OrdMap & HashMap

• AVL is not worse than RBTree and is also a feasible way to resolve Hash Collision Attack.
• This package exposes two public structs: OrdMap which implemented by optimized AVL, HashMap whose every index contains an AVL-Tree.
• To improve performance, raw pointer is used frequently. Because Rust uses a similar memory model to C/C++, two classic macros offset_of and container_of are used to dereference member variables into main struct. Fastbin is implemented to reduce the cost of memory allocation.
• insert and remove operations are optimized by selectively skipping AVL Rebalance, because under 95% of indexes, there are less than 3 nodes.
• Since SipHash is not good at performance, FnvBuildHasher is used as the default BuildHasher.

• The whole structure of HashMap is like: ``` HashMap: ------------------------------------------ | HashIndex | HashIndex | ... ------------------------------------------ ---------------- HashIndex: | ...... | ---------------- ---------------- | AVL Node | | ........ | | AVL Root |==> ---------------- ... <==>| ListHead |<==>| ListHead |<==> ... <==> HEAD <==> ... ---------------- ----------------

  InternalHashEntry ---------------- | HashNode | | Value | ----------------

  HashNode ---------------- ----------------- | ...... | | HashValue | | ...... | | Key | ... <==>| AVL Node |<==>| AVL Node |<==> ... ---------------- ----------------- ```

  Usage

Notice the Trait. Usage of most functions is same as STL HashMap, you can find examples in test case or Documentation. impl<K, V, S> HashMap<K, V, S> where K: Ord + Hash, S: BuildHasher impl<K, V> OrdMap<K, V> where K: Ord

Performance Test

AVL Compare with RBTree

cargo run --release --example avl_cmp_rbtree * Obviously, optimized AVL is not worse than RBTree, and with the advantage of smaller height and Fastbin, it performs better. ``` avl tree size 1000000 build time PT0.086414625S contain count 1000000 find time PT0.079008516S remove time PT0.047414057S insert after remove time PT0.068621561S clear time PT0.020838042S

rbtree size 1000000 build time PT0.255747347S contain count 1000000 find time PT0.128373407S remove time PT0.129552620S insert after remove time PT0.256210620S

clear time PT0.051735117S

avl tree size 10000000 build time PT0.996452066S contain count 10000000 find time PT0.862577411S remove time PT0.641826155S insert after remove time PT0.779849807S clear time PT0.206050905S

rbtree size 10000000 build time PT3.348531156S contain count 10000000 find time PT1.482958388S remove time PT1.538498453S insert after remove time PT3.361221077S

clear time PT0.505872813S

```

HashMap Competition

• Because the operations of String themselves take too much time, we just show the competition with key usize.

Run command: cargo run --release --example hash_map_cmp_usize Our implement performs much better, especially in the case of searching. ``` test hash avl map insert time PT0.111996300S max node num of single index: 1 find 1000000, time PT0.042600200S remove time PT0.108599100S

test stl hash map insert time PT0.141726400S find 1000000, time PT0.117749600S

remove time PT0.139572800S

test hash avl map insert time PT0.661286300S max node num of single index: 2 find 5000000, time PT0.251113S remove time PT0.638360800S

test stl hash map insert time PT0.746569600S find 5000000, time PT0.647037200S

remove time PT0.828150S

test hash avl map insert time PT1.385713200S max node num of single index: 2 find 10000000, time PT0.521490300S remove time PT1.368300300S

test stl hash map insert time PT1.632164300S find 10000000, time PT1.426744300S

remove time PT1.703507200S

* When facing Collision Attack, the runtime complexity of STL HashMap can be _O(n^2)_, but ours is _O(n log n)_. cargo run --release --example hashmapcmp_collision test hash avl map insert time PT0.000739762S max node num of single index: 10000 find 10000, time PT0.000690444S remove time PT0.000497103S

test stl hash map insert time PT0.093169479S find 10000, time PT0.079600027S

remove time PT0.089153558S

```