Remove Duplicate Lines

Duplicate line removal identifies and eliminates repeated lines from text, keeping only unique entries. The algorithm typically uses a hash set (or similar data structure) to track seen lines efficiently. As the algorithm processes each line, it checks if the line exists in the set: if not present, the line is unique and added to both the result and the set; if already present, the line is a duplicate and skipped. This approach has O(n) time complexity where n is the number of lines - very efficient even for large texts. The algorithm can operate in case-sensitive mode (treating "Apple" and "apple" as different) or case-insensitive mode (treating them as duplicates). Whitespace handling is configurable: strict mode considers leading/trailing spaces significant; trimmed mode ignores them. Advanced options include preserving first occurrence vs last occurrence of duplicates, sorting results (alphabetically or maintaining original order), and counting duplicate instances. The implementation must handle edge cases like empty lines, whitespace-only lines, and very long lines. Hash-based detection is much faster than naive nested-loop comparison (O(n²)), making it practical for processing large datasets, logs, or lists with millions of entries.

1. Data Cleaning & List Processing
Remove duplicate entries from email lists, contact databases, product catalogs, and customer records. Marketing teams clean mailing lists to avoid sending duplicate emails. Data analysts deduplicate datasets before analysis to ensure accurate counts and prevent double-counting in statistics.

2. Log File Analysis
Filter duplicate log entries to focus on unique events, errors, or warnings. System administrators process verbose logs with repeated messages, removing duplicates to identify distinct issues. Debugging becomes easier when log files show only unique error conditions rather than thousands of repeated messages.

3. Code & Configuration Management
Remove duplicate import statements, configuration entries, or dependency declarations. Developers clean up messy code with redundant imports or configurations. Build scripts use deduplication to ensure package manifests don't list the same dependency multiple times, which can cause installation errors.

4. SEO & Keyword Research
Deduplicate keyword lists, remove repeated URLs from sitemap drafts, and clean CSV exports from SEO tools. Digital marketers consolidate keyword research from multiple sources, removing duplicates before import into SEO platforms. Sitemap generators remove duplicate URLs before submission to search engines.

5. Text Processing & Writing
Remove duplicate sentences or paragraphs when consolidating multiple document versions. Writers merge content from different sources and remove redundant passages. Translation memory tools deduplicate translation segments to optimize translation databases and reduce costs.

6. Database & Spreadsheet Cleanup
Prepare data for database import by ensuring unique keys and removing duplicate records. Administrators export database tables, remove duplicates, then reimport cleaned data. Spreadsheet users clean columns with repeated values before using them as unique identifiers or performing analysis.

• Choose case-sensitivity based on your data: case-sensitive for code, case-insensitive for names/emails

• Enable whitespace trimming to catch duplicates with inconsistent spacing

• Preserve original line order if sequence matters; sort alphabetically if order is irrelevant

• For large files, use command-line tools (sort | uniq) for better performance than browser-based tools

• Keep first occurrence to maintain original entry details; keep last to preserve most recent version

• Count duplicates before removing to understand data quality issues

• Consider partial matching for fuzzy deduplication (similar but not identical lines)

• Save a backup before removing duplicates from important data