TxtToPG is a dedicated graphical data conversion software developed by Withdata Software designed to bulk-import delimited text files (such as .txt, .csv, .tsv, and log files) into a PostgreSQL database. It is widely used by database administrators to execute rapid data migrations without writing complex COPY scripts manually.
Note: In recent distribution cycles, Withdata Software has merged TxtToPG into their comprehensive all-in-one suite named FileToDB. However, the core mechanisms for accelerating text-to-PostgreSQL loading remain identical. Step-by-Step Guide to Fast Data Loading
TxtToPG achieves fast data loading by bypassing line-by-line standard INSERT statements and leveraging structural optimizations. Here is how to configure and execute a fast bulk load: 1. Establish the Database Connection Open TxtToPG (or FileToDB).
Fill in your PostgreSQL instance details: host, port, database name, username, and password. Click Connect to fetch your target database schema. 2. Source File Selection
Select the single source text file, or choose an entire directory to queue up multi-file batch imports. Define your source formatting structure:
Delimiters: Specify if the text uses commas, tabs, pipes, or custom characters.
Quotes & Enclosures: Define the text qualifiers (e.g., double quotes ””) to prevent field parsing errors.
Header Rows: If your text file contains headers, check the box to skip the first row so it isn’t parsed as raw data. 3. Target Table Mapping Select your target PostgreSQL table.
Map your source text columns to the destination database columns. TxtToPG features an Auto-Map button that instantly pairs columns with matching or highly similar names. 4. Configure Fast Loading Parameters
To maximize ingestion throughput and achieve maximum speed, configure these execution settings:
Change the Import Mode: Switch the engine from simple Insert to Bulk Copy. This instructs the backend to utilize PostgreSQL’s native high-performance binary protocol.
Adjust Batch Size: Increase the commit batch size (e.g., setting it to 10000 or 50000 rows per commit). This minimizes network roundtrips and transaction log overhead.
Data Type Handling: Adjust parameters for date/time formatting rules to prevent processing bottlenecks during type conversion. 5. Execute and Monitor
Save your import configuration as a session file (.wfs) if you want to rerun it later.
Click Import to start the pipeline. The visual logging panel will track throughput metrics, total rows processed, and error rates in real time. Pro-Tips for Maximizing Load Speed
If you are dealing with massive datasets (millions of rows), the software can still be bottlenecked by database constraints. To unlock maximum speed, apply these database-side best practices alongside the tool:
Drop Indexes and Constraints: Temporarily drop secondary indexes and foreign keys on the target PostgreSQL table before running the tool, then recreate them after the load finishes. This prevents PostgreSQL from performing expensive index re-indexing calculations on every batch.
Unlogged Tables: If the data can be re-imported easily in case of a crash, alter your target table to an UNLOGGED state before running TxtToPG. This stops data from being written to the Write-Ahead Log (WAL), which often doubles ingestion speed.
Automate via CLI: For recurring large loads, run TxtToPG via its command-line interface using your saved session files. You can schedule these tasks using Windows Task Scheduler or Linux Cron jobs to run during low-traffic hours.
If you are setting this up for a specific workflow, let me know:
What is the average file size or row count you are importing?
Do you need to frequently schedule this import, or is it a one-time migration?
Are you running into any specific data type errors (like dates or special characters)?
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