I tried this countless times and, despite what I read above, I do not agree with most of either the process or the conclusion.

The process

If you're to compare two methods, adding thick layers of SQLAlchemy or pandasSQL_builder (that is pandas.io.sql.pandasSQL_builder, without so much as an import) and other such non self-contained fragments is not helpful to say the least. The only way to compare two methods without noise is to just use them as clean as possible and, at the very least, in similar circumstances.

The assumptions

There is a saying about assumptions... Between assuming the difference is not noticeable and bringing up useless considerations about pd.read_sql_query, the point gets severely blurred. The only obvious consideration here is that if anyone is comparing pd.read_sql_query and pd.read_sql_table, it's the table, the whole table and nothing but the table. Invoking where, join and others is just a waste of time.

Furthermore, the question explicitly asks for the difference between read_sql_table and read_sql_query with a SELECT * FROM table.

My code

I ran this over and over again on SQLite, MariaDB and PostgreSQL. I use SQLAlchemy exclusively to create the engines, because pandas requires this. The data comes from the coffee-quality-database and I preloaded the file data/arabica_data_cleaned.csv in all three engines, to a table called arabica in a DB called coffee

Here's a summarised version of my script:

import time
import pandas as pd
from sqlalchemy import create_engine

sqlite_engine = create_engine('sqlite:///coffee.db', echo=False)
mariadb_engine = create_engine('mariadb+mariadbconnector://root:[email protected]:3306/coffee')
postgres_engine = create_engine('postgresql://postgres:[email protected]:5432/coffee')

for engine in [sqlite_engine, mariadb_engine, postgres_engine]:
    print(engine)
    print('\tpd.read_sql_query:')
    startTime = time.time()
    for i in range(100):
        pd.read_sql_query('SELECT * FROM arabica;', engine)
    print(f"\t[-- TIME --] {time.time()-startTime:.2f} sec\n")
    print('\tpd.read_sql_table:')
    startTime = time.time()
    for i in range(100):
        pd.read_sql_table('arabica', engine)
    print(f"\t[-- TIME --] {time.time()-startTime:.2f} sec\n")

The versions are:

• Python: 3.9.0
• pandas: 1.2.4
• SQLAlchemy: 1.4.13
• time: built-in

My results

Here's a sample output:

Engine(sqlite:///coffee.db)
        pd.read_sql_query:
        [-- TIME --] 2.58 sec

        pd.read_sql_table:
        [-- TIME --] 3.60 sec

Engine(mariadb+mariadbconnector://root:***@127.0.0.1:3306/coffee)
        pd.read_sql_query:
        [-- TIME --] 2.84 sec

        pd.read_sql_table:
        [-- TIME --] 4.15 sec

Engine(postgresql://postgres:***@127.0.0.1:5432/coffee)
        pd.read_sql_query:
        [-- TIME --] 2.18 sec

        pd.read_sql_table:
        [-- TIME --] 4.01 sec

Conclusion

The above are a sample output, but I ran this over and over again and the only observation is that in every single run, pd.read_sql_table ALWAYS takes longer than pd.read_sql_query. This sounds very counter-intuitive, but that's why we actually isolate the issue and test prior to pouring knowledge here.

I haven't had the chance to run a proper statistical analysis on the results, but at first glance, I would risk stating that the differences are significant, as both "columns" (query and table timings) come back within close ranges (from run to run) and are both quite distanced. In some runs, table takes twice the time for some of the engines.

If/when I get the chance to run such an analysis, I will complement this answer with results and a matplotlib evidence.

Context

My initial idea was to investigate the suitability of SQL vs. MongoDB when tables reach thousands of columns. pdmongo.read_mongo (from the pdmongo package) devastates pd.read_sql_table — which performs very poorly against large tables — but falls short of pd.read_sql_query.

With around 900 columns, pd.read_sql_query outperforms pd.read_sql_table by 5 to 10 times!