Understanding the Challenges of Uploading Pandas DataFrames to Exasol Tables using Python

In this article, we will delve into the complexities of uploading a Pandas DataFrame to an Exasol SQL table using Python. We’ll explore the limitations of the Pandas to_sql function when dealing with Exasol-specific syntax and provide solutions using alternative approaches.

Introduction

Exasol is a column-store database management system designed for high-performance analytics workloads. While it shares some similarities with traditional relational databases, its unique architecture poses challenges when working with external Python libraries like Pandas. In this article, we’ll examine the issues surrounding uploading Pandas DataFrames to Exasol tables using to_sql and discuss alternative methods that can help overcome these hurdles.

Overview of the Problem

The original question from Stack Overflow highlights a common issue faced by developers working with Exasol and Pandas:

# connect to Exasol DB
exaString='DSN=exa'
conDB = pyodbc.connect(exaString)   

# get some data from somewhere, works without error
sqlString = "SELECT * FROM SOMETABLE"
data = pd.read_sql(sqlString, conDB)

# now upload this data to a new table
data.to_sql('MYTABLENAME', conDB, flavor='mysql')

The to_sql function fails with the following error message:

pyodbc.ProgrammingError: ('42000', "[42000] [EXASOL][EXASolution driver]syntax error, unexpected identifier_chain2, expecting 
  assignment_operator or ':' [line 1, column 6] (-1)
  (SQLExecDirectW)")

This error occurs because Pandas does not support Exasol-specific syntax by default. To resolve this issue, we’ll explore alternative methods using the exasol Python package and adjusting the Pandas configuration to accommodate Exasol dialects.

Alternative Approaches

Using the exasol Package

One solution is to utilize the exasol Python package, which provides a native interface for interacting with Exasol databases. This package allows you to read and write data directly from Exasol tables using Pandas-like functionality:

import exasol
con = exasol.connect(dsn='EXA') # normal pyodbc connection with additional functions

data = con.readData('SELECT * FROM services') # pandas data frame per default
con.writeData(data, table = 'services2')

This approach eliminates the need for manual configuration and provides a more streamlined workflow.

Adapting Pandas Configuration

Another solution involves modifying the Pandas configuration to recognize Exasol dialects. We’ll use the mysql flavor as an example, which is commonly used with column-store databases like Exasol:

import pyodbc
import pandas as pd

con = pyodbc.connect('DSN=EXA')
con.execute('OPEN SCHEMA TEST2')

# configure pandas to understand EXASOL as mysql flavor
pd.io.sql._SQL_TYPES['int']['mysql'] = 'INT'
pd.io.sql._SQL_SYMB['mysql']['br_l'] = ''
pd.io.sql._SQL_SYMB['mysql']['br_r'] = ''
pd.io.sql._SQL_SYMB['mysql']['wld'] = '?'
pd.io.sql.PandasSQLLegacy.has_table = \
    lambda self, name: name.upper() in [t[0].upper() for t in con.execute('SELECT table_name FROM cat').fetchall()]

data = pd.read_sql('SELECT * FROM services', con)
data.to_sql('SERVICES2', con, flavor = 'mysql', index = False)

By modifying the Pandas configuration, we can tell it to recognize Exasol dialects and avoid the syntax error encountered earlier.

Conclusion

Uploading a Pandas DataFrame to an Exasol SQL table using Python requires careful consideration of the underlying database architecture. By exploring alternative approaches using the exasol package or adapting Pandas configuration to accommodate Exasol dialects, we can overcome common challenges associated with this workflow.

When working with column-store databases like Exasol, it’s essential to be aware of their unique syntax and architecture. By understanding these nuances and using the right tools for the job, you can efficiently transfer data between Pandas DataFrames and Exasol tables, ensuring seamless integration in your analytics workflows.

Last modified on 2023-11-19