Mastering Linux’s Three Musketeers: grep, sed, and awk

In Linux, grep , sed and awk are collectively known as the “three musketeers” of text processing, tightly coupled with regular expressions to provide powerful data‑handling capabilities.

grep

grep (Global Regular Expression Print) searches files for lines matching a pattern. Common options include: -i: ignore case -v: invert match -n: show line numbers -r: recursive search -o: output only matching parts -E or --extended-regexp: use extended regular expressions

Example file test.txt:

a b c d e
1 2 3 4 5
G E d C T G B
l k j h g f

Typical commands and results:

grep c test.txt

ab c d e

grep -i c test.txt

ab c d e
G E d C T G B

grep -io c test.txt

c
C

Matching multiple patterns:

grep -E 'c|h' test.txt

ab c d e
l k j h g f

grep 'c\|h' test.txt

ab c d e
l k j h g f

sed

sed (stream editor) filters and transforms text streams. The substitution command s/old/new/g replaces all occurrences of old with new. Adding -i edits files in place. sed 's/old/new/g' file In‑place editing example:

sed -i 's/原字符串/替换字符串/g' filename

Counting lines with wc -l counts newline characters, which may differ from the actual number of logical lines. Using sed: sed -n "$=" filename returns the line number of the last line, effectively counting lines even when the final newline is missing. The -n flag suppresses default output, and $= prints the line number of the last line.

awk

awk excels at column‑oriented processing. The -F option sets the field separator; in the examples the separator is |@| (escaped as \|\@\|).

Count fields in the last record: awk -F '\|\@\|' 'END{print NF}' filename Print the first column: awk -F '\|\@\|' '{print $1}' filename Print the first and third columns: awk -F '\|\@\|' '{print $1,$3}' filename Filter rows where the sixth column contains the word “money”:

awk -F '\|\@\|' '{print $6}' filename | grep money

Conclusion

These three tools are indispensable for Linux text processing. grep shines at searching, sed at editing streams, and awk at generating reports from columnar data. Mastering the specific strengths of each dramatically improves data‑handling efficiency.