Implementing a Tail-like Function

Here’s a concise roadmap of three approaches to implement a tail‑like function in Python.

• Full‑file read: Read the entire file into memory and slice the last n lines; simple but uses O(file_size) memory.
• Fixed‑size queue: Stream lines one by one, retaining only the last n in a collections.deque(maxlen=n), so memory is bounded by O(n).
• Backward block buffer: Seek from the file’s end in binary mode, read fixed‑size blocks backward, count newlines until enough are found, then decode and print—uses minimal memory even for very large files.

1. Naïve Full‑File Read (No Memory Consideration)

Code Example

def tail_simple(num_lines: int, file_path: str = "./file.log") -> None:
    with open(file_path, "r", encoding="utf-8", errors="replace") as f:
        lines = f.readlines()                   # reads entire file into memory
        for line in lines[-num_lines:]:          # slices last n lines
            print(line.rstrip("\n"))

Nuances

• Memory usage: f.readlines() loads every line into a list, so memory scales with file size.
• Performance: Fast for small to medium files, but will crash or swap if the file is huge.

2. Bounded Memory via a Fixed‑Size Queue

Code Example

from collections import deque

def tail_queue(num_lines: int, file_path: str = "./file.log") -> None:
    buffer = deque(maxlen=num_lines)           # only holds last n lines
    with open(file_path, "r", encoding="utf-8", errors="replace") as f:
        for line in f:
            buffer.append(line.rstrip("\n"))   # discard oldest when full
    for line in buffer:
        print(line)

Nuances

• Memory bound: Uses at most n lines of memory, regardless of file size.
• Streaming: Reads line by line; useful for very large files or when you don’t know file size in advance.
• Overhead: Python’s generator overhead per line; slightly slower than block reads but generally fine.

3. Block‑Buffered Reverse Read (Constant Small Memory)

When num_lines is small relative to file size, reading backwards in chunks can be far more efficient.

Incremental Development

• Open in binary and seek to end

   import os

   with open(file_path, "rb") as f:
       f.seek(0, os.SEEK_END)                # go to file end :contentReference[oaicite:5]{index=5}

• Read fixed‑size blocks backwards

   block_size = 1024
   file_size = f.tell()
   buffer = bytearray()
   while file_size > 0 and buffer.count(b"\n") <= num_lines:
       read_size = min(block_size, file_size)
       f.seek(file_size - read_size)
       buffer[:0] = f.read(read_size)        # prepend new data
       file_size -= read_size

• Extract and decode the last lines

   lines = buffer.splitlines()[-num_lines:]
   for line in lines:
       print(line.decode("utf-8"))

Full Function

import os

def tail(num_lines: int, file_path: str = "./file.log") -> None:
    with open(file_path, "rb") as f:
        f.seek(0, os.SEEK_END)
        file_size = f.tell()

        buffer = bytearray()
        lines_found = 0
        block_size = 1024

        # Read backwards until we find enough newlines
        while file_size > 0 and lines_found <= num_lines:
            read_size = min(block_size, file_size)
            f.seek(file_size - read_size)
            data = f.read(read_size)
            buffer[:0] = data
            lines_found = buffer.count(b"\n")
            file_size -= read_size

        # Decode and print the desired lines
        lines = buffer.splitlines()[-num_lines:]
        for line in lines:
            print(line.decode("utf-8"))

Nuances

• Binary mode avoids issues with variable‑length encodings when seeking.
• Block size: Choose based on typical line length; too small and you incur many seeks, too large and you waste memory.
• Byte vs. text splitting: Counts b"\n" and splits on raw bytes, then decodes.
• Edge cases: Handles files smaller than block_size, and files with fewer than num_lines lines gracefully.

Conclusion

• Full‑file read is easiest to implement but not scalable.
• Deque‑based offers O(n) memory bound and simple streaming.
• Block‑buffered reverse read provides near‑constant memory overhead and is ideal for very large files.

Choose the approach that best fits your file sizes and memory constraints.