profiling upgrades (dfinity#91)

* upgrade for basic_dao

* nft

* rerun CI

* collection motoko

* rust collections

* fix

* fix

* add utils

* use utils in collections

* refactor

* fix

* certified_map upgrade

* readme

* fix nft did

* fix

Author: chenyan-dfinity

.github/workflows/perf.yml

@@ -12,9 +12,9 @@ jobs:
       fail-fast: false
     env:
       DFX_VERSION: 0.15.0
-      IC_REPL_VERSION: 0.4.3
+      IC_REPL_VERSION: 0.5.0
       MOC_VERSION: 0.10.0
-      IC_WASM_VERSION: 0.5.0
+      IC_WASM_VERSION: 0.5.1
     steps:
       - uses: actions/checkout@v3
       - name: Checkout out gh-pages report

Cargo.lock

@@ -1,5 +1,6 @@
 [workspace]
 members = [
+    "utils/rust",
     "collections/rust/src/hashmap",
     "collections/rust/src/btreemap",
     "collections/rust/src/heap",
@@ -24,5 +25,6 @@ opt-level = 2
 [workspace.dependencies]
 ic-cdk = "0.10.0"
 ic-cdk-timers = "0.4.0"
-candid = "0.9"
+candid = "0.9.8"
 serde = "1"
+ic-stable-structures = "0.5"

Cargo.toml

@@ -11,6 +11,7 @@ the same elements, and the queries are exactly the same. Below we explain the me
 * batch_get 50. Find 50 elements from the collection.
 * batch_put 50. Insert 50 elements to the collection.
 * batch_remove 50. Remove 50 elements from the collection.
+* upgrade. Upgrade the canister with the same Wasm module. The map state is persisted by serializing and deserializing states into stable memory.
 
 ## **💎 Takeaways**
 
@@ -22,7 +23,12 @@ the same elements, and the queries are exactly the same. Below we explain the me
 > **Note**
 >
 > * The Candid interface of the benchmark is minimal, therefore the serialization cost is negligible in this measurement.
-> * Due to the instrumentation overhead and cycle limit, we cannot profile computations with large collections. Hopefully, when deterministic time slicing is ready, we can measure the performance on larger memory footprint.
+> * Due to the instrumentation overhead and cycle limit, we cannot profile computations with very large collections.
+> * The `upgrade` column uses Candid for serializing stable data. In Rust, you may get better cycle cost by using a different serialization format. Another slowdown in Rust is that `ic-stable-structures` tends to be slower than the region memory in Motoko.
+> * Different library has different ways for persisting data during upgrades, there are mainly three categories:
+>   + Use stable variable directly in Motoko: `zhenya_hashmap`, `btree`, `vector`
+>   + Expose and serialize external state (`share/unshare` in Motoko, `candid::Encode` in Rust): `rbtree`, `heap`, `btreemap_rs`, `hashmap_rs`, `heap_rs`, `vector_rs`
+>   + Use pre/post-upgrade hooks to convert data into an array: `hashmap`, `splay`, `triemap`, `buffer`, `imrc_hashmap_rs`
 > * `hashmap` uses amortized data structure. When the initial capacity is reached, it has to copy the whole array, thus the cost of `batch_put 50` is much higher than other data structures.
 > * `btree` comes from [mops.one/stableheapbtreemap](https://mops.one/stableheapbtreemap).
 > * `zhenya_hashmap` comes from [mops.one/map](https://mops.one/map).

collections/README.md

@@ -4,8 +4,11 @@ import Hash "mo:base/Hash";
 import Iter "mo:base/Iter";
 import Option "mo:base/Option";
 import Random "random";
+import Profiling "../../../utils/motoko/Profiling";
 
 actor {
+    stable let profiling = Profiling.init();
+    
     func f_hash(x : Nat64) : Nat32 = Hash.hash(Nat64.toNat x);
     let hash : HashMap.HashUtils<Nat64> = (f_hash, Nat64.equal);
     stable var map = HashMap.new<Nat64, Nat64>();

collections/motoko/src/ZhenyaHashmap.mo

@@ -3,8 +3,11 @@ import Nat64 "mo:base/Nat64";
 import Iter "mo:base/Iter";
 import Option "mo:base/Option";
 import Random "random";
+import Profiling "../../../utils/motoko/Profiling";
 
 actor {
+    stable let profiling = Profiling.init();
+    
     stable var map = Map.init<Nat64, Nat64>(null);
     let rand = Random.new(null, 42);
 

collections/motoko/src/btreemap.mo

@@ -3,9 +3,20 @@ import Iter "mo:base/Iter";
 import Random "random";
 import Nat64 "mo:base/Nat64";
 import Option "mo:base/Option";
+import Profiling "../../../utils/motoko/Profiling";
 
 actor {
+    stable let profiling = Profiling.init();
+    
     var buffer = Buffer.Buffer<Nat64>(0);
+    stable var stableBuffer : [Nat64] = [];
+
+    system func preupgrade() {
+        stableBuffer := Buffer.toArray(buffer);
+    };
+    system func postupgrade() {
+        buffer := Buffer.fromArray(stableBuffer);
+    };
 
     public func generate(n: Nat) : async () {
         buffer.clear();

collections/motoko/src/buffer.mo

@@ -5,12 +5,23 @@ import Hash "mo:base/Hash";
 import Iter "mo:base/Iter";
 import Option "mo:base/Option";
 import Random "random";
+import Profiling "../../../utils/motoko/Profiling";
 
 actor {
+    stable let profiling = Profiling.init();
+    
     func hash(x: Nat64) : Nat32 = Hash.hash(Nat64.toNat x);
     var map = HashMap.HashMap<Nat64, Nat64>(0, Nat64.equal, hash);
+    stable var stableMap: [(Nat64, Nat64)] = [];
     let rand = Random.new(null, 42);
 
+    system func preupgrade() {
+        stableMap := Iter.toArray(map.entries())
+    };
+    system func postupgrade() {
+        map := HashMap.fromIter(stableMap.vals(), stableMap.size(), Nat64.equal, hash);
+    };
+
     public func generate(size: Nat32) : async () {
         let rand = Random.new(?size, 1);
         let iter = Iter.map<Nat64, (Nat64, Nat64)>(rand, func x = (x, x));

collections/motoko/src/hashmap.mo

@@ -4,10 +4,21 @@ import Iter "mo:base/Iter";
 import Option "mo:base/Option";
 import Random "random";
 import O "mo:base/Order";
+import Profiling "../../../utils/motoko/Profiling";
 
 actor {
+    stable let profiling = Profiling.init();
+    
     var map = Heap.Heap<Nat64>(Nat64.compare);
+    stable var stableMap : Heap.Tree<Nat64> = null;
     let rand = Random.new(null, 42);
+
+    system func preupgrade() {
+        stableMap := map.share();
+    };
+    system func postupgrade() {
+        map.unsafeUnshare(stableMap);
+    };
 
     public func generate(size: Nat32) : async () {
         let rand = Random.new(?size, 1);

collections/motoko/src/heap.mo

@@ -3,11 +3,22 @@ import Nat64 "mo:base/Nat64";
 import Iter "mo:base/Iter";
 import Option "mo:base/Option";
 import Random "random";
+import Profiling "../../../utils/motoko/Profiling";
 
 actor {
+    stable let profiling = Profiling.init();
+    
     var map = RBTree.RBTree<Nat64, Nat64>(Nat64.compare);
+    stable var stableMap : RBTree.Tree<Nat64, Nat64> = #leaf;
     let rand = Random.new(null, 42);
 
+    system func preupgrade() {
+        stableMap := map.share();
+    };
+    system func postupgrade() {
+        map.unshare(stableMap);
+    };
+    
     public func generate(size: Nat32) : async () {
         let rand = Random.new(?size, 1);
         let iter = Iter.map<Nat64, (Nat64, Nat64)>(rand, func x = (x, x));