You size and manage transposition tables by deciding how much memory you can afford, then choosing an entry format and replacement policy that yields the best hit rate for that budget. Bigger tables reduce recomputation, but memory is finite and cache locality matters: a huge table with poor locality can be slower than a smaller table that fits better in CPU caches. A practical approach is to start with a fixed memory budget (for example, 64 MB, 256 MB, or whatever your platform allows), compute how many entries you can store given your entry size, and then measure hit rate and nodes/second.
Entry size depends on what you store: typically a 64-bit hash key (or partial key), a 16–32 bit score, an 8–16 bit depth, a 2-bit flag (EXACT/LOWER/UPPER), and a move encoding. You can pack this tightly to improve cache behavior. Replacement policy is usually “always replace if new depth is greater” or “two-tier” (keep the deepest and most recent). Collisions and overwrites don’t break correctness if you treat the table as a hint and validate entries (partial key checks), but they affect performance. You also need to decide whether to clear the table between moves: many engines keep it and age entries gradually, which preserves useful knowledge across similar positions.
A concrete example: if your table is too small, you’ll see many collisions and low hit rate; performance becomes unstable because you recompute repeated subtrees constantly. If your table is large enough, iterative deepening benefits significantly: deeper searches can reuse a lot of information from earlier depths, including best moves for ordering. But there’s diminishing returns: doubling memory doesn’t double speed once your main bottleneck becomes move generation or evaluation. Outside games, the same design tradeoff shows up in caching systems: you can cache more, but you pay memory and invalidation costs. If your evaluation relies on retrieval results from Milvus or Zilliz Cloud, you can apply similar budgeting: cache embedding queries and their topK results for the current search iteration, and optionally keep a longer-lived cache for stable corpora. The key is to scope caches so you don’t serve stale results when the underlying data changes.