-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Migration for new data package format (#122)
* Migration for new data package format * more layers * moved pandas function to seperate file
- Loading branch information
1 parent
ff4bee6
commit 5dbb54d
Showing
7 changed files
with
236 additions
and
51 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| import argparse | ||
| import enum | ||
| import io | ||
| import json | ||
| import os | ||
|
|
||
| import awswrangler | ||
| import boto3 | ||
| import pandas | ||
| from rich import progress | ||
|
|
||
|
|
||
| class JsonFilename(enum.Enum): | ||
| """stores names of expected kinds of persisted S3 JSON files""" | ||
|
|
||
| COLUMN_TYPES = "column_types" | ||
| TRANSACTIONS = "transactions" | ||
| DATA_PACKAGES = "data_packages" | ||
| STUDY_PERIODS = "study_periods" | ||
|
|
||
|
|
||
| class BucketPath(enum.Enum): | ||
| """stores root level buckets for managing data processing state""" | ||
|
|
||
| ADMIN = "admin" | ||
| AGGREGATE = "aggregates" | ||
| ARCHIVE = "archive" | ||
| CACHE = "cache" | ||
| CSVAGGREGATE = "csv_aggregates" | ||
| ERROR = "error" | ||
| LAST_VALID = "last_valid" | ||
| LATEST = "latest" | ||
| META = "metadata" | ||
| STUDY_META = "study_metadata" | ||
| UPLOAD = "site_upload" | ||
|
|
||
|
|
||
| def get_csv_column_datatypes(dtypes): | ||
| """helper for generating column type for dashboard API""" | ||
| column_dict = {} | ||
| for column in dtypes.index: | ||
| if column.endswith("year"): | ||
| column_dict[column] = "year" | ||
| elif column.endswith("month"): | ||
| column_dict[column] = "month" | ||
| elif column.endswith("week"): | ||
| column_dict[column] = "week" | ||
| elif column.endswith("day") or str(dtypes[column]) == "datetime64": | ||
| column_dict[column] = "day" | ||
| elif "cnt" in column or str(dtypes[column]) in ( | ||
| "Int8", | ||
| "Int16", | ||
| "Int32", | ||
| "Int64", | ||
| "UInt8", | ||
| "UInt16", | ||
| "UInt32", | ||
| "UInt64", | ||
| ): | ||
| column_dict[column] = "integer" | ||
| elif str(dtypes[column]) in ("Float32", "Float64"): | ||
| column_dict[column] = "float" | ||
| elif str(dtypes[column]) == "boolean": | ||
| column_dict[column] = "float" | ||
| else: | ||
| column_dict[column] = "string" | ||
| return column_dict | ||
|
|
||
|
|
||
| def _put_s3_data(key: str, bucket_name: str, client, data: dict) -> None: | ||
| """Convenience class for writing a dict to S3""" | ||
| b_data = io.BytesIO(json.dumps(data).encode()) | ||
| client.upload_fileobj(Bucket=bucket_name, Key=key, Fileobj=b_data) | ||
|
|
||
|
|
||
| def update_column_type_metadata(bucket: str): | ||
| """creates a new metadata dict for column types. | ||
| By design, this will replaces an existing column type dict if one already exists. | ||
| """ | ||
| client = boto3.client("s3") | ||
| res = client.list_objects_v2(Bucket=bucket, Prefix="aggregates/") | ||
| contents = res["Contents"] | ||
| output = {} | ||
| for resource in progress.track(contents): | ||
| dirs = resource["Key"].split("/") | ||
| study = dirs[1] | ||
| subscription = dirs[2].split("__")[1] | ||
| version = dirs[3] | ||
| bytes_buffer = io.BytesIO() | ||
| client.download_fileobj(Bucket=bucket, Key=resource["Key"], Fileobj=bytes_buffer) | ||
| df = pandas.read_parquet(bytes_buffer) | ||
| type_dict = get_csv_column_datatypes(df.dtypes) | ||
| output.setdefault(study, {}) | ||
| output[study].setdefault(subscription, {}) | ||
| output[study][subscription].setdefault(version, {}) | ||
| output[study][subscription][version]["column_types_format_version"] = 2 | ||
| output[study][subscription][version]["columns"] = type_dict | ||
| output[study][subscription][version]["last_data_update"] = ( | ||
| resource["LastModified"].now().isoformat() | ||
| ) | ||
| output[study][subscription][version]["s3_path"] = resource["Key"][:-8] + ".csv" | ||
| output[study][subscription][version]["total"] = int(df["cnt"][0]) | ||
| _put_s3_data("metadata/column_types.json", bucket, client, output) | ||
|
|
||
|
|
||
| def get_s3_json_as_dict(bucket, key: str): | ||
| """reads a json object as dict (typically metadata in this case)""" | ||
| s3_client = boto3.client("s3") | ||
| bytes_buffer = io.BytesIO() | ||
| print(bucket) | ||
| print(key) | ||
| s3_client.download_fileobj( | ||
| Bucket=bucket, | ||
| Key=key, | ||
| Fileobj=bytes_buffer, | ||
| ) | ||
| return json.loads(bytes_buffer.getvalue().decode()) | ||
|
|
||
|
|
||
| def cache_api_data(s3_bucket_name: str, db: str) -> None: | ||
| s3_client = boto3.client("s3") | ||
| df = awswrangler.athena.read_sql_query( | ||
| ( | ||
| f"SELECT table_name FROM information_schema.tables " # noqa: S608 | ||
| f"WHERE table_schema = '{db}'" # nosec | ||
| ), | ||
| database=db, | ||
| s3_output=f"s3://{s3_bucket_name}/awswrangler", | ||
| workgroup=os.environ.get("WORKGROUP_NAME"), | ||
| ) | ||
| data_packages = df[df["table_name"].str.contains("__")].iloc[:, 0] | ||
| column_types = get_s3_json_as_dict( | ||
| s3_bucket_name, | ||
| f"{BucketPath.META.value}/{JsonFilename.COLUMN_TYPES.value}.json", | ||
| ) | ||
| dp_details = [] | ||
| for dp in list(data_packages): | ||
| dp_detail = { | ||
| "study": dp.split("__", 1)[0], | ||
| "name": dp.split("__", 1)[1], | ||
| } | ||
| try: | ||
| versions = column_types[dp_detail["study"]][dp_detail["name"]] | ||
| for version in versions: | ||
| dp_details.append( | ||
| { | ||
| **dp_detail, | ||
| **versions[version], | ||
| "version": version, | ||
| "id": dp + "__" + version, | ||
| } | ||
| ) | ||
| except KeyError: | ||
| continue | ||
| s3_client.put_object( | ||
| Bucket=s3_bucket_name, | ||
| Key=f"{BucketPath.CACHE.value}/{JsonFilename.DATA_PACKAGES.value}.json", | ||
| Body=json.dumps(dp_details), | ||
| ) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser( | ||
| description="""Creates data package metadata for existing aggregates. """ | ||
| ) | ||
| parser.add_argument("-b", "--bucket", help="bucket name") | ||
| parser.add_argument("-d", "--db", help="database name") | ||
| args = parser.parse_args() | ||
| update_column_type_metadata(args.bucket) | ||
| cache_api_data(args.bucket, args.db) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| """Pandas functions used across different functions""" | ||
|
|
||
| import pandas | ||
|
|
||
|
|
||
| def get_column_datatypes(dtypes: pandas.DataFrame): | ||
| """helper for generating column type for dashboard API""" | ||
| column_dict = {} | ||
| for column in dtypes.index: | ||
| if column.endswith("year"): | ||
| column_dict[column] = "year" | ||
| elif column.endswith("month"): | ||
| column_dict[column] = "month" | ||
| elif column.endswith("week"): | ||
| column_dict[column] = "week" | ||
| elif column.endswith("day") or str(dtypes[column]) == "datetime64": | ||
| column_dict[column] = "day" | ||
| elif column.startswith("cnt") or str(dtypes[column]) in ( | ||
| "Int8", | ||
| "Int16", | ||
| "Int32", | ||
| "Int64", | ||
| "UInt8", | ||
| "UInt16", | ||
| "UInt32", | ||
| "UInt64", | ||
| ): | ||
| column_dict[column] = "integer" | ||
| elif str(dtypes[column]) in ("Float32", "Float64"): | ||
| column_dict[column] = "float" | ||
| elif str(dtypes[column]) == "boolean": | ||
| column_dict[column] = "boolean" | ||
| else: | ||
| column_dict[column] = "string" | ||
| return column_dict |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters