`diff_tables`¶

Row-level diff of two files: return the rows present in only one side. Where compare_schemas diffs the column metadata, diff_tables diffs the actual rows.

When to use¶

To answer "what records changed between these two files / versions?" without pulling both tables and comparing them yourself.

How rows are compared¶

Each row is keyed by its whole-row content (every column, in order, rendered to text). A row in A matches a row in B when their keys are equal. Columns are therefore compared positionally - the two files should share the same column order for the result to be meaningful. Because matching is on rendered values, the diff works across formats (a CSV row matches the equivalent Parquet row).

Input schema¶

Parameter	Type	Required?	Default	Description
`path_a`	string	yes	(no default)	Path to the first file (side A)
`path_b`	string	yes	(no default)	Path to the second file (side B)
`table_a`	string	no	(no default)	Specific table to read from A (multi-table sources)
`table_b`	string	no	(no default)	Specific table to read from B (multi-table sources)
`limit`	int	no	server default (1000)	Max rows returned per side. `0` = unlimited
`unlimited`	bool	no	`false`	Lift the 5,000,000-row file-loader cap for both files

Response shape¶

{
  "only_in_a": { "schema": [...], "rows": [...], "row_count": <n>, "truncated": <bool>, ... },
  "only_in_b": { "schema": [...], "rows": [...], "row_count": <n>, "truncated": <bool>, ... },
  "only_in_a_count": <n>,
  "only_in_b_count": <n>,
  "shared_keys": <n>
}

only_in_a / only_in_b are each a read_table-style payload carrying the rows unique to that side (so limit and the per-cell byte cap apply to each). shared_keys is the number of distinct row keys present in both files. Rows present in both are not returned.

Example call¶

{
  "name": "diff_tables",
  "arguments": { "path_a": "/tmp/users_v1.csv", "path_b": "/tmp/users_v2.csv" }
}

diff_tables¶