# line-based reading and parallel processing
this outlines a strategy for processing newline-separated data from streams or files using multiple threads.

# use cases
processing large volumes of newline-separated data with:
* efficient handling of large input
* support for multithreaded execution
* compatibility with streams and multiple large files

# the algorithm
* buffers:
  * two buffers, data[0] and data[1], alternate during reading
  * data[0] is initially filled from the input source
* newline search:
  * after data is read into data[0], the algorithm locates the last newline in the second half of the buffer
  * if no newline is found, the buffer is either expanded or an error is returned if a line exceeds the allowed length
* line handling:
  * any data after the last newline is moved to the beginning of data[1]
  * the next read continues into data[1], with buffers alternating to ensure all lines are preserved and completed

input is assumed to be line-aligned, with eof or end of stream marked by a newline.

# thread coordination
* buffer chains: multiple threads process data via a sequence of buffers. only one thread reads from the input, but all threads can operate on their respective buffers
* file metadata optimization:
  * file size metadata is used to group small files and partition large ones efficiently
  * threads may seek to predefined offsets in large files, enabling parallel file processing

# fgets() overhead in c
the fgets() function reads a line from a stream into a buffer. while simple and portable, it introduces overhead:
* buffer size constraint: predefined buffer sizes may be insufficient for long lines, requiring multiple reads
* data copying: data is copied from internal to user buffers, incurring memory overhead
* per-call cost: each fgets() call performs checks and incurs function overhead, reducing throughput on large data sets

modern libraries may optimize fgets() through:
* internal buffering to reduce system calls
* simd-based character scanning
* thread-safe implementations with minimal locking

however, these optimizations are insufficient for cases involving extremely large data volumes or strict memory/performance control.

# improvements over fgets()
this algorithm mitigates fgets() limitations by:
* reading large chunks to reduce system call frequency and call overhead
* exposing direct pointers into the buffer to avoid unnecessary data copies
* scanning large blocks for newlines to process multiple lines per operation

# handling critical cases
* long lines: the buffer can grow dynamically if no newline is found. alternatively, a line-length cap can be enforced and excess data skipped
* incomplete lines: partial lines at the end of a buffer are copied to the start of the next buffer to ensure continuity
* thread usage: each thread processes an independent buffer to parallelize data parsing and improve throughput

# scalability and efficiency
* synchronization: thread contention is minimized since reading is serialized but parsing is parallel
* i/o vs. cpu bound: reading large chunks benefits i/o-bound applications. cpu-bound gains depend on parsing cost
* error handling: partial reads, i/o failures, and buffer overflows must be explicitly managed for robust behavior

# conclusion
this algorithm offers benefits over fgets() for:
* large-scale data processing where minimizing system calls and copies matters
* scenarios involving long lines or fragmented input
* environments where multithreaded processing improves throughput