Merge pull request #48 from basho/mra/merkle-cleanup
Add merkle library
This commit is contained in:
commit
e9b1134cd9
4 changed files with 375 additions and 0 deletions
1
.gitignore
vendored
1
.gitignore
vendored
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@ -26,3 +26,4 @@ rel/machi
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current_counterexample.eqc
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foo*
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typescript*
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*.swp
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20
include/machi_merkle_tree.hrl
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20
include/machi_merkle_tree.hrl
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%% machi merkle tree records
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-record(naive, {
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chunk_size = 1048576 :: pos_integer(), %% default 1 MB
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recalc = true :: boolean(),
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root :: 'undefined' | binary(),
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lvl1 = [] :: [ binary() ],
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lvl2 = [] :: [ binary() ],
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lvl3 = [] :: [ binary() ],
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leaves = [] :: [ { Offset :: pos_integer(),
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Size :: pos_integer(),
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Csum :: binary()} ]
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}).
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-record(mt, {
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filename :: string(),
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tree :: #naive{},
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backend = 'naive' :: 'naive'
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}).
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156
src/machi_merkle_tree.erl
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156
src/machi_merkle_tree.erl
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%% -------------------------------------------------------------------
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%%
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%% Copyright (c) 2007-2015 Basho Technologies, Inc. All Rights Reserved.
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%%
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%% This file is provided to you under the Apache License,
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%% Version 2.0 (the "License"); you may not use this file
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%% except in compliance with the License. You may obtain
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%% a copy of the License at
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%%
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%% http://www.apache.org/licenses/LICENSE-2.0
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%%
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%% Unless required by applicable law or agreed to in writing,
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%% software distributed under the License is distributed on an
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%% "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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%% KIND, either express or implied. See the License for the
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%% specific language governing permissions and limitations
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%% under the License.
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%%
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%% -------------------------------------------------------------------
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%% @doc Creates a Merkle tree per file based on the checksum data for
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%% a given data file.
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%%
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%% The `naive' implementation representation is:
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%%
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%% `<<Length:64, Offset:32, 0>>' for unwritten bytes
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%% `<<Length:64, Offset:32, 1>>' for trimmed bytes
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%% `<<Length:64, Offset:32, Csum/binary>>' for written bytes
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%%
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%% The tree feeds these leaf nodes into hashes representing chunks of a minimum
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%% size of at least 1024 KB (1 MB), but if the file size is larger, we will try
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%% to get about 100 chunks for the first rollup "Level 1." We aim for around 10
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%% hashes at level 2, and then 2 hashes level 3 and finally the root.
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-module(machi_merkle_tree).
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-include("machi.hrl").
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-include("machi_merkle_tree.hrl").
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-ifdef(TEST).
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-compile(export_all).
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-else.
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-export([
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open/2,
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open/3,
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tree/1,
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filename/1,
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diff/2
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]).
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-endif.
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-define(TRIMMED, <<1>>).
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-define(UNWRITTEN, <<0>>).
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-define(NAIVE_ENCODE(Offset, Size, Data), <<Offset:64/unsigned-big, Size:32/unsigned-big, Data/binary>>).
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-define(MINIMUM_CHUNK, 1048576). %% 1024 * 1024
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-define(LEVEL_SIZE, 10).
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-define(H, sha).
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%% public API
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open(Filename, DataDir) ->
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open(Filename, DataDir, naive).
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open(Filename, DataDir, Type) ->
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Tree = load_filename(Filename, DataDir, Type),
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{ok, #mt{ filename = Filename, tree = Tree, backend = Type}}.
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tree(#mt{ tree = T, backend = naive }) ->
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case T#naive.recalc of
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true -> build_tree(T);
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false -> T
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end.
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filename(#mt{ filename = F }) -> F.
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diff(#mt{backend = naive, tree = T1}, #mt{backend = naive, tree = T2}) ->
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case T1#naive.root == T2#naive.root of
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true -> same;
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false -> naive_diff(T1, T2)
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end;
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diff(_, _) -> error(badarg).
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%% private
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% @private
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load_filename(Filename, DataDir, naive) ->
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{Last, M} = do_load(Filename, DataDir, fun insert_csum_naive/2, []),
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ChunkSize = max(?MINIMUM_CHUNK, Last div 100),
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T = #naive{ leaves = lists:reverse(M), chunk_size = ChunkSize, recalc = true },
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build_tree(T).
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do_load(Filename, DataDir, FoldFun, AccInit) ->
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CsumFile = machi_util:make_checksum_filename(DataDir, Filename),
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{ok, T} = machi_csum_table:open(CsumFile, []),
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Acc = machi_csum_table:foldl_chunks(FoldFun, {0, AccInit}, T),
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ok = machi_csum_table:close(T),
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Acc.
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% @private
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insert_csum_naive({Last, Size, _Csum}=In, {Last, MT}) ->
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%% no gap
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{Last+Size, update_acc(In, MT)};
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insert_csum_naive({Offset, Size, _Csum}=In, {Last, MT}) ->
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Hole = Offset - Last,
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MT0 = update_acc({Last, Hole, unwritten}, MT),
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{Offset+Size, update_acc(In, MT0)}.
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% @private
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update_acc({Offset, Size, unwritten}, MT) ->
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[ {Offset, Size, ?NAIVE_ENCODE(Offset, Size, ?UNWRITTEN)} | MT ];
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update_acc({Offset, Size, trimmed}, MT) ->
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[ {Offset, Size, ?NAIVE_ENCODE(Offset, Size, ?TRIMMED)} | MT ];
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update_acc({Offset, Size, <<_Tag:8, Csum/binary>>}, MT) ->
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[ {Offset, Size, ?NAIVE_ENCODE(Offset, Size, Csum)} | MT ].
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build_tree(MT = #naive{ leaves = L, chunk_size = ChunkSize }) ->
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Lvl1s = build_level_1(ChunkSize, L, 1, [ crypto:hash_init(?H) ]),
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Mod2 = length(Lvl1s) div ?LEVEL_SIZE,
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Lvl2s = build_int_level(Mod2, Lvl1s, 1, [ crypto:hash_init(?H) ]),
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Mod3 = length(Lvl2s) div 2,
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Lvl3s = build_int_level(Mod3, Lvl2s, 1, [ crypto:hash_init(?H) ]),
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Root = build_root(Lvl3s, crypto:hash_init(?H)),
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MT#naive{ root = Root, lvl1 = Lvl1s, lvl2 = Lvl2s, lvl3 = Lvl3s, recalc = false }.
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build_root([], Ctx) ->
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crypto:hash_final(Ctx);
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build_root([H|T], Ctx) ->
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build_root(T, crypto:hash_update(Ctx, H)).
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build_int_level(_Mod, [], _Cnt, [ Ctx | Rest ]) ->
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lists:reverse( [ crypto:hash_final(Ctx) | Rest ] );
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build_int_level(Mod, [H|T], Cnt, [ Ctx | Rest ]) when Cnt rem Mod == 0 ->
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NewCtx = crypto:hash_init(?H),
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build_int_level(Mod, T, Cnt + 1, [ crypto:hash_update(NewCtx, H), crypto:hash_final(Ctx) | Rest ]);
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build_int_level(Mod, [H|T], Cnt, [ Ctx | Rest ]) ->
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build_int_level(Mod, T, Cnt+1, [ crypto:hash_update(Ctx, H) | Rest ]).
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build_level_1(_Size, [], _Multiple, [ Ctx | Rest ]) ->
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lists:reverse([ crypto:hash_final(Ctx) | Rest ]);
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build_level_1(Size, [{Pos, Len, Hash}|T], Multiple, [ Ctx | Rest ])
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when ( Pos + Len ) > ( Size * Multiple ) ->
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NewCtx = crypto:hash_init(?H),
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build_level_1(Size, T, Multiple+1,
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[ crypto:hash_update(NewCtx, Hash), crypto:hash_final(Ctx) | Rest ]);
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build_level_1(Size, [{Pos, Len, Hash}|T], Multiple, [ Ctx | Rest ])
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when ( Pos + Len ) =< ( Size * Multiple ) ->
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build_level_1(Size, T, Multiple, [ crypto:hash_update(Ctx, Hash) | Rest ]).
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naive_diff(#naive{lvl1 = L1}, #naive{lvl1=L2, chunk_size=CS2}) ->
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Set1 = gb_sets:from_list(lists:zip(lists:seq(1, length(L1)), L1)),
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Set2 = gb_sets:from_list(lists:zip(lists:seq(1, length(L2)), L2)),
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%% The byte ranges in list 2 that do not match in list 1
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%% Or should we do something else?
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[ {(X-1)*CS2, CS2, SHA} || {X, SHA} <- gb_sets:to_list(gb_sets:subtract(Set1, Set2)) ].
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198
test/machi_merkle_tree_test.erl
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198
test/machi_merkle_tree_test.erl
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%% -------------------------------------------------------------------
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%%
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%% Copyright (c) 2007-2015 Basho Technologies, Inc. All Rights Reserved.
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%%
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%% This file is provided to you under the Apache License,
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%% Version 2.0 (the "License"); you may not use this file
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%% except in compliance with the License. You may obtain
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%% a copy of the License at
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%%
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%% http://www.apache.org/licenses/LICENSE-2.0
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%%
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%% Unless required by applicable law or agreed to in writing,
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%% software distributed under the License is distributed on an
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%% "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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%% KIND, either express or implied. See the License for the
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%% specific language governing permissions and limitations
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%% under the License.
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%%
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%% -------------------------------------------------------------------
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-module(machi_merkle_tree_test).
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-compile([export_all]).
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-include("machi_merkle_tree.hrl").
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-include_lib("eunit/include/eunit.hrl").
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-include_lib("kernel/include/file.hrl").
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-define(GAP_CHANCE, 0.10).
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%% unit tests
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basic_test() ->
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random:seed(os:timestamp()),
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Fsz = choose_size() * 1024,
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Filesize = max(Fsz, 10*1024*1024),
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ChunkSize = max(1048576, Filesize div 100),
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N = make_leaf_nodes(Filesize),
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D0 = #naive{ leaves = N, chunk_size = ChunkSize, recalc = true },
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T1 = machi_merkle_tree:build_tree(D0),
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D1 = #naive{ leaves = tl(N), chunk_size = ChunkSize, recalc = true },
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T2 = machi_merkle_tree:build_tree(D1),
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?assertNotEqual(T1#naive.root, T2#naive.root),
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?assertEqual(1, length(machi_merkle_tree:naive_diff(T1, T2))).
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make_leaf_nodes(Filesize) ->
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lists:reverse(
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lists:foldl(fun(T, Acc) -> machi_merkle_tree:update_acc(T, Acc) end,
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[],
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generate_offsets(Filesize, 1024, []))
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).
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choose_int(Factor) ->
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random:uniform(1024*Factor).
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small_int() ->
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choose_int(10).
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medium_int() ->
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choose_int(1024).
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large_int() ->
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choose_int(4096).
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generate_offsets(Filesize, Current, Acc) when Current < Filesize ->
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Length0 = choose_size(),
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Length = case Length0 + Current > Filesize of
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false -> Length0;
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true -> Filesize - Current
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end,
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Data = term_to_binary(os:timestamp()),
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Checksum = machi_util:make_tagged_csum(client_sha, machi_util:checksum_chunk(Data)),
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Gap = maybe_gap(random:uniform()),
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generate_offsets(Filesize, Current + Length + Gap, [ {Current, Length, Checksum} | Acc ]);
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generate_offsets(_Filesize, _Current, Acc) ->
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lists:reverse(Acc).
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random_from_list(L) ->
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N = random:uniform(length(L)),
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lists:nth(N, L).
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choose_size() ->
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F = random_from_list([fun small_int/0, fun medium_int/0, fun large_int/0]),
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F().
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maybe_gap(Chance) when Chance < ?GAP_CHANCE ->
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choose_size();
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maybe_gap(_) -> 0.
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%% Define or remove these ifdefs if benchmarking is desired.
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-ifdef(BENCH).
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generate_offsets(FH, Filesize, Current, Acc) when Current < Filesize ->
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Length0 = choose_size(),
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Length = case Length0 + Current > Filesize of
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false -> Length0;
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true -> Filesize - Current
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end,
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{ok, Data} = file:pread(FH, Current, Length),
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Checksum = machi_util:make_tagged_csum(client_sha, machi_util:checksum_chunk(Data)),
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Gap = maybe_gap(random:uniform()),
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generate_offsets(FH, Filesize, Current + Length + Gap, [ {Current, Length, Checksum} | Acc ]);
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generate_offsets(_FH, _Filesize, _Current, Acc) ->
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lists:reverse(Acc).
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make_offsets_from_file(Filename) ->
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{ok, Info} = file:read_file_info(Filename),
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Filesize = Info#file_info.size,
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{ok, FH} = file:open(Filename, [read, raw, binary]),
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Offsets = generate_offsets(FH, Filesize, 1024, []),
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file:close(FH),
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Offsets.
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choose_filename() ->
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random_from_list([
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"def^c5ea7511-d649-47d6-a8c3-2b619379c237^1",
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"jkl^b077eff7-b2be-4773-a73f-fea4acb8a732^1",
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"stu^553fa47a-157c-4fac-b10f-2252c7d8c37a^1",
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"vwx^ae015d68-7689-4c9f-9677-926c6664f513^1",
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"yza^4c784dc2-19bf-4ac6-91f6-58bbe5aa88e0^1"
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]).
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make_csum_file(DataDir, Filename, Offsets) ->
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Path = machi_util:make_checksum_filename(DataDir, Filename),
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filelib:ensure_dir(Path),
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{ok, MC} = machi_csum_table:open(Path, []),
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lists:foreach(fun({Offset, Size, Checksum}) ->
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machi_csum_table:write(MC, Offset, Size, Checksum) end,
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Offsets),
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machi_csum_table:close(MC).
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test() ->
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test(100).
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test(N) ->
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{ok, F} = file:open("results.txt", [raw, write]),
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lists:foreach(fun(X) -> format_and_store(F, run_test(X)) end, lists:seq(1, N)).
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format_and_store(F, {OffsetNum, {MTime, MSize}, {NTime, NSize}}) ->
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S = io_lib:format("~w\t~w\t~w\t~w\t~w\n", [OffsetNum, MTime, MSize, NTime, NSize]),
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ok = file:write(F, S).
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run_test(C) ->
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random:seed(os:timestamp()),
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OffsetFn = "test/" ++ choose_filename(),
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O = make_offsets_from_file(OffsetFn),
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Fn = "csum_" ++ integer_to_list(C),
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make_csum_file(".", Fn, O),
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Osize = length(O),
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{MTime, {ok, M}} = timer:tc(fun() -> machi_merkle_tree:open(Fn, ".", merklet) end),
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{NTime, {ok, N}} = timer:tc(fun() -> machi_merkle_tree:open(Fn, ".", naive) end),
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?assertEqual(Fn, machi_merkle_tree:filename(M)),
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?assertEqual(Fn, machi_merkle_tree:filename(N)),
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MTree = machi_merkle_tree:tree(M),
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MSize = byte_size(term_to_binary(MTree)),
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NTree = machi_merkle_tree:tree(N),
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NSize = byte_size(term_to_binary(NTree)),
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?assertEqual(same, machi_merkle_tree:diff(N, N)),
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?assertEqual(same, machi_merkle_tree:diff(M, M)),
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{Osize, {MTime, MSize}, {NTime, NSize}}.
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torture_test(C) ->
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Results = [ run_torture_test() || _ <- lists:seq(1, C) ],
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{ok, F} = file:open("torture_results.txt", [raw, write]),
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lists:foreach(fun({MSize, MTime, NSize, NTime}) ->
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file:write(F, io_lib:format("~p\t~p\t~p\t~p\n",
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[MSize, MTime, NSize, NTime]))
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end, Results),
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ok = file:close(F).
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run_torture_test() ->
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{NTime, N} = timer:tc(fun() -> naive_torture() end),
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MSize = byte_size(term_to_binary(M)),
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NSize = byte_size(term_to_binary(N)),
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{MSize, MTime, NSize, NTime}.
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naive_torture() ->
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N = lists:foldl(fun(T, Acc) -> machi_merkle_tree:update_acc(T, Acc) end, [], torture_generator()),
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T = #naive{ leaves = lists:reverse(N), chunk_size = 10010, recalc = true },
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machi_merkle_tree:build_tree(T).
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torture_generator() ->
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[ {O, 1, crypto:hash(sha, term_to_binary(now()))} || O <- lists:seq(1024, 1000000) ].
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-endif. % BENCH
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