_active_runs=1, tags=['dtc-de'], ) as dag: download_dataset_task = BashOperator( task_id="download_dataset_task", bash_command=f"curl -sSL {dataset_url} > {path_to_local_home}/{dataset_file}" ) format_to_parquet_task = PythonOperator( task_id="format_to_parquet_task", python_callable=format_to_parquet, op_kwargs={ "src_file": f"{path_to_local_home}/{dataset_file}", }, ) # TODO: Homework - research and try XCOM to communicate output values between 2 tasks/operators local_to_gcs_task = PythonOperator( task_id="local_to_gcs_task", python_callable=upload_to_gcs, op_kwargs={ "bucket": BUCKET, "object_name": f"raw/{parquet_file}", "local_file": f"{path_to_local_home}/{parquet_file}", }, ) bigquery_external_table_task = BigQueryCreateExternalTableOperator( task_id="bigquery_external_table_task", table_resource={ "tableReference": { "projectId": PROJECT_ID, "datasetId": BIGQUERY_DATASET, "tableId": "external_table", }, "externalDataConfiguration": { "sourceFormat": "PARQUET", "sourceUris": [f"gs://{BUCKET}/raw/{parquet_file}"], }, }, ) download_dataset_task >> format_to_parquet_task >> local_to_gcs_task >> bigquery_external_table_task
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
Free Data Engineering course!
15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
import os from datetime import datetime from airflow import DAG from airflow.operators.bash import BashOperator from airflow.operators.python import PythonOperator from ingest_script import ingest_callable AIRFLOW_HOME = os.environ.get("AIRFLOW_HOME", "/opt/airflow/") PG_HOST = os.getenv('PG_HOST') PG_USER = os.getenv('PG_USER') PG_PASSWORD = os.getenv('PG_PASSWORD') PG_PORT = os.getenv('PG_PORT') PG_DATABASE = os.getenv('PG_DATABASE') local_workflow = DAG( "LocalIngestionDag", schedule_interval="0 6 2 * *", start_date=datetime(2021, 1, 1) ) URL_PREFIX = 'https://s3.amazonaws.com/nyc-tlc/trip+data' URL_TEMPLATE = URL_PREFIX + '/yellow_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' OUTPUT_FILE_TEMPLATE = AIRFLOW_HOME + '/output_{{ execution_date.strftime(\'%Y-%m\') }}.csv' TABLE_NAME_TEMPLATE = 'yellow_taxi_{{ execution_date.strftime(\'%Y_%m\') }}' with local_workflow: wget_task = BashOperator( task_id='wget', bash_command=f'curl -sSL {URL_TEMPLATE} > {OUTPUT_FILE_TEMPLATE}' ) ingest_task = PythonOperator( task_id="ingest", python_callable=ingest_callable, op_kwargs=dict( user=PG_USER, password=PG_PASSWORD, host=PG_HOST, port=PG_PORT, db=PG_DATABASE, table_name=TABLE_NAME_TEMPLATE, csv_file=OUTPUT_FILE_TEMPLATE ), ) wget_task >> ingest_task
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
Free Data Engineering course!
15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
import os from time import time import pandas as pd from sqlalchemy import create_engine def ingest_callable(user, password, host, port, db, table_name, csv_file, execution_date): print(table_name, csv_file, execution_date) engine = create_engine(f'postgresql://{user}:{password}@{host}:{port}/{db}') engine.connect() print('connection established successfully, inserting data...') t_start = time() df_iter = pd.read_csv(csv_file, iterator=True, chunksize=100000) df = next(df_iter) df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime) df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime) df.head(n=0).to_sql(name=table_name, con=engine, if_exists='replace') df.to_sql(name=table_name, con=engine, if_exists='append') t_end = time() print('inserted the first chunk, took %.3f second' % (t_end - t_start)) while True: t_start = time() try: df = next(df_iter) except StopIteration: print("completed") break df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime) df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime) df.to_sql(name=table_name, con=engine, if_exists='append') t_end = time() print('inserted another chunk, took %.3f second' % (t_end - t_start))
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
import os from datetime import datetime from airflow import DAG from airflow.utils.dates import days_ago from airflow.operators.bash import BashOperator from airflow.operators.python import PythonOperator from google.cloud import storage PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "pivotal-surfer-336713") BUCKET = os.environ.get("GCP_GCS_BUCKET", "dtc_data_lake_pivotal-surfer-336713") dataset_file = "yellow_tripdata_2021-01.csv" dataset_url = f"https://s3.amazonaws.com/nyc-tlc/trip+data/{dataset_file}" path_to_local_home = os.environ.get("AIRFLOW_HOME", "/opt/airflow/") path_to_creds = f"{path_to_local_home}/google_credentials.json" default_args = { "owner": "airflow", "start_date": days_ago(1), "depends_on_past": False, "retries": 1, } # # Takes 15-20 mins to run. Good case for using Spark (distributed processing, in place of chunks) # def upload_to_gcs(bucket, object_name, local_file): # """ # Ref: https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python # :param bucket: GCS bucket name # :param object_name: target path & file-name # :param local_file: source path & file-name # :return: # """ # # WORKAROUND to prevent timeout for files > 6 MB on 800 kbps upload link. # # (Ref: https://github.com/googleapis/python-storage/issues/74) # storage.blob._MAX_MULTIPART_SIZE = 5 * 1024 * 1024 # 5 MB # storage.blob._DEFAULT_CHUNKSIZE = 5 * 1024 * 1024 # 5 MB # # client = storage.Client() # bucket = client.bucket(bucket) # # blob = bucket.blob(object_name) # # blob.chunk_size = 5 * 1024 * 1024 # blob.upload_from_filename(local_file) with DAG( dag_id="data_ingestion_gcs_dag", schedule_interval="@daily", default_args=default_args, catchup=True, max_active_runs=1, ) as dag: # Takes ~2 mins, depending upon your internet's download speed download_dataset_task = BashOperator( task_id="download_dataset_task", bash_command=f"curl -sS {dataset_url} > {path_to_local_home}/{dataset_file}" # "&& unzip {zip_file} && rm {zip_file}" ) # # APPROACH 1: (takes 20 mins, at an upload speed of 800Kbps. Faster if your internet has a better upload speed) # upload_to_gcs_task = PythonOperator( # task_id="upload_to_gcs_task", # python_callable=upload_to_gcs, # op_kwargs={ # "bucket": BUCKET, # "object_name": f"raw/{dataset_file}", # "local_file": f"{path_to_local_home}/{dataset_file}", # # }, # ) # OR APPROACH 2: (takes 20 mins, at an upload speed of 800Kbps. Faster if your internet has a better upload speed) # Ref: https://cloud.google.com/blog/products/gcp/optimizing-your-cloud-storage-performance-google-cloud-performance-atlas upload_to_gcs_task = BashOperator( task_id="upload_to_gcs_task", bash_command=f"gcloud auth activate-service-account --key-file={path_to_creds} && \ gsutil -m cp {path_to_local_home}/{dataset_file} gs://{BUCKET}", ) download_dataset_task >> upload_to_gcs_task
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
import os import logging from datetime import datetime from airflow import DAG from airflow.utils.dates import days_ago from airflow.operators.bash import BashOperator from airflow.operators.python import PythonOperator from google.cloud import storage import pyarrow.csv as pv import pyarrow.parquet as pq PROJECT_ID = os.environ.get("GCP_PROJECT_ID") BUCKET = os.environ.get("GCP_GCS_BUCKET") AIRFLOW_HOME = os.environ.get("AIRFLOW_HOME", "/opt/airflow/") def format_to_parquet(src_file, dest_file): if not src_file.endswith('.csv'): logging.error("Can only accept source files in CSV format, for the moment") return table = pv.read_csv(src_file) pq.write_table(table, dest_file) def upload_to_gcs(bucket, object_name, local_file): client = storage.Client() bucket = client.bucket(bucket) blob = bucket.blob(object_name) blob.upload_from_filename(local_file) default_args = { "owner": "airflow", #"start_date": days_ago(1), "depends_on_past": False, "retries": 1, } def donwload_parquetize_upload_dag( dag, url_template, local_csv_path_template, local_parquet_path_template, gcs_path_template ): with dag: download_dataset_task = BashOperator( task_id="download_dataset_task", bash_command=f"curl -sSLf {url_template} > {local_csv_path_template}" ) format_to_parquet_task = PythonOperator( task_id="format_to_parquet_task", python_callable=format_to_parquet, op_kwargs={ "src_file": local_csv_path_template, "dest_file": local_parquet_path_template }, ) local_to_gcs_task = PythonOperator( task_id="local_to_gcs_task", python_callable=upload_to_gcs, op_kwargs={ "bucket": BUCKET, "object_name": gcs_path_template, "local_file": local_parquet_path_template, }, ) rm_task = BashOperator( task_id="rm_task", bash_command=f"rm {local_csv_path_template} {local_parquet_path_template}" ) download_dataset_task >> format_to_parquet_task >> local_to_gcs_task >> rm_task URL_PREFIX = 'https://s3.amazonaws.com/nyc-tlc/trip+data' YELLOW_TAXI_URL_TEMPLATE = URL_PREFIX + '/yellow_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' YELLOW_TAXI_CSV_FILE_TEMPLATE = AIRFLOW_HOME + '/yellow_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' YELLOW_TAXI_PARQUET_FILE_TEMPLATE = AIRFLOW_HOME + '/yellow_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.parquet' YELLOW_TAXI_GCS_PATH_TEMPLATE = "raw/yellow_tripdata/{{ execution_date.strftime(\'%Y\') }}/yellow_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.parquet" yellow_taxi_data_dag = DAG( dag_id="yellow_taxi_data_v2", schedule_interval="0 6 2 * *", start_date=datetime(2019, 1, 1), default_args=default_args, catchup=True, max_active_runs=3, tags=['dtc-de'], ) donwload_parquetize_upload_dag( dag=yellow_taxi_data_dag, url_template=YELLOW_TAXI_URL_TEMPLATE, local_csv_path_template=YELLOW_TAXI_CSV_FILE_TEMPLATE, local_parquet_path_template=YELLOW_TAXI_PARQUET_FILE_TEMPLATE, gcs_path_template=YELLOW_TAXI_GCS_PATH_TEMPLATE ) # https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2021-01.csv GREEN_TAXI_URL_TEMPLATE = URL_PREFIX + '/green_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' GREEN_TAXI_CSV_FILE_TEMPLATE = AIRFLOW_HOME + '/green_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' GREEN_TAXI_PARQUET_FILE_TEMPLATE = AIRFLOW_HOME + '/green_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.parquet' GREEN_TAXI_GCS_PATH_TEMPLATE = "raw/green_tripdata/{{ execution_date.strftime(\'%Y\') }}/green_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.parquet" green_taxi_data_dag = DAG( dag_id="green_taxi_data_v1", schedule_interval="0 7 2 * *", start_date=datetime(2019, 1, 1), default_args=default_args, catchup=True, max_active_runs=3, tags=['dtc-de'], ) donwload_parquetize_upload_dag( dag=green_taxi_data_dag, url_template=GREEN_TAXI_URL_TEMPLATE, local_csv_path_template=GREEN_TAXI_CSV_FILE_TEMPLATE, local_parquet_path_template=GREEN_TAXI_PARQUET_FILE_TEMPLATE, gcs_path_template=GREEN_TAXI_GCS_PATH_TEMPLATE ) # https://nyc-tlc.s3.amazonaws.com/trip+data/fhv_tripdata_2021-01.csv FHV_TAXI_URL_TEMPLATE = URL_PREFIX + '/fhv_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' FHV_TAXI_CSV_FILE_TEMPLATE = AIRFLOW_HOME + '/fhv_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.csv' FHV_TAXI_PARQUET_FILE_TEMPLATE = AIRFLOW_HOME + '/fhv_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.parquet' FHV_TAXI_GCS_PATH_TEMPLATE = "raw/fhv_tripdata/{{ execution_date.strftime(\'%Y\') }}/fhv_tripdata_{{ execution_date.strftime(\'%Y-%m\') }}.parquet" fhv_taxi_data_dag = DAG( dag_id="hfv_taxi_data_v1", schedule_interval="0 8 2 * *", start_date=datetime(2019, 1, 1), end_date=datetime(2020, 1, 1), default_args=default_args, catchup=True, max_active_runs=3, tags=['dtc-de'], ) donwload_parquetize_upload_dag( dag=fhv_taxi_data_dag, url_template=FHV_TAXI_URL_TEMPLATE, local_csv_path_template=FHV_TAXI_CSV_FILE_TEMPLATE, local_parquet_path_template=FHV_TAXI_PARQUET_FILE_TEMPLATE, gcs_path_template=FHV_TAXI_GCS_PATH_TEMPLATE ) # https://s3.amazonaws.com/nyc-tlc/misc/taxi+_zone_lookup.csv ZONES_URL_TEMPLATE = 'https://s3.amazonaws.com/nyc-tlc/misc/taxi+_zone_lookup.csv' ZONES_CSV_FILE_TEMPLATE = AIRFLOW_HOME + '/taxi_zone_lookup.csv' ZONES_PARQUET_FILE_TEMPLATE = AIRFLOW_HOME + '/taxi_zone_lookup.parquet' ZONES_GCS_PATH_TEMPLATE = "raw/taxi_zone/taxi_zone_lookup.parquet" zones_data_dag = DAG( dag_id="zones_data_v1", schedule_interval="@once", start_date=days_ago(1), default_args=default_args, catchup=True, max_active_runs=3, tags=['dtc-de'], ) donwload_parquetize_upload_dag( dag=zones_data_dag, url_template=ZONES_URL_TEMPLATE, local_csv_path_template=ZONES_CSV_FILE_TEMPLATE, local_parquet_path_template=ZONES_PARQUET_FILE_TEMPLATE, gcs_path_template=ZONES_GCS_PATH_TEMPLATE )
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
Free Data Engineering course!
15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
import os import logging from airflow import DAG from airflow.utils.dates import days_ago from airflow.providers.google.cloud.operators.bigquery import BigQueryCreateExternalTableOperator, BigQueryInsertJobOperator from airflow.providers.google.cloud.transfers.gcs_to_gcs import GCSToGCSOperator PROJECT_ID = os.environ.get("GCP_PROJECT_ID") BUCKET = os.environ.get("GCP_GCS_BUCKET") path_to_local_home = os.environ.get("AIRFLOW_HOME", "/opt/airflow/") BIGQUERY_DATASET = os.environ.get("BIGQUERY_DATASET", 'trips_data_all') DATASET = "tripdata" COLOUR_RANGE = {'yellow': 'tpep_pickup_datetime', 'green': 'lpep_pickup_datetime'} INPUT_PART = "raw" INPUT_FILETYPE = "parquet" default_args = { "owner": "airflow", "start_date": days_ago(1), "depends_on_past": False, "retries": 1, } # NOTE: DAG declaration - using a Context Manager (an implicit way) with DAG( dag_id="gcs_2_bq_dag", schedule_interval="@daily", default_args=default_args, catchup=False, max_active_runs=1, tags=['dtc-de'], ) as dag: for colour, ds_col in COLOUR_RANGE.items(): move_files_gcs_task = GCSToGCSOperator( task_id=f'move_{colour}_{DATASET}_files_task', source_bucket=BUCKET, source_object=f'{INPUT_PART}/{colour}_{DATASET}*.{INPUT_FILETYPE}', destination_bucket=BUCKET, destination_object=f'{colour}/{colour}_{DATASET}', move_object=True ) bigquery_external_table_task = BigQueryCreateExternalTableOperator( task_id=f"bq_{colour}_{DATASET}_external_table_task", table_resource={ "tableReference": { "projectId": PROJECT_ID, "datasetId": BIGQUERY_DATASET, "tableId": f"{colour}_{DATASET}_external_table", }, "externalDataConfiguration": { "autodetect": "True", "sourceFormat": f"{INPUT_FILETYPE.upper()}", "sourceUris": [f"gs://{BUCKET}/{colour}/*"], }, }, ) CREATE_BQ_TBL_QUERY = ( f"CREATE OR REPLACE TABLE {BIGQUERY_DATASET}.{colour}_{DATASET} \ PARTITION BY DATE({ds_col}) \ AS \ SELECT * FROM {BIGQUERY_DATASET}.{colour}_{DATASET}_external_table;" ) # Create a partitioned table from external table bq_create_partitioned_table_job = BigQueryInsertJobOperator( task_id=f"bq_create_{colour}_{DATASET}_partitioned_table_task", configuration={ "query": { "query": CREATE_BQ_TBL_QUERY, "useLegacySql": False, } } ) move_files_gcs_task >> bigquery_external_table_task >> bq_create_partitioned_table_job
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
Free Data Engineering course!
15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
from confluent_kafka import Producer import argparse import csv from typing import Dict from time import sleep from settings import CONFLUENT_CLOUD_CONFIG, \ GREEN_TAXI_TOPIC, FHV_TAXI_TOPIC, \ GREEN_TRIP_DATA_PATH, FHV_TRIP_DATA_PATH class RideCSVProducer: def __init__(self, probs: Dict, ride_type: str): self.producer = Producer(**probs) self.ride_type = ride_type def parse_row(self, row): if self.ride_type == 'green': record = f'{row[5]}, {row[6]}' # PULocationID, DOLocationID key = str(row[0]) # vendor_id elif self.ride_type == 'fhv': record = f'{row[3]}, {row[4]}' # PULocationID, DOLocationID, key = str(row[0]) # dispatching_base_num return key, record def read_records(self, resource_path: str): records, ride_keys = [], [] with open(resource_path, 'r') as f: reader = csv.reader(f) header = next(reader) # skip the header for row in reader: key, record = self.parse_row(row) ride_keys.append(key) records.append(record) return zip(ride_keys, records) def publish(self, records: [str, str], topic: str): for key_value in records: key, value = key_value try: self.producer.poll(0) self.producer.produce(topic=topic, key=key, value=value) print(f"Producing record for <key: {key}, value:{value}>") except KeyboardInterrupt: break except BufferError as bfer: self.producer.poll(0.1) except Exception as e: print(f"Exception while producing record - {value}: {e}") self.producer.flush() sleep(10) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Kafka Consumer') parser.add_argument('--type', type=str, default='green') args = parser.parse_args() if args.type == 'green': kafka_topic = GREEN_TAXI_TOPIC data_path = GREEN_TRIP_DATA_PATH elif args.type == 'fhv': kafka_topic = FHV_TAXI_TOPIC data_path = FHV_TRIP_DATA_PATH producer = RideCSVProducer(ride_type=args.type, probs=CONFLUENT_CLOUD_CONFIG) ride_records = producer.read_records(resource_path=data_path) producer.publish(records=ride_records, topic=kafka_topic)
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
import pyspark.sql.types as T INPUT_DATA_PATH = '../../resources/rides.csv' BOOTSTRAP_SERVERS = 'localhost:9092' TOPIC_WINDOWED_VENDOR_ID_COUNT = 'vendor_counts_windowed' PRODUCE_TOPIC_RIDES_CSV = CONSUME_TOPIC_RIDES_CSV = 'rides_csv' RIDE_SCHEMA = T.StructType( [T.StructField("vendor_id", T.IntegerType()), T.StructField('tpep_pickup_datetime', T.TimestampType()), T.StructField('tpep_dropoff_datetime', T.TimestampType()), T.StructField("passenger_count", T.IntegerType()), T.StructField("trip_distance", T.FloatType()), T.StructField("payment_type", T.IntegerType()), T.StructField("total_amount", T.FloatType()), ])
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data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Python
from pyspark.sql import SparkSession import pyspark.sql.functions as F from settings import CONFLUENT_CLOUD_CONFIG, GREEN_TAXI_TOPIC, FHV_TAXI_TOPIC, RIDES_TOPIC, ALL_RIDE_SCHEMA def read_from_kafka(consume_topic: str): # Spark Streaming DataFrame, connect to Kafka topic served at host in bootrap.servers option df_stream = spark \ .readStream \ .format("kafka") \ .option("kafka.bootstrap.servers", CONFLUENT_CLOUD_CONFIG['bootstrap.servers']) \ .option("subscribe", consume_topic) \ .option("startingOffsets", "earliest") \ .option("checkpointLocation", "checkpoint") \ .option("kafka.security.protocol", "SASL_SSL") \ .option("kafka.sasl.mechanism", "PLAIN") \ .option("kafka.sasl.jaas.config", f"""org.apache.kafka.common.security.plain.PlainLoginModule required username="{CONFLUENT_CLOUD_CONFIG['sasl.username']}" password="{CONFLUENT_CLOUD_CONFIG['sasl.password']}";""") \ .option("failOnDataLoss", False) \ .load() return df_stream def parse_rides(df, schema): """ take a Spark Streaming df and parse value col based on <schema>, return streaming df cols in schema """ assert df.isStreaming is True, "DataFrame doesn't receive streaming data" df = df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") # split attributes to nested array in one Column col = F.split(df['value'], ', ') # expand col to multiple top-level columns for idx, field in enumerate(schema): df = df.withColumn(field.name, col.getItem(idx).cast(field.dataType)) df = df.na.drop() df.printSchema() return df.select([field.name for field in schema]) def sink_console(df, output_mode: str = 'complete', processing_time: str = '5 seconds'): query = df.writeStream \ .outputMode(output_mode) \ .trigger(processingTime=processing_time) \ .format("console") \ .option("truncate", False) \ .start() \ .awaitTermination() return query # pyspark.sql.streaming.StreamingQuery def sink_kafka(df, topic, output_mode: str = 'complete'): query = df.writeStream \ .format("kafka") \ .option("kafka.bootstrap.servers", "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092") \ .outputMode(output_mode) \ .option("topic", topic) \ .option("checkpointLocation", "checkpoint") \ .option("kafka.security.protocol", "SASL_SSL") \ .option("kafka.sasl.mechanism", "PLAIN") \ .option("kafka.sasl.jaas.config", f"""org.apache.kafka.common.security.plain.PlainLoginModule required username="{CONFLUENT_CLOUD_CONFIG['sasl.username']}" password="{CONFLUENT_CLOUD_CONFIG['sasl.password']}";""") \ .option("failOnDataLoss", False) \ .start() return query def op_groupby(df, column_names): df_aggregation = df.groupBy(column_names).count() return df_aggregation if __name__ == "__main__": spark = SparkSession.builder.appName('streaming-homework').getOrCreate() spark.sparkContext.setLogLevel('WARN') # Step 1: Consume GREEN_TAXI_TOPIC and FHV_TAXI_TOPIC df_green_rides = read_from_kafka(consume_topic=GREEN_TAXI_TOPIC) df_fhv_rides = read_from_kafka(consume_topic=FHV_TAXI_TOPIC) # Step 2: Publish green and fhv rides to RIDES_TOPIC kafka_sink_green_query = sink_kafka(df=df_green_rides, topic=RIDES_TOPIC, output_mode='append') kafka_sink_fhv_query = sink_kafka(df=df_fhv_rides, topic=RIDES_TOPIC, output_mode='append') # Step 3: Read RIDES_TOPIC and parse it in ALL_RIDE_SCHEMA df_all_rides = read_from_kafka(consume_topic=RIDES_TOPIC) df_all_rides = parse_rides(df_all_rides, ALL_RIDE_SCHEMA) # Step 4: Apply Aggregation on the all_rides df_pu_location_count = op_groupby(df_all_rides, ['PULocationID']) df_pu_location_count = df_pu_location_count.sort(F.col('count').desc()) # Step 5: Sink Aggregation Streams to Console console_sink_pu_location = sink_console(df_pu_location_count, output_mode='complete')
39.87
197
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2021-10-21 09:32:50+00:00
1,561
null
Python
#!/usr/bin/env python # coding: utf-8 import os import argparse from time import time import pandas as pd from sqlalchemy import create_engine def main(params): user = params.user password = params.password host = params.host port = params.port db = params.db table_name = params.table_name url = params.url # the backup files are gzipped, and it's important to keep the correct extension # for pandas to be able to open the file if url.endswith('.csv.gz'): csv_name = 'output.csv.gz' else: csv_name = 'output.csv' os.system(f"wget {url} -O {csv_name}") engine = create_engine(f'postgresql://{user}:{password}@{host}:{port}/{db}') df_iter = pd.read_csv(csv_name, iterator=True, chunksize=100000) df = next(df_iter) df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime) df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime) df.head(n=0).to_sql(name=table_name, con=engine, if_exists='replace') df.to_sql(name=table_name, con=engine, if_exists='append') while True: try: t_start = time() df = next(df_iter) df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime) df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime) df.to_sql(name=table_name, con=engine, if_exists='append') t_end = time() print('inserted another chunk, took %.3f second' % (t_end - t_start)) except StopIteration: print("Finished ingesting data into the postgres database") break if __name__ == '__main__': parser = argparse.ArgumentParser(description='Ingest CSV data to Postgres') parser.add_argument('--user', required=True, help='user name for postgres') parser.add_argument('--password', required=True, help='password for postgres') parser.add_argument('--host', required=True, help='host for postgres') parser.add_argument('--port', required=True, help='port for postgres') parser.add_argument('--db', required=True, help='database name for postgres') parser.add_argument('--table_name', required=True, help='name of the table where we will write the results to') parser.add_argument('--url', required=True, help='url of the csv file') args = parser.parse_args() main(args)
29.417722
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1,561
null
Python
import sys import pandas as pd print(sys.argv) day = sys.argv[1] # some fancy stuff with pandas print(f'job finished successfully for day = {day}')
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1,561
null
Python
import io import os import requests import pandas as pd from google.cloud import storage """ Pre-reqs: 1. `pip install pandas pyarrow google-cloud-storage` 2. Set GOOGLE_APPLICATION_CREDENTIALS to your project/service-account key 3. Set GCP_GCS_BUCKET as your bucket or change default value of BUCKET """ # services = ['fhv','green','yellow'] init_url = 'https://github.com/DataTalksClub/nyc-tlc-data/releases/download/' # switch out the bucketname BUCKET = os.environ.get("GCP_GCS_BUCKET", "dtc-data-lake-bucketname") def upload_to_gcs(bucket, object_name, local_file): """ Ref: https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python """ # # WORKAROUND to prevent timeout for files > 6 MB on 800 kbps upload speed. # # (Ref: https://github.com/googleapis/python-storage/issues/74) # storage.blob._MAX_MULTIPART_SIZE = 5 * 1024 * 1024 # 5 MB # storage.blob._DEFAULT_CHUNKSIZE = 5 * 1024 * 1024 # 5 MB client = storage.Client() bucket = client.bucket(bucket) blob = bucket.blob(object_name) blob.upload_from_filename(local_file) def web_to_gcs(year, service): for i in range(12): # sets the month part of the file_name string month = '0'+str(i+1) month = month[-2:] # csv file_name file_name = f"{service}_tripdata_{year}-{month}.csv.gz" # download it using requests via a pandas df request_url = f"{init_url}{service}/{file_name}" r = requests.get(request_url) open(file_name, 'wb').write(r.content) print(f"Local: {file_name}") # read it back into a parquet file df = pd.read_csv(file_name, compression='gzip') file_name = file_name.replace('.csv.gz', '.parquet') df.to_parquet(file_name, engine='pyarrow') print(f"Parquet: {file_name}") # upload it to gcs upload_to_gcs(BUCKET, f"{service}/{file_name}", file_name) print(f"GCS: {service}/{file_name}") web_to_gcs('2019', 'green') web_to_gcs('2020', 'green') # web_to_gcs('2019', 'yellow') # web_to_gcs('2020', 'yellow')
30.671642
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1,561
null
Python
#!/usr/bin/env python # coding: utf-8 import argparse import pyspark from pyspark.sql import SparkSession from pyspark.sql import functions as F parser = argparse.ArgumentParser() parser.add_argument('--input_green', required=True) parser.add_argument('--input_yellow', required=True) parser.add_argument('--output', required=True) args = parser.parse_args() input_green = args.input_green input_yellow = args.input_yellow output = args.output spark = SparkSession.builder \ .appName('test') \ .getOrCreate() df_green = spark.read.parquet(input_green) df_green = df_green \ .withColumnRenamed('lpep_pickup_datetime', 'pickup_datetime') \ .withColumnRenamed('lpep_dropoff_datetime', 'dropoff_datetime') df_yellow = spark.read.parquet(input_yellow) df_yellow = df_yellow \ .withColumnRenamed('tpep_pickup_datetime', 'pickup_datetime') \ .withColumnRenamed('tpep_dropoff_datetime', 'dropoff_datetime') common_colums = [ 'VendorID', 'pickup_datetime', 'dropoff_datetime', 'store_and_fwd_flag', 'RatecodeID', 'PULocationID', 'DOLocationID', 'passenger_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'payment_type', 'congestion_surcharge' ] df_green_sel = df_green \ .select(common_colums) \ .withColumn('service_type', F.lit('green')) df_yellow_sel = df_yellow \ .select(common_colums) \ .withColumn('service_type', F.lit('yellow')) df_trips_data = df_green_sel.unionAll(df_yellow_sel) df_trips_data.registerTempTable('trips_data') df_result = spark.sql(""" SELECT -- Reveneue grouping PULocationID AS revenue_zone, date_trunc('month', pickup_datetime) AS revenue_month, service_type, -- Revenue calculation SUM(fare_amount) AS revenue_monthly_fare, SUM(extra) AS revenue_monthly_extra, SUM(mta_tax) AS revenue_monthly_mta_tax, SUM(tip_amount) AS revenue_monthly_tip_amount, SUM(tolls_amount) AS revenue_monthly_tolls_amount, SUM(improvement_surcharge) AS revenue_monthly_improvement_surcharge, SUM(total_amount) AS revenue_monthly_total_amount, SUM(congestion_surcharge) AS revenue_monthly_congestion_surcharge, -- Additional calculations AVG(passenger_count) AS avg_montly_passenger_count, AVG(trip_distance) AS avg_montly_trip_distance FROM trips_data GROUP BY 1, 2, 3 """) df_result.coalesce(1) \ .write.parquet(output, mode='overwrite')
21.75
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1,561
null
Python
#!/usr/bin/env python # coding: utf-8 import argparse import pyspark from pyspark.sql import SparkSession from pyspark.sql import functions as F parser = argparse.ArgumentParser() parser.add_argument('--input_green', required=True) parser.add_argument('--input_yellow', required=True) parser.add_argument('--output', required=True) args = parser.parse_args() input_green = args.input_green input_yellow = args.input_yellow output = args.output spark = SparkSession.builder \ .appName('test') \ .getOrCreate() spark.conf.set('temporaryGcsBucket', 'dataproc-temp-europe-west6-828225226997-fckhkym8') df_green = spark.read.parquet(input_green) df_green = df_green \ .withColumnRenamed('lpep_pickup_datetime', 'pickup_datetime') \ .withColumnRenamed('lpep_dropoff_datetime', 'dropoff_datetime') df_yellow = spark.read.parquet(input_yellow) df_yellow = df_yellow \ .withColumnRenamed('tpep_pickup_datetime', 'pickup_datetime') \ .withColumnRenamed('tpep_dropoff_datetime', 'dropoff_datetime') common_colums = [ 'VendorID', 'pickup_datetime', 'dropoff_datetime', 'store_and_fwd_flag', 'RatecodeID', 'PULocationID', 'DOLocationID', 'passenger_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'payment_type', 'congestion_surcharge' ] df_green_sel = df_green \ .select(common_colums) \ .withColumn('service_type', F.lit('green')) df_yellow_sel = df_yellow \ .select(common_colums) \ .withColumn('service_type', F.lit('yellow')) df_trips_data = df_green_sel.unionAll(df_yellow_sel) df_trips_data.registerTempTable('trips_data') df_result = spark.sql(""" SELECT -- Reveneue grouping PULocationID AS revenue_zone, date_trunc('month', pickup_datetime) AS revenue_month, service_type, -- Revenue calculation SUM(fare_amount) AS revenue_monthly_fare, SUM(extra) AS revenue_monthly_extra, SUM(mta_tax) AS revenue_monthly_mta_tax, SUM(tip_amount) AS revenue_monthly_tip_amount, SUM(tolls_amount) AS revenue_monthly_tolls_amount, SUM(improvement_surcharge) AS revenue_monthly_improvement_surcharge, SUM(total_amount) AS revenue_monthly_total_amount, SUM(congestion_surcharge) AS revenue_monthly_congestion_surcharge, -- Additional calculations AVG(passenger_count) AS avg_montly_passenger_count, AVG(trip_distance) AS avg_montly_trip_distance FROM trips_data GROUP BY 1, 2, 3 """) df_result.write.format('bigquery') \ .option('table', output) \ .save()
22.069565
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1,561
null
Python
import argparse from typing import Dict, List from kafka import KafkaConsumer from settings import BOOTSTRAP_SERVERS, CONSUME_TOPIC_RIDES_CSV class RideCSVConsumer: def __init__(self, props: Dict): self.consumer = KafkaConsumer(**props) def consume_from_kafka(self, topics: List[str]): self.consumer.subscribe(topics=topics) print('Consuming from Kafka started') print('Available topics to consume: ', self.consumer.subscription()) while True: try: # SIGINT can't be handled when polling, limit timeout to 1 second. msg = self.consumer.poll(1.0) if msg is None or msg == {}: continue for msg_key, msg_values in msg.items(): for msg_val in msg_values: print(f'Key:{msg_val.key}-type({type(msg_val.key)}), ' f'Value:{msg_val.value}-type({type(msg_val.value)})') except KeyboardInterrupt: break self.consumer.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Kafka Consumer') parser.add_argument('--topic', type=str, default=CONSUME_TOPIC_RIDES_CSV) args = parser.parse_args() topic = args.topic config = { 'bootstrap_servers': [BOOTSTRAP_SERVERS], 'auto_offset_reset': 'earliest', 'enable_auto_commit': True, 'key_deserializer': lambda key: int(key.decode('utf-8')), 'value_deserializer': lambda value: value.decode('utf-8'), 'group_id': 'consumer.group.id.csv-example.1', } csv_consumer = RideCSVConsumer(props=config) csv_consumer.consume_from_kafka(topics=[topic])
35.25
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1,561
null
Python
import csv from time import sleep from typing import Dict from kafka import KafkaProducer from settings import BOOTSTRAP_SERVERS, INPUT_DATA_PATH, PRODUCE_TOPIC_RIDES_CSV def delivery_report(err, msg): if err is not None: print("Delivery failed for record {}: {}".format(msg.key(), err)) return print('Record {} successfully produced to {} [{}] at offset {}'.format( msg.key(), msg.topic(), msg.partition(), msg.offset())) class RideCSVProducer: def __init__(self, props: Dict): self.producer = KafkaProducer(**props) # self.producer = Producer(producer_props) @staticmethod def read_records(resource_path: str): records, ride_keys = [], [] i = 0 with open(resource_path, 'r') as f: reader = csv.reader(f) header = next(reader) # skip the header for row in reader: # vendor_id, passenger_count, trip_distance, payment_type, total_amount records.append(f'{row[0]}, {row[1]}, {row[2]}, {row[3]}, {row[4]}, {row[9]}, {row[16]}') ride_keys.append(str(row[0])) i += 1 if i == 5: break return zip(ride_keys, records) def publish(self, topic: str, records: [str, str]): for key_value in records: key, value = key_value try: self.producer.send(topic=topic, key=key, value=value) print(f"Producing record for <key: {key}, value:{value}>") except KeyboardInterrupt: break except Exception as e: print(f"Exception while producing record - {value}: {e}") self.producer.flush() sleep(1) if __name__ == "__main__": config = { 'bootstrap_servers': [BOOTSTRAP_SERVERS], 'key_serializer': lambda x: x.encode('utf-8'), 'value_serializer': lambda x: x.encode('utf-8') } producer = RideCSVProducer(props=config) ride_records = producer.read_records(resource_path=INPUT_DATA_PATH) print(ride_records) producer.publish(topic=PRODUCE_TOPIC_RIDES_CSV, records=ride_records)
33.571429
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1,561
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Python
from typing import List, Dict class RideRecord: def __init__(self, arr: List[str]): self.vendor_id = int(arr[0]) self.passenger_count = int(arr[1]) self.trip_distance = float(arr[2]) self.payment_type = int(arr[3]) self.total_amount = float(arr[4]) @classmethod def from_dict(cls, d: Dict): return cls(arr=[ d['vendor_id'], d['passenger_count'], d['trip_distance'], d['payment_type'], d['total_amount'] ] ) def __repr__(self): return f'{self.__class__.__name__}: {self.__dict__}' def dict_to_ride_record(obj, ctx): if obj is None: return None return RideRecord.from_dict(obj) def ride_record_to_dict(ride_record: RideRecord, ctx): return ride_record.__dict__
21.648649
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1,561
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Python
from typing import Dict class RideRecordKey: def __init__(self, vendor_id): self.vendor_id = vendor_id @classmethod def from_dict(cls, d: Dict): return cls(vendor_id=d['vendor_id']) def __repr__(self): return f'{self.__class__.__name__}: {self.__dict__}' def dict_to_ride_record_key(obj, ctx): if obj is None: return None return RideRecordKey.from_dict(obj) def ride_record_key_to_dict(ride_record_key: RideRecordKey, ctx): return ride_record_key.__dict__
20.04
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Python
from typing import List, Dict from decimal import Decimal from datetime import datetime class Ride: def __init__(self, arr: List[str]): self.vendor_id = arr[0] self.tpep_pickup_datetime = datetime.strptime(arr[1], "%Y-%m-%d %H:%M:%S"), self.tpep_dropoff_datetime = datetime.strptime(arr[2], "%Y-%m-%d %H:%M:%S"), self.passenger_count = int(arr[3]) self.trip_distance = Decimal(arr[4]) self.rate_code_id = int(arr[5]) self.store_and_fwd_flag = arr[6] self.pu_location_id = int(arr[7]) self.do_location_id = int(arr[8]) self.payment_type = arr[9] self.fare_amount = Decimal(arr[10]) self.extra = Decimal(arr[11]) self.mta_tax = Decimal(arr[12]) self.tip_amount = Decimal(arr[13]) self.tolls_amount = Decimal(arr[14]) self.improvement_surcharge = Decimal(arr[15]) self.total_amount = Decimal(arr[16]) self.congestion_surcharge = Decimal(arr[17]) @classmethod def from_dict(cls, d: Dict): return cls(arr=[ d['vendor_id'], d['tpep_pickup_datetime'][0], d['tpep_dropoff_datetime'][0], d['passenger_count'], d['trip_distance'], d['rate_code_id'], d['store_and_fwd_flag'], d['pu_location_id'], d['do_location_id'], d['payment_type'], d['fare_amount'], d['extra'], d['mta_tax'], d['tip_amount'], d['tolls_amount'], d['improvement_surcharge'], d['total_amount'], d['congestion_surcharge'], ] ) def __repr__(self): return f'{self.__class__.__name__}: {self.__dict__}'
32.396226
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1,561
null
Python
import faust from taxi_rides import TaxiRide from faust import current_event app = faust.App('datatalksclub.stream.v3', broker='kafka://localhost:9092', consumer_auto_offset_reset="earliest") topic = app.topic('datatalkclub.yellow_taxi_ride.json', value_type=TaxiRide) high_amount_rides = app.topic('datatalks.yellow_taxi_rides.high_amount') low_amount_rides = app.topic('datatalks.yellow_taxi_rides.low_amount') @app.agent(topic) async def process(stream): async for event in stream: if event.total_amount >= 40.0: await current_event().forward(high_amount_rides) else: await current_event().forward(low_amount_rides) if __name__ == '__main__': app.main()
31.318182
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1,561
null
Python
import csv from json import dumps from kafka import KafkaProducer from time import sleep producer = KafkaProducer(bootstrap_servers=['localhost:9092'], key_serializer=lambda x: dumps(x).encode('utf-8'), value_serializer=lambda x: dumps(x).encode('utf-8')) file = open('../../resources/rides.csv') csvreader = csv.reader(file) header = next(csvreader) for row in csvreader: key = {"vendorId": int(row[0])} value = {"vendorId": int(row[0]), "passenger_count": int(row[3]), "trip_distance": float(row[4]), "payment_type": int(row[9]), "total_amount": float(row[16])} producer.send('datatalkclub.yellow_taxi_ride.json', value=value, key=key) print("producing") sleep(1)
36
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1,561
null
Python
import faust from taxi_rides import TaxiRide app = faust.App('datatalksclub.stream.v2', broker='kafka://localhost:9092') topic = app.topic('datatalkclub.yellow_taxi_ride.json', value_type=TaxiRide) @app.agent(topic) async def start_reading(records): async for record in records: print(record) if __name__ == '__main__': app.main()
19.823529
76
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1,561
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Python
import faust from taxi_rides import TaxiRide app = faust.App('datatalksclub.stream.v2', broker='kafka://localhost:9092') topic = app.topic('datatalkclub.yellow_taxi_ride.json', value_type=TaxiRide) vendor_rides = app.Table('vendor_rides', default=int) @app.agent(topic) async def process(stream): async for event in stream.group_by(TaxiRide.vendorId): vendor_rides[event.vendorId] += 1 if __name__ == '__main__': app.main()
23.833333
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null
Python
import faust class TaxiRide(faust.Record, validation=True): vendorId: str passenger_count: int trip_distance: float payment_type: int total_amount: float
16.7
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1,561
null
Python
from datetime import timedelta import faust from taxi_rides import TaxiRide app = faust.App('datatalksclub.stream.v2', broker='kafka://localhost:9092') topic = app.topic('datatalkclub.yellow_taxi_ride.json', value_type=TaxiRide) vendor_rides = app.Table('vendor_rides_windowed', default=int).tumbling( timedelta(minutes=1), expires=timedelta(hours=1), ) @app.agent(topic) async def process(stream): async for event in stream.group_by(TaxiRide.vendorId): vendor_rides[event.vendorId] += 1 if __name__ == '__main__': app.main()
23.26087
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1,561
null
Python
from pyspark.sql import SparkSession import pyspark.sql.functions as F from settings import RIDE_SCHEMA, CONSUME_TOPIC_RIDES_CSV, TOPIC_WINDOWED_VENDOR_ID_COUNT def read_from_kafka(consume_topic: str): # Spark Streaming DataFrame, connect to Kafka topic served at host in bootrap.servers option df_stream = spark \ .readStream \ .format("kafka") \ .option("kafka.bootstrap.servers", "localhost:9092,broker:29092") \ .option("subscribe", consume_topic) \ .option("startingOffsets", "earliest") \ .option("checkpointLocation", "checkpoint") \ .load() return df_stream def parse_ride_from_kafka_message(df, schema): """ take a Spark Streaming df and parse value col based on <schema>, return streaming df cols in schema """ assert df.isStreaming is True, "DataFrame doesn't receive streaming data" df = df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") # split attributes to nested array in one Column col = F.split(df['value'], ', ') # expand col to multiple top-level columns for idx, field in enumerate(schema): df = df.withColumn(field.name, col.getItem(idx).cast(field.dataType)) return df.select([field.name for field in schema]) def sink_console(df, output_mode: str = 'complete', processing_time: str = '5 seconds'): write_query = df.writeStream \ .outputMode(output_mode) \ .trigger(processingTime=processing_time) \ .format("console") \ .option("truncate", False) \ .start() return write_query # pyspark.sql.streaming.StreamingQuery def sink_memory(df, query_name, query_template): query_df = df \ .writeStream \ .queryName(query_name) \ .format("memory") \ .start() query_str = query_template.format(table_name=query_name) query_results = spark.sql(query_str) return query_results, query_df def sink_kafka(df, topic): write_query = df.writeStream \ .format("kafka") \ .option("kafka.bootstrap.servers", "localhost:9092,broker:29092") \ .outputMode('complete') \ .option("topic", topic) \ .option("checkpointLocation", "checkpoint") \ .start() return write_query def prepare_df_to_kafka_sink(df, value_columns, key_column=None): columns = df.columns df = df.withColumn("value", F.concat_ws(', ', *value_columns)) if key_column: df = df.withColumnRenamed(key_column, "key") df = df.withColumn("key", df.key.cast('string')) return df.select(['key', 'value']) def op_groupby(df, column_names): df_aggregation = df.groupBy(column_names).count() return df_aggregation def op_windowed_groupby(df, window_duration, slide_duration): df_windowed_aggregation = df.groupBy( F.window(timeColumn=df.tpep_pickup_datetime, windowDuration=window_duration, slideDuration=slide_duration), df.vendor_id ).count() return df_windowed_aggregation if __name__ == "__main__": spark = SparkSession.builder.appName('streaming-examples').getOrCreate() spark.sparkContext.setLogLevel('WARN') # read_streaming data df_consume_stream = read_from_kafka(consume_topic=CONSUME_TOPIC_RIDES_CSV) print(df_consume_stream.printSchema()) # parse streaming data df_rides = parse_ride_from_kafka_message(df_consume_stream, RIDE_SCHEMA) print(df_rides.printSchema()) sink_console(df_rides, output_mode='append') df_trip_count_by_vendor_id = op_groupby(df_rides, ['vendor_id']) df_trip_count_by_pickup_date_vendor_id = op_windowed_groupby(df_rides, window_duration="10 minutes", slide_duration='5 minutes') # write the output out to the console for debugging / testing sink_console(df_trip_count_by_vendor_id) # write the output to the kafka topic df_trip_count_messages = prepare_df_to_kafka_sink(df=df_trip_count_by_pickup_date_vendor_id, value_columns=['count'], key_column='vendor_id') kafka_sink_query = sink_kafka(df=df_trip_count_messages, topic=TOPIC_WINDOWED_VENDOR_ID_COUNT) spark.streams.awaitAnyTermination()
35.586207
115
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data-engineering-zoomcamp
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1,561
null
Java
/** * Autogenerated by Avro * * DO NOT EDIT DIRECTLY */ package schemaregistry; import org.apache.avro.generic.GenericArray; import org.apache.avro.specific.SpecificData; import org.apache.avro.util.Utf8; import org.apache.avro.message.BinaryMessageEncoder; import org.apache.avro.message.BinaryMessageDecoder; import org.apache.avro.message.SchemaStore; @org.apache.avro.specific.AvroGenerated public class RideRecord extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord { private static final long serialVersionUID = 6805437803204402942L; public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"RideRecord\",\"namespace\":\"schemaregistry\",\"fields\":[{\"name\":\"vendor_id\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"passenger_count\",\"type\":\"int\"},{\"name\":\"trip_distance\",\"type\":\"double\"}]}"); public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; } private static final SpecificData MODEL$ = new SpecificData(); private static final BinaryMessageEncoder<RideRecord> ENCODER = new BinaryMessageEncoder<>(MODEL$, SCHEMA$); private static final BinaryMessageDecoder<RideRecord> DECODER = new BinaryMessageDecoder<>(MODEL$, SCHEMA$); /** * Return the BinaryMessageEncoder instance used by this class. * @return the message encoder used by this class */ public static BinaryMessageEncoder<RideRecord> getEncoder() { return ENCODER; } /** * Return the BinaryMessageDecoder instance used by this class. * @return the message decoder used by this class */ public static BinaryMessageDecoder<RideRecord> getDecoder() { return DECODER; } /** * Create a new BinaryMessageDecoder instance for this class that uses the specified {@link SchemaStore}. * @param resolver a {@link SchemaStore} used to find schemas by fingerprint * @return a BinaryMessageDecoder instance for this class backed by the given SchemaStore */ public static BinaryMessageDecoder<RideRecord> createDecoder(SchemaStore resolver) { return new BinaryMessageDecoder<>(MODEL$, SCHEMA$, resolver); } /** * Serializes this RideRecord to a ByteBuffer. * @return a buffer holding the serialized data for this instance * @throws java.io.IOException if this instance could not be serialized */ public java.nio.ByteBuffer toByteBuffer() throws java.io.IOException { return ENCODER.encode(this); } /** * Deserializes a RideRecord from a ByteBuffer. * @param b a byte buffer holding serialized data for an instance of this class * @return a RideRecord instance decoded from the given buffer * @throws java.io.IOException if the given bytes could not be deserialized into an instance of this class */ public static RideRecord fromByteBuffer( java.nio.ByteBuffer b) throws java.io.IOException { return DECODER.decode(b); } private java.lang.String vendor_id; private int passenger_count; private double trip_distance; /** * Default constructor. Note that this does not initialize fields * to their default values from the schema. If that is desired then * one should use <code>newBuilder()</code>. */ public RideRecord() {} /** * All-args constructor. * @param vendor_id The new value for vendor_id * @param passenger_count The new value for passenger_count * @param trip_distance The new value for trip_distance */ public RideRecord(java.lang.String vendor_id, java.lang.Integer passenger_count, java.lang.Double trip_distance) { this.vendor_id = vendor_id; this.passenger_count = passenger_count; this.trip_distance = trip_distance; } @Override public org.apache.avro.specific.SpecificData getSpecificData() { return MODEL$; } @Override public org.apache.avro.Schema getSchema() { return SCHEMA$; } // Used by DatumWriter. Applications should not call. @Override public java.lang.Object get(int field$) { switch (field$) { case 0: return vendor_id; case 1: return passenger_count; case 2: return trip_distance; default: throw new IndexOutOfBoundsException("Invalid index: " + field$); } } // Used by DatumReader. Applications should not call. @Override @SuppressWarnings(value="unchecked") public void put(int field$, java.lang.Object value$) { switch (field$) { case 0: vendor_id = value$ != null ? value$.toString() : null; break; case 1: passenger_count = (java.lang.Integer)value$; break; case 2: trip_distance = (java.lang.Double)value$; break; default: throw new IndexOutOfBoundsException("Invalid index: " + field$); } } /** * Gets the value of the 'vendor_id' field. * @return The value of the 'vendor_id' field. */ public java.lang.String getVendorId() { return vendor_id; } /** * Sets the value of the 'vendor_id' field. * @param value the value to set. */ public void setVendorId(java.lang.String value) { this.vendor_id = value; } /** * Gets the value of the 'passenger_count' field. * @return The value of the 'passenger_count' field. */ public int getPassengerCount() { return passenger_count; } /** * Sets the value of the 'passenger_count' field. * @param value the value to set. */ public void setPassengerCount(int value) { this.passenger_count = value; } /** * Gets the value of the 'trip_distance' field. * @return The value of the 'trip_distance' field. */ public double getTripDistance() { return trip_distance; } /** * Sets the value of the 'trip_distance' field. * @param value the value to set. */ public void setTripDistance(double value) { this.trip_distance = value; } /** * Creates a new RideRecord RecordBuilder. * @return A new RideRecord RecordBuilder */ public static schemaregistry.RideRecord.Builder newBuilder() { return new schemaregistry.RideRecord.Builder(); } /** * Creates a new RideRecord RecordBuilder by copying an existing Builder. * @param other The existing builder to copy. * @return A new RideRecord RecordBuilder */ public static schemaregistry.RideRecord.Builder newBuilder(schemaregistry.RideRecord.Builder other) { if (other == null) { return new schemaregistry.RideRecord.Builder(); } else { return new schemaregistry.RideRecord.Builder(other); } } /** * Creates a new RideRecord RecordBuilder by copying an existing RideRecord instance. * @param other The existing instance to copy. * @return A new RideRecord RecordBuilder */ public static schemaregistry.RideRecord.Builder newBuilder(schemaregistry.RideRecord other) { if (other == null) { return new schemaregistry.RideRecord.Builder(); } else { return new schemaregistry.RideRecord.Builder(other); } } /** * RecordBuilder for RideRecord instances. */ @org.apache.avro.specific.AvroGenerated public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<RideRecord> implements org.apache.avro.data.RecordBuilder<RideRecord> { private java.lang.String vendor_id; private int passenger_count; private double trip_distance; /** Creates a new Builder */ private Builder() { super(SCHEMA$, MODEL$); } /** * Creates a Builder by copying an existing Builder. * @param other The existing Builder to copy. */ private Builder(schemaregistry.RideRecord.Builder other) { super(other); if (isValidValue(fields()[0], other.vendor_id)) { this.vendor_id = data().deepCopy(fields()[0].schema(), other.vendor_id); fieldSetFlags()[0] = other.fieldSetFlags()[0]; } if (isValidValue(fields()[1], other.passenger_count)) { this.passenger_count = data().deepCopy(fields()[1].schema(), other.passenger_count); fieldSetFlags()[1] = other.fieldSetFlags()[1]; } if (isValidValue(fields()[2], other.trip_distance)) { this.trip_distance = data().deepCopy(fields()[2].schema(), other.trip_distance); fieldSetFlags()[2] = other.fieldSetFlags()[2]; } } /** * Creates a Builder by copying an existing RideRecord instance * @param other The existing instance to copy. */ private Builder(schemaregistry.RideRecord other) { super(SCHEMA$, MODEL$); if (isValidValue(fields()[0], other.vendor_id)) { this.vendor_id = data().deepCopy(fields()[0].schema(), other.vendor_id); fieldSetFlags()[0] = true; } if (isValidValue(fields()[1], other.passenger_count)) { this.passenger_count = data().deepCopy(fields()[1].schema(), other.passenger_count); fieldSetFlags()[1] = true; } if (isValidValue(fields()[2], other.trip_distance)) { this.trip_distance = data().deepCopy(fields()[2].schema(), other.trip_distance); fieldSetFlags()[2] = true; } } /** * Gets the value of the 'vendor_id' field. * @return The value. */ public java.lang.String getVendorId() { return vendor_id; } /** * Sets the value of the 'vendor_id' field. * @param value The value of 'vendor_id'. * @return This builder. */ public schemaregistry.RideRecord.Builder setVendorId(java.lang.String value) { validate(fields()[0], value); this.vendor_id = value; fieldSetFlags()[0] = true; return this; } /** * Checks whether the 'vendor_id' field has been set. * @return True if the 'vendor_id' field has been set, false otherwise. */ public boolean hasVendorId() { return fieldSetFlags()[0]; } /** * Clears the value of the 'vendor_id' field. * @return This builder. */ public schemaregistry.RideRecord.Builder clearVendorId() { vendor_id = null; fieldSetFlags()[0] = false; return this; } /** * Gets the value of the 'passenger_count' field. * @return The value. */ public int getPassengerCount() { return passenger_count; } /** * Sets the value of the 'passenger_count' field. * @param value The value of 'passenger_count'. * @return This builder. */ public schemaregistry.RideRecord.Builder setPassengerCount(int value) { validate(fields()[1], value); this.passenger_count = value; fieldSetFlags()[1] = true; return this; } /** * Checks whether the 'passenger_count' field has been set. * @return True if the 'passenger_count' field has been set, false otherwise. */ public boolean hasPassengerCount() { return fieldSetFlags()[1]; } /** * Clears the value of the 'passenger_count' field. * @return This builder. */ public schemaregistry.RideRecord.Builder clearPassengerCount() { fieldSetFlags()[1] = false; return this; } /** * Gets the value of the 'trip_distance' field. * @return The value. */ public double getTripDistance() { return trip_distance; } /** * Sets the value of the 'trip_distance' field. * @param value The value of 'trip_distance'. * @return This builder. */ public schemaregistry.RideRecord.Builder setTripDistance(double value) { validate(fields()[2], value); this.trip_distance = value; fieldSetFlags()[2] = true; return this; } /** * Checks whether the 'trip_distance' field has been set. * @return True if the 'trip_distance' field has been set, false otherwise. */ public boolean hasTripDistance() { return fieldSetFlags()[2]; } /** * Clears the value of the 'trip_distance' field. * @return This builder. */ public schemaregistry.RideRecord.Builder clearTripDistance() { fieldSetFlags()[2] = false; return this; } @Override @SuppressWarnings("unchecked") public RideRecord build() { try { RideRecord record = new RideRecord(); record.vendor_id = fieldSetFlags()[0] ? this.vendor_id : (java.lang.String) defaultValue(fields()[0]); record.passenger_count = fieldSetFlags()[1] ? this.passenger_count : (java.lang.Integer) defaultValue(fields()[1]); record.trip_distance = fieldSetFlags()[2] ? this.trip_distance : (java.lang.Double) defaultValue(fields()[2]); return record; } catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { throw new org.apache.avro.AvroRuntimeException(e); } } } @SuppressWarnings("unchecked") private static final org.apache.avro.io.DatumWriter<RideRecord> WRITER$ = (org.apache.avro.io.DatumWriter<RideRecord>)MODEL$.createDatumWriter(SCHEMA$); @Override public void writeExternal(java.io.ObjectOutput out) throws java.io.IOException { WRITER$.write(this, SpecificData.getEncoder(out)); } @SuppressWarnings("unchecked") private static final org.apache.avro.io.DatumReader<RideRecord> READER$ = (org.apache.avro.io.DatumReader<RideRecord>)MODEL$.createDatumReader(SCHEMA$); @Override public void readExternal(java.io.ObjectInput in) throws java.io.IOException { READER$.read(this, SpecificData.getDecoder(in)); } @Override protected boolean hasCustomCoders() { return true; } @Override public void customEncode(org.apache.avro.io.Encoder out) throws java.io.IOException { out.writeString(this.vendor_id); out.writeInt(this.passenger_count); out.writeDouble(this.trip_distance); } @Override public void customDecode(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException { org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff(); if (fieldOrder == null) { this.vendor_id = in.readString(); this.passenger_count = in.readInt(); this.trip_distance = in.readDouble(); } else { for (int i = 0; i < 3; i++) { switch (fieldOrder[i].pos()) { case 0: this.vendor_id = in.readString(); break; case 1: this.passenger_count = in.readInt(); break; case 2: this.trip_distance = in.readDouble(); break; default: throw new java.io.IOException("Corrupt ResolvingDecoder."); } } } } }
29.533473
377
0.659381
data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Java
/** * Autogenerated by Avro * * DO NOT EDIT DIRECTLY */ package schemaregistry; import org.apache.avro.generic.GenericArray; import org.apache.avro.specific.SpecificData; import org.apache.avro.util.Utf8; import org.apache.avro.message.BinaryMessageEncoder; import org.apache.avro.message.BinaryMessageDecoder; import org.apache.avro.message.SchemaStore; @org.apache.avro.specific.AvroGenerated public class RideRecordCompatible extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord { private static final long serialVersionUID = 7163300507090021229L; public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"RideRecordCompatible\",\"namespace\":\"schemaregistry\",\"fields\":[{\"name\":\"vendorId\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"passenger_count\",\"type\":\"int\"},{\"name\":\"trip_distance\",\"type\":\"double\"},{\"name\":\"pu_location_id\",\"type\":[\"null\",\"long\"],\"default\":null}]}"); public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; } private static final SpecificData MODEL$ = new SpecificData(); private static final BinaryMessageEncoder<RideRecordCompatible> ENCODER = new BinaryMessageEncoder<>(MODEL$, SCHEMA$); private static final BinaryMessageDecoder<RideRecordCompatible> DECODER = new BinaryMessageDecoder<>(MODEL$, SCHEMA$); /** * Return the BinaryMessageEncoder instance used by this class. * @return the message encoder used by this class */ public static BinaryMessageEncoder<RideRecordCompatible> getEncoder() { return ENCODER; } /** * Return the BinaryMessageDecoder instance used by this class. * @return the message decoder used by this class */ public static BinaryMessageDecoder<RideRecordCompatible> getDecoder() { return DECODER; } /** * Create a new BinaryMessageDecoder instance for this class that uses the specified {@link SchemaStore}. * @param resolver a {@link SchemaStore} used to find schemas by fingerprint * @return a BinaryMessageDecoder instance for this class backed by the given SchemaStore */ public static BinaryMessageDecoder<RideRecordCompatible> createDecoder(SchemaStore resolver) { return new BinaryMessageDecoder<>(MODEL$, SCHEMA$, resolver); } /** * Serializes this RideRecordCompatible to a ByteBuffer. * @return a buffer holding the serialized data for this instance * @throws java.io.IOException if this instance could not be serialized */ public java.nio.ByteBuffer toByteBuffer() throws java.io.IOException { return ENCODER.encode(this); } /** * Deserializes a RideRecordCompatible from a ByteBuffer. * @param b a byte buffer holding serialized data for an instance of this class * @return a RideRecordCompatible instance decoded from the given buffer * @throws java.io.IOException if the given bytes could not be deserialized into an instance of this class */ public static RideRecordCompatible fromByteBuffer( java.nio.ByteBuffer b) throws java.io.IOException { return DECODER.decode(b); } private java.lang.String vendorId; private int passenger_count; private double trip_distance; private java.lang.Long pu_location_id; /** * Default constructor. Note that this does not initialize fields * to their default values from the schema. If that is desired then * one should use <code>newBuilder()</code>. */ public RideRecordCompatible() {} /** * All-args constructor. * @param vendorId The new value for vendorId * @param passenger_count The new value for passenger_count * @param trip_distance The new value for trip_distance * @param pu_location_id The new value for pu_location_id */ public RideRecordCompatible(java.lang.String vendorId, java.lang.Integer passenger_count, java.lang.Double trip_distance, java.lang.Long pu_location_id) { this.vendorId = vendorId; this.passenger_count = passenger_count; this.trip_distance = trip_distance; this.pu_location_id = pu_location_id; } @Override public org.apache.avro.specific.SpecificData getSpecificData() { return MODEL$; } @Override public org.apache.avro.Schema getSchema() { return SCHEMA$; } // Used by DatumWriter. Applications should not call. @Override public java.lang.Object get(int field$) { switch (field$) { case 0: return vendorId; case 1: return passenger_count; case 2: return trip_distance; case 3: return pu_location_id; default: throw new IndexOutOfBoundsException("Invalid index: " + field$); } } // Used by DatumReader. Applications should not call. @Override @SuppressWarnings(value="unchecked") public void put(int field$, java.lang.Object value$) { switch (field$) { case 0: vendorId = value$ != null ? value$.toString() : null; break; case 1: passenger_count = (java.lang.Integer)value$; break; case 2: trip_distance = (java.lang.Double)value$; break; case 3: pu_location_id = (java.lang.Long)value$; break; default: throw new IndexOutOfBoundsException("Invalid index: " + field$); } } /** * Gets the value of the 'vendorId' field. * @return The value of the 'vendorId' field. */ public java.lang.String getVendorId() { return vendorId; } /** * Sets the value of the 'vendorId' field. * @param value the value to set. */ public void setVendorId(java.lang.String value) { this.vendorId = value; } /** * Gets the value of the 'passenger_count' field. * @return The value of the 'passenger_count' field. */ public int getPassengerCount() { return passenger_count; } /** * Sets the value of the 'passenger_count' field. * @param value the value to set. */ public void setPassengerCount(int value) { this.passenger_count = value; } /** * Gets the value of the 'trip_distance' field. * @return The value of the 'trip_distance' field. */ public double getTripDistance() { return trip_distance; } /** * Sets the value of the 'trip_distance' field. * @param value the value to set. */ public void setTripDistance(double value) { this.trip_distance = value; } /** * Gets the value of the 'pu_location_id' field. * @return The value of the 'pu_location_id' field. */ public java.lang.Long getPuLocationId() { return pu_location_id; } /** * Sets the value of the 'pu_location_id' field. * @param value the value to set. */ public void setPuLocationId(java.lang.Long value) { this.pu_location_id = value; } /** * Creates a new RideRecordCompatible RecordBuilder. * @return A new RideRecordCompatible RecordBuilder */ public static schemaregistry.RideRecordCompatible.Builder newBuilder() { return new schemaregistry.RideRecordCompatible.Builder(); } /** * Creates a new RideRecordCompatible RecordBuilder by copying an existing Builder. * @param other The existing builder to copy. * @return A new RideRecordCompatible RecordBuilder */ public static schemaregistry.RideRecordCompatible.Builder newBuilder(schemaregistry.RideRecordCompatible.Builder other) { if (other == null) { return new schemaregistry.RideRecordCompatible.Builder(); } else { return new schemaregistry.RideRecordCompatible.Builder(other); } } /** * Creates a new RideRecordCompatible RecordBuilder by copying an existing RideRecordCompatible instance. * @param other The existing instance to copy. * @return A new RideRecordCompatible RecordBuilder */ public static schemaregistry.RideRecordCompatible.Builder newBuilder(schemaregistry.RideRecordCompatible other) { if (other == null) { return new schemaregistry.RideRecordCompatible.Builder(); } else { return new schemaregistry.RideRecordCompatible.Builder(other); } } /** * RecordBuilder for RideRecordCompatible instances. */ @org.apache.avro.specific.AvroGenerated public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<RideRecordCompatible> implements org.apache.avro.data.RecordBuilder<RideRecordCompatible> { private java.lang.String vendorId; private int passenger_count; private double trip_distance; private java.lang.Long pu_location_id; /** Creates a new Builder */ private Builder() { super(SCHEMA$, MODEL$); } /** * Creates a Builder by copying an existing Builder. * @param other The existing Builder to copy. */ private Builder(schemaregistry.RideRecordCompatible.Builder other) { super(other); if (isValidValue(fields()[0], other.vendorId)) { this.vendorId = data().deepCopy(fields()[0].schema(), other.vendorId); fieldSetFlags()[0] = other.fieldSetFlags()[0]; } if (isValidValue(fields()[1], other.passenger_count)) { this.passenger_count = data().deepCopy(fields()[1].schema(), other.passenger_count); fieldSetFlags()[1] = other.fieldSetFlags()[1]; } if (isValidValue(fields()[2], other.trip_distance)) { this.trip_distance = data().deepCopy(fields()[2].schema(), other.trip_distance); fieldSetFlags()[2] = other.fieldSetFlags()[2]; } if (isValidValue(fields()[3], other.pu_location_id)) { this.pu_location_id = data().deepCopy(fields()[3].schema(), other.pu_location_id); fieldSetFlags()[3] = other.fieldSetFlags()[3]; } } /** * Creates a Builder by copying an existing RideRecordCompatible instance * @param other The existing instance to copy. */ private Builder(schemaregistry.RideRecordCompatible other) { super(SCHEMA$, MODEL$); if (isValidValue(fields()[0], other.vendorId)) { this.vendorId = data().deepCopy(fields()[0].schema(), other.vendorId); fieldSetFlags()[0] = true; } if (isValidValue(fields()[1], other.passenger_count)) { this.passenger_count = data().deepCopy(fields()[1].schema(), other.passenger_count); fieldSetFlags()[1] = true; } if (isValidValue(fields()[2], other.trip_distance)) { this.trip_distance = data().deepCopy(fields()[2].schema(), other.trip_distance); fieldSetFlags()[2] = true; } if (isValidValue(fields()[3], other.pu_location_id)) { this.pu_location_id = data().deepCopy(fields()[3].schema(), other.pu_location_id); fieldSetFlags()[3] = true; } } /** * Gets the value of the 'vendorId' field. * @return The value. */ public java.lang.String getVendorId() { return vendorId; } /** * Sets the value of the 'vendorId' field. * @param value The value of 'vendorId'. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder setVendorId(java.lang.String value) { validate(fields()[0], value); this.vendorId = value; fieldSetFlags()[0] = true; return this; } /** * Checks whether the 'vendorId' field has been set. * @return True if the 'vendorId' field has been set, false otherwise. */ public boolean hasVendorId() { return fieldSetFlags()[0]; } /** * Clears the value of the 'vendorId' field. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder clearVendorId() { vendorId = null; fieldSetFlags()[0] = false; return this; } /** * Gets the value of the 'passenger_count' field. * @return The value. */ public int getPassengerCount() { return passenger_count; } /** * Sets the value of the 'passenger_count' field. * @param value The value of 'passenger_count'. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder setPassengerCount(int value) { validate(fields()[1], value); this.passenger_count = value; fieldSetFlags()[1] = true; return this; } /** * Checks whether the 'passenger_count' field has been set. * @return True if the 'passenger_count' field has been set, false otherwise. */ public boolean hasPassengerCount() { return fieldSetFlags()[1]; } /** * Clears the value of the 'passenger_count' field. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder clearPassengerCount() { fieldSetFlags()[1] = false; return this; } /** * Gets the value of the 'trip_distance' field. * @return The value. */ public double getTripDistance() { return trip_distance; } /** * Sets the value of the 'trip_distance' field. * @param value The value of 'trip_distance'. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder setTripDistance(double value) { validate(fields()[2], value); this.trip_distance = value; fieldSetFlags()[2] = true; return this; } /** * Checks whether the 'trip_distance' field has been set. * @return True if the 'trip_distance' field has been set, false otherwise. */ public boolean hasTripDistance() { return fieldSetFlags()[2]; } /** * Clears the value of the 'trip_distance' field. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder clearTripDistance() { fieldSetFlags()[2] = false; return this; } /** * Gets the value of the 'pu_location_id' field. * @return The value. */ public java.lang.Long getPuLocationId() { return pu_location_id; } /** * Sets the value of the 'pu_location_id' field. * @param value The value of 'pu_location_id'. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder setPuLocationId(java.lang.Long value) { validate(fields()[3], value); this.pu_location_id = value; fieldSetFlags()[3] = true; return this; } /** * Checks whether the 'pu_location_id' field has been set. * @return True if the 'pu_location_id' field has been set, false otherwise. */ public boolean hasPuLocationId() { return fieldSetFlags()[3]; } /** * Clears the value of the 'pu_location_id' field. * @return This builder. */ public schemaregistry.RideRecordCompatible.Builder clearPuLocationId() { pu_location_id = null; fieldSetFlags()[3] = false; return this; } @Override @SuppressWarnings("unchecked") public RideRecordCompatible build() { try { RideRecordCompatible record = new RideRecordCompatible(); record.vendorId = fieldSetFlags()[0] ? this.vendorId : (java.lang.String) defaultValue(fields()[0]); record.passenger_count = fieldSetFlags()[1] ? this.passenger_count : (java.lang.Integer) defaultValue(fields()[1]); record.trip_distance = fieldSetFlags()[2] ? this.trip_distance : (java.lang.Double) defaultValue(fields()[2]); record.pu_location_id = fieldSetFlags()[3] ? this.pu_location_id : (java.lang.Long) defaultValue(fields()[3]); return record; } catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { throw new org.apache.avro.AvroRuntimeException(e); } } } @SuppressWarnings("unchecked") private static final org.apache.avro.io.DatumWriter<RideRecordCompatible> WRITER$ = (org.apache.avro.io.DatumWriter<RideRecordCompatible>)MODEL$.createDatumWriter(SCHEMA$); @Override public void writeExternal(java.io.ObjectOutput out) throws java.io.IOException { WRITER$.write(this, SpecificData.getEncoder(out)); } @SuppressWarnings("unchecked") private static final org.apache.avro.io.DatumReader<RideRecordCompatible> READER$ = (org.apache.avro.io.DatumReader<RideRecordCompatible>)MODEL$.createDatumReader(SCHEMA$); @Override public void readExternal(java.io.ObjectInput in) throws java.io.IOException { READER$.read(this, SpecificData.getDecoder(in)); } @Override protected boolean hasCustomCoders() { return true; } @Override public void customEncode(org.apache.avro.io.Encoder out) throws java.io.IOException { out.writeString(this.vendorId); out.writeInt(this.passenger_count); out.writeDouble(this.trip_distance); if (this.pu_location_id == null) { out.writeIndex(0); out.writeNull(); } else { out.writeIndex(1); out.writeLong(this.pu_location_id); } } @Override public void customDecode(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException { org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff(); if (fieldOrder == null) { this.vendorId = in.readString(); this.passenger_count = in.readInt(); this.trip_distance = in.readDouble(); if (in.readIndex() != 1) { in.readNull(); this.pu_location_id = null; } else { this.pu_location_id = in.readLong(); } } else { for (int i = 0; i < 4; i++) { switch (fieldOrder[i].pos()) { case 0: this.vendorId = in.readString(); break; case 1: this.passenger_count = in.readInt(); break; case 2: this.trip_distance = in.readDouble(); break; case 3: if (in.readIndex() != 1) { in.readNull(); this.pu_location_id = null; } else { this.pu_location_id = in.readLong(); } break; default: throw new java.io.IOException("Corrupt ResolvingDecoder."); } } } } }
30.317073
462
0.657858
data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
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1,561
null
Java
/** * Autogenerated by Avro * * DO NOT EDIT DIRECTLY */ package schemaregistry; import org.apache.avro.generic.GenericArray; import org.apache.avro.specific.SpecificData; import org.apache.avro.util.Utf8; import org.apache.avro.message.BinaryMessageEncoder; import org.apache.avro.message.BinaryMessageDecoder; import org.apache.avro.message.SchemaStore; @org.apache.avro.specific.AvroGenerated public class RideRecordNoneCompatible extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord { private static final long serialVersionUID = -4618980179396772493L; public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"RideRecordNoneCompatible\",\"namespace\":\"schemaregistry\",\"fields\":[{\"name\":\"vendorId\",\"type\":\"int\"},{\"name\":\"passenger_count\",\"type\":\"int\"},{\"name\":\"trip_distance\",\"type\":\"double\"}]}"); public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; } private static final SpecificData MODEL$ = new SpecificData(); private static final BinaryMessageEncoder<RideRecordNoneCompatible> ENCODER = new BinaryMessageEncoder<>(MODEL$, SCHEMA$); private static final BinaryMessageDecoder<RideRecordNoneCompatible> DECODER = new BinaryMessageDecoder<>(MODEL$, SCHEMA$); /** * Return the BinaryMessageEncoder instance used by this class. * @return the message encoder used by this class */ public static BinaryMessageEncoder<RideRecordNoneCompatible> getEncoder() { return ENCODER; } /** * Return the BinaryMessageDecoder instance used by this class. * @return the message decoder used by this class */ public static BinaryMessageDecoder<RideRecordNoneCompatible> getDecoder() { return DECODER; } /** * Create a new BinaryMessageDecoder instance for this class that uses the specified {@link SchemaStore}. * @param resolver a {@link SchemaStore} used to find schemas by fingerprint * @return a BinaryMessageDecoder instance for this class backed by the given SchemaStore */ public static BinaryMessageDecoder<RideRecordNoneCompatible> createDecoder(SchemaStore resolver) { return new BinaryMessageDecoder<>(MODEL$, SCHEMA$, resolver); } /** * Serializes this RideRecordNoneCompatible to a ByteBuffer. * @return a buffer holding the serialized data for this instance * @throws java.io.IOException if this instance could not be serialized */ public java.nio.ByteBuffer toByteBuffer() throws java.io.IOException { return ENCODER.encode(this); } /** * Deserializes a RideRecordNoneCompatible from a ByteBuffer. * @param b a byte buffer holding serialized data for an instance of this class * @return a RideRecordNoneCompatible instance decoded from the given buffer * @throws java.io.IOException if the given bytes could not be deserialized into an instance of this class */ public static RideRecordNoneCompatible fromByteBuffer( java.nio.ByteBuffer b) throws java.io.IOException { return DECODER.decode(b); } private int vendorId; private int passenger_count; private double trip_distance; /** * Default constructor. Note that this does not initialize fields * to their default values from the schema. If that is desired then * one should use <code>newBuilder()</code>. */ public RideRecordNoneCompatible() {} /** * All-args constructor. * @param vendorId The new value for vendorId * @param passenger_count The new value for passenger_count * @param trip_distance The new value for trip_distance */ public RideRecordNoneCompatible(java.lang.Integer vendorId, java.lang.Integer passenger_count, java.lang.Double trip_distance) { this.vendorId = vendorId; this.passenger_count = passenger_count; this.trip_distance = trip_distance; } @Override public org.apache.avro.specific.SpecificData getSpecificData() { return MODEL$; } @Override public org.apache.avro.Schema getSchema() { return SCHEMA$; } // Used by DatumWriter. Applications should not call. @Override public java.lang.Object get(int field$) { switch (field$) { case 0: return vendorId; case 1: return passenger_count; case 2: return trip_distance; default: throw new IndexOutOfBoundsException("Invalid index: " + field$); } } // Used by DatumReader. Applications should not call. @Override @SuppressWarnings(value="unchecked") public void put(int field$, java.lang.Object value$) { switch (field$) { case 0: vendorId = (java.lang.Integer)value$; break; case 1: passenger_count = (java.lang.Integer)value$; break; case 2: trip_distance = (java.lang.Double)value$; break; default: throw new IndexOutOfBoundsException("Invalid index: " + field$); } } /** * Gets the value of the 'vendorId' field. * @return The value of the 'vendorId' field. */ public int getVendorId() { return vendorId; } /** * Sets the value of the 'vendorId' field. * @param value the value to set. */ public void setVendorId(int value) { this.vendorId = value; } /** * Gets the value of the 'passenger_count' field. * @return The value of the 'passenger_count' field. */ public int getPassengerCount() { return passenger_count; } /** * Sets the value of the 'passenger_count' field. * @param value the value to set. */ public void setPassengerCount(int value) { this.passenger_count = value; } /** * Gets the value of the 'trip_distance' field. * @return The value of the 'trip_distance' field. */ public double getTripDistance() { return trip_distance; } /** * Sets the value of the 'trip_distance' field. * @param value the value to set. */ public void setTripDistance(double value) { this.trip_distance = value; } /** * Creates a new RideRecordNoneCompatible RecordBuilder. * @return A new RideRecordNoneCompatible RecordBuilder */ public static schemaregistry.RideRecordNoneCompatible.Builder newBuilder() { return new schemaregistry.RideRecordNoneCompatible.Builder(); } /** * Creates a new RideRecordNoneCompatible RecordBuilder by copying an existing Builder. * @param other The existing builder to copy. * @return A new RideRecordNoneCompatible RecordBuilder */ public static schemaregistry.RideRecordNoneCompatible.Builder newBuilder(schemaregistry.RideRecordNoneCompatible.Builder other) { if (other == null) { return new schemaregistry.RideRecordNoneCompatible.Builder(); } else { return new schemaregistry.RideRecordNoneCompatible.Builder(other); } } /** * Creates a new RideRecordNoneCompatible RecordBuilder by copying an existing RideRecordNoneCompatible instance. * @param other The existing instance to copy. * @return A new RideRecordNoneCompatible RecordBuilder */ public static schemaregistry.RideRecordNoneCompatible.Builder newBuilder(schemaregistry.RideRecordNoneCompatible other) { if (other == null) { return new schemaregistry.RideRecordNoneCompatible.Builder(); } else { return new schemaregistry.RideRecordNoneCompatible.Builder(other); } } /** * RecordBuilder for RideRecordNoneCompatible instances. */ @org.apache.avro.specific.AvroGenerated public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<RideRecordNoneCompatible> implements org.apache.avro.data.RecordBuilder<RideRecordNoneCompatible> { private int vendorId; private int passenger_count; private double trip_distance; /** Creates a new Builder */ private Builder() { super(SCHEMA$, MODEL$); } /** * Creates a Builder by copying an existing Builder. * @param other The existing Builder to copy. */ private Builder(schemaregistry.RideRecordNoneCompatible.Builder other) { super(other); if (isValidValue(fields()[0], other.vendorId)) { this.vendorId = data().deepCopy(fields()[0].schema(), other.vendorId); fieldSetFlags()[0] = other.fieldSetFlags()[0]; } if (isValidValue(fields()[1], other.passenger_count)) { this.passenger_count = data().deepCopy(fields()[1].schema(), other.passenger_count); fieldSetFlags()[1] = other.fieldSetFlags()[1]; } if (isValidValue(fields()[2], other.trip_distance)) { this.trip_distance = data().deepCopy(fields()[2].schema(), other.trip_distance); fieldSetFlags()[2] = other.fieldSetFlags()[2]; } } /** * Creates a Builder by copying an existing RideRecordNoneCompatible instance * @param other The existing instance to copy. */ private Builder(schemaregistry.RideRecordNoneCompatible other) { super(SCHEMA$, MODEL$); if (isValidValue(fields()[0], other.vendorId)) { this.vendorId = data().deepCopy(fields()[0].schema(), other.vendorId); fieldSetFlags()[0] = true; } if (isValidValue(fields()[1], other.passenger_count)) { this.passenger_count = data().deepCopy(fields()[1].schema(), other.passenger_count); fieldSetFlags()[1] = true; } if (isValidValue(fields()[2], other.trip_distance)) { this.trip_distance = data().deepCopy(fields()[2].schema(), other.trip_distance); fieldSetFlags()[2] = true; } } /** * Gets the value of the 'vendorId' field. * @return The value. */ public int getVendorId() { return vendorId; } /** * Sets the value of the 'vendorId' field. * @param value The value of 'vendorId'. * @return This builder. */ public schemaregistry.RideRecordNoneCompatible.Builder setVendorId(int value) { validate(fields()[0], value); this.vendorId = value; fieldSetFlags()[0] = true; return this; } /** * Checks whether the 'vendorId' field has been set. * @return True if the 'vendorId' field has been set, false otherwise. */ public boolean hasVendorId() { return fieldSetFlags()[0]; } /** * Clears the value of the 'vendorId' field. * @return This builder. */ public schemaregistry.RideRecordNoneCompatible.Builder clearVendorId() { fieldSetFlags()[0] = false; return this; } /** * Gets the value of the 'passenger_count' field. * @return The value. */ public int getPassengerCount() { return passenger_count; } /** * Sets the value of the 'passenger_count' field. * @param value The value of 'passenger_count'. * @return This builder. */ public schemaregistry.RideRecordNoneCompatible.Builder setPassengerCount(int value) { validate(fields()[1], value); this.passenger_count = value; fieldSetFlags()[1] = true; return this; } /** * Checks whether the 'passenger_count' field has been set. * @return True if the 'passenger_count' field has been set, false otherwise. */ public boolean hasPassengerCount() { return fieldSetFlags()[1]; } /** * Clears the value of the 'passenger_count' field. * @return This builder. */ public schemaregistry.RideRecordNoneCompatible.Builder clearPassengerCount() { fieldSetFlags()[1] = false; return this; } /** * Gets the value of the 'trip_distance' field. * @return The value. */ public double getTripDistance() { return trip_distance; } /** * Sets the value of the 'trip_distance' field. * @param value The value of 'trip_distance'. * @return This builder. */ public schemaregistry.RideRecordNoneCompatible.Builder setTripDistance(double value) { validate(fields()[2], value); this.trip_distance = value; fieldSetFlags()[2] = true; return this; } /** * Checks whether the 'trip_distance' field has been set. * @return True if the 'trip_distance' field has been set, false otherwise. */ public boolean hasTripDistance() { return fieldSetFlags()[2]; } /** * Clears the value of the 'trip_distance' field. * @return This builder. */ public schemaregistry.RideRecordNoneCompatible.Builder clearTripDistance() { fieldSetFlags()[2] = false; return this; } @Override @SuppressWarnings("unchecked") public RideRecordNoneCompatible build() { try { RideRecordNoneCompatible record = new RideRecordNoneCompatible(); record.vendorId = fieldSetFlags()[0] ? this.vendorId : (java.lang.Integer) defaultValue(fields()[0]); record.passenger_count = fieldSetFlags()[1] ? this.passenger_count : (java.lang.Integer) defaultValue(fields()[1]); record.trip_distance = fieldSetFlags()[2] ? this.trip_distance : (java.lang.Double) defaultValue(fields()[2]); return record; } catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { throw new org.apache.avro.AvroRuntimeException(e); } } } @SuppressWarnings("unchecked") private static final org.apache.avro.io.DatumWriter<RideRecordNoneCompatible> WRITER$ = (org.apache.avro.io.DatumWriter<RideRecordNoneCompatible>)MODEL$.createDatumWriter(SCHEMA$); @Override public void writeExternal(java.io.ObjectOutput out) throws java.io.IOException { WRITER$.write(this, SpecificData.getEncoder(out)); } @SuppressWarnings("unchecked") private static final org.apache.avro.io.DatumReader<RideRecordNoneCompatible> READER$ = (org.apache.avro.io.DatumReader<RideRecordNoneCompatible>)MODEL$.createDatumReader(SCHEMA$); @Override public void readExternal(java.io.ObjectInput in) throws java.io.IOException { READER$.read(this, SpecificData.getDecoder(in)); } @Override protected boolean hasCustomCoders() { return true; } @Override public void customEncode(org.apache.avro.io.Encoder out) throws java.io.IOException { out.writeInt(this.vendorId); out.writeInt(this.passenger_count); out.writeDouble(this.trip_distance); } @Override public void customDecode(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException { org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff(); if (fieldOrder == null) { this.vendorId = in.readInt(); this.passenger_count = in.readInt(); this.trip_distance = in.readDouble(); } else { for (int i = 0; i < 3; i++) { switch (fieldOrder[i].pos()) { case 0: this.vendorId = in.readInt(); break; case 1: this.passenger_count = in.readInt(); break; case 2: this.trip_distance = in.readDouble(); break; default: throw new java.io.IOException("Corrupt ResolvingDecoder."); } } } } }
30.607966
344
0.675975
data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
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2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Java
package org.example; import com.opencsv.CSVReader; import com.opencsv.exceptions.CsvException; import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig; import io.confluent.kafka.serializers.KafkaAvroSerializer; import org.apache.kafka.clients.producer.KafkaProducer; import org.apache.kafka.clients.producer.ProducerConfig; import org.apache.kafka.clients.producer.ProducerRecord; import org.apache.kafka.streams.StreamsConfig; import schemaregistry.RideRecord; import java.io.FileReader; import java.io.IOException; import java.util.List; import java.util.Properties; import java.util.concurrent.ExecutionException; import java.util.stream.Collectors; public class AvroProducer { private Properties props = new Properties(); public AvroProducer() { props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(ProducerConfig.ACKS_CONFIG, "all"); props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer"); props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, KafkaAvroSerializer.class.getName()); props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "https://psrc-kk5gg.europe-west3.gcp.confluent.cloud"); props.put("basic.auth.credentials.source", "USER_INFO"); props.put("basic.auth.user.info", Secrets.SCHEMA_REGISTRY_KEY+":"+Secrets.SCHEMA_REGISTRY_SECRET); } public List<RideRecord> getRides() throws IOException, CsvException { var ridesStream = this.getClass().getResource("/rides.csv"); var reader = new CSVReader(new FileReader(ridesStream.getFile())); reader.skip(1); return reader.readAll().stream().map(row -> RideRecord.newBuilder() .setVendorId(row[0]) .setTripDistance(Double.parseDouble(row[4])) .setPassengerCount(Integer.parseInt(row[3])) .build() ).collect(Collectors.toList()); } public void publishRides(List<RideRecord> rides) throws ExecutionException, InterruptedException { KafkaProducer<String, RideRecord> kafkaProducer = new KafkaProducer<>(props); for (RideRecord ride : rides) { var record = kafkaProducer.send(new ProducerRecord<>("rides_avro", String.valueOf(ride.getVendorId()), ride), (metadata, exception) -> { if (exception != null) { System.out.println(exception.getMessage()); } }); System.out.println(record.get().offset()); Thread.sleep(500); } } public static void main(String[] args) throws IOException, CsvException, ExecutionException, InterruptedException { var producer = new AvroProducer(); var rideRecords = producer.getRides(); producer.publishRides(rideRecords); } }
44.767123
192
0.688323
data-engineering-zoomcamp
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15,757
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2021-10-21 09:32:50+00:00
1,561
null
Java
package org.example; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.clients.consumer.ConsumerRecord; import org.apache.kafka.clients.consumer.KafkaConsumer; import org.apache.kafka.clients.producer.ProducerConfig; import org.example.data.Ride; import java.time.Duration; import java.time.temporal.ChronoUnit; import java.time.temporal.TemporalUnit; import java.util.List; import java.util.Properties; import io.confluent.kafka.serializers.KafkaJsonDeserializerConfig; public class JsonConsumer { private Properties props = new Properties(); private KafkaConsumer<String, Ride> consumer; public JsonConsumer() { props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer"); props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "io.confluent.kafka.serializers.KafkaJsonDeserializer"); props.put(ConsumerConfig.GROUP_ID_CONFIG, "kafka_tutorial_example.jsonconsumer.v2"); props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest"); props.put(KafkaJsonDeserializerConfig.JSON_VALUE_TYPE, Ride.class); consumer = new KafkaConsumer<String, Ride>(props); consumer.subscribe(List.of("rides")); } public void consumeFromKafka() { System.out.println("Consuming form kafka started"); var results = consumer.poll(Duration.of(1, ChronoUnit.SECONDS)); var i = 0; do { for(ConsumerRecord<String, Ride> result: results) { System.out.println(result.value().DOLocationID); } results = consumer.poll(Duration.of(1, ChronoUnit.SECONDS)); System.out.println("RESULTS:::" + results.count()); i++; } while(!results.isEmpty() || i < 10); } public static void main(String[] args) { JsonConsumer jsonConsumer = new JsonConsumer(); jsonConsumer.consumeFromKafka(); } }
42.631579
192
0.697104
data-engineering-zoomcamp
https://github.com/DataTalksClub/data-engineering-zoomcamp
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15,757
3,602
2023-12-05 01:08:47+00:00
2021-10-21 09:32:50+00:00
1,561
null
Java
package org.example; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.common.serialization.Serdes; import org.apache.kafka.streams.KafkaStreams; import org.apache.kafka.streams.StreamsBuilder; import org.apache.kafka.streams.StreamsConfig; import org.apache.kafka.streams.Topology; import org.apache.kafka.streams.kstream.Consumed; import org.apache.kafka.streams.kstream.Produced; import org.example.customserdes.CustomSerdes; import org.example.data.Ride; import java.util.Properties; public class JsonKStream { private Properties props = new Properties(); public JsonKStream() { props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(StreamsConfig.APPLICATION_ID_CONFIG, "kafka_tutorial.kstream.count.plocation.v1"); props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest"); props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0); } public Topology createTopology() { StreamsBuilder streamsBuilder = new StreamsBuilder(); var ridesStream = streamsBuilder.stream("rides", Consumed.with(Serdes.String(), CustomSerdes.getSerde(Ride.class))); var puLocationCount = ridesStream.groupByKey().count().toStream(); puLocationCount.to("rides-pulocation-count", Produced.with(Serdes.String(), Serdes.Long())); return streamsBuilder.build(); } public void countPLocation() throws InterruptedException { var topology = createTopology(); var kStreams = new KafkaStreams(topology, props); kStreams.start(); while (kStreams.state() != KafkaStreams.State.RUNNING) { System.out.println(kStreams.state()); Thread.sleep(1000); } System.out.println(kStreams.state()); Runtime.getRuntime().addShutdownHook(new Thread(kStreams::close)); } public static void main(String[] args) throws InterruptedException { var object = new JsonKStream(); object.countPLocation(); } }
42.175439
192
0.707724
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1,561
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Java
package org.example; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.common.serialization.Serdes; import org.apache.kafka.streams.KafkaStreams; import org.apache.kafka.streams.StreamsBuilder; import org.apache.kafka.streams.StreamsConfig; import org.apache.kafka.streams.Topology; import org.apache.kafka.streams.errors.StreamsUncaughtExceptionHandler; import org.apache.kafka.streams.kstream.*; import org.example.customserdes.CustomSerdes; import org.example.data.PickupLocation; import org.example.data.Ride; import org.example.data.VendorInfo; import java.time.Duration; import java.util.Optional; import java.util.Properties; public class JsonKStreamJoins { private Properties props = new Properties(); public JsonKStreamJoins() { props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(StreamsConfig.APPLICATION_ID_CONFIG, "kafka_tutorial.kstream.joined.rides.pickuplocation.v1"); props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest"); props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0); } public Topology createTopology() { StreamsBuilder streamsBuilder = new StreamsBuilder(); KStream<String, Ride> rides = streamsBuilder.stream(Topics.INPUT_RIDE_TOPIC, Consumed.with(Serdes.String(), CustomSerdes.getSerde(Ride.class))); KStream<String, PickupLocation> pickupLocations = streamsBuilder.stream(Topics.INPUT_RIDE_LOCATION_TOPIC, Consumed.with(Serdes.String(), CustomSerdes.getSerde(PickupLocation.class))); var pickupLocationsKeyedOnPUId = pickupLocations.selectKey((key, value) -> String.valueOf(value.PULocationID)); var joined = rides.join(pickupLocationsKeyedOnPUId, (ValueJoiner<Ride, PickupLocation, Optional<VendorInfo>>) (ride, pickupLocation) -> { var period = Duration.between(ride.tpep_dropoff_datetime, pickupLocation.tpep_pickup_datetime); if (period.abs().toMinutes() > 10) return Optional.empty(); else return Optional.of(new VendorInfo(ride.VendorID, pickupLocation.PULocationID, pickupLocation.tpep_pickup_datetime, ride.tpep_dropoff_datetime)); }, JoinWindows.ofTimeDifferenceAndGrace(Duration.ofMinutes(20), Duration.ofMinutes(5)), StreamJoined.with(Serdes.String(), CustomSerdes.getSerde(Ride.class), CustomSerdes.getSerde(PickupLocation.class))); joined.filter(((key, value) -> value.isPresent())).mapValues(Optional::get) .to(Topics.OUTPUT_TOPIC, Produced.with(Serdes.String(), CustomSerdes.getSerde(VendorInfo.class))); return streamsBuilder.build(); } public void joinRidesPickupLocation() throws InterruptedException { var topology = createTopology(); var kStreams = new KafkaStreams(topology, props); kStreams.setUncaughtExceptionHandler(exception -> { System.out.println(exception.getMessage()); return StreamsUncaughtExceptionHandler.StreamThreadExceptionResponse.SHUTDOWN_APPLICATION; }); kStreams.start(); while (kStreams.state() != KafkaStreams.State.RUNNING) { System.out.println(kStreams.state()); Thread.sleep(1000); } System.out.println(kStreams.state()); Runtime.getRuntime().addShutdownHook(new Thread(kStreams::close)); } public static void main(String[] args) throws InterruptedException { var object = new JsonKStreamJoins(); object.joinRidesPickupLocation(); } }
50.922078
192
0.718039
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Java
package org.example; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.common.serialization.Serdes; import org.apache.kafka.streams.KafkaStreams; import org.apache.kafka.streams.StreamsBuilder; import org.apache.kafka.streams.StreamsConfig; import org.apache.kafka.streams.Topology; import org.apache.kafka.streams.kstream.Consumed; import org.apache.kafka.streams.kstream.Produced; import org.apache.kafka.streams.kstream.TimeWindows; import org.apache.kafka.streams.kstream.WindowedSerdes; import org.example.customserdes.CustomSerdes; import org.example.data.Ride; import java.time.Duration; import java.time.temporal.ChronoUnit; import java.util.Properties; public class JsonKStreamWindow { private Properties props = new Properties(); public JsonKStreamWindow() { props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(StreamsConfig.APPLICATION_ID_CONFIG, "kafka_tutorial.kstream.count.plocation.v1"); props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest"); props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0); } public Topology createTopology() { StreamsBuilder streamsBuilder = new StreamsBuilder(); var ridesStream = streamsBuilder.stream("rides", Consumed.with(Serdes.String(), CustomSerdes.getSerde(Ride.class))); var puLocationCount = ridesStream.groupByKey() .windowedBy(TimeWindows.ofSizeAndGrace(Duration.ofSeconds(10), Duration.ofSeconds(5))) .count().toStream(); var windowSerde = WindowedSerdes.timeWindowedSerdeFrom(String.class, 10*1000); puLocationCount.to("rides-pulocation-window-count", Produced.with(windowSerde, Serdes.Long())); return streamsBuilder.build(); } public void countPLocationWindowed() { var topology = createTopology(); var kStreams = new KafkaStreams(topology, props); kStreams.start(); Runtime.getRuntime().addShutdownHook(new Thread(kStreams::close)); } public static void main(String[] args) { var object = new JsonKStreamWindow(); object.countPLocationWindowed(); } }
41.983607
192
0.724151
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1,561
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Java
package org.example; import com.opencsv.CSVReader; import com.opencsv.exceptions.CsvException; import org.apache.kafka.clients.producer.*; import org.apache.kafka.streams.StreamsConfig; import org.example.data.Ride; import java.io.FileReader; import java.io.IOException; import java.time.LocalDateTime; import java.util.List; import java.util.Properties; import java.util.concurrent.ExecutionException; import java.util.stream.Collectors; public class JsonProducer { private Properties props = new Properties(); public JsonProducer() { props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(ProducerConfig.ACKS_CONFIG, "all"); props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer"); props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "io.confluent.kafka.serializers.KafkaJsonSerializer"); } public List<Ride> getRides() throws IOException, CsvException { var ridesStream = this.getClass().getResource("/rides.csv"); var reader = new CSVReader(new FileReader(ridesStream.getFile())); reader.skip(1); return reader.readAll().stream().map(arr -> new Ride(arr)) .collect(Collectors.toList()); } public void publishRides(List<Ride> rides) throws ExecutionException, InterruptedException { KafkaProducer<String, Ride> kafkaProducer = new KafkaProducer<String, Ride>(props); for(Ride ride: rides) { ride.tpep_pickup_datetime = LocalDateTime.now().minusMinutes(20); ride.tpep_dropoff_datetime = LocalDateTime.now(); var record = kafkaProducer.send(new ProducerRecord<>("rides", String.valueOf(ride.DOLocationID), ride), (metadata, exception) -> { if(exception != null) { System.out.println(exception.getMessage()); } }); System.out.println(record.get().offset()); System.out.println(ride.DOLocationID); Thread.sleep(500); } } public static void main(String[] args) throws IOException, CsvException, ExecutionException, InterruptedException { var producer = new JsonProducer(); var rides = producer.getRides(); producer.publishRides(rides); } }
44.278689
192
0.682361
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1,561
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Java
package org.example; import com.opencsv.exceptions.CsvException; import org.apache.kafka.clients.producer.KafkaProducer; import org.apache.kafka.clients.producer.ProducerConfig; import org.apache.kafka.clients.producer.ProducerRecord; import org.example.data.PickupLocation; import java.io.IOException; import java.time.LocalDateTime; import java.util.Properties; import java.util.concurrent.ExecutionException; public class JsonProducerPickupLocation { private Properties props = new Properties(); public JsonProducerPickupLocation() { props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "pkc-75m1o.europe-west3.gcp.confluent.cloud:9092"); props.put("security.protocol", "SASL_SSL"); props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username='"+Secrets.KAFKA_CLUSTER_KEY+"' password='"+Secrets.KAFKA_CLUSTER_SECRET+"';"); props.put("sasl.mechanism", "PLAIN"); props.put("client.dns.lookup", "use_all_dns_ips"); props.put("session.timeout.ms", "45000"); props.put(ProducerConfig.ACKS_CONFIG, "all"); props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer"); props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "io.confluent.kafka.serializers.KafkaJsonSerializer"); } public void publish(PickupLocation pickupLocation) throws ExecutionException, InterruptedException { KafkaProducer<String, PickupLocation> kafkaProducer = new KafkaProducer<String, PickupLocation>(props); var record = kafkaProducer.send(new ProducerRecord<>("rides_location", String.valueOf(pickupLocation.PULocationID), pickupLocation), (metadata, exception) -> { if (exception != null) { System.out.println(exception.getMessage()); } }); System.out.println(record.get().offset()); } public static void main(String[] args) throws IOException, CsvException, ExecutionException, InterruptedException { var producer = new JsonProducerPickupLocation(); producer.publish(new PickupLocation(186, LocalDateTime.now())); } }
47.577778
192
0.730892
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Java
package org.example; public class Secrets { public static final String KAFKA_CLUSTER_KEY = "REPLACE_WITH_YOUR_KAFKA_CLUSTER_KEY"; public static final String KAFKA_CLUSTER_SECRET = "REPLACE_WITH_YOUR_KAFKA_CLUSTER_SECRET"; public static final String SCHEMA_REGISTRY_KEY = "REPLACE_WITH_SCHEMA_REGISTRY_KEY"; public static final String SCHEMA_REGISTRY_SECRET = "REPLACE_WITH_SCHEMA_REGISTRY_SECRET"; }
37.181818
95
0.761337
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Java
package org.example; public class Topics { public static final String INPUT_RIDE_TOPIC = "rides"; public static final String INPUT_RIDE_LOCATION_TOPIC = "rides_location"; public static final String OUTPUT_TOPIC = "vendor_info"; }
29.5
76
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Java
package org.example.customserdes; import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig; import io.confluent.kafka.serializers.KafkaJsonDeserializer; import io.confluent.kafka.serializers.KafkaJsonSerializer; import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde; import org.apache.avro.specific.SpecificRecordBase; import org.apache.kafka.common.serialization.Deserializer; import org.apache.kafka.common.serialization.Serde; import org.apache.kafka.common.serialization.Serdes; import org.apache.kafka.common.serialization.Serializer; import org.example.data.PickupLocation; import org.example.data.Ride; import org.example.data.VendorInfo; import java.util.HashMap; import java.util.Map; public class CustomSerdes { public static <T> Serde<T> getSerde(Class<T> classOf) { Map<String, Object> serdeProps = new HashMap<>(); serdeProps.put("json.value.type", classOf); final Serializer<T> mySerializer = new KafkaJsonSerializer<>(); mySerializer.configure(serdeProps, false); final Deserializer<T> myDeserializer = new KafkaJsonDeserializer<>(); myDeserializer.configure(serdeProps, false); return Serdes.serdeFrom(mySerializer, myDeserializer); } public static <T extends SpecificRecordBase> SpecificAvroSerde getAvroSerde(boolean isKey, String schemaRegistryUrl) { var serde = new SpecificAvroSerde<T>(); Map<String, Object> serdeProps = new HashMap<>(); serdeProps.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, schemaRegistryUrl); serde.configure(serdeProps, isKey); return serde; } }
37.325581
122
0.763206
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Java
package org.example.data; import java.time.LocalDateTime; public class PickupLocation { public PickupLocation(long PULocationID, LocalDateTime tpep_pickup_datetime) { this.PULocationID = PULocationID; this.tpep_pickup_datetime = tpep_pickup_datetime; } public PickupLocation() { } public long PULocationID; public LocalDateTime tpep_pickup_datetime; }
22.352941
82
0.724747
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Java
package org.example.data; import java.nio.DoubleBuffer; import java.time.LocalDate; import java.time.LocalDateTime; import java.time.format.DateTimeFormatter; public class Ride { public Ride(String[] arr) { VendorID = arr[0]; tpep_pickup_datetime = LocalDateTime.parse(arr[1], DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")); tpep_dropoff_datetime = LocalDateTime.parse(arr[2], DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")); passenger_count = Integer.parseInt(arr[3]); trip_distance = Double.parseDouble(arr[4]); RatecodeID = Long.parseLong(arr[5]); store_and_fwd_flag = arr[6]; PULocationID = Long.parseLong(arr[7]); DOLocationID = Long.parseLong(arr[8]); payment_type = arr[9]; fare_amount = Double.parseDouble(arr[10]); extra = Double.parseDouble(arr[11]); mta_tax = Double.parseDouble(arr[12]); tip_amount = Double.parseDouble(arr[13]); tolls_amount = Double.parseDouble(arr[14]); improvement_surcharge = Double.parseDouble(arr[15]); total_amount = Double.parseDouble(arr[16]); congestion_surcharge = Double.parseDouble(arr[17]); } public Ride(){} public String VendorID; public LocalDateTime tpep_pickup_datetime; public LocalDateTime tpep_dropoff_datetime; public int passenger_count; public double trip_distance; public long RatecodeID; public String store_and_fwd_flag; public long PULocationID; public long DOLocationID; public String payment_type; public double fare_amount; public double extra; public double mta_tax; public double tip_amount; public double tolls_amount; public double improvement_surcharge; public double total_amount; public double congestion_surcharge; }
35.56
112
0.681445
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Java
package org.example.data; import java.time.LocalDateTime; public class VendorInfo { public VendorInfo(String vendorID, long PULocationID, LocalDateTime pickupTime, LocalDateTime lastDropoffTime) { VendorID = vendorID; this.PULocationID = PULocationID; this.pickupTime = pickupTime; this.lastDropoffTime = lastDropoffTime; } public VendorInfo() { } public String VendorID; public long PULocationID; public LocalDateTime pickupTime; public LocalDateTime lastDropoffTime; }
23.590909
116
0.72037
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Java
package org.example; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.common.internals.Topic; import org.apache.kafka.common.serialization.Serdes; import org.apache.kafka.streams.*; import org.example.customserdes.CustomSerdes; import org.example.data.PickupLocation; import org.example.data.Ride; import org.example.data.VendorInfo; import org.example.helper.DataGeneratorHelper; import org.junit.jupiter.api.AfterAll; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; import javax.xml.crypto.Data; import java.util.Properties; import static org.junit.jupiter.api.Assertions.*; class JsonKStreamJoinsTest { private Properties props = new Properties(); private static TopologyTestDriver testDriver; private TestInputTopic<String, Ride> ridesTopic; private TestInputTopic<String, PickupLocation> pickLocationTopic; private TestOutputTopic<String, VendorInfo> outputTopic; private Topology topology = new JsonKStreamJoins().createTopology(); @BeforeEach public void setup() { props = new Properties(); props.setProperty(StreamsConfig.APPLICATION_ID_CONFIG, "testing_count_application"); props.setProperty(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "dummy:1234"); if (testDriver != null) { testDriver.close(); } testDriver = new TopologyTestDriver(topology, props); ridesTopic = testDriver.createInputTopic(Topics.INPUT_RIDE_TOPIC, Serdes.String().serializer(), CustomSerdes.getSerde(Ride.class).serializer()); pickLocationTopic = testDriver.createInputTopic(Topics.INPUT_RIDE_LOCATION_TOPIC, Serdes.String().serializer(), CustomSerdes.getSerde(PickupLocation.class).serializer()); outputTopic = testDriver.createOutputTopic(Topics.OUTPUT_TOPIC, Serdes.String().deserializer(), CustomSerdes.getSerde(VendorInfo.class).deserializer()); } @Test public void testIfJoinWorksOnSameDropOffPickupLocationId() { Ride ride = DataGeneratorHelper.generateRide(); PickupLocation pickupLocation = DataGeneratorHelper.generatePickUpLocation(ride.DOLocationID); ridesTopic.pipeInput(String.valueOf(ride.DOLocationID), ride); pickLocationTopic.pipeInput(String.valueOf(pickupLocation.PULocationID), pickupLocation); assertEquals(outputTopic.getQueueSize(), 1); var expected = new VendorInfo(ride.VendorID, pickupLocation.PULocationID, pickupLocation.tpep_pickup_datetime, ride.tpep_dropoff_datetime); var result = outputTopic.readKeyValue(); assertEquals(result.key, String.valueOf(ride.DOLocationID)); assertEquals(result.value.VendorID, expected.VendorID); assertEquals(result.value.pickupTime, expected.pickupTime); } @AfterAll public static void shutdown() { testDriver.close(); } }
44.460317
178
0.754803
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1,561
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Java
package org.example; import org.apache.kafka.common.serialization.Serdes; import org.apache.kafka.streams.*; import org.example.customserdes.CustomSerdes; import org.example.data.Ride; import org.example.helper.DataGeneratorHelper; import org.junit.jupiter.api.AfterAll; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; import static org.junit.jupiter.api.Assertions.*; import java.util.Properties; class JsonKStreamTest { private Properties props; private static TopologyTestDriver testDriver; private TestInputTopic<String, Ride> inputTopic; private TestOutputTopic<String, Long> outputTopic; private Topology topology = new JsonKStream().createTopology(); @BeforeEach public void setup() { props = new Properties(); props.setProperty(StreamsConfig.APPLICATION_ID_CONFIG, "testing_count_application"); props.setProperty(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "dummy:1234"); if (testDriver != null) { testDriver.close(); } testDriver = new TopologyTestDriver(topology, props); inputTopic = testDriver.createInputTopic("rides", Serdes.String().serializer(), CustomSerdes.getSerde(Ride.class).serializer()); outputTopic = testDriver.createOutputTopic("rides-pulocation-count", Serdes.String().deserializer(), Serdes.Long().deserializer()); } @Test public void testIfOneMessageIsPassedToInputTopicWeGetCountOfOne() { Ride ride = DataGeneratorHelper.generateRide(); inputTopic.pipeInput(String.valueOf(ride.DOLocationID), ride); assertEquals(outputTopic.readKeyValue(), KeyValue.pair(String.valueOf(ride.DOLocationID), 1L)); assertTrue(outputTopic.isEmpty()); } @Test public void testIfTwoMessageArePassedWithDifferentKey() { Ride ride1 = DataGeneratorHelper.generateRide(); ride1.DOLocationID = 100L; inputTopic.pipeInput(String.valueOf(ride1.DOLocationID), ride1); Ride ride2 = DataGeneratorHelper.generateRide(); ride2.DOLocationID = 200L; inputTopic.pipeInput(String.valueOf(ride2.DOLocationID), ride2); assertEquals(outputTopic.readKeyValue(), KeyValue.pair(String.valueOf(ride1.DOLocationID), 1L)); assertEquals(outputTopic.readKeyValue(), KeyValue.pair(String.valueOf(ride2.DOLocationID), 1L)); assertTrue(outputTopic.isEmpty()); } @Test public void testIfTwoMessageArePassedWithSameKey() { Ride ride1 = DataGeneratorHelper.generateRide(); ride1.DOLocationID = 100L; inputTopic.pipeInput(String.valueOf(ride1.DOLocationID), ride1); Ride ride2 = DataGeneratorHelper.generateRide(); ride2.DOLocationID = 100L; inputTopic.pipeInput(String.valueOf(ride2.DOLocationID), ride2); assertEquals(outputTopic.readKeyValue(), KeyValue.pair("100", 1L)); assertEquals(outputTopic.readKeyValue(), KeyValue.pair("100", 2L)); assertTrue(outputTopic.isEmpty()); } @AfterAll public static void tearDown() { testDriver.close(); } }
37.7375
139
0.715623
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null
Java
package org.example.helper; import org.example.data.PickupLocation; import org.example.data.Ride; import org.example.data.VendorInfo; import java.time.LocalDateTime; import java.time.format.DateTimeFormatter; import java.util.List; public class DataGeneratorHelper { public static Ride generateRide() { var arrivalTime = LocalDateTime.now().format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")); var departureTime = LocalDateTime.now().minusMinutes(30).format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")); return new Ride(new String[]{"1", departureTime, arrivalTime,"1","1.50","1","N","238","75","2","8","0.5","0.5","0","0","0.3","9.3","0"}); } public static PickupLocation generatePickUpLocation(long pickupLocationId) { return new PickupLocation(pickupLocationId, LocalDateTime.now()); } }
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Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import LoadDimensionOperator from helpers import SqlQueries def load_dimension_subdag( parent_dag_name, task_id, redshift_conn_id, sql_statement, delete_load, table_name, *args, **kwargs): dag = DAG(f"{parent_dag_name}.{task_id}", **kwargs) load_dimension_table = LoadDimensionOperator( task_id=task_id, dag=dag, redshift_conn_id=redshift_conn_id, sql_query = sql_statement, delete_load = delete_load, table_name = table_name, ) load_dimension_table return dag
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0.657064
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from datetime import datetime, timedelta import os from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import ( CreateTableOperator, StageToRedshiftOperator, LoadFactOperator, LoadDimensionOperator, DataQualityOperator) from helpers import SqlQueries from sparkify_dimension_subdag import load_dimension_subdag from airflow.operators.subdag_operator import SubDagOperator #AWS_KEY = os.environ.get('AWS_KEY') #AWS_SECRET = os.environ.get('AWS_SECRET') s3_bucket = 'udacity-dend-warehouse' song_s3_key = "song_data" log_s3_key = "log-data" log_json_file = "log_json_path.json" default_args = { 'owner': 'udacity', 'depends_on_past': True, 'start_date': datetime(2019, 1, 12), 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), 'catchup': True } dag_name = 'udac_example_dag' dag = DAG(dag_name, default_args=default_args, description='Load and transform data in Redshift with Airflow', schedule_interval='0 * * * *', max_active_runs = 1 ) start_operator = DummyOperator(task_id='Begin_execution', dag=dag) create_tables_in_redshift = CreateTableOperator( task_id = 'create_tables_in_redshift', redshift_conn_id = 'redshift', dag = dag ) stage_events_to_redshift = StageToRedshiftOperator( task_id='Stage_events', table_name="staging_events", s3_bucket = s3_bucket, s3_key = log_s3_key, file_format="JSON", log_json_file = log_json_file, redshift_conn_id = "redshift", aws_credential_id="aws_credentials", dag=dag, provide_context=True ) stage_songs_to_redshift = StageToRedshiftOperator( task_id='Stage_songs', table_name="staging_songs", s3_bucket = s3_bucket, s3_key = song_s3_key, file_format="JSON", redshift_conn_id = "redshift", aws_credential_id="aws_credentials", dag=dag, provide_context=True ) load_songplays_table = LoadFactOperator( task_id='Load_songplays_fact_table', redshift_conn_id = 'redshift', sql_query = SqlQueries.songplay_table_insert, dag=dag ) load_user_dimension_table = SubDagOperator( subdag=load_dimension_subdag( parent_dag_name=dag_name, task_id="Load_user_dim_table", redshift_conn_id="redshift", start_date=default_args['start_date'], sql_statement=SqlQueries.user_table_insert, delete_load = True, table_name = "users", ), task_id="Load_user_dim_table", dag=dag, ) load_song_dimension_table = SubDagOperator( subdag=load_dimension_subdag( parent_dag_name=dag_name, task_id="Load_song_dim_table", redshift_conn_id="redshift", start_date=default_args['start_date'], sql_statement=SqlQueries.song_table_insert, delete_load = True, table_name = "songs", ), task_id="Load_song_dim_table", dag=dag, ) load_artist_dimension_table = SubDagOperator( subdag=load_dimension_subdag( parent_dag_name=dag_name, task_id="Load_artist_dim_table", redshift_conn_id="redshift", start_date=default_args['start_date'], sql_statement=SqlQueries.artist_table_insert, delete_load = True, table_name = "artists", ), task_id="Load_artist_dim_table", dag=dag, ) load_time_dimension_table = SubDagOperator( subdag=load_dimension_subdag( parent_dag_name=dag_name, task_id="Load_time_dim_table", redshift_conn_id="redshift", start_date=default_args['start_date'], sql_statement=SqlQueries.time_table_insert, delete_load = True, table_name = "time", ), task_id="Load_time_dim_table", dag=dag, ) run_quality_checks = DataQualityOperator( task_id='Run_data_quality_checks', dag=dag, redshift_conn_id = "redshift", tables = ["artists", "songplays", "songs", "time", "users"] ) end_operator = DummyOperator(task_id='Stop_execution', dag=dag) start_operator >> create_tables_in_redshift create_tables_in_redshift >> [stage_songs_to_redshift, stage_events_to_redshift] >> load_songplays_table load_songplays_table >> [load_user_dimension_table, load_song_dimension_table, load_artist_dimension_table, load_time_dimension_table] >> run_quality_checks >> end_operator
27.06962
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0.657871
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
0
0
0
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import configparser # CONFIG config = configparser.ConfigParser() config.read('dwh.cfg') # DROP TABLES staging_events_table_drop = "DROP TABle IF EXISTS staging_events;" staging_songs_table_drop = "DROP TABLE IF EXISTS staging_songs;" songplay_table_drop = "DROP TABLE IF EXISTS songplays;" user_table_drop = "DROP TABLE IF EXISTS users;" song_table_drop = "DROP TABLE IF EXISTS songs;" artist_table_drop = "DROP TABLE IF EXISTS artists;" time_table_drop = "DROP TABLE IF EXISTS time;" # CREATE TABLES staging_events_table_create= (""" CREATE TABLE IF NOT EXISTS staging_events ( artist VARCHAR, auth VARCHAR, firstName VARCHAR(50), gender CHAR, itemInSession INTEGER, lastName VARCHAR(50), length FLOAT, level VARCHAR, location VARCHAR, method VARCHAR, page VARCHAR, registration FLOAT, sessionId INTEGER, song VARCHAR, status INTEGER, ts BIGINT, userAgent VARCHAR, userId INTEGER ); """) staging_songs_table_create = (""" CREATE TABLE IF NOT EXISTS staging_songs ( num_songs INTEGER, artist_id VARCHAR, artist_latitude FLOAT, artist_longitude FLOAT, artist_location VARCHAR, artist_name VARCHAR, song_id VARCHAR, title VARCHAR, duration FLOAT, year FLOAT ); """) songplay_table_create = (""" CREATE TABLE IF NOT EXISTS songplays ( songplay_id INTEGER IDENTITY (1, 1) PRIMARY KEY , start_time TIMESTAMP, user_id INTEGER, level VARCHAR, song_id VARCHAR, artist_id VARCHAR, session_id INTEGER, location VARCHAR, user_agent VARCHAR ) DISTSTYLE KEY DISTKEY ( start_time ) SORTKEY ( start_time ); """) user_table_create = (""" CREATE TABLE IF NOT EXISTS users ( userId INTEGER PRIMARY KEY, firsname VARCHAR(50), lastname VARCHAR(50), gender CHAR(1) ENCODE BYTEDICT, level VARCHAR ENCODE BYTEDICT ) SORTKEY (userId); """) song_table_create = (""" CREATE TABLE IF NOT EXISTS songs ( song_id VARCHAR PRIMARY KEY, title VARCHAR, artist_id VARCHAR, year INTEGER ENCODE BYTEDICT, duration FLOAT ) SORTKEY (song_id); """) artist_table_create = (""" CREATE TABLE IF NOT EXISTS artists ( artist_id VARCHAR PRIMARY KEY , name VARCHAR, location VARCHAR, latitude FLOAT, longitude FLOAT ) SORTKEY (artist_id); """) time_table_create = (""" CREATE TABLE IF NOT EXISTS time ( start_time TIMESTAMP PRIMARY KEY , hour INTEGER, day INTEGER, week INTEGER, month INTEGER, year INTEGER ENCODE BYTEDICT , weekday VARCHAR(9) ENCODE BYTEDICT ) DISTSTYLE KEY DISTKEY ( start_time ) SORTKEY (start_time); """) # STAGING TABLES staging_events_copy = (""" COPY staging_events FROM {} iam_role {} FORMAT AS json {}; """).format(config['S3']['LOG_DATA'], config['IAM_ROLE']['ARN'], config['S3']['LOG_JSONPATH']) staging_songs_copy = (""" COPY staging_songs FROM {} iam_role {} FORMAT AS json 'auto'; """).format(config['S3']['SONG_DATA'], config['IAM_ROLE']['ARN']) # FINAL TABLES songplay_table_insert = (""" INSERT INTO songplays (START_TIME, USER_ID, LEVEL, SONG_ID, ARTIST_ID, SESSION_ID, LOCATION, USER_AGENT) SELECT DISTINCT TIMESTAMP 'epoch' + (se.ts / 1000) * INTERVAL '1 second' as start_time, se.userId, se.level, ss.song_id, ss.artist_id, se.sessionId, se.location, se.userAgent FROM staging_songs ss INNER JOIN staging_events se ON (ss.title = se.song AND se.artist = ss.artist_name) AND se.page = 'NextSong'; """) user_table_insert = (""" INSERT INTO users SELECT DISTINCT userId, firstName, lastName, gender, level FROM staging_events WHERE userId IS NOT NULL AND page = 'NextSong'; """) song_table_insert = (""" INSERT INTO songs SELECT DISTINCT song_id, title, artist_id, year, duration FROM staging_songs WHERE song_id IS NOT NULL; """) artist_table_insert = (""" INSERT INTO artists SELECT DISTINCT artist_id, artist_name, artist_location, artist_latitude, artist_longitude FROM staging_songs; """) time_table_insert = (""" insert into time SELECT DISTINCT TIMESTAMP 'epoch' + (ts/1000) * INTERVAL '1 second' as start_time, EXTRACT(HOUR FROM start_time) AS hour, EXTRACT(DAY FROM start_time) AS day, EXTRACT(WEEKS FROM start_time) AS week, EXTRACT(MONTH FROM start_time) AS month, EXTRACT(YEAR FROM start_time) AS year, to_char(start_time, 'Day') AS weekday FROM staging_events; """) # QUERY LISTS create_table_queries = [staging_events_table_create, staging_songs_table_create, songplay_table_create, user_table_create, song_table_create, artist_table_create, time_table_create] drop_table_queries = [staging_events_table_drop, staging_songs_table_drop, songplay_table_drop, user_table_drop, song_table_drop, artist_table_drop, time_table_drop] copy_table_queries = [staging_events_copy, staging_songs_copy] insert_table_queries = [songplay_table_insert, user_table_insert, song_table_insert, artist_table_insert, time_table_insert]
23.429952
181
0.68038
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from airflow.hooks.postgres_hook import PostgresHook from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults class CreateTableOperator(BaseOperator): ui_color = '#358140' @apply_defaults def __init__(self, redshift_conn_id = "", *args, **kwargs): super(CreateTableOperator, self).__init__(*args, **kwargs) self.redshift_conn_id = redshift_conn_id def execute(self, context): self.log.info('Creating Postgres SQL Hook') redshift = PostgresHook(postgres_conn_id = self.redshift_conn_id) self.log.info('Executing creating tables in Redshift.') queries = open('/home/workspace/airflow/create_tables.sql', 'r').read() redshift.run(queries) self.log.info("Tables created ")
26.7
80
0.651807
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from airflow.hooks.postgres_hook import PostgresHook from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults class DataQualityOperator(BaseOperator): ui_color = '#89DA59' @apply_defaults def __init__(self, redshift_conn_id="", tables = [], *args, **kwargs): super(DataQualityOperator, self).__init__(*args, **kwargs) self.redshift_conn_id = redshift_conn_id self.tables = tables def execute(self, context): redshift_hook = PostgresHook(postgres_conn_id = self.redshift_conn_id) for table in self.tables: self.log.info(f"Starting data quality validation on table : {table}") records = redshift_hook.get_records(f"select count(*) from {table};") if len(records) < 1 or len(records[0]) < 1 or records[0][0] < 1: self.log.error(f"Data Quality validation failed for table : {table}.") raise ValueError(f"Data Quality validation failed for table : {table}") self.log.info(f"Data Quality Validation Passed on table : {table}!!!") self.log.info('DataQualityOperator not implemented yet')
36.823529
94
0.595331
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from airflow.hooks.postgres_hook import PostgresHook from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults class LoadDimensionOperator(BaseOperator): ui_color = '#80BD9E' @apply_defaults def __init__(self, redshift_conn_id="", sql_query = "", delete_load = False, table_name = "", *args, **kwargs): super(LoadDimensionOperator, self).__init__(*args, **kwargs) self.redshift_conn_id = redshift_conn_id self.sql_query = sql_query self.table_name = table_name self.delete_load = delete_load def execute(self, context): redshift_hook = PostgresHook(postgres_conn_id = self.redshift_conn_id) if self.delete_load: self.log.info(f"Delete load operation set to TRUE. Running delete statement on table {self.table_name}") redshift_hook.run(f"DELETE FROM {self.table_name}") self.log.info(f"Running query to load data into Dimension Table {self.table_name}") redshift_hook.run(self.sql_query) self.log.info(f"Dimension Table {self.table_name} loaded.")
36.65625
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0.622924
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from airflow.hooks.postgres_hook import PostgresHook from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults class LoadFactOperator(BaseOperator): ui_color = '#F98866' @apply_defaults def __init__(self, redshift_conn_id="", sql_query = "", *args, **kwargs): super(LoadFactOperator, self).__init__(*args, **kwargs) self.redshift_conn_id = redshift_conn_id self.sql_query = sql_query def execute(self, context): redshift_hook = PostgresHook(postgres_conn_id = self.redshift_conn_id) redshift_hook.run(self.sql_query)
27.416667
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Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from airflow.hooks.postgres_hook import PostgresHook from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults from airflow.contrib.hooks.aws_hook import AwsHook class StageToRedshiftOperator(BaseOperator): ui_color = '#358140' copy_query = " COPY {} \ FROM '{}' \ ACCESS_KEY_ID '{}' \ SECRET_ACCESS_KEY '{}' \ FORMAT AS json '{}'; \ " @apply_defaults def __init__(self, redshift_conn_id="", aws_credential_id="", table_name = "", s3_bucket="", s3_key = "", file_format = "", log_json_file = "", *args, **kwargs): super(StageToRedshiftOperator, self).__init__(*args, **kwargs) self.redshift_conn_id = redshift_conn_id self.aws_credential_id = aws_credential_id self.table_name = table_name self.s3_bucket = s3_bucket self.s3_key = s3_key self.file_format = file_format self.log_json_file = log_json_file self.execution_date = kwargs.get('execution_date') def execute(self, context): aws_hook = AwsHook(self.aws_credential_id) credentials = aws_hook.get_credentials() s3_path = "s3://{}/{}".format(self.s3_bucket, self.s3_key) self.log.info(f"Picking staging file for table {self.table_name} from location : {s3_path}") if self.log_json_file != "": self.log_json_file = "s3://{}/{}".format(self.s3_bucket, self.log_json_file) copy_query = self.copy_query.format(self.table_name, s3_path, credentials.access_key, credentials.secret_key, self.log_json_file) else: copy_query = self.copy_query.format(self.table_name, s3_path, credentials.access_key, credentials.secret_key, 'auto') self.log.info(f"Running copy query : {copy_query}") redshift_hook = PostgresHook(postgres_conn_id = self.redshift_conn_id) redshift_hook.run(copy_query) self.log.info(f"Table {self.table_name} staged successfully!!")
37.280702
141
0.577258
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import configparser from pathlib import Path config = configparser.ConfigParser() config.read_file(open(f"{Path(__file__).parents[0]}/config.cfg")) api_key = config['KEYS']['API_KEY'] headers = {'Authorization': 'Bearer %s' % api_key}
28.625
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0.711864
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
# This is request module of this project from request import Request from auth import headers import json class BusinessSearch: def __init__(self, term, location, price=None): self._param = {'term' : term, 'location' : location} if price: self._param['price'] = price self._base_url = 'https://api.yelp.com/v3/businesses/search' self._business_list = self._search_business() def _search_business(self): business_search_request = Request.get_content(url=self._base_url, param=self._param) return business_search_request['businesses'] if business_search_request is not None else [] def _parse_results(self, data): # Categories data : 'categories': [{'alias': 'bakeries', 'title': 'Bakeries'}] categories = ' '.join([category['title'] for category in data['categories']]) # Longitude and latitude data : 'coordinates': {'latitude': 45.5232, 'longitude': -73.583459} longitude = data['coordinates']['longitude'] latitude = data['coordinates']['latitude'] # Location example : 'location': { 'display_address': ['316 Avenue du Mont-Royal E', 'Montreal, QC H2T 1P7', 'Canada']} location = ','.join(data['location']['display_address']) return {"id" : data['id'], "name" : self._add_escape_character(data['name']), "image_url" : data['image_url'], "url" : data['url'], "review_count" : data['review_count'], "categories" : categories, "rating" : data['rating'], "latitude" : latitude, "longitude" : longitude, "price" : data['price'], "location" : location, "display_phone" : data['display_phone'] } def _add_escape_character(self, data): return data.replace("'", "''") def get_results(self): return [self._parse_results(business) for business in self._business_list]
47.512821
139
0.615547
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import psycopg2 import configparser from pathlib import Path from queries import create_business_schema, create_business_table config = configparser.ConfigParser() config.read_file(open(f"{Path(__file__).parents[0]}/config.cfg")) class DatabaseDriver: def __init__(self): self._conn = psycopg2.connect("host={} dbname={} user={} password={} port={}".format(*config['DATABASE'].values())) self._cur = self._conn.cursor() def execute_query(self, query): self._cur.execute(query) def setup(self): self.execute_query(create_business_schema) self.execute_query(create_business_table)
30.8
123
0.685039
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import configparser from pathlib import Path from businesssearch import BusinessSearch from queries import create_business_schema, create_business_table, insert_business_table from databasedriver import DatabaseDriver import argparse config = configparser.ConfigParser() config.read_file(open(f"{Path(__file__).parents[0]}/config.cfg")) parser = argparse.ArgumentParser( description="A Example yelp business finder based on parameters such as term, location, price, ") api_key = config['KEYS']['API_KEY'] headers = {'Authorization': 'Bearer %s' % api_key} def to_string(data): return [str(value) for value in data.values()] def main(): args = parser.parse_args() # Pricing levels to filter the search result with: 1 = $, 2 = $$, 3 = $$$, 4 = $$$$. b = BusinessSearch(term=args.term, location=args.location, price=args.price) db = DatabaseDriver() db.setup() queries = [insert_business_table.format(*to_string(result)) for result in b.get_results()] query_to_execute = "BEGIN; \n" + '\n'.join(queries) + "\nCOMMIT;" db.execute_query(query_to_execute) if __name__ == "__main__": parser._action_groups.pop() required = parser.add_argument_group('required arguments') optional = parser.add_argument_group('optional arguments') required.add_argument("-t", "--term", metavar='', required=True, help="Search term, for example \"food\" or \"restaurants\". The term may also be business names, such as \"Starbucks.\".") required.add_argument("-l", "--location", metavar='', required=True, help="This string indicates the geographic area to be used when searching for businesses. ") optional.add_argument("-p", "--price", type=int, metavar='', required=False, default=1, help="Pricing levels to filter the search result with: 1 = $, 2 = $$, 3 = $$$, 4 = $$$$.") main()
44.690476
148
0.657977
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
create_business_schema = """CREATE SCHEMA IF NOT EXISTS yelp;""" create_business_table = """ CREATE TABLE IF NOT EXISTS yelp.business ( business_id varchar PRIMARY KEY, business_name varchar, image_url varchar, url varchar, review_count int, categories varchar, rating float, latitude float, longitude float, price varchar, location varchar, phone varchar ); """ insert_business_table = """INSERT INTO yelp.business VALUES ('{}', '{}', '{}', '{}', {}, '{}', {}, {}, {}, '{}', '{}', '{}') ON CONFLICT (business_id) DO UPDATE SET business_id = EXCLUDED.business_id, business_name = EXCLUDED.business_name, image_url = EXCLUDED.image_url, url = EXCLUDED.url, review_count = EXCLUDED.review_count, categories = EXCLUDED.categories, rating = EXCLUDED.rating, latitude = EXCLUDED.latitude, longitude = EXCLUDED.longitude, price = EXCLUDED.price, location = EXCLUDED.location, phone = EXCLUDED.phone; """
35.371429
124
0.505503
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import requests from auth import headers import json class Request: def __init__(self): self._header = headers @staticmethod def get_content(url, param): response = requests.get(url, headers=headers, params=param) if response.status_code == 200: return json.loads(response.content) else: print(f"Request completed with Error. Response Code : {response.status_code}") return None
27.875
90
0.635575
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import configparser import psycopg2 from sql_queries import copy_table_queries, insert_table_queries def load_staging_tables(cur, conn): for query in copy_table_queries: cur.execute(query) conn.commit() def insert_tables(cur, conn): for query in insert_table_queries: cur.execute(query) conn.commit() def main(): config = configparser.ConfigParser() config.read('dwh.cfg') conn = psycopg2.connect("host={} dbname={} user={} password={} port={}".format(*config['CLUSTER'].values())) cur = conn.cursor() load_staging_tables(cur, conn) insert_tables(cur, conn) conn.close() if __name__ == "__main__": main()
20.625
112
0.638205
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
import configparser import psycopg2 from sql_queries import create_table_queries, drop_table_queries def drop_tables(cur, conn): for query in drop_table_queries: cur.execute(query) conn.commit() def create_tables(cur, conn): for query in create_table_queries: cur.execute(query) conn.commit() def main(): config = configparser.ConfigParser() config.read('dwh.cfg') conn = psycopg2.connect("host={} dbname={} user={} password={} port={}".format(*config['CLUSTER'].values())) cur = conn.cursor() drop_tables(cur, conn) create_tables(cur, conn) conn.close() if __name__ == "__main__": main()
20
112
0.636364
Udacity-Data-Engineering-Projects
https://github.com/san089/Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
1,219
433
2023-12-04 20:08:27+00:00
2020-01-20 22:50:03+00:00
2,128
Other
Python
from create_tables import main as create_table_main from etl import main as etl_main if __name__ == "__main__": create_table_main() etl_main()
20.857143
51
0.664474

Dataset Card for "Customizable-Code-Assistant-Data"

Dataset Summary

This dataset contains is a dummy Version of the Customizable Code Assistant Dataset.

Supported Tasks and Leaderboards

Customizable Code Assistant is a dataset for code completion. The task is to predict the next token in a code snippet. The dataset is designed to be customizable, so that it can be used for different programming languages and different code completion tasks.

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