Optimizing Pandas to_sql() for Teradata Database: Performance Bottlenecks and Optimization Techniques

Optimizing Pandas to_sql() for Teradata Database

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Writing large datasets to a database can be a challenging task, especially when dealing with performance-critical operations like data ingestion. In this article, we’ll explore the performance bottlenecks of Pandas’ to_sql() function when writing to Teradata databases and provide actionable recommendations for optimization.

Understanding Teradata Database Performance

Before diving into the optimization strategies, it’s essential to understand how Teradata databases perform. Teradata is a distributed relational database management system that uses MPP (Massively Parallel Processing) architecture to improve performance. However, this also means that data ingestion can be slower due to the need for parallel processing.

Performance Bottlenecks in Pandas to_sql()

The provided code snippet demonstrates how Pandas’ to_sql() function is being used to write large datasets to a Teradata database. While the code looks straightforward, performance bottlenecks can occur due to several factors:

1. Chunking and Iterators

The use of chunking and iterators in pd.read_fwf() can lead to slower performance. This is because Pandas needs to process each chunk individually, which can result in additional overhead.

chunksize = 100

for file_path in glob.glob('C:/files/*.txt'):
    for chunk in pd.read_fwf(file_path,widths = fwidths, names = cols, iterator = True, skiprows=1, chunksize = chunksize):

2. Database Connection Management

Creating and managing database connections can be a performance bottleneck. In this case, the connection is created using create_engine() and then reused for each chunk.

conn = create_engine('teradata://username:password')

3. Data Type Conversions

Teradata uses specific data types, such as VARCHAR(length=255), which can lead to additional overhead when converting data types in Pandas.

dtype={'ACT_NUMBER': sqlalchemy.types.VARCHAR(length=255), 'Subject_Code': sqlalchemy.types.VARCHAR(length=255), 'Segment': sqlalchemy.types.VARCHAR(length=255)}

Optimizing Performance with Teradata-specific Techniques

To optimize performance, we can apply the following techniques:

1. Using Teradata’s `copy_into` Method

Instead of using Pandas’ to_sql() function, which can be slower due to chunking and iterators, we can use Teradata’s built-in copy_into method.

import teradata

td = teradata.TeraData(
    'database',
    'username',
    'password',
    'host:port'
)

table_name = 'new_table'
schema_name = 'mySchema'

with td.copy_into(table_name, schema_name) as conn:
    # Insert data here
    insert_query = "INSERT INTO {} ({}) VALUES ({})"
    for file_path in glob.glob('C:/files/*.txt'):
        with open(file_path, 'r') as f:
            csv_reader = pd.read_csv(f, width=255, names=cols)
            for row in csv_reader.itertuples(index=False):
                query = insert_query.format(table_name, ', '.join(cols), ', '.join(['{}' for _ in range(len(row))]))
                conn.execute(query, row)

2. Minimizing Data Type Conversions

To reduce data type conversions, we can define the schema and data types explicitly.

# Define schema and data types
schema = {
    'ACT_NUMBER': teradata.VARCHAR(255),
    'Subject_Code': teradata.VARCHAR(255),
    'Segment': teradata.VARCHAR(255)
}

# Use teradata's copy_into method with schema and data types
with td.copy_into(table_name, schema_name) as conn:
    # Insert data here

3. Using Teradata’s `chunk_size` Parameter

When using pd.read_fwf(), we can specify the chunk size to reduce overhead.

chunksize = teradata.TeraData.chunks

Additional Optimization Techniques

Here are some additional optimization techniques that you can consider:

1. Partitioning Data

To improve performance, you can partition your data by creating separate partitions for each chunk.

# Define partition function
def get_partition(file_path):
    return file_path.split('/')[-1]

# Use teradata's copy_into method with partition function
with td.copy_into(table_name, schema_name) as conn:
    # Insert data here
    insert_query = "INSERT INTO {} ({}) VALUES ({})"
    for file_path in glob.glob('C:/files/*.txt'):
        with open(file_path, 'r') as f:
            csv_reader = pd.read_csv(f, width=255, names=cols)
            for i, row in enumerate(csv_reader.itertuples(index=False)):
                partition = get_partition(file_path)
                query = insert_query.format(table_name, ', '.join(cols), ', '.join(['{}' for _ in range(len(row))]))
                conn.execute(query, (row[partition],) + tuple(row[i+1:]))

2. Using Teradata’s `merge_into` Method

Instead of using pd.merge() to join data, you can use Teradata’s built-in merge_into method.

# Define merge function
def merge_data(left_df, right_df):
    return pd.merge(left_df, right_df, on=cols)

# Use teradata's copy_into method with merge function
with td.copy_into(table_name, schema_name) as conn:
    # Insert data here
    insert_query = "INSERT INTO {} ({}) VALUES ({})"
    for file_path in glob.glob('C:/files/*.txt'):
        with open(file_path, 'r') as f:
            csv_reader = pd.read_csv(f, width=255, names=cols)
            merged_df = merge_data(csv_reader, other_df)  # Replace other_df with your data
            for row in merged_df.itertuples(index=False):
                query = insert_query.format(table_name, ', '.join(cols), ', '.join(['{}' for _ in range(len(row))]))
                conn.execute(query, row)

By applying these optimization techniques and techniques, you can significantly improve the performance of Pandas’ to_sql() function when writing to Teradata databases.

Last modified on 2024-03-25