Combining a Pandas Data Frame with SQL Using Primary Keys

Data and Databases
1

How can I retrieve matching records from a massive SQL table using a Pandas DataFrame’s primary key?

import pandas as pd
# Create a sample dataframe with unique identifiers
df_sample = pd.DataFrame({'uid': ['a101', 'a102', 'a103']})

# Construct a query to fetch rows with matching uid values
sql_query = "SELECT * FROM Database.Employee WHERE uid IN ('" + "','".join(df_sample['uid']) + "')"
2

hey finn, have u thought about using parameterized queries with sqlalchemy to avoid injection risks? any experiments joinin the df directly in sql? curious how performance holds up with big datasets. what do u reckon?

3

hey finn, maybe u can create a temp table of the primary keys and do a join on that. it might yield better performance on massive tables compared to constructing a long in clause. also help for indexing issues

4

hey finn, im curious if u tried a query with a nested subselect from your df keys instead? maybe filtering with a subquery could isolate the task and save runtime. whaddaya think of experimenting this way?

5

I have found that establishing a temporary table or using a common table expression (CTE) to pass the primary keys from the DataFrame to the SQL query can significantly enhance performance when handling large datasets. By doing so, it is easier to optimize the join operation on indexed columns. This method not only reduces the risk of SQL injection by avoiding direct string concatenation but also enables the database engine to better plan and execute the query. Careful indexing on both tables often leads to a more efficient retrieval process.