1.
pd.{read/to}_sql is simple but
not fast
Uwe Korn – QuantCo – November 2020

2.
About me
• Engineering at QuantCo
• Apache {Arrow, Parquet} PMC
• Turbodbc Maintainer
• Other OSS stuff
@xhochy
@xhochy
mail@uwekorn.com
https://uwekorn.com

3.
Our setting
• We like tabular data
• Thus we use pandas
• We want large amounts of this data in pandas
• The traditional storage for it is SQL databases
• How do we get from one to another?

4.
SQL
• Very very brief intro:
• „domain-specific language for accessing data held in a relational
database management system“
• The one language in data systems that precedes all the Python, R,
Julia, … we use as our „main“ language, also much wider user
base
• SELECT * FROM table
INSERT INTO table

5.
• Two main arguments:
• sql: SQL query to be executed or a table name.
• con: SQLAlchemy connectable, str, or sqlite3 connection

6.
• Two main arguments:
• name: Name of SQL table.
• con: SQLAlchemy connectable, str, or sqlite3 connection

7.
• Let’s look at the other nice bits („additional arguments“)
• if_exists: „What should we do when the target already exists?“
• fail
• replace
• append

8.
• index: „What should we with this one magical column?“ (bool)
• index_label
• chunksize: „Write less data at once“
• dtype: „What should we with this one magical column?“ (bool)
• method: „Supply some magic insertion hook“ (callable)

9.
SQLAlchemy
• SQLAlchemy is a Python SQL toolkit and Object Relational Mapper
(ORM)
• We only use the toolkit part for:
• Metadata about schema and tables (incl. creation)
• Engine for connecting to various databases using a uniform
interface

10.
Under the bonnet

11.
How does it work (read_sql)?
• pandas.read_sql [1] calls SQLDatabase.read_query [2]
• This then does
• Depending on whether a chunksize was given, this fetches all or
parts of the result
[1] https://github.com/pandas-dev/pandas/blob/d9fff2792bf16178d4e450fe7384244e50635733/pandas/io/sql.py#L509-L516
[2] https://github.com/pandas-dev/pandas/blob/d9fff2792bf16178d4e450fe7384244e50635733/pandas/io/sql.py#L1243

12.
How does it work (read_sql)?
• Passes in the data into the from_records constructor
• Optionally parses dates and sets an index

13.
How does it work (to_sql)?
• This is more tricky as we modify the database.
• to_sql [1] may need to create the target
• If not existing, it will call CREATE TABLE [2]
• Afterwards, we INSERT [3] into the (new) table
• The insertion step is where we convert from DataFrame back into
records [4]
[1] https://github.com/pandas-dev/pandas/blob/d9fff2792bf16178d4e450fe7384244e50635733/pandas/io/sql.py#L1320
[2] https://github.com/pandas-dev/pandas/blob/d9fff2792bf16178d4e450fe7384244e50635733/pandas/io/sql.py#L1383-L1393
[3] https://github.com/pandas-dev/pandas/blob/d9fff2792bf16178d4e450fe7384244e50635733/pandas/io/sql.py#L1398
[4] https://github.com/pandas-dev/pandas/blob/d9fff2792bf16178d4e450fe7384244e50635733/pandas/io/sql.py#L734-L747

14.
Why is it slow?
No benchmarks yet, theory first.

15.
Why is it slow?

16.
Thanks
Slides will come after PyData Global
Follow me on Twitter: @xhochy
How to get fast?

17.
ODBC
• Open Database Connectivity (ODBC) is a standard API for accessing
databases
• Most databases provide an ODBC interface, some of them are
efficient
• Two popular Python libraries for that:
• https://github.com/mkleehammer/pyodbc
• https://github.com/blue-yonder/turbodbc

18.
ODBC
Turbodbc has support for Apache Arrow: https://arrow.apache.org/
blog/2017/06/16/turbodbc-arrow/

19.
ODBC
• With turbodbc + Arrow we get the following performance
improvements:
• 3-4x for MS SQL, see https://youtu.be/B-uj8EDcjLY?t=1208
• 3-4x speedup for Exasol, see https://youtu.be/B-uj8EDcjLY?t=1390

20.
Snowflake
• Turbodbc is a solution that retrofits performance
• Snowflake drivers already come with built-in speed
• Default response is JSON-based, BUT:
• The database server can answer directly with Arrow
• Client only needs the Arrow->pandas conversion (lightning fast⚡)
• Up to 10x faster, see https://www.snowflake.com/blog/fetching-
query-results-from-snowflake-just-got-a-lot-faster-with-apache-
arrow/

21.
JDBC
• Blogged about this at: https://uwekorn.com/2019/11/17/fast-jdbc-
access-in-python-using-pyarrow-jvm.html
• Not yet so convenient and read-only
• First, you need all your Java dependencies incl arrow-jdbc in your
classpath
• Start JVM and load the driver, setup Arrow Java

22.
JDBC
• Then:
• Fetch result using the Arrow Java JDBC adapter
• Use pyarrow.jvm to get a Python reference to the JVM memory
• Convert to pandas 136x speedup!

23.
Postgres
Not yet opensourced but this is how it works:

24.
How do we get this
into pandas.read_sql?

25.
API troubles
• pandas’ simple API:
• turbodbc

26.
API troubles
• pandas’ simple API:
• Snowflake

27.
API troubles
• pandas’ simple API:
• pyarrow.jvm + JDBC

28.
Building a better API
• We want to use pandas’ simple API but with the nice performance
benefits
• One idea: Dispatching based on the connection class
• User doesn’t need to learn a new API
• Performance improvements come via optional packages

29.
Building a better API
Alternative idea:

30.
Building a better API
Discussion in https://github.com/pandas-dev/pandas/issues/36893

31.
Thanks
Follow me on Twitter: @xhochy