How to get result from BigQuery based on user input parameters - sql

Question

This is a continuation of my previous post for making api that takes url parameter , passes it to BigQuery and if the luid record has data in orderid column, it returns True . How to check whether data exists in specific column on BigQuery with Flask?
I changed sql and it seems this sql works well on GCP console but as you can see , it returns Flase({'f0_': 0})) if you input correct parameter from browser. Do I need to fix this sql ??
[URL:https://test-989898.df.r.appspot.com?luid=U77777]
The output of return str(row)
↓
Row((True,), {'f0_': 0})
The output of SQL with same luid above on console
↓
row | f0_
1 | true
SELECT EXISTS(SELECT 1
FROM `test-266110.conversion_log.conversion_log_2020*` as p
WHERE luid = "U77777" AND orderid != '' limit 1000)
and I tried this article as below . User input parameter can not be available in BigQuery ??
https://cloud.google.com/bigquery/docs/parameterized-queries
#app.route('/')
def get_request():
luid = request.args.get('luid') or ''
client = bigquery.Client()
query = """SELECT EXISTS(SELECT 1
FROM `test-266110.conversion_log.conversion_log_2020*` as p
WHERE #luid = p.luid AND orderid != '' limit 1000)"""
job_config = bigquery.QueryJobConfig(
query_parameters=[
bigquery.ScalarQueryParameter("luid", "STRING", luid),
]
)
query_job = client.query(query, job_config=job_config)
query_res = query_job.result()
for row in query_res:
return str(row)
↓
Row((True,), {'f0_': 0})
I've been stack in this problem for a while , I'm welcome to any idea . Anyone has good solutions ??
from flask import Flask, request, jsonify
from google.cloud import bigquery
app = Flask(__name__)
#app.route('/')
def get_request():
luid = request.args.get('luid') or ''
client = bigquery.Client()
query = """SELECT EXISTS(SELECT 1
FROM `test-266110.conversion_log.conversion_log_2020*` as p
WHERE #luid = p.luid AND orderid != '' limit 1000)"""
job_config = bigquery.QueryJobConfig(
query_parameters=[
bigquery.ScalarQueryParameter("luid", "STRING", luid),
]
)
query_job = client.query(query, job_config=job_config)
query_res = query_job.result()
# first_row = next(iter(query_job.result()))
for row in query_res:
return str(row)
#return jsonify({luid:query_res.total_rows})
"""
if query_res == :
return jsonify({luid: str(True)})
else:
return jsonify({luid: str(False)})
"""
if __name__ == "__main__":
app.run()
↓
Row((True,), {'f0_': 0})

Answer 1

You seem to have solved most of the bits, it's just a question of getting them working together. Here's a quick sample that should help with the BigQuery things, and shows a different way of writing your query pattern using a public dataset table.
from google.cloud import bigquery
client = bigquery.Client()
# assume you get this from your flask app's param. this is the "luid" you're checking.
value = "treason"
# rewriting the sql demonstrate a similar thing with a public dataset table
sql = "SELECT COUNTIF(word=#luid AND corpus='sonnets') > 0 as word_is_sonnet FROM `bigquery-public-data.samples.shakespeare`"
config = bigquery.QueryJobConfig(
query_parameters=[
bigquery.ScalarQueryParameter("luid", "STRING", value),
]
)
job = client.query(sql, job_config=config)
# this is a bit odd, but in this case we know we're dealing with a single row
# coming from the iterable based on the query structure.
first_row = next(iter(job.result()))
print(first_row.get("word_is_sonnet"))
However, that said I'd make sure you're understanding how BigQuery works and charges for queries. You seem to be doing point lookups for a range of tables (the wildcard table in your original query), which means you're potentially doing a lot of table scanning to satisfy this request.
I just wanted to call that out so you're not surprised by either the performance or the costs if the intent is that you're issuing many requests like this.