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data_source_test_creator.py
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55 lines (45 loc) · 1.7 KB
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import contextlib
import random
import tempfile
import time
from typing import Iterator
from google.cloud import bigquery
from feast import BigQuerySource, FileSource
from feast.data_format import ParquetFormat
@contextlib.contextmanager
def prep_file_source(df, timestamp_field=None) -> Iterator[FileSource]:
with tempfile.NamedTemporaryFile(suffix=".parquet") as f:
f.close()
df.to_parquet(f.name)
file_source = FileSource(
file_format=ParquetFormat(),
path=f.name,
timestamp_field=timestamp_field,
)
yield file_source
def simple_bq_source_using_table_arg(df, timestamp_field=None) -> BigQuerySource:
client = bigquery.Client()
gcp_project = client.project
bigquery_dataset = f"ds_{time.time_ns()}"
dataset = bigquery.Dataset(f"{gcp_project}.{bigquery_dataset}")
client.create_dataset(dataset, exists_ok=True)
dataset.default_table_expiration_ms = (
1000
* 60
* 60 # 60 minutes in milliseconds (seems to be minimum limit for gcloud)
)
client.update_dataset(dataset, ["default_table_expiration_ms"])
table = f"{gcp_project}.{bigquery_dataset}.table_{random.randrange(100, 999)}"
job = client.load_table_from_dataframe(df, table)
job.result()
return BigQuerySource(
table=table,
timestamp_field=timestamp_field,
)
def simple_bq_source_using_query_arg(df, timestamp_field=None) -> BigQuerySource:
bq_source_using_table = simple_bq_source_using_table_arg(df, timestamp_field)
return BigQuerySource(
name=bq_source_using_table.table,
query=f"SELECT * FROM {bq_source_using_table.table}",
timestamp_field=timestamp_field,
)