# Troubleshooting & FAQ

This vignette covers common issues you may encounter when using dbverse packages and how to resolve them.

    library(dbProject)

    project_path <- tempfile("dbproject-troubleshooting-")
    proj <- dbProject$new(path = project_path)
    #> Creating new version '20260504T194458Z-20342'
    #> Writing to pin 'cachedConnection'
    #> Manifest file written to root folder of board, as `_pins.yaml`
    proj$is_connected()
    #> [1] TRUE
    proj$disconnect()
    proj$is_connected()
    #> [1] FALSE
    unlink(project_path, recursive = TRUE)

## Database Lock Errors

### Error: “Could not set lock on file”

    Error: Could not set lock on file "path/to/database.duckdb":
    Conflicting lock is held in /path/to/R (PID 12345) by user...

Cause: DuckDB uses file-level locking to ensure data integrity. Only one process can write to a database file at a time. This error occurs when:

- Another R session is connected to the same database
- A previous R session didn’t disconnect properly before closing
- Multiple scripts are trying to access the same database simultaneously

Solutions:

1. Kill the conflicting process (shown in the error message):

       # Replace 12345 with the actual PID from the error
       kill 12345

2. Restart RStudio to close all R processes

3. Properly disconnect before closing sessions:

       # Always disconnect when done
       proj$disconnect()

       # Or for direct DBI connections:
       DBI::dbDisconnect(con, shutdown = TRUE)

4. Use read-only mode for concurrent access:

       # Multiple processes can read simultaneously
       con <- DBI::dbConnect(duckdb::duckdb(), dbdir = "path/to/db", read_only = TRUE)

## Connection Issues

### Error: “No active or cached connection available”

Cause: The `dbProject` connection has been closed or was never established. Solution: Reconnect to the database:

    proj$reconnect()

### Objects showing “Error: Column references …” after restart

Cause: Lazy table references become stale after R restarts because the connection is lost.

Solution: Use `dbProject` for automatic reconnection:

    # Create project (saves connection info)
    proj <- dbProject$new(path = "my_project", dbdir = "data.duckdb")

    # After restart, just reload
    proj <- dbProject$new(path = "my_project", dbdir = "data.duckdb")
    proj$reconnect()

    # Pinned objects are automatically reconnected
    my_data <- proj$pin_read("my_table")

## Memory Issues

### Large datasets causing R to crash

Cause: Accidentally collecting large lazy tables into memory.

Prevention:

1. Avoid `collect()` on large tables:

       # BAD - loads entire table into memory
       big_df <- big_table |> collect()

       # GOOD - keep lazy, push computation to database and only collect what's needed
       result <- big_table |>
         filter(gene == "BRCA1") |>
         collect()

2. Use `pin_write()` to materialize intermediates to disk:

       # Materialize to database, not memory
       filtered_data <- proj$pin_write(x = lazy_filtered, name = "filtered")

## Package-Specific Issues

### dbMatrix: Slow operations after many transformations

Cause: Complex lazy query chains can cause performance issues.

Solution: Use `dplyr::compute()` or `pin_write()` to materialize intermediate results:

    # Materialize after expensive operations
    complex_result <- my_dbMatrix |>
      some_expensive_transform() |> # e.g. a function containing several SQL joins
      dplyr::compute(name = "materialized", temporary = FALSE)

## Getting Help

If you encounter issues not covered here:

1. Checkout the specific dbverse package on our GitHub organization.
2. Ensure you’re using the latest dbverse package versions.
3. Include a minimal reproducible example when reporting issues.