In Google's Bigquery, SQL queries can be parameterized. If you're not familiar with this concept, it basically means that you can write SQL queries as parameterized templates like this:
INSERT INTO mydataset.mytable(columnA, columnB)
VALUES (@valueA, @valueB)
And pass the values separately. This has numerous benefits:
- The query is more readable than when it's built by string concatenation
- The code is more robust and industrialized
- It's a great protection against SQL injection attacks (mandatory XKCD)
The passing of query parameters from a Python script appears straightforward... at first sight. For example:
from google.cloud.bigquery import (
Client,
ScalarQueryParameter,
ArrayQueryParameter,
StructQueryParameter,
QueryJobConfig,
)
client=Client()
client.query("
INSERT INTO mydataset.mytable(columnA, columnB)
VALUES (@valueA, @valueB)
", job_config=QueryJobConfig(
query_parameters=[
ScalarQueryParameter("valueA","STRING","A"),
ScalarQueryParameter("valueB","STRING","B")
])
The example above inserts simple ("Scalar") values in columns A and B. But you can also pass more complex parameters:
- Arrays (ArrayQueryParameter)
- Structs (StructQueryParameter)
Problems arise when you want to insert arrays of structs : there are many gotchas, almost no documentation and very few resources on the subject on the web. The goal of this article is to fill this gap.
How to persist an array of structs in bigquery using parameterized queries
Let's define the following object that we want to store in our destination table
from dataclasses import dataclass
@dataclass
class Country:
name: str
capital_city: str
@dataclass
class Continent:
name: str
countries: list[Country]
by invoking this parameterized query
query = UPDATE continents SET countries=@countries WHERE name="Oceania"
The first try by following the shallow documentation would be
client.query(query,
job_config=QueryJobConfig(query_parameters=[
ArrayQueryParameter("countries", "RECORD", [
{name="New Zealand", capital_city="Wellington"},
{name="Fiji", capital_city="Suva"} ...]
]))
which would fail miserably
AttributeError: 'dict' object has no attribute 'to_api_repr'
Gotcha n°1: ArrayQueryParameter's values must be instances of StructQueryParameter
It turns out that the third argument of the constructor - values
- must be a collection of StructQueryParameter
instances, not the wanted values directly. So let's build them:
client.query(query,
job_config=QueryJobConfig(query_parameters=[
ArrayQueryParameter("countries", "RECORD", [
StructQueryParameter("countries",
ScalarQueryParameter("name", "STRING", ct.name),
ScalarQueryParameter("capital_city", "STRING", ct.capital_city)
)
for ct in countries])
]))
This time it works... Until you try to set an empty array
client.query(query,
job_config=QueryJobConfig(
query_parameters=[
ArrayQueryParameter("countries", "RECORD", [])
]))
ValueError: Missing detailed struct item type info for an empty array, please provide a StructQueryParameterType instance.
Gotcha n°2: Provide the full structure type as second argument
The error message is pretty clear: "RECORD" is not enough for Bigquery to know what to do with your empty array. It needs the fully detailed structure. So be it
client.query(query, job_config=QueryJobConfig(query_parameters=[
ArrayQueryParameter("countries",
StructQueryParameterType(
ScalarQueryParameterType("STRING","name"),
ScalarQueryParameterType("STRING","capital_city")
), [])
]))
(Notice how the order of the arguments of the ...ParameterType
constructor is the reverse of ...Parameter
constructor. Just another trap on the road...)
And now it works for empty arrays too, yay !
One last gotcha to be aware of: every subfield of a StructQueryParameterType must have a name, even if the second parameter (name
) is optional in the constructor. It's actually mandatory for subfields, otherwise you'll get a new kind of error
Empty struct field name
I think that's all we need to know to complete the usage of arrays of records in query parameters, I hope this helps !
Thanks for reading! I’m Matthieu, data engineer at Stack Labs.
If you want to discover the Stack Labs Data Platform or join an enthousiast Data Engineering team, please contact us.
Photo de Denys Nevozhai sur Unsplash