SQL Formatter

Tool Overview

This SQL formatter helps you make SQL queries clean and easy to read. It takes one long line or messy SQL and turns it into a clear, well structured query. It also checks your SQL for basic problems and supports many different SQL dialects.

You paste your SQL into the tool, and it automatically applies indentation, line breaks, and keyword casing. You can choose the database dialect, indentation width, preset style, and whether keywords appear in upper or lower case. The tool shows a formatted version of your query and can also show a diff view that compares original and formatted SQL line by line.

This tool is for developers, data engineers, analysts, DBAs, and anyone who writes SQL by hand. It is helpful when working with complex joins, nested subqueries, reporting queries, or long stored procedure bodies. You do not need to be an expert, but you should know basic SQL to get the best value from the formatter.

The main problem it solves is that SQL for real systems often becomes long and hard to read. When queries are written on a single line, or with random spacing, it is hard to see joins, filters, and groups. This tool makes the structure visible so you can understand, debug, and maintain SQL with less effort.

Background & Concept Explanation

SQL stands for Structured Query Language. It is used to ask questions from databases, change data, and define tables and views. SQL is used in many database engines such as MySQL, PostgreSQL, SQL Server, Oracle, SQLite, and cloud data warehouses.

A SQL query is made of clauses such as SELECT, FROM, WHERE, GROUP BY, and ORDER BY. Each clause has a clear role. SELECT chooses which columns to return. FROM chooses which tables or views to read from. WHERE filters rows. GROUP BY and HAVING handle aggregation. ORDER BY decides the sort order. A related operation involves formatting Python code as part of a similar workflow.

In practice, SQL queries can become long. Queries may join many tables, use nested subqueries, window functions, and conditional expressions. When all of this is written without good formatting, it becomes hard to see the logical structure. Developers and analysts then struggle to answer simple questions like where a filter is applied or why a join behaves a certain way.

Good SQL formatting solves this by using indentation and line breaks to show the hierarchy. Each major clause appears on its own line. Conditions inside WHERE or JOIN are aligned in a readable way. Long lists of columns or expressions are broken into multiple lines. Keyword casing is consistent so that language keywords stand out from table and column names.

Different databases also have slightly different SQL dialects. For example, some engines support LIMIT while others use TOP or FETCH. Data warehouse engines add extra functions and syntax. A flexible formatter must understand these dialects so that it does not reject valid syntax for a given engine.

The SQL formatter in this tool uses a dedicated SQL formatting engine. It reads your SQL query, applies dialect aware rules, and produces a formatted version. It also gathers simple statistics such as number of lines, words, and keywords, which help you understand query size and complexity.

Key Features

• Support for many SQL dialects. You can choose from several dialects such as generic SQL, MySQL, PostgreSQL, SQLite, MariaDB, DB2, Oracle, PL SQL, T SQL, Redshift, BigQuery, Snowflake, and Spark. The formatter passes the selected dialect to the formatting engine so it can handle dialect specific syntax correctly. This matters because it reduces false errors and keeps vendor specific SQL readable.
• Preset formatting styles. You can choose between standard, compact, expanded, and data warehouse presets. Presets control line spacing and formatting choices such as how many blank lines appear between queries. This lets you match different use cases, such as very compact code for quick scanning or more spaced out formatting for teaching and code review.
• Keyword casing control. You can turn uppercase mode on or off. When uppercase is on, SQL keywords such as SELECT, FROM, WHERE, and JOIN appear in upper case. When it is off, keywords appear in lower case. Consistent casing helps you visually separate language keywords from column names and values.
• Indentation width options. You can choose indentation width of 2, 4, or 8 spaces. The formatter passes this value to the SQL formatting engine as tab width. This allows teams to match their preferred style across projects and editors.
• Real time formatting with debouncing. The tool formats your SQL automatically as you type or paste. It uses a debounce helper so that formatting runs only after a short pause. This keeps the interface responsive even when you work with large queries.
• Size validation for safety. The tool checks input size before formatting. It accepts queries up to 10 megabytes. If the input is larger, it reports a clear error instead of trying to format and possibly freezing the browser.
• Syntax validation feedback. When formatting fails, the tool returns your original SQL and a validation object with an error message. This can happen if the query has a syntax problem or does not match the selected dialect. The status bar shows whether the SQL is valid or if there is a potential problem.
• Diff view for before and after. The tool generates a diff between your original and formatted SQL. It marks unchanged lines, removed lines, and added lines with different colors. The diff view helps you verify that only formatting changed and no content was lost.
• SQL statistics. The formatter calculates size, line count, word count, and keyword count for your input. These numbers are displayed in the interface beneath the input panel. They give a quick sense of query length and density of SQL keywords.
• AI powered explanation and optimization. You can ask the tool to explain a query in plain language or to suggest optimizations. The tool sends a truncated version of your SQL to a backend AI service, which returns an explanation or optimization hints. The result is shown in a separate banner so you can keep your code and the explanation side by side.
• Copy and upload support. You can upload .sql or .txt files from your computer. You can also copy the formatted SQL to your clipboard with a single button. This makes it easy to move between the formatter and your database tools or editors.

Common Use Cases

A backend developer is handed a long, unformatted query from an old system. They paste it into the formatter, select the correct dialect, and instantly see a version with clear indentation and keyword casing. This makes it easier to spot logic problems and plan refactors. For adjacent tasks, formatting SQL syntax addresses a complementary step.

A data analyst builds complex reporting queries with many joins and subqueries. Before sharing the query with teammates, they run it through the formatter so that each clause appears on its own line and the join conditions are lined up. This reduces misunderstandings during review and helps others reuse the query.

A data engineer works with a cloud data warehouse and writes long analytic queries. They use the data warehouse preset and a wider indent width to make nested structures easier to see on wide screens. The statistics help them judge when a query has grown too large and should be broken into steps.

A DBA is called to debug a slow query from an application log. The query is logged on a single line. They paste it into the formatter, view the diff, and then ask the AI explanation feature to summarize what the query is doing. This speeds up understanding and helps them design indexes or rewrite parts of the query.

A developer learning SQL needs to understand how different parts of a query fit together. They type a simple SELECT with WHERE and JOIN clauses and watch how the formatter breaks it into lines. This visual layout reinforces best practices for writing readable SQL from the start.

A team that uses mixed databases needs to maintain queries for MySQL, PostgreSQL, and SQL Server. They switch the dialect setting to match each environment and rely on the formatter to respect the chosen dialect. This reduces accidental use of syntax that only works in a different engine. When working with related formats, formatting PostgreSQL queries can be a useful part of the process.

How to Use This Tool (Step-by-Step)

1. Choose the SQL dialect from the dropdown at the top. Pick the one that matches your database, such as MySQL, PostgreSQL, or a data warehouse engine.
2. Select a preset style. Use standard for general work, compact for smaller output, expanded for more spacing between queries, or data warehouse for large analytic queries if available in the preset list.
3. Decide whether you want keywords in upper case. Turn on the UPPERCASE option if you want SELECT, FROM, and WHERE to stand out. Turn it off if your team prefers lower case keywords.
4. Set the indent width by choosing 2, 4, or 8 spaces. Pick a value that matches your team or your editor settings. The formatter will use this indent width in the formatted SQL.
5. Paste your SQL into the input area on the left. You can also click the upload button to load a .sql or .txt file from your computer. The tool checks that the content is not larger than 10 megabytes.
6. Wait a moment while the tool formats your SQL. Formatting happens automatically after a short pause so you do not need to press a format button. The status bar shows when the formatter is processing.
7. Look at the status bar just below the controls. If the SQL is valid, you will see a valid message along with line, word, and keyword counts. If there is a problem, you will see an error message instead.
8. View the formatted output on the right side. Make sure the structure looks correct and that all clauses and joins appear as you expect. If needed, adjust the dialect or preset and let the tool reformat.
9. Switch to the diff tab to see the differences between original and formatted SQL. Added lines, removed lines, and unchanged lines are shown with different colors. Use this to confirm that changes are cosmetic and do not alter the query meaning.
10. If you want help understanding or improving the query, use the Explain or Optimize buttons. Explain asks the AI service to describe what the query does in plain language. Optimize asks for improvement suggestions and may update the input with an optimized version.
11. Once you are happy with the result, use the copy button to copy the formatted SQL. Paste it into your database tool, version control system, or code editor. You can also keep the tool open while you experiment with further changes.
12. Use the clear button if you want to start over with a new query. This resets the input, output, diff, statistics, and AI result.

Calculations & Logic

The formatter first checks the size of your input to make sure it is below 10 megabytes. It uses the browser Blob API to measure the number of bytes and compares this to a fixed limit. If the query is too large, it returns a validation error instead of trying to format it.

For formatting, the tool calls a dedicated SQL formatting library. It passes the selected dialect, indent width, keyword case choice, and a few preset based options such as lines between queries. The library parses the SQL and produces a new string that follows its internal rules for indentation, line breaks, and spacing.

The diff view is built by comparing the original and formatted SQL line by line. The tool walks through both lists of lines and records whether each line is unchanged, removed, or added. Each diff entry includes the line number and content so that the UI can show color coded output.

Statistics are computed in a simple way. Size is measured using the same Blob based byte count. Line count is the number of newline separated lines. Word count is the number of non empty tokens when splitting on whitespace. Keyword count is computed by scanning for common SQL keywords and counting how often they appear.

Validation is derived from whether the formatting library finishes without throwing an error. If formatting throws, the tool marks the query as invalid and returns the original SQL along with an error message. This message is displayed in the status bar so you know the query may have a syntax issue or dialect mismatch. In some workflows, formatting JSON data is a relevant follow-up operation.

AI explanation and optimization send a truncated copy of your query to a backend service. The tool limits the text to about fifty kilobytes to control processing cost. The backend returns either a natural language explanation or optimization suggestions, which the tool displays in a banner without changing your SQL unless an optimized version is clearly provided.

Tips, Limitations & Best Practices

Always choose the correct dialect before formatting. Using the wrong dialect can cause the formatter to misread valid syntax or mark it as invalid. If you see errors for a query that runs fine in your database, check the dialect setting first.

Use uppercase keywords if you often scan queries by eye. This makes clauses stand out and helps you separate language keywords from identifiers. If your team prefers lower case, turn uppercase off to match your shared style.

Keep your queries focused. If a formatted query becomes very long and complex, consider breaking it into smaller steps using temporary tables or common table expressions. The statistics panel can help you spot very long queries that may be hard to maintain.

Remember that formatting only changes whitespace and line breaks. It does not change the logic of the query when the underlying library works correctly. Still, after formatting, run your query again in a safe environment to confirm that the results are the same. For related processing needs, formatting PHP code handles a complementary task.

Treat AI explanations and optimizations as suggestions, not rules. Always review them with your own understanding of the data and performance needs. Do not apply any change blindly, especially in production systems.

Be careful when pasting very large log files or query dumps. The size limit protects your browser, but very large inputs can still feel slow to work with. If needed, extract the specific query you want to format instead of pasting an entire log.

Use the diff view to build confidence with the formatter. When you see that differences are purely cosmetic, you will feel safer about using the tool regularly. If you ever see unexpected changes in the diff, adjust presets or settings and try again.

Save important queries in version control or a separate script file after formatting. That way you can track how they change over time and always return to a known good version.

Remember that keyword counting is approximate. It looks for common SQL keywords in the text and counts matches. It does not understand context, so a keyword inside a comment or string literal will also be counted.

For very large or performance critical queries, use this formatter as a first step. Then test execution plans, indexes, and statistics in your database. Good formatting makes this deeper tuning easier but does not replace it.