Feat: Onboard NOAA Passive Bioacoustic dataset

datasets/noaa_passive_bioacoustic/infra/noaa_passive_bioacoustic_dataset.tf

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+/**
+ * Copyright 2022 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+resource "google_bigquery_dataset" "noaa_passive_bioacoustic" {
+  dataset_id  = "noaa_passive_bioacoustic"
+  project     = var.project_id
+  description = "This dataset basically contains the metadata corresponding to the audio data files present in google cloud storage bucket gs://noaa-passive-bioacoustic/big_query_metadata/"
+}
+
+output "bigquery_dataset-noaa_passive_bioacoustic-dataset_id" {
+  value = google_bigquery_dataset.noaa_passive_bioacoustic.dataset_id
+}

datasets/noaa_passive_bioacoustic/infra/provider.tf

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+/**
+ * Copyright 2022 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+provider "google" {
+  project                     = var.project_id
+  impersonate_service_account = var.impersonating_acct
+  region                      = var.region
+}
+
+data "google_client_openid_userinfo" "me" {}
+
+output "impersonating-account" {
+  value = data.google_client_openid_userinfo.me.email
+}

datasets/noaa_passive_bioacoustic/infra/variables.tf

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+/**
+ * Copyright 2022 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+variable "project_id" {}
+variable "bucket_name_prefix" {}
+variable "impersonating_acct" {}
+variable "region" {}
+variable "env" {}
+variable "iam_policies" {
+  default = {}
+}
+

datasets/noaa_passive_bioacoustic/pipelines/_images/run_csv_transform_kub/Dockerfile

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+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# The base image for this build
+FROM python:3.8
+
+# Allow statements and log messages to appear in Cloud logs
+ENV PYTHONUNBUFFERED True
+
+# Copy the requirements file into the image
+COPY requirements.txt ./
+
+# Install the packages specified in the requirements file
+RUN python3 -m pip install --no-cache-dir -r requirements.txt
+
+# The WORKDIR instruction sets the working directory for any RUN, CMD,
+# ENTRYPOINT, COPY and ADD instructions that follow it in the Dockerfile.
+# If the WORKDIR doesn’t exist, it will be created even if it’s not used in
+# any subsequent Dockerfile instruction
+WORKDIR /custom
+
+# Copy the specific data processing script/s in the image under /custom/*
+COPY ./csv_transform.py .
+COPY ./schema.json .
+
+# Command to run the data processing script when the container is run
+CMD ["python3", "csv_transform.py"]

datasets/noaa_passive_bioacoustic/pipelines/_images/run_csv_transform_kub/csv_transform.py

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+import csv
+import json
+import logging
+import os
+import typing
+
+import pandas as pd
+from google.api_core.exceptions import NotFound
+from google.cloud import bigquery, storage
+
+
+def main(
+    source_gcs_path: str,
+    destination_gcs_path: str,
+    project_id: str,
+    dataset_id: str,
+    gcs_bucket: str,
+    schema_filepath: str,
+    header: typing.List[str],
+) -> None:
+    logging.info("Getting the names of the source files")
+    source_file_names = fetch_gcs_file_names(source_gcs_path, gcs_bucket)
+    for each_file in source_file_names:
+        pipeline_name = each_file
+        table_id = each_file
+        logging.info(f"Started ETL process for {pipeline_name}")
+        execute_pipeline(
+            source_gcs_path,
+            destination_gcs_path,
+            project_id,
+            dataset_id,
+            gcs_bucket,
+            pipeline_name,
+            table_id,
+            schema_filepath,
+            header,
+        )
+        logging.info(f"Finished process for {pipeline_name} ------>")
+        logging.info("------ NEXT PIPELINE ------")
+    logging.info("Finished the entire process for all the pipelines.")
+
+
+def fetch_gcs_file_names(source_gcs_path: str, gcs_bucket: str) -> list:
+    client = storage.Client()
+    blobs = client.list_blobs(gcs_bucket, prefix=source_gcs_path)
+    source_file_names = []
+    for blob in blobs:
+        if blob.name.split("/")[-1]:
+            source_file_names.append(blob.name.split("/")[-1])
+    logging.info(f"{len(source_file_names)} tables to be loaded in bq")
+    return source_file_names
+
+
+def execute_pipeline(
+    source_gcs_path: str,
+    destination_gcs_path: str,
+    project_id: str,
+    dataset_id: str,
+    gcs_bucket: str,
+    pipeline_name: str,
+    table_id: str,
+    schema_filepath: str,
+    header: typing.List[str],
+):
+    if pipeline_name.endswith(".xlsx"):
+        table_id = table_id[:-5].lower()
+        logging.info(f"Downloading and transforming {pipeline_name}")
+        upload_file = transform_xlsx(gcs_bucket, source_gcs_path, pipeline_name)
+    elif pipeline_name.endswith(".csv"):
+        table_id = table_id[:-4].lower()
+        logging.info(f"Downloading and transforming {pipeline_name}")
+        upload_file = transform_csv(gcs_bucket, source_gcs_path, pipeline_name, header)
+    pipeline_name = upload_file
+    blob = None
+    if upload_file:
+        logging.info("Uploading final file to gcs")
+        upload_file_to_gcs(gcs_bucket, destination_gcs_path, upload_file=upload_file)
+        source_gcs_path = destination_gcs_path
+        client = storage.Client()
+        blob = client.list_blobs(gcs_bucket, prefix=source_gcs_path + pipeline_name)
+    if blob:
+        table_exists = create_dest_table(
+            project_id=project_id,
+            dataset_id=dataset_id,
+            table_id=table_id,
+            gcs_bucket=gcs_bucket,
+            schema_filepath=schema_filepath,
+            drop_table=True,
+        )
+        if table_exists:
+            load_data_to_bq(
+                project_id=project_id,
+                dataset_id=dataset_id,
+                table_id=table_id,
+                gcs_bucket=gcs_bucket,
+                source_gcs_path=source_gcs_path,
+                truncate_table=True,
+                field_delimiter="|",
+            )
+        else:
+            error_msg = f"Error: Data was not loaded because the destination table {project_id}.{dataset_id}.{table_id} does not exist and/or could not be created."
+            raise ValueError(error_msg)
+    else:
+        logging.info(f"Informational: The data file {blob} is unavailable")
+
+
+def upload_file_to_gcs(gcs_bucket: str, destination_gcs_path: str, upload_file: str):
+    gcs_upload_file = upload_file.lower()
+    client = storage.Client()
+    bucket = client.bucket(gcs_bucket)
+    blob = bucket.blob(destination_gcs_path + "/" + gcs_upload_file)
+    blob.upload_from_filename(upload_file)
+    logging.info(f"Completed uploading {upload_file} to gcs")
+
+
+def transform_xlsx(gcs_bucket: str, source_gcs_path: str, pipeline_name: str) -> str:
+    download_file(gcs_bucket, source_gcs_path, pipeline_name)
+    try:
+        df = pd.read_excel(pipeline_name)
+        upload_file = f"{pipeline_name[:-5]}.csv"
+        upload_file = upload_file.lower()
+        df.to_csv(upload_file, index=False, sep=",")
+    except ValueError:
+        logging.info(f"{pipeline_name} file is corrupted, skipping and moving ahead.")
+        return None
+    logging.info("Completed transforming file.")
+    if upload_file:
+        return upload_file
+
+
+def transform_csv(
+    gcs_bucket: str, source_gcs_path: str, pipeline_name: str, header: typing.List[str]
+) -> str:
+    download_file(gcs_bucket, source_gcs_path, pipeline_name)
+    if pipeline_name == "NCEI_NEFSC_PAD_metadata.csv":
+        data = []
+        with open(pipeline_name) as f:
+            content = csv.reader(f)
+            for row in content:
+                while row[-1] == "":
+                    row.pop()
+                if "MULTIPOINT" in row[-1]:
+                    temp = row.pop()
+                    row.extend(temp.split(","))
+                data.append(row)
+        data[0] = header
+        upload_file = f"_{pipeline_name[:-4]}.csv"
+        upload_file = upload_file.lower()
+        with open(upload_file, "w", newline="") as f:
+            content = csv.writer(f)
+            for row in data:
+                content.writerow(row)
+        df = pd.read_csv(upload_file)
+        upload_file = upload_file[1:]
+        df.to_csv(
+            upload_file, index=False
+        )  # Ensures column number consistency by padding blank fields
+    else:
+        upload_file = pipeline_name
+        logging.info("No transformations required. Moving ahead.")
+        return upload_file
+    logging.info("Completed transforming file.")
+    return upload_file.lower()
+
+
+def download_file(gcs_bucket: str, source_gcs_path: str, pipeline_name: str):
+    client = storage.Client()
+    bucket = client.bucket(gcs_bucket)
+    blob = bucket.blob(source_gcs_path + "/" + pipeline_name)
+    blob.download_to_filename(pipeline_name)
+    logging.info("Completed downloading file.")
+
+
+def create_dest_table(
+    project_id: str,
+    dataset_id: str,
+    table_id: str,
+    gcs_bucket: str,
+    schema_filepath: str,
+    drop_table: bool,
+) -> bool:
+    table_ref = f"{project_id}.{dataset_id}.{table_id}"
+    logging.info(f"Attempting to create table {table_id} if it doesn't already exist")
+    client = bigquery.Client()
+    try:
+        table = client.get_table(table_ref)
+        table_exists_id = table.table_id
+        logging.info(f"Table {table_exists_id} currently exists.")
+        if drop_table:
+            logging.info("Dropping existing table")
+            client.delete_table(table)
+            table = None
+    except NotFound:
+        table = None
+    if not table:
+        logging.info(
+            f"Table {table_ref} currently does not exist.  Attempting to create table."
+        )
+        if schema_filepath:
+            schema = create_table_schema(schema_filepath, table_id)
+            table = bigquery.Table(table_ref, schema=schema)
+            client.create_table(table)
+            logging.info(f"Table {table_id} was created")
+            table_exists = True
+        else:
+            logging.info(f"Schema {schema_filepath} file not found")
+            table_exists = False
+    else:
+        table_exists = True
+    return table_exists
+
+
+def create_table_schema(schema_filepath: str, table_id: str) -> list:
+    logging.info("Defining table schema")
+    schema = []
+    with open(schema_filepath) as f:
+        sc = f.read()
+    schema_struct = json.loads(sc)
+    for table_field in schema_struct:
+        if table_field == table_id:
+            for schema_field in schema_struct[table_field]:
+                fld_name = schema_field["name"]
+                fld_type = schema_field["type"]
+                try:
+                    fld_descr = schema_field["description"]
+                except KeyError:
+                    fld_descr = ""
+                fld_mode = schema_field["mode"]
+                schema.append(
+                    bigquery.SchemaField(
+                        name=fld_name,
+                        field_type=fld_type,
+                        mode=fld_mode,
+                        description=fld_descr,
+                    )
+                )
+    return schema
+
+
+def load_data_to_bq(
+    project_id: str,
+    dataset_id: str,
+    table_id: str,
+    gcs_bucket: str,
+    source_gcs_path: str,
+    truncate_table: bool,
+    field_delimiter: str = "|",
+) -> None:
+    logging.info(f"Loading output data from {source_gcs_path} into {table_id}")
+    client = bigquery.Client(project=project_id)
+    table_ref = f"{project_id}.{dataset_id}.{table_id}"
+    job_config = bigquery.LoadJobConfig(
+        skip_leading_rows=1, source_format=bigquery.SourceFormat.CSV
+    )
+    job = client.load_table_from_uri(
+        f"gs://{gcs_bucket}/{source_gcs_path}/{table_id}.csv",
+        table_ref,
+        job_config=job_config,
+    )
+    logging.info(job.result())
+    logging.info("Loading table completed")
+
+
+if __name__ == "__main__":
+    logging.getLogger().setLevel(logging.INFO)
+    main(
+        source_gcs_path=os.environ.get("SOURCE_GCS_PATH"),
+        destination_gcs_path=os.environ.get("DESTINATION_GCS_PATH"),
+        project_id=os.environ.get("PROJECT_ID"),
+        dataset_id=os.environ.get("DATASET_ID"),
+        gcs_bucket=os.environ.get("GCS_BUCKET"),
+        schema_filepath=os.environ.get("SCHEMA_FILEPATH", ""),
+        header=json.loads(os.environ.get("HEADER")),
+    )