How to Update a Postgres Table's Column with Data from a CSV File While Preserving Other Columns?

Updating Postgres Tables with CSV Data: A Selective Approach

This guide demonstrates how to selectively update a Postgres table's column using data from a CSV file, leaving other columns untouched. Let's say we need to update the "Banana" column in our table while preserving the "Apple" column's existing values.

Solution:

This method utilizes a temporary table for efficient and safe data manipulation.

1. Create a Temporary Table:

First, create a temporary table (tmp_x) mirroring the structure of your target table, including the primary key ("ID"):

<code class="language-sql">CREATE TEMP TABLE tmp_x (id int, apple text, banana text);</code>

2. Import CSV Data:

Next, import your CSV data into the temporary table using the COPY command. Remember to replace /absolute/path/to/file with the actual path to your CSV file:

<code class="language-sql">COPY tmp_x FROM '/absolute/path/to/file' (FORMAT csv);</code>

3. Update the Target Table:

Now, update the "Banana" column in your main table (tbl) using data from the temporary table, matching rows based on the "ID" column:

<code class="language-sql">UPDATE tbl
SET    banana = tmp_x.banana
FROM   tmp_x
WHERE  tbl.id = tmp_x.id;</code>

4. Remove the Temporary Table:

Finally, remove the temporary table to free up resources:

<code class="language-sql">DROP TABLE tmp_x;</code>

Important Considerations:

• Privileges: Prior to Postgres 11, the COPY command typically requires superuser privileges. From Postgres 11 onwards, roles like pg_read_server_files or pg_write_server_files can be used. Alternatively, the copy meta-command in psql avoids superuser requirements.

• Performance: For large CSV files, enhance performance by adjusting temp_buffers and creating an index on the temporary table's "ID" column. Manual analysis of the temporary table (ANALYZE tmp_x;) is recommended as it's not automatically analyzed.

This approach ensures a clean and efficient update process, minimizing the risk of unintended data modifications. Remember to always back up your data before performing any significant database updates.

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