pd.{read/to}_sql is simple but not fast

Pandas provides convenience methods to read and write to databases using to_sql and read_sql. They provide great usability and a uniform interface for all databases that support an SQL Alchemy connection. Sadly, the layer of convenience also introduces a performance loss. Luckily, for a lot of databases, a performant access layer is available.

Transcript

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

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

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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?
   

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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
   

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

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

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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
   

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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)
   

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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
   

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10. Under the bonnet
    

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15. 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
    

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

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17. 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
    

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

    

    

    

    

    

    

    

    

    

    

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19. Why is it slow?
    

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

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21. 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
    

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

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23. 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
    

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24. 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/
    

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25. 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
    

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26. 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!
    

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

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

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29. API troubles
    • pandas’ simple API: 

    
    
    • turbodbc

    

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30. API troubles
    • pandas’ simple API: 

    
    
    • Snowflake

    

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31. API troubles
    • pandas’ simple API: 

    
    
    • pyarrow.jvm + JDBC

    

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32. 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

    

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33. Building a better API
    Alternative idea:
    

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

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35. Thanks
    Follow me on Twitter: @xhochy
    

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