When implementing algorithms, prioritize data structure choices that minimize resource usage while maintaining functionality. Consider if a simpler approach with counters, efficient state tracking, or built-in functions can replace complex custom implementations.
When implementing algorithms, prioritize data structure choices that minimize resource usage while maintaining functionality. Consider if a simpler approach with counters, efficient state tracking, or built-in functions can replace complex custom implementations.
For hierarchical or nested data:
For example, instead of creating one structure per level like this:
/* Creates one structure per level - O(n) space complexity */
while (SubtransDdlLevel != 0)
{
DdlHashTable *new_table = MemoryContextAlloc(TopTransactionContext, sizeof(DdlHashTable));
new_table->prev_table = CurrentDdlTable;
CurrentDdlTable = new_table;
SubtransDdlLevel -= 1;
}
Consider storing level information in the structure itself:
/* Creates structures only when needed - O(1) space complexity for most cases */
if (need_new_table)
{
DdlHashTable *new_table = MemoryContextAlloc(TopTransactionContext, sizeof(DdlHashTable));
new_table->prev_table = CurrentDdlTable;
new_table->subtrans_level = SubtransDdlLevel - 1;
CurrentDdlTable = new_table;
}
Similarly, for string parsing operations, prefer standard library functions over custom implementations:
/* More efficient and clearer than manual parsing */
int pos;
if (sscanf(safekeepers_list, "g#%u:%n", generation, &pos) == 1) {
return safekeepers_list + pos;
} else {
return safekeepers_list;
}
Enter the URL of a public GitHub repository