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Command documentation sourced from the linux-command project This comprehensive command reference is part of the linux-command documentation project.

colrm - Remove Columns from File

The colrm command is a simple yet powerful text processing utility that removes specified columns from lines of text read from standard input. It processes input line by line, deleting characters starting from a specified column position to either a specific ending column or the end of the line. The command is particularly useful for processing fixed-width format files, removing unnecessary whitespace, or extracting specific portions of structured text data. Colrm works with character positions starting from 1 and operates on each line independently, making it ideal for tabular data processing and log file manipulation.

Basic Syntax​

colrm [START [STOP]]

Parameters​

  • START - Starting column number to begin removal (1-based indexing)
  • STOP - Ending column number to stop removal (inclusive)

Parameter Behavior​

  • No parameters: Acts as a passthrough (copies input to output unchanged)
  • START only: Removes from column START to the end of each line
  • START and STOP: Removes columns from START through STOP (inclusive)
  • Column indexing: Uses 1-based indexing (first character is column 1)
  • Line processing: Processes each line independently
  • Character-based: Operates on characters, not words or fields

Usage Examples​

Basic Column Removal​

Single Column to End​

# Remove from column 5 to end of line
echo "Hello World Linux" | colrm 5
# Output: Hell

# Remove from column 3 onwards
echo "1234567890" | colrm 3
# Output: 12

# Process file removing from column 10
colrm 10 < input.txt > output.txt

# Remove everything after position 20
printf "This is a test line for demonstration\n" | colrm 21
# Output: This is a test line

Column Range Removal​

# Remove columns 3 through 6
echo "1234567890" | colrm 3 6
# Output: 127890

# Remove columns 5 through 8
echo "Linux Command Line" | colrm 5 8
# Output: Lin Command Line

# Process file removing columns 2-4
colrm 2 4 < data.txt > processed.txt

# Multiple range removals in pipeline
echo "ABCDEFGHIJKLMN" | colrm 3 5 | colrm 8 10
# Output: ABGJKLMN

Text Processing Examples​

Log File Processing​

# Remove timestamps from log files (assuming fixed-width timestamps)
# Log format: "2023-12-01 10:30:45 [INFO] Message here"
cat application.log | colrm 1 20
# Output: "[INFO] Message here"

# Remove IP addresses from access logs
# Log format: "192.168.1.1 - - [01/Dec/2023:10:30:45] GET /path"
cat access.log | colrm 1 16
# Output: "- - [01/Dec/2023:10:30:45] GET /path"

# Process system logs
cat /var/log/syslog | colrm 1 16 | head

# Remove date/time from custom log format
# Format: "2023-12-01|10:30:45|ERROR|System message"
echo "2023-12-01|10:30:45|ERROR|System message" | colrm 1 20
# Output: "ERROR|System message"

# Clean up multi-part timestamps
echo "[2023-12-01 10:30:45.123] Process started" | colrm 2 24
# Output: "Process started"

Data File Processing​

# Process fixed-width data files
# Format: ID(3) Name(20) Age(3) City(15)
# Input: "001John Smith              25New York City   "
cat data.txt | colrm 4 23
# Output: "001 25New York City   "

# Remove middle columns
cat data.txt | colrm 4 23 | colrm 6 8
# Output: "001  New York City   "

# Process CSV-like fixed-width data
cat fixed_width.txt | colrm 11 30

# Extract specific data columns
echo "001John Doe            025New York        NY10001" | colrm 4 22 | colrm 8 23
# Output: "001  New York        NY10001"

# Clean up padded database export
echo "CUSTOMER001  John Smith           ACTIVE  " | colrm 12 32
# Output: "CUSTOMER001  ACTIVE  "

Advanced Text Manipulation​

Multiple Operations​

# Remove multiple sections using pipeline
echo "1234567890ABCDEFGHIJ" | colrm 3 5 | colrm 8 10
# Output: 1267890ABCDEFGHIJ

# Process text with multiple removals
cat textfile.txt | colrm 1 5 | colrm 20 25 > cleaned.txt

# Remove header and footer from fixed-width reports
cat report.txt | colrm 1 10 | colrm 70 80

# Complex text transformation
echo "HEADER_INFO_12345_MAIN_DATA_FOOTER_INFO" | \
    colrm 1 12 | colrm 17 26 | colrm 35 45
# Output: "_MAIN_DATA_"

# Nested operations for precise control
printf "Line1: abcdefghijklmnopqrstuvwxyz\n" | \
    colrm 6 15 | colrm 12 20
# Output: "Line1: abcde"

Working with Multiple Files​

# Process multiple files with loop
for file in *.txt; do
    colrm 1 5 < "$file" > "processed_$file"
done

# Batch process logs
for log in app_*.log; do
    colrm 16 25 < "$log" > "cleaned_$log"
done

# Create processed versions
find . -name "*.dat" -exec sh -c 'colrm 1 8 < "$1" > "proc_$1"' _ {} \;

# Parallel processing with xargs
ls *.log | xargs -I {} -P 4 sh -c 'colrm 1 20 < "{}" > "clean_{}"'

# Process with error handling
for file in data_*.txt; do
    if [ -f "$file" ]; then
        colrm 5 15 < "$file" > "${file%.txt}_processed.txt" || \
            echo "Error processing $file"
    fi
done

Practical Applications​

System Administration​

Log File Cleanup​

# Clean up system logs by removing timestamps
tail -f /var/log/messages | colrm 1 16

# Process Apache access logs
cat /var/log/apache2/access.log | colrm 1 27 > clean_access.log

# Remove user information from process listings
ps aux | colrm 1 80 | colrm 9 25

# Clean up mail logs
cat /var/log/mail.log | colrm 1 20 > mail_clean.log

# Real-time log monitoring with column removal
tail -f /var/log/syslog | colrm 1 16 | grep "ERROR"

# Process nginx logs
# Format: "192.168.1.1 - user [01/Dec/2023:10:30:45] request"
cat nginx_access.log | colrm 1 12 | colrm 3 15 > clean_nginx.log

# Remove process IDs from ps output
ps aux | colrm 10 15 > ps_no_pid.txt

# Clean up cron logs
cat /var/log/cron | colrm 1 17 | colrm 25 30

Configuration File Processing​

# Remove comments column from config files
cat config.txt | colrm 40 80 > clean_config.txt

# Process host files (remove IP addresses)
cat /etc/hosts | colrm 1 16 | grep -v "^#"

# Clean up passwd file (remove password field)
cat /etc/passwd | colrm 30 45 > clean_passwd

# Remove commented sections from config
cat /etc/sudoers | colrm 1 8 | grep -v "^#" | colrm 30 80

# Process fstab entries
cat /etc/fstab | colrm 40 80 > fstab_brief

# Clean up group file
cat /etc/group | colrm 15 50 > group_simple

# Process crontab files
crontab -l | colrm 1 15 > crontab_clean

# Remove options from sshd_config
cat /etc/ssh/sshd_config | colrm 25 80 > sshd_basic

Data Processing​

Fixed-Width Data Files​

# Process mainframe-style data files
# Format: CUSTID(5) NAME(25) ADDRESS(30) CITY(15) STATE(2) ZIP(5)
cat customers.dat | colrm 6 30 > customer_ids.txt
cat customers.dat | colrm 1 5 | colrm 26 50 > customer_addresses.txt

# Extract specific columns from financial data
cat financial.txt | colrm 1 10 | colrm 20 35 > key_data.txt

# Process inventory data
cat inventory.txt | colrm 15 25 | colrm 40 50 > clean_inventory.txt

# Database export cleanup
# Format: ID(8) NAME(30) DEPT(20) SALARY(10) DATE(10)
cat employees.txt | colrm 9 38 | colrm 48 58 > employees_summary.txt

# Process COBOL-style data
cat cobol_data.txt | colrm 1 5 | colrm 25 45 | colrm 60 80

# Legacy system data conversion
echo "RECORD001FIELD_DATA_000001234567890" | colrm 1 6 | colrm 20 35
# Output: "FIELD_DATA_00000"

# Clean up EDI data segments
cat edi_file.txt | colrm 4 20 | colrm 30 50

Report Generation​

# Create summary reports by removing detail columns
cat detailed_report.txt | colrm 25 60 > summary_report.txt

# Process performance data
cat perf_data.txt | colrm 10 20 > key_metrics.txt

# Clean up database exports
cat db_export.txt | colrm 1 15 | colrm 50 100 > clean_export.txt

# Generate executive summary
cat full_report.txt | colrm 1 30 | colrm 70 120 > exec_summary.txt

# Create tabular summary from verbose output
df -h | colrm 1 15 | colrm 45 60 | column -t

# Process system monitoring data
cat vmstat_output.txt | colrm 1 20 | colrm 60 80 > vmstat_clean.txt

# Clean up application logs for reporting
cat app_log.txt | colrm 1 25 | grep "ERROR\|WARN" > error_summary.txt

# Create statistical summary
cat stats_data.txt | colrm 10 30 | colrm 50 70 > stats_summary.txt

Development Workflows​

Code Processing​

# Remove line numbers from source code
cat numbered_code.c | colrm 1 5 > clean_code.c

# Process generated code files
cat generated.txt | colrm 1 10 | colrm 80 120 > processed.txt

# Clean up debug output
cat debug.log | colrm 20 40 > clean_debug.log

# Remove copyright headers from source files
find . -name "*.py" -exec sh -c 'colrm 1 70 < "$1" > "$1.tmp" && mv "$1.tmp" "$1"' _ {} \;

# Process compiler output
cat compile_output.txt | colrm 1 30 | grep "error\|warning"

# Clean up makefile output
make 2>&1 | colrm 1 25 | grep -E "(error|undefined|undefined)"

# Remove timestamps from build logs
cat build.log | colrm 1 23 > build_clean.log

# Process test results
cat test_output.txt | colrm 1 15 | colrm 40 60 > test_summary.txt

# Clean up profiler output
cat profiler.txt | colrm 1 20 | colrm 50 80 > profiler_clean.txt

Text Analysis​

# Remove padding from text files
cat padded.txt | colrm 61 80 > unpadded.txt

# Process survey data
cat survey.txt | colrm 1 8 | colrm 50 80 > responses.txt

# Clean up OCR output
cat ocr_output.txt | colrm 70 100 > cleaned_ocr.txt

# Remove page numbers from document dumps
cat document.txt | colrm 70 80 > doc_no_page_nums.txt

# Process form data
cat form_submissions.txt | colrm 1 12 | colrm 45 65 > form_data.txt

# Clean up imported CSV data with fixed positions
cat import_data.txt | colrm 1 10 | colrm 25 35 | colrm 50 60 > clean_import.txt

# Remove line numbers from academic papers
cat paper_with_lines.txt | colrm 1 5 > paper_no_lines.txt

# Process bibliography entries
cat bibliography.txt | colrm 1 25 | colrm 80 120 > biblio_clean.txt

Advanced Techniques​

Combining with Other Commands​

Pipeline Operations​

# Combine with grep for filtered processing
cat data.txt | grep "ERROR" | colrm 1 15 > errors.txt

# Use with sort after column removal
cat data.txt | colrm 1 10 | sort > sorted_data.txt

# Process with awk after colrm
cat file.txt | colrm 5 20 | awk '{print $1,$3}'

# Chain multiple text processing commands
cat raw_data.txt | colrm 1 8 | tr '[:upper:]' '[:lower:]' | uniq > final_data.txt

# Use with cut for complex column extraction
cat complex_data.txt | colrm 1 5 | cut -c1-10,20-30

# Combine with sed for pattern-based cleanup
cat messy_data.txt | colrm 1 10 | sed 's/\s\+/ /g' | colrm 20 30

# Use with awk for conditional processing
cat data.txt | awk '$3 > 100' | colrm 5 15

# Pipe through multiple filters
cat log.txt | grep "ERROR" | colrm 1 20 | sort | uniq > error_summary.txt

Complex Text Processing​

# Multi-step data transformation
cat input.txt | colrm 1 5 | colrm 20 30 | sed 's/\s\+/ /g' > output.txt

# Process and format data
cat data.txt | colrm 1 10 | fmt -w 60 > formatted.txt

# Create formatted reports
cat raw_data | colrm 5 25 | column -t > nice_report.txt

# Clean up and reformat text
cat ugly_text.txt | colrm 1 8 | tr -s ' ' | fold -w 72 > clean_text.txt

# Extract and reorganize data
echo "12345DATA789MORE12345INFO" | colrm 6 8 | colrm 9 13
# Output: "12345MORE12345INFO"

# Complex data cleanup pipeline
cat dirty_data.txt | colrm 1 15 | \
    sed 's/[^[:print:]]//g' | \
    tr -s '\n' | \
    colrm 50 100 > clean_data.txt

# Process logs with multiple filters
cat app.log | colrm 1 12 | \
    grep -E "(ERROR|WARN)" | \
    colrm 20 30 | \
    sort | \
    uniq -c > error_counts.txt

# Advanced text manipulation
cat file.txt | \
    colrm 1 5 | \
    awk '{printf "%-20s %s\n", $1, $2}' | \
    colrm 25 40 > formatted_output.txt

Script Integration​

Backup Script Example​

#!/bin/bash
# Process log files before backup

LOG_DIR="/var/log"
BACKUP_DIR="/backup"
DATE=$(date +%Y%m%d)
TEMP_DIR="/tmp/colrm_temp$$"

# Create temporary directory
mkdir -p "$TEMP_DIR"

# Function to process log file
process_log() {
    local input_file="$1"
    local output_file="$2"

    # Remove timestamps and sensitive info
    if [[ -f "$input_file" ]]; then
        colrm 1 16 < "$input_file" | colrm 50 80 > "$output_file"
        echo "Processed: $(basename "$input_file")"
    else
        echo "Warning: $input_file not found"
    fi
}

# Process and backup logs
for log_file in "$LOG_DIR"/*.log; do
    if [[ -f "$log_file" ]]; then
        basename=$(basename "$log_file")
        output_file="$TEMP_DIR/${basename}_$DATE"

        process_log "$log_file" "$output_file"
    fi
done

# Create archive of processed logs
cd "$TEMP_DIR"
tar -czf "$BACKUP_DIR/processed_logs_$DATE.tar.gz" *

# Cleanup
rm -rf "$TEMP_DIR"

echo "Log files processed and backed up successfully"

Data Processing Script​

#!/bin/bash
# Advanced fixed-width data processing

INPUT_DIR="/data/input"
OUTPUT_DIR="/data/output"
ERROR_LOG="/var/log/data_processing.log"

# Create output directory
mkdir -p "$OUTPUT_DIR"

# Function to process data file
process_data_file() {
    local input_file="$1"
    local filename=$(basename "$input_file" .dat)
    local output_file="$OUTPUT_DIR/${filename}_processed.txt"

    echo "Processing: $filename" >> "$ERROR_LOG"

    # Remove header and detail columns, handle errors
    if colrm 1 10 < "$input_file" | colrm 40 80 > "$output_file" 2>> "$ERROR_LOG"; then
        echo "Successfully processed: $filename" >> "$ERROR_LOG"
        return 0
    else
        echo "Error processing: $filename" >> "$ERROR_LOG"
        return 1
    fi
}

# Count processed files
processed_count=0
error_count=0

# Process all .dat files
for data_file in "$INPUT_DIR"/*.dat; do
    if [[ -f "$data_file" ]]; then
        if process_data_file "$data_file"; then
            ((processed_count++))
        else
            ((error_count++))
        fi
    fi
done

# Generate summary report
echo "Data processing completed on $(date)" >> "$ERROR_LOG"
echo "Files processed: $processed_count" >> "$ERROR_LOG"
echo "Errors encountered: $error_count" >> "$ERROR_LOG"

echo "Data processing completed"
echo "Processed: $processed_count files"
echo "Errors: $error_count files"

Automated Log Analysis​

#!/bin/bash
# Automated log analysis with colrm

LOG_SOURCES=(
    "/var/log/apache2/access.log"
    "/var/log/nginx/access.log"
    "/var/log/application.log"
)

OUTPUT_DIR="/tmp/log_analysis"
DATE=$(date +%Y%m%d_%H%M%S)

mkdir -p "$OUTPUT_DIR"

# Function to analyze log file
analyze_log() {
    local log_file="$1"
    local base_name=$(basename "$log_file" .log)

    # Extract key information by removing columns
    cat "$log_file" | \
        colrm 1 27 | \  # Remove timestamp and IP
        grep -E "(GET|POST|PUT|DELETE)" | \
        colrm 30 80 | \  # Remove protocol and status
        sort | \
        uniq -c | \
        sort -nr > "$OUTPUT_DIR/${base_name}_endpoints_$DATE.txt"

    # Extract error messages
    cat "$log_file" | \
        grep -E "(ERROR|WARN|CRIT)" | \
        colrm 1 20 | \  # Remove timestamp
        colrm 50 100 > "$OUTPUT_DIR/${base_name}_errors_$DATE.txt"
}

# Process all logs
for log_source in "${LOG_SOURCES[@]}"; do
    if [[ -f "$log_source" ]]; then
        echo "Analyzing: $log_source"
        analyze_log "$log_source"
    else
        echo "Log file not found: $log_source"
    fi
done

# Generate summary
echo "Log analysis completed at $(date)" > "$OUTPUT_DIR/analysis_summary_$DATE.txt"
echo "Output files created in: $OUTPUT_DIR"

echo "Log analysis complete. Check $OUTPUT_DIR for results."

Special Operations​

Working with Encodings​

Multi-byte Character Handling​

# Note: colrm works with bytes, not characters
# For UTF-8 files, be careful with multi-byte characters

# Set locale for proper handling
export LANG=C
export LC_ALL=C

# Process ASCII-only files safely
cat ascii_file.txt | colrm 5 15 > output.txt

# For UTF-8 files, consider using other tools
# colrm may split multi-byte characters

# Safe UTF-8 processing with iconv first
iconv -f UTF-8 -t ASCII//TRANSLIT input.txt > temp_ascii.txt
colrm 5 15 < temp_ascii.txt > output.txt
rm temp_ascii.txt

# Test for multi-byte issues
echo "café résumé naïve" | colrm 5 6
# May produce corrupted output due to UTF-8 byte splitting

# Workaround for UTF-8 files
awk '{print substr($0,1,4) substr($0,7)}' utf8_file.txt

# Detect potential issues
file -bi input.txt | grep -q utf-8 && \
    echo "Warning: UTF-8 file detected, colrm may cause character corruption"

Binary File Processing​

# Process binary files with caution
# colrm works byte-by-byte

# Remove headers from binary files
dd if=program.bin bs=1 skip=100 | colrm 50 100 > program_clean.bin

# Extract data portions
cat binary.dat | colrm 1 20 > extracted_data.bin

# Process with hexdump for verification
hexdump -C input.bin | head
colrm 10 20 < input.bin > output.bin
hexdump -C output.bin | head

# Safe binary processing with dd
dd if=input.bin bs=1 count=100 of=header.bin
dd if=input.bin bs=1 skip=100 of=body.bin
colrm 50 100 < body.bin > body_clean.bin
cat header.bin body_clean.bin > final.bin

# Binary file analysis
cat unknown_file.bin | colrm 1 16 | file -

Performance Considerations​

Large File Processing​

# Process large files efficiently
# colrm is memory efficient as it processes line by line

# Process very large files
colrm 1 10 < huge_file.txt > processed.txt

# Use with pv for progress monitoring
pv huge_file.txt | colrm 1 10 > processed.txt

# Batch process multiple large files
find . -name "*.log" -size +100M -exec bash -c \
    'echo "Processing $1"; colrm 1 16 < "$1" > "clean_$1"' _ {} \;

# Parallel processing with GNU parallel
ls *.txt | parallel -j 4 'colrm 1 20 < {} > processed_{.}'

# Memory-efficient streaming
tail -f large_log.txt | colrm 1 15 | grep "ERROR"

# Process in chunks for very large files
split -l 1000000 huge_file.txt chunk_
for chunk in chunk_*; do
    colrm 5 25 < "$chunk" > "processed_$chunk" &
done
wait
cat processed_chunk_* > final_processed.txt
rm chunk_* processed_chunk_*

Optimization Tips​

# colrm is generally fast for simple operations
# For complex column operations, consider:

# Use cut for delimiter-based column removal
cut -c1-5,20- file.txt  # Similar to multiple colrm operations

# Use awk for pattern-based processing
awk '{print substr($0,1,4) substr($0,21)}' file.txt

# Use sed for pattern-based column removal
sed 's/\(.\{4\}\).\{15\}/\1/' file.txt

# Performance comparison
echo "Testing colrm performance:"
time colrm 1 10 < large_file.txt > /dev/null

echo "Testing cut performance:"
time cut -c11- large_file.txt > /dev/null

echo "Testing awk performance:"
time awk '{print substr($0,11)}' large_file.txt > /dev/null

# Choose appropriate tool based on:
# - Simplicity: colrm for fixed-position removal
# - Flexibility: awk for conditional logic
# - Speed: cut for simple character extraction
# - Memory: colrm processes line by line

Integration and Automation​

Cron Jobs​

# Add to crontab for daily log processing
# 0 2 * * * /usr/local/bin/process_logs.sh

# Daily log cleanup script
#!/bin/bash
LOG_FILE="/var/log/application.log"
CLEAN_LOG="/var/log/application_clean.log"
ARCHIVE_DIR="/var/log/archive"
DATE=$(date +%Y%m%d)

# Process today's log
if [[ -f "$LOG_FILE" ]]; then
    # Remove sensitive information and timestamps
    colrm 1 16 < "$LOG_FILE" | colrm 45 80 > "$CLEAN_LOG"

    # Archive original log
    mv "$LOG_FILE" "$ARCHIVE_DIR/application_$DATE.log"

    # Create symlink for continuity
    ln -sf "$ARCHIVE_DIR/application_$DATE.log" "$LOG_FILE"

    echo "Log processing completed for $(date)"
else
    echo "Log file not found: $LOG_FILE" >&2
    exit 1
fi

System Logging Integration​

# Real-time log processing with tail
tail -f /var/log/syslog | colrm 1 16 | tee clean_syslog.log

# Process logs as they arrive
tail -F /var/log/messages | colrm 16 25 >> processed_messages.log

# Multi-log monitoring with colrm
tail -f /var/log/{apache,nginx}/*.log | \
    colrm 1 27 | \
    grep -E "(ERROR|404|500)" | \
    tee critical_errors.log

# Log rotation with processing
#!/bin/bash
for log in /var/log/*.log; do
    if [[ -s "$log" ]]; then
        # Process and compress
        colrm 1 20 < "$log" | gzip > "${log}.processed.gz"
        # Truncate original
        > "$log"
    fi
done

Pipeline Integration​

# Complex data processing pipeline
cat raw_data.csv | \
    colrm 1 15 | \  # Remove headers
    grep -v "^#" | \  # Remove comments
    colrm 20 40 | \  # Remove middle columns
    sort | uniq > processed_data.txt

# Log analysis pipeline
tail -f access.log | \
    colrm 1 27 | \  # Remove IP and timestamp
    grep "POST" | \  # Filter POST requests
    colrm 30 80 | \  # Remove protocol info
    sort | uniq -c | sort -nr > post_requests.txt

# Real-time monitoring
netstat -an | \
    grep ESTABLISHED | \
    colrm 1 30 | \  # Remove protocol and addresses
    colrm 20 50 | \  # Remove state info
    sort | uniq > active_connections.txt

Troubleshooting​

Common Issues​

Character Encoding Problems​

# Problem: Corrupted output with UTF-8 files
# Solution: Convert to ASCII or use locale-appropriate tools

# Check file encoding
file -bi problematic_file.txt

# Convert UTF-8 to ASCII before processing
iconv -f UTF-8 -t ASCII//TRANSLIT file.txt > ascii_file.txt
colrm 1 10 < ascii_file.txt > output.txt

# Use different locale settings
LANG=C colrm 1 10 < utf8_file.txt > output.txt

# Alternative: Use awk for UTF-8 safe processing
awk '{print substr($0,1,4) substr($0,11)}' utf8_file.txt

Incorrect Column Numbers​

# Problem: Wrong columns removed
# Solution: Count characters carefully

# Show character positions with numbering
echo "1234567890" | cat -n
#      1  1234567890

# Test with sample data first
echo "Sample text for testing" | colrm 7 12
echo "Sample text for testing" | cat -A  # Show all characters

# Use od to see exact byte positions
echo "Test string" | od -c
echo "Test string" | od -b

# Create column reference
printf "%s\n" {1..80} | tr '\n' ' '
printf "\nSample text here\n"

# Verify with hexdump
echo "Test" | hexdump -C

Performance Issues​

# Problem: Slow processing of large files
# Solution: Use appropriate buffer sizes or alternative tools

# Use dd for large binary files
dd if=input.txt bs=1M | colrm 1 100 > output.txt

# Consider using more efficient tools for complex operations
cut -c1-50,101- large_file.txt > output.txt

# Process in parallel
ls *.txt | parallel -j 4 'colrm 1 20 {} > processed_{.}'

# Use buffering for better I/O
stdbuf -oL colrm 1 10 < large_file.txt > output.txt

# Monitor system resources during processing
/usr/bin/time -v colrm 1 10 < huge_file.txt > /dev/null

# Test different approaches
echo "Testing colrm:"
time colrm 1 10 < test_file.txt > /dev/null

echo "Testing cut:"
time cut -c11- test_file.txt > /dev/null

echo "Testing awk:"
time awk '{print substr($0,11)}' test_file.txt > /dev/null

Memory and File Size Issues​

# Problem: Out of memory with very large files
# Solution: Process in chunks or use streaming

# Process file in chunks
split -l 500000 huge_file.txt part_
for part in part_*; do
    colrm 1 20 < "$part" >> output.txt
    rm "$part"
done

# Use streaming to avoid loading entire file
tail -f huge_file.txt | colrm 1 10 | head -1000 > sample.txt

# Monitor memory usage
while true; do
    ps aux | grep colrm
    sleep 1
done &

colrm 1 10 < huge_file.txt > output.txt
kill %1
  • cut - Remove sections from each line of files
  • awk - Pattern scanning and processing language
  • sed - Stream editor for filtering and transforming text
  • tr - Translate or delete characters
  • paste - Merge lines of files
  • column - Columnate lists
  • fmt - Simple optimal text formatter
  • pr - Convert text files for printing
  • expand - Convert tabs to spaces
  • unexpand - Convert spaces to tabs

Best Practices​

  1. Test on sample data before processing large files to verify correct column positions
  2. Count character positions carefully - colrm uses 1-based indexing, not 0-based
  3. Use absolute paths in scripts for reliability and reproducibility
  4. Backup original files before batch processing to prevent data loss
  5. Consider character encoding when working with non-ASCII text to avoid corruption
  6. Validate input format - colrm works best with fixed-width text, not delimited data
  7. Use appropriate tools - consider cut or awk for delimiter-based data processing
  8. Monitor performance with large files using pv or time to avoid system overload
  9. Handle edge cases like empty lines or short lines gracefully in scripts
  10. Document transformations for future reference and team collaboration
  11. Check for multi-byte characters in UTF-8 files to prevent character splitting
  12. Use error handling in scripts to catch processing failures early

Performance Tips​

  1. colrm is memory-efficient - processes line by line, suitable for large files
  2. Fast for simple column removal operations compared to more complex tools
  3. Use ASCII-only input for predictable results and maximum performance
  4. Process large files in chunks if memory is constrained or system load is high
  5. Consider alternative tools for complex pattern-based operations (awk, sed)
  6. Use cut for delimiter-based column operations when dealing with structured data
  7. Use awk for conditional column removal based on content or patterns
  8. Test with sample data to verify correct column positions before batch processing
  9. Pipe directly from source to avoid intermediate files and reduce I/O overhead
  10. Use buffering for I/O intensive operations with stdbuf or appropriate tools
  11. Parallel processing can speed up multiple file operations using xargs or GNU parallel
  12. Avoid unnecessary operations - combine multiple colrm operations when possible

The colrm command provides a simple, efficient way to remove fixed-width columns from text files. While it has a specific use case compared to more flexible tools like cut and awk, it excels at processing fixed-width text files, logs, and mainframe-style data where character positions are consistent across lines. Its straightforward syntax and low memory overhead make it an excellent choice for high-throughput text processing pipelines and automated data cleanup tasks.

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