Lightweight database query profiler.
Project description
Database Query Profiler 🗃️⏱️
Lightweight database query profiler.
This tool is database-agnostic -- just provide a class that connects to your database with an execute
method, and the queries that you want to profile.
[!WARNING]
This is NOT a replacement for analysing the query plan. This should just support the analysis done with it.
Installation ⬇️
Grab a copy from PyPI like usual:
pip install db-query-profiler
Sample Output 📝
Given a set of queries (details below), this package prints the average time in seconds taken to run each query, as well as the percentage of the total time taken by each query.
The tqdm
package is used to show progress of the queries being run.
A typical output will look something like this:
Start time: 2023-05-07 12:38:06.879738
----------------------------------------
100%|██████████| 5/5 [00:01<00:00, 3.29it/s]
query-1.sql: 0.10063192s (33.4%)
query-2.sql: 0.20044784s (66.6%)
----------------------------------------
End time: 2023-05-07 12:38:08.757555
Usage 📖
The package exposes a single function, time_queries
, which currently requires:
- A database connection/cursor class that implements an
execute
method. - The number of times to re-run each query.
- A directory containing the SQL files with the queries to run.
There should only be a single query in each file, and the file name will be used as the query name in the output.
For the following examples, assume that there are SQL files in the queries
directory.
SQLite Example
Official documentation: https://docs.python.org/3/library/sqlite3.html
import sqlite3
import db_query_profiler
def main() -> None:
db_conn = sqlite3.connect(":memory:") # Or a path to a database file
db_query_profiler.time_queries(
conn=db_conn,
repeat=5,
directory="queries"
)
if __name__ == "__main__":
main()
Snowflake Example
Official documentation: https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-example
Some databases, like Snowflake, have extra layers of caching that can affect the results of the profiling. To avoid this and make the runtime comparisons more genuine, it's recommended to turn off these extra caching options (where this is supported).
import db_query_profiler
import snowflake.connector # snowflake-connector-python
# This dictionary is just for illustration purposes and
# you should use whatever connection method you prefer
CREDENTIALS = {
"user": "XXX",
"password": "XXX",
"account": "XXX",
"warehouse": "XXX",
"role": "XXX",
"database": "XXX",
}
def main() -> None:
db_conn = snowflake.connector.SnowflakeConnection(**CREDENTIALS)
with db_conn.cursor() as cursor:
cursor.execute("""ALTER SESSION SET USE_CACHED_RESULT = FALSE;""")
db_query_profiler.time_queries(
conn=cursor,
repeat=5,
directory="queries",
)
cursor.execute("""ALTER SESSION SET USE_CACHED_RESULT = TRUE;""")
db_conn.close()
if __name__ == "__main__":
main()
Warnings ⚠️
This package will open and run all the files in the specified directory, so be careful about what you put in there -- potentially unsafe SQL commands could be run.
This package only reads from the database, so it's encouraged to configure your database connection in a read-only way.
SQLite
Official documentation:
To connect to a SQLite database in a read-only way, use the uri=True
parameter with file:
and ?mode=ro
surrounding the database path when connecting:
db_conn = sqlite3.connect("file:path/to/database.db?mode=ro", uri=True)
Contributing 🤝
The Python packaging is managed with Poetry (check which version in the poetry.lock file), but that should be the only dependency.
To get started, just clone the repo, install the dependencies, and enable pre-commit:
poetry install --sync --with dev,test
pre-commit install --install-hooks
Happy coding! 🎉
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