How to select value from a table where value is a dictionary, and the key of the dictionary is a number using SQL query?

Question

So, I have a table from BigQuery public tables (Google Analytics):

print(bigquery_client.query(
"""
SELECT hits.0.productName
from `bigquery-public-data.google_analytics_sample.ga_sessions_*`,
where date between '20160101' and '20161231'
""").to_dataframe())

Additional code:

import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] ='/Users/<UserName>/Desktop/folder/key/<Key_name>.json'
bigquery_client = bigquery.Client()

ERROR in Jupiter Notebook:

BadRequest                                Traceback (most recent call last)
<ipython-input-31-424833cf8827> in <module>
----> 1 print(bigquery_client.query(
      2 """
      3 SELECT hits.0.productName
      4 from `bigquery-public-data.google_analytics_sample.ga_sessions_*`,
      5 where date between '20160101' and '20161231'

~/opt/anaconda3/lib/python3.8/site-packages/google/cloud/bigquery/job/query.py in to_dataframe(self, bqstorage_client, dtypes, progress_bar_type, create_bqstorage_client, date_as_object, max_results, geography_as_object)
   1563                 :mod:`shapely` library cannot be imported.
   1564         """
-> 1565         query_result = wait_for_query(self, progress_bar_type, max_results=max_results)
   1566         return query_result.to_dataframe(
   1567             bqstorage_client=bqstorage_client,

~/opt/anaconda3/lib/python3.8/site-packages/google/cloud/bigquery/_tqdm_helpers.py in wait_for_query(query_job, progress_bar_type, max_results)
     86     )
     87     if progress_bar is None:
---> 88         return query_job.result(max_results=max_results)
     89 
     90     i = 0

~/opt/anaconda3/lib/python3.8/site-packages/google/cloud/bigquery/job/query.py in result(self, page_size, max_results, retry, timeout, start_index, job_retry)
   1370                 do_get_result = job_retry(do_get_result)
   1371 
-> 1372             do_get_result()
   1373 
   1374         except exceptions.GoogleAPICallError as exc:

~/opt/anaconda3/lib/python3.8/site-packages/google/api_core/retry.py in retry_wrapped_func(*args, **kwargs)
    281                 self._initial, self._maximum, multiplier=self._multiplier
    282             )
--> 283             return retry_target(
    284                 target,
    285                 self._predicate,

~/opt/anaconda3/lib/python3.8/site-packages/google/api_core/retry.py in retry_target(target, predicate, sleep_generator, deadline, on_error)
    188     for sleep in sleep_generator:
    189         try:
--> 190             return target()
    191 
    192         # pylint: disable=broad-except

~/opt/anaconda3/lib/python3.8/site-packages/google/cloud/bigquery/job/query.py in do_get_result()
   1360                     self._job_retry = job_retry
   1361 
-> 1362                 super(QueryJob, self).result(retry=retry, timeout=timeout)
   1363 
   1364                 # Since the job could already be "done" (e.g. got a finished job

~/opt/anaconda3/lib/python3.8/site-packages/google/cloud/bigquery/job/base.py in result(self, retry, timeout)
    711 
    712         kwargs = {} if retry is DEFAULT_RETRY else {"retry": retry}
--> 713         return super(_AsyncJob, self).result(timeout=timeout, **kwargs)
    714 
    715     def cancelled(self):

~/opt/anaconda3/lib/python3.8/site-packages/google/api_core/future/polling.py in result(self, timeout, retry)
    135             # pylint: disable=raising-bad-type
    136             # Pylint doesn't recognize that this is valid in this case.
--> 137             raise self._exception
    138 
    139         return self._result

BadRequest: 400 Syntax error: Unexpected keyword WHERE at [4:1]

(job ID: 3c15e031-ee7d-4594-a577-0237f8282695)

                    -----Query Job SQL Follows-----                    

    |    .    |    .    |    .    |    .    |    .    |    .    |
   1:
   2:SELECT hits.0.productName
   3:from `bigquery-public-data.google_analytics_sample.ga_sessions_*`,
   4:where date between '20160101' and '20161231'
    |    .    |    .    |    .    |    .    |    .    |    .    |

As seen at the screenshot I have hits column, which value is a dictionary and I need to fetch the inner dictionary value from '0' column, but there is the error. Actually, I need to take 'productName' values from all numeric columns.

Answer 1

An approach you can take to solve this will filter the data you want directly in the query.  

Filtering from BigQuery:

First for a better understanding, take a look on the data schema for the fields that contains product names:

Picture-schema

• The first possible field could be hits.item.productName

  • hits is a RECORD
  • item is a RECORD inside item
  • productName is the string hits.item

• The second field could be hits.product.v2ProductName

  • product is a RECORD inside item
  • v2ProductName is the string hits.product   For query a RECORD, you have to 'flat' is, turning it into a table using the expression UNNEST([record]) as described here:   So to return all the unique product names from hits.product.v2ProductName query :  

 
from google.cloud import bigquery
import pandas as pd
client = bigquery.Client()
 
sql = """
SELECT
DISTINCT p.v2productname
FROM
`bigquery-public-data.google_analytics_sample.ga_sessions_*`,
UNNEST(product) AS p
WHERE
date  BETWEEN  '20160101'
AND  '20161231'
AND (p.v2productname IS  NOT  NULL);
"""
v2productname = client.query(sql).to_dataframe()
print(v2productname)

For use the field hits.item.productName run the following, but all records are null:  

SELECT
DISTINCT h.item.productname
FROM
`bigquery-public-data.google_analytics_sample.ga_sessions_*`,
UNNEST(hits) AS h,
UNNEST(product) AS p
WHERE
date  BETWEEN  '20160101'
AND  '20161231'
AND (h.item.productname IS  NOT  NULL);

         

Filtering from the dataframe:

I tried to process it using a dataframe but its not possible due to the chain of records in the datasets, the function to_dataframe() is not able to process it.  

In resume:

Try to filter and process as much of the data as possible in the BigQuery, it will faster and more cost effectively.