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docs: add sample to run DML query #591
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tswast
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tswast:b178085085-bigquery_update_with_dml
Apr 13, 2021
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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 | ||
# | ||
# https://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. | ||
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from google.cloud import bigquery | ||
import pytest | ||
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from conftest import resource_prefix | ||
import update_with_dml | ||
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@pytest.fixture | ||
def table_id(bigquery_client: bigquery.Client, project_id: str, dataset_id: str): | ||
table_id = f"{resource_prefix()}_update_with_dml" | ||
yield table_id | ||
full_table_id = f"{project_id}.{dataset_id}.{table_id}" | ||
bigquery_client.delete_table(full_table_id, not_found_ok=True) | ||
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def test_update_with_dml(bigquery_client_patch, dataset_id, table_id): | ||
override_values = { | ||
"dataset_id": dataset_id, | ||
"table_id": table_id, | ||
} | ||
num_rows = update_with_dml.run_sample(override_values=override_values) | ||
assert num_rows > 0 |
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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 | ||
# | ||
# https://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. | ||
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# [START bigquery_update_with_dml] | ||
import pathlib | ||
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from google.cloud import bigquery | ||
from google.cloud.bigquery import enums | ||
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def load_from_newline_delimited_json( | ||
client: bigquery.Client, | ||
filepath: pathlib.Path, | ||
project_id: str, | ||
dataset_id: str, | ||
table_id: str, | ||
): | ||
full_table_id = f"{project_id}.{dataset_id}.{table_id}" | ||
job_config = bigquery.LoadJobConfig() | ||
job_config.source_format = enums.SourceFormat.NEWLINE_DELIMITED_JSON | ||
job_config.schema = [ | ||
bigquery.SchemaField("id", enums.SqlTypeNames.STRING), | ||
bigquery.SchemaField("user_id", enums.SqlTypeNames.INTEGER), | ||
bigquery.SchemaField("login_time", enums.SqlTypeNames.TIMESTAMP), | ||
bigquery.SchemaField("logout_time", enums.SqlTypeNames.TIMESTAMP), | ||
bigquery.SchemaField("ip_address", enums.SqlTypeNames.STRING), | ||
] | ||
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with open(filepath, "rb") as json_file: | ||
load_job = client.load_table_from_file( | ||
json_file, full_table_id, job_config=job_config | ||
) | ||
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# Wait for load job to finish. | ||
load_job.result() | ||
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def update_with_dml( | ||
client: bigquery.Client, project_id: str, dataset_id: str, table_id: str | ||
): | ||
query_text = f""" | ||
UPDATE `{project_id}.{dataset_id}.{table_id}` | ||
SET ip_address = REGEXP_REPLACE(ip_address, r"(\\.[0-9]+)$", ".0") | ||
WHERE TRUE | ||
""" | ||
query_job = client.query(query_text) | ||
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# Wait for query job to finish. | ||
query_job.result() | ||
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print(f"DML query modified {query_job.num_dml_affected_rows} rows.") | ||
return query_job.num_dml_affected_rows | ||
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def run_sample(override_values={}): | ||
client = bigquery.Client() | ||
filepath = pathlib.Path(__file__).parent / "user_sessions_data.json" | ||
project_id = client.project | ||
dataset_id = "sample_db" | ||
table_id = "UserSessions" | ||
# [END bigquery_update_with_dml] | ||
# To facilitate testing, we replace values with alternatives | ||
# provided by the testing harness. | ||
dataset_id = override_values.get("dataset_id", dataset_id) | ||
table_id = override_values.get("table_id", table_id) | ||
# [START bigquery_update_with_dml] | ||
load_from_newline_delimited_json(client, filepath, project_id, dataset_id, table_id) | ||
return update_with_dml(client, project_id, dataset_id, table_id) | ||
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# [END bigquery_update_with_dml] |
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{"id":"2ad525d6-c832-4c3d-b7fe-59d104885519","user_id":"38","login_time":"1.47766087E9","logout_time":"1.477661109E9","ip_address":"192.0.2.12"} | ||
{"id":"53d65e20-6ea9-4650-98d9-a2111fbd1122","user_id":"88","login_time":"1.47707544E9","logout_time":"1.477075519E9","ip_address":"192.0.2.88"} | ||
{"id":"5e6c3021-d5e7-4ccd-84b2-adfa9176d13d","user_id":"39","login_time":"1.474022869E9","logout_time":"1.474022961E9","ip_address":"203.0.113.52"} | ||
{"id":"6196eefa-1498-4567-8ef0-498845b888d9","user_id":"52","login_time":"1.478604612E9","logout_time":"1.478604691E9","ip_address":"203.0.113.169"} | ||
{"id":"70656dc5-7e0f-49cf-9e00-f06ed93c1f5b","user_id":"46","login_time":"1.474089924E9","logout_time":"1.474090227E9","ip_address":"192.0.2.10"} | ||
{"id":"aafa5eef-ad49-49a7-9a0f-fbc7fd639bd3","user_id":"40","login_time":"1.478031161E9","logout_time":"1.478031388E9","ip_address":"203.0.113.18"} | ||
{"id":"d2792fc2-24dd-4260-9456-3fbe6cdfdd90","user_id":"5","login_time":"1.481259081E9","logout_time":"1.481259247E9","ip_address":"192.0.2.140"} | ||
{"id":"d835dc49-32f9-4790-b4eb-dddee62e0dcc","user_id":"62","login_time":"1.478892977E9","logout_time":"1.478893219E9","ip_address":"203.0.113.83"} | ||
{"id":"f4a0d3c7-351f-471c-8e11-e093e7a6ce75","user_id":"89","login_time":"1.459031555E9","logout_time":"1.459031831E9","ip_address":"203.0.113.233"} | ||
{"id":"f6e9f526-5b22-4679-9c3e-56a636e815bb","user_id":"97","login_time":"1.482426034E9","logout_time":"1.482426415E9","ip_address":"203.0.113.167"} |
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This cleanup code looks great. Since
dataset
hascreated
property so technically there's no need to have timestamp in the resource name, but it's also good when we manually check the resources.Only concern is this code might fail and the test fails too. It might annoy us. If it annoys us too much, maybe we can consider ignoring failure here.
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The
not_found_ok
will ignore the most common error of 404. Other than that, I've not found thedelete_dataset
operation to be all that flaky with the default retry options.