-- SQL CLI Configuration for Large Datasets
-- Use this config when working with large datasets where wide columns
-- may appear later in the data (e.g., FIX file selectors with 3k+ rows)

require('sql-cli').setup({
   command = "sql-cli", -- Or full path like "/home/user/sql-cli/target/release/sql-cli"

   -- Split configuration
   split = {
      direction = "vertical",
      size = 0.5,
   },

   -- Output format
   output_format = "table",

   -- Table output settings optimized for large datasets
   table_output = {
      -- Set to 0 for unlimited column width (no truncation)
      -- Or set to a higher value like 150 if you want some limit
      max_col_width = 0,

      -- Set to 0 to scan ALL rows for column width calculation
      -- This ensures wide values appearing late in the dataset are not truncated
      -- Note: This may be slower for very large datasets (10k+ rows)
      col_sample_rows = 0,

      -- Hide columns that are entirely NULL/empty
      -- Useful for sparse datasets (e.g., spot trades with empty option columns)
      auto_hide_empty = true,
   },

   -- Auto-detect features
   auto_detect = {
      csv_files = true,
      data_hints = true,
   },

   -- Output settings
   output = {
      focus_on_run = false,
      clear_on_run = true,
      wrap = false,
      number = false,
   },
})

-- Example use cases:
--
-- 1. FIX file selector with 3k rows:
--    With col_sample_rows = 0, it will scan all 3k rows to find the widest
--    tag values, preventing truncation with "..." in later rows.
--
-- 2. WEB CTE responses with variable-length fields:
--    Unlimited column width ensures full data visibility without truncation.
--
-- 3. Alternative balanced approach:
--    If scanning all rows is too slow, try:
--      max_col_width = 150      -- Reasonable limit
--      col_sample_rows = 1000   -- Sample first 1000 rows
--
-- Performance note:
--   - col_sample_rows = 0 (all rows) is slower but accurate
--   - col_sample_rows = 100 (default) is fast but may truncate late data
--   - Choose based on your dataset size and width distribution
