dbSendQuery fetches all rows when querying DuckDB

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Summary

The issue at hand involves the dbSendQuery function in R, which is used to send a query to a database and retrieve the results. In this case, when querying a DuckDB database, the function fetches all rows instead of allowing for batch processing, leading to excessive memory usage. This behavior is not observed when using the same database from Python.

Root Cause

The root cause of this issue lies in the implementation of the DBI (Database Interface) package in R, specifically in how it interacts with DuckDB. The key factors contributing to this behavior include:

  • The dbSendQuery function not providing an option to limit the number of rows fetched initially.
  • The result set being stored in memory, which can lead to high memory usage for large tables.

Why This Happens in Real Systems

This issue occurs in real systems due to:

  • Inefficient data retrieval mechanisms that do not account for large datasets.
  • Lack of batch processing support in the database interface, leading to the need for workarounds.
  • Inadequate memory management in the application, failing to handle large result sets.

Real-World Impact

The impact of this issue includes:

  • Increased memory usage, potentially leading to system crashes or slow performance.
  • Reduced scalability, as the application may not be able to handle large datasets.
  • Decreased reliability, as the system may become unstable or unresponsive.

Example or Code

# Create a connection to the DuckDB database
con <- dbConnect(RDuckDB::duckdb(), "example.db")

# Send a query to the database
rs <- dbSendQuery(con, "SELECT * FROM big_table;")

# Fetch rows in batches
while (!dbHasCompleted(rs)) {
  x <- dbFetch(rs, n = 1e5)
  # Process the fetched rows
}

# Clear the result set
dbClearResult(rs)

How Senior Engineers Fix It

Senior engineers address this issue by:

  • Implementing custom batch processing using loops and conditional statements to control the number of rows fetched.
  • Utilizing database-specific features, such as cursors or streaming results, to reduce memory usage.
  • Optimizing database queries to minimize the amount of data transferred and processed.

Why Juniors Miss It

Junior engineers may overlook this issue due to:

  • Lack of experience with large datasets and memory management.
  • Insufficient understanding of the database interface and its limitations.
  • Inadequate testing, failing to account for edge cases and real-world scenarios.