Change score assessment
Wans't actually calculating scores correctly previously.
This commit is contained in:
Martin Sumner
parent
908f762a48
commit
d9229c9782
1 changed files with 68 additions and 108 deletions
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@ -118,6 +118,7 @@
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compaction_perc :: float() | undefined}).
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-type iclerk_options() :: #iclerk_options{}.
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-type candidate():: #candidate{}.
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%%%============================================================================
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%%% API
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@ -229,9 +230,9 @@ handle_cast({compact, Checker, InitiateFun, CloseFun, FilterFun, Inker, _TO},
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{MaxRunLength,
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State#state.maxrunlength_compactionperc,
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State#state.singlefile_compactionperc},
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BestRun0 = assess_candidates(Candidates, ScoreParams),
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case score_run(BestRun0, ScoreParams) of
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Score when Score > 0.0 ->
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{BestRun0, Score} = assess_candidates(Candidates, ScoreParams),
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case Score > 0.0 of
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true ->
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BestRun1 = sort_run(BestRun0),
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print_compaction_run(BestRun1, ScoreParams),
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ManifestSlice = compact_files(BestRun1,
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@ -257,7 +258,7 @@ handle_cast({compact, Checker, InitiateFun, CloseFun, FilterFun, Inker, _TO},
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ok = CloseFun(FilterServer),
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{noreply, State}
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end;
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Score ->
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false ->
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leveled_log:log("IC003", [Score]),
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ok = leveled_inker:ink_compactioncomplete(Inker),
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ok = CloseFun(FilterServer),
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@ -457,54 +458,42 @@ fetch_inbatches(PositionList, BatchSize, CDB, CheckedList) ->
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fetch_inbatches(Tail, BatchSize, CDB, CheckedList ++ KL_List).
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-spec assess_candidates(list(candidate()), tuple())
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-> {list(candidate()), float()}.
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%% @doc
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%% For each run length we need to assess all the runs of candidates
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%% The best scoring run should emerge as the winner
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%% Although this requires many loops over the list of the candidate, as the
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%% file scores have already been calculated the cost per loop should not be
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%% a high burden. Reducing the maximum run length, will reduce the cost of
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%% this exercise should be a problem
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assess_candidates(AllCandidates, Params) ->
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NaiveBestRun = assess_candidates(AllCandidates, Params, [], []),
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MaxRunLength = element(1, Params),
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case length(AllCandidates) of
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L when L > MaxRunLength, MaxRunLength > 1 ->
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%% Assess with different offsets from the start
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AssessFold =
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fun(Counter, BestRun) ->
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SubList = lists:nthtail(Counter, AllCandidates),
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assess_candidates(SubList, Params, [], BestRun)
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MaxRunLength = min(element(1, Params), length(AllCandidates)),
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NaiveBestRun = lists:sublist(AllCandidates, MaxRunLength),
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FoldFun =
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fun(RunLength, Best) ->
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assess_for_runlength(RunLength, AllCandidates, Params, Best)
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end,
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lists:foldl(AssessFold,
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NaiveBestRun,
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lists:seq(1, MaxRunLength - 1));
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_ ->
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NaiveBestRun
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end.
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assess_candidates([], _Params, _CurrentRun0, BestAssessment) ->
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BestAssessment;
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assess_candidates([HeadC|Tail], Params, CurrentRun0, BestAssessment) ->
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CurrentRun1 = choose_best_assessment(CurrentRun0 ++ [HeadC],
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[HeadC],
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Params),
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assess_candidates(Tail,
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Params,
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CurrentRun1,
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choose_best_assessment(CurrentRun1,
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BestAssessment,
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Params)).
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% Check all run lengths to find the best candidate. Reverse the list of
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% run lengths, so that longer runs win on equality of score
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lists:foldl(FoldFun,
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{NaiveBestRun, score_run(NaiveBestRun, Params)},
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lists:reverse(lists:seq(1, MaxRunLength))).
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choose_best_assessment(RunToAssess, BestRun, Params) ->
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{MaxRunLength, _MR_CT, _SF_CT} = Params,
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case length(RunToAssess) of
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LR1 when LR1 > MaxRunLength ->
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BestRun;
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_ ->
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AssessScore = score_run(RunToAssess, Params),
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BestScore = score_run(BestRun, Params),
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if
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AssessScore > BestScore ->
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RunToAssess;
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true ->
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BestRun
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assess_for_runlength(RunLength, AllCandidates, Params, Best) ->
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NumberOfRuns = 1 + length(AllCandidates) - RunLength,
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FoldFun =
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fun(Offset, {BestRun, BestScore}) ->
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Run = lists:sublist(AllCandidates, Offset, RunLength),
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Score = score_run(Run, Params),
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case Score > BestScore of
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true -> {Run, Score};
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false -> {BestRun, BestScore}
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end
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end.
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end,
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lists:foldl(FoldFun, Best, lists:seq(1, NumberOfRuns)).
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score_run([], _Params) ->
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@ -740,22 +729,6 @@ simple_score_test() ->
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Run3 = [#candidate{compaction_perc = 100.0}],
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?assertMatch(-60.0, score_run(Run3, {4, 70.0, 40.0})).
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score_compare_test() ->
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Run1 = [#candidate{compaction_perc = 55.0},
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#candidate{compaction_perc = 55.0},
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#candidate{compaction_perc = 56.0},
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#candidate{compaction_perc = 50.0}],
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?assertMatch(16.0, score_run(Run1, {4, 70.0, 40.0})),
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Run2 = [#candidate{compaction_perc = 55.0}],
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?assertMatch(Run1,
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choose_best_assessment(Run1,
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Run2,
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{4, 60.0, 40.0})),
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?assertMatch(Run2,
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choose_best_assessment(Run1 ++ Run2,
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Run2,
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{4, 60.0, 40.0})).
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file_gc_test() ->
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State = #state{waste_path="test/waste/",
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waste_retention_period=1},
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@ -771,6 +744,11 @@ file_gc_test() ->
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{ok, ClearedJournals2} = file:list_dir(State#state.waste_path),
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?assertMatch([], ClearedJournals2).
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check_bestrun(CandidateList, Params) ->
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{BestRun, _Score} = assess_candidates(CandidateList, Params),
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lists:map(fun(C) -> C#candidate.filename end, BestRun).
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find_bestrun_test() ->
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%% Tests dependent on these defaults
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%% -define(MAX_COMPACTION_RUN, 4).
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@ -778,53 +756,35 @@ find_bestrun_test() ->
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%% -define(MAXRUNLENGTH_COMPACTION_TARGET, 60.0).
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%% Tested first with blocks significant as no back-tracking
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Params = {4, 60.0, 40.0},
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Block1 = [#candidate{compaction_perc = 55.0},
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#candidate{compaction_perc = 65.0},
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#candidate{compaction_perc = 42.0},
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#candidate{compaction_perc = 50.0}],
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Block2 = [#candidate{compaction_perc = 38.0},
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#candidate{compaction_perc = 75.0},
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#candidate{compaction_perc = 75.0},
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#candidate{compaction_perc = 45.0}],
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Block3 = [#candidate{compaction_perc = 70.0},
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#candidate{compaction_perc = 100.0},
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#candidate{compaction_perc = 100.0},
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#candidate{compaction_perc = 100.0}],
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Block4 = [#candidate{compaction_perc = 55.0},
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#candidate{compaction_perc = 56.0},
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#candidate{compaction_perc = 57.0},
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#candidate{compaction_perc = 40.0}],
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Block5 = [#candidate{compaction_perc = 60.0},
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#candidate{compaction_perc = 60.0}],
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Block1 = [#candidate{compaction_perc = 55.0, filename = "a"},
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#candidate{compaction_perc = 65.0, filename = "b"},
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#candidate{compaction_perc = 42.0, filename = "c"},
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#candidate{compaction_perc = 50.0, filename = "d"}],
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Block2 = [#candidate{compaction_perc = 38.0, filename = "e"},
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#candidate{compaction_perc = 75.0, filename = "f"},
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#candidate{compaction_perc = 75.0, filename = "g"},
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#candidate{compaction_perc = 45.0, filename = "h"}],
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Block3 = [#candidate{compaction_perc = 70.0, filename = "i"},
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#candidate{compaction_perc = 100.0, filename = "j"},
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#candidate{compaction_perc = 100.0, filename = "k"},
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#candidate{compaction_perc = 100.0, filename = "l"}],
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Block4 = [#candidate{compaction_perc = 55.0, filename = "m"},
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#candidate{compaction_perc = 56.0, filename = "n"},
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#candidate{compaction_perc = 57.0, filename = "o"},
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#candidate{compaction_perc = 40.0, filename = "p"}],
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Block5 = [#candidate{compaction_perc = 60.0, filename = "q"},
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#candidate{compaction_perc = 60.0, filename = "r"}],
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CList0 = Block1 ++ Block2 ++ Block3 ++ Block4 ++ Block5,
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?assertMatch(Block4, assess_candidates(CList0, Params, [], [])),
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CList1 = CList0 ++ [#candidate{compaction_perc = 20.0}],
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?assertMatch([#candidate{compaction_perc = 20.0}],
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assess_candidates(CList1, Params, [], [])),
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?assertMatch(["b", "c", "d", "e"], check_bestrun(CList0, Params)),
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CList1 = CList0 ++ [#candidate{compaction_perc = 20.0, filename="s"}],
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?assertMatch(["s"], check_bestrun(CList1, Params)),
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CList2 = Block4 ++ Block3 ++ Block2 ++ Block1 ++ Block5,
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?assertMatch(Block4, assess_candidates(CList2, Params, [], [])),
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?assertMatch(["h", "a", "b", "c"], check_bestrun(CList2, Params)),
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CList3 = Block5 ++ Block1 ++ Block2 ++ Block3 ++ Block4,
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?assertMatch([#candidate{compaction_perc = 42.0},
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#candidate{compaction_perc = 50.0},
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#candidate{compaction_perc = 38.0}],
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assess_candidates(CList3, Params)),
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%% Now do some back-tracking to get a genuinely optimal solution without
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%% needing to re-order
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?assertMatch([#candidate{compaction_perc = 42.0},
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#candidate{compaction_perc = 50.0},
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#candidate{compaction_perc = 38.0}],
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assess_candidates(CList0, Params)),
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?assertMatch([#candidate{compaction_perc = 42.0},
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#candidate{compaction_perc = 50.0},
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#candidate{compaction_perc = 38.0}],
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assess_candidates(CList0, setelement(1, Params, 5))),
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?assertMatch([#candidate{compaction_perc = 42.0},
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#candidate{compaction_perc = 50.0},
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#candidate{compaction_perc = 38.0},
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#candidate{compaction_perc = 75.0},
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#candidate{compaction_perc = 75.0},
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#candidate{compaction_perc = 45.0}],
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assess_candidates(CList0, setelement(1, Params, 6))).
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?assertMatch(["b", "c", "d", "e"],check_bestrun(CList3, Params)).
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handle_emptycandidatelist_test() ->
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?assertMatch({[], 0.0}, assess_candidates([], {4, 60.0, 40.0})).
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test_ledgerkey(Key) ->
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{o, "Bucket", Key, null}.
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