Introduction to Data Extraction and Storage in SQL Server and Apache Parquet

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As data volumes continue to grow, the need for efficient data extraction and storage solutions becomes increasingly important. In this article, we will explore how to extract large datasets from a SQL Server database to Parquet files without using Hadoop.

Background on SQL Server, Apache Arrow, and Apache Parquet

SQL Server

SQL Server is a relational database management system (RDBMS) developed by Microsoft. It provides a robust platform for storing, managing, and querying large amounts of data.

Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data processing. It provides a set of libraries and APIs for working with tabular data, including SQL tables, query results, and file formats like Parquet.

Apache Parquet

Apache Parquet is a columnar storage format that is designed to be efficient in terms of storage and compression. It supports a wide range of data types, including numeric, string, and datetime values.

The Challenge: Extracting 1 Terabyte of Data from SQL Server to Parquet Files

The original question posed on Stack Overflow highlights the challenge of extracting large datasets from a SQL Server database to Parquet files without using Hadoop. The query options provided are limited by the amount of RAM available and the inability to stream data directly to Parquet files.

Solution Overview

To address this challenge, we will explore three possible solutions:

1. Batch Processing with Apache Arrow: Using Apache Arrow to retrieve result in batches from SQL Server.
2. Chunked Data Extraction with fetchnumpybatches: Retrieving data in a chunked fashion using the fetchnumpybatches library.
3. Optimized Parquet Export with fetchallarrow and Partitioning: Optimized approach for exporting data as multiple smaller Parquet files.

Solution 1: Batch Processing with Apache Arrow

Batch processing involves breaking down the large dataset into smaller chunks, processing each chunk individually, and then reassembling the results. We will demonstrate how to use Apache Arrow to retrieve result in batches from SQL Server.

Using Turbodbc with Apache Arrow

Turbodbc is a library that provides a Python interface for interacting with SQL Server databases. It supports batched queries using Apache Arrow.

# Install required libraries
pip install turbodbc apache-arrow pythonarrow

# Import necessary libraries
import turbodbc
from pyarrow import array, table

# Create Turbodbc connection to SQL Server
conn = turbodbc.connect(
    host='localhost',
    database='my_database',
    username='my_username',
    password='my_password'
)

# Define batch size and chunk count
batch_size = 10000
chunk_count = 1000

# Execute batched query using Apache Arrow
cursor = conn.cursor()
query = "SELECT * FROM my_table"
for i in range(chunk_count):
    start_idx = i * batch_size
    end_idx = (i + 1) * batch_size - 1
    cursor.execute(query, params={"start": start_idx, "end": end_idx})
    rows = cursor.fetchall()
    if len(rows) > 0:
        # Create Apache Arrow table from fetched rows
        arrow_table = pyarrow.Table.from_pandas(pd.DataFrame(rows), ['column1', 'column2'])
        # Export data to Parquet file
        arrow_table.write_to_csv("my_parquet_file.parquet", compression="snappy")

Solution 2: Chunked Data Extraction with fetchnumpybatches

Another approach is to use the fetchnumpybatches library, which provides a way to retrieve result in chunks from SQL Server.

# Install required libraries
pip install fetchnumpybatches

# Import necessary libraries
import fetchnumpybatches as ftnp
from pyarrow import array, table

# Define chunk size and total rows
chunk_size = 10000
total_rows = 1000000

# Initialize SQL Server connection
conn = ftnp.connect(
    host='localhost',
    database='my_database',
    username='my_username',
    password='my_password'
)

# Execute chunked query using fetchnumpybatches
with conn.cursor(chunk_size=chunk_size) as cursor:
    query = "SELECT * FROM my_table"
    for row in ftnp.fetch_with_chunks(query, total_rows):
        # Create Apache Arrow table from fetched row
        arrow_table = pyarrow.Table.from_pandas(pd.DataFrame([row]), ['column1', 'column2'])
        # Export data to Parquet file
        arrow_table.write_to_csv("my_parquet_file.parquet", compression="snappy")

Solution 3: Optimized Parquet Export with fetchallarrow and Partitioning

A third approach is to optimize the export process using Apache Arrow’s fetchallarrow method, which allows writing data to Parquet files in a single pass. We will also explore partitioning strategies for optimal performance.

# Import necessary libraries
import turbodbc
from pyarrow import array, table

# Create Turbodbc connection to SQL Server
conn = turbodbc.connect(
    host='localhost',
    database='my_database',
    username='my_username',
    password='my_password'
)

# Define partitioning strategy (e.g., by date or range)
partition_strategies = ["DATE", "RANGE"]

# Iterate over different partitioning strategies
for partition_strategy in partition_strategies:
    # Execute batched query using Apache Arrow
    cursor = conn.cursor()
    query = f"SELECT * FROM my_table PARTITION ({partition_strategy})"
    with cursor.batched(query, fetchallarrow=True) as cursor_batched:
        for row_batch in cursor_batched:
            # Export data to Parquet file
            arrow_table = pyarrow.Table.from_pandas(pd.DataFrame(row_batch), ['column1', 'column2'])
            arrow_table.write_to_csv("my_parquet_file.parquet", compression="snappy")

Conclusion and Recommendations

When working with large datasets, efficient data extraction and storage strategies are crucial for performance and scalability. In this article, we explored three possible solutions for extracting 1 terabyte of data from SQL Server to Parquet files using batch processing, chunked data extraction, and optimized Parquet export.

Recommendations:

• Use Apache Arrow’s fetchallarrow method for optimal performance when exporting data to Parquet files.
• Explore partitioning strategies (e.g., by date or range) to optimize the export process.
• Consider using batch processing with Turbodbc and Apache Arrow for large-scale applications.

Last modified on 2023-11-16