trino multi insert python

Daniel Parker

3 min read

·

25-02-2025

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Trino, a distributed SQL query engine, offers incredible speed and scalability for querying data across various sources. However, efficiently inserting large amounts of data into Trino can be a challenge. This article will guide you through optimizing your data ingestion process using Python and Trino's multi-insert capabilities, focusing on maximizing performance and minimizing overhead. We'll explore various approaches and best practices for handling large datasets.

Understanding Trino's INSERT Statement Limitations

Before diving into multi-insert strategies, it's crucial to understand the limitations of single INSERT statements in Trino. Single INSERT statements can be slow when dealing with substantial datasets. The overhead of numerous individual queries can significantly impact performance. This is where multi-insert techniques come in handy.

Method 1: Using Multiple INSERT Statements with Batching

One straightforward approach is to batch your data into smaller chunks and execute multiple INSERT statements. This approach distributes the load and prevents overwhelming Trino's resources. This is suitable for moderately sized datasets.

import trino

# Establish connection
connection = trino.dbapi.connect(
    host='your_trino_host',
    port=your_trino_port,
    user='your_user',
    catalog='your_catalog',
    schema='your_schema'
)
cursor = connection.cursor()

# Sample data (replace with your data)
data = [
    (1, 'Value 1'),
    (2, 'Value 2'),
    (3, 'Value 3'),
    # ... more data
]

# Batch size
batch_size = 1000

for i in range(0, len(data), batch_size):
    batch = data[i:i + batch_size]
    sql = "INSERT INTO your_table (id, value) VALUES %s" % (', '.join(['(%s, %s)' for _ in batch]))
    cursor.execute(sql, batch)
    connection.commit()

cursor.close()
connection.close()

Key Considerations:

• Batch Size Optimization: Experiment to find the optimal batch size for your specific setup. Too small, and you lose efficiency; too large, and you risk exceeding memory limits.
• Error Handling: Implement robust error handling to manage potential issues during insertion. Consider using try-except blocks to catch exceptions and handle them appropriately.

Method 2: Leveraging COPY Statement (For Enhanced Performance)

For significantly larger datasets, the COPY statement in Trino offers far superior performance. COPY is designed for high-throughput data loading and bypasses many of the overheads associated with individual INSERT statements. This method is ideal for loading massive datasets efficiently.

import trino
import csv
from io import StringIO

# ... (Connection details as before) ...

# Sample data (replace with your actual data)
data = [
    (1, 'Value 1'),
    (2, 'Value 2'),
    (3, 'Value 3'),
    # ... more data
]

# Create a CSV-formatted string from data
csv_buffer = StringIO()
writer = csv.writer(csv_buffer)
writer.writerows(data)
csv_buffer.seek(0)

cursor.execute(f"COPY your_table FROM STDIN WITH (format CSV, header 'false')")
cursor.copy_expert(csv_buffer.read())
connection.commit()

cursor.close()
connection.close()

Key Considerations:

• File Format: COPY supports various formats (CSV, Parquet, ORC, etc.). Choose the format best suited for your data and Trino's configuration.
• Compression: Compressing your data before loading can significantly reduce transfer time and improve performance.
• Data Cleaning: Ensure your data is properly formatted and cleaned before using COPY to avoid errors and ensure data integrity.

Choosing the Right Approach

The optimal method depends on your data volume and system resources:

• Small to Medium Datasets: Batching with multiple INSERT statements is usually sufficient.
• Large Datasets: Utilize the COPY statement for significantly improved performance and scalability.

Error Handling and Best Practices

• Transactions: Wrap your INSERT or COPY operations within a transaction to ensure atomicity. This guarantees either all data is inserted or none is, preventing partial data loads.
• Monitoring: Monitor the performance of your data loading process to identify bottlenecks and optimize your approach.
• Connection Pooling: For large-scale operations, use connection pooling to efficiently manage database connections.

By understanding these techniques and implementing best practices, you can significantly improve the efficiency and speed of your Trino multi-insert operations using Python. Remember to adapt these examples to your specific table schema and data format. Always test and optimize your solution based on your unique requirements.