Change default compaction settings
Need to allow specific settings to be passed into unit tests. Also, too much journal compaction may lead to intermittent failures on the basic_SUITE space_clear_on_delete test. think this is because there are less “deletes” to reload in on startup to trigger the cascade down and clear up?
This commit is contained in:
Martin Sumner
parent
39fd3e5c87
commit
15c52ae118
2 changed files with 68 additions and 49 deletions
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@ -95,10 +95,10 @@
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-define(BATCH_SIZE, 32).
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-define(BATCHES_TO_CHECK, 8).
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%% How many consecutive files to compact in one run
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-define(MAX_COMPACTION_RUN, 6).
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-define(MAX_COMPACTION_RUN, 8).
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%% Sliding scale to allow preference of longer runs up to maximum
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-define(SINGLEFILE_COMPACTION_TARGET, 40.0).
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-define(MAXRUN_COMPACTION_TARGET, 60.0).
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-define(MAXRUN_COMPACTION_TARGET, 70.0).
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-define(CRC_SIZE, 4).
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-define(DEFAULT_RELOAD_STRATEGY, leveled_codec:inker_reload_strategy([])).
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-define(DEFAULT_WASTE_RETENTION_PERIOD, 86400).
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@ -383,47 +383,55 @@ fetch_inbatches(PositionList, BatchSize, CDB, CheckedList) ->
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KL_List = leveled_cdb:cdb_directfetch(CDB, Batch, key_size),
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fetch_inbatches(Tail, BatchSize, CDB, CheckedList ++ KL_List).
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assess_candidates(AllCandidates, MaxRunLength) ->
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NaiveBestRun = assess_candidates(AllCandidates, MaxRunLength, [], []),
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assess_candidates(AllCandidates, MaxRunLength) when is_integer(MaxRunLength) ->
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% This will take the defaults for other params.
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% Unit tests should pass tuple as params including tested defaults
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assess_candidates(AllCandidates,
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{MaxRunLength,
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?MAXRUN_COMPACTION_TARGET,
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?SINGLEFILE_COMPACTION_TARGET});
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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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SqL = lists:seq(1, MaxRunLength - 1),
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lists:foldl(fun(Counter, BestRun) ->
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SubList = lists:nthtail(Counter,
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AllCandidates),
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assess_candidates(SubList,
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MaxRunLength,
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[],
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BestRun)
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end,
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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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end,
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lists:foldl(AssessFold,
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NaiveBestRun,
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SqL);
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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([], _MaxRunLength, _CurrentRun0, BestAssessment) ->
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assess_candidates([], _Params, _CurrentRun0, BestAssessment) ->
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BestAssessment;
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assess_candidates([HeadC|Tail], MaxRunLength, CurrentRun0, 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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MaxRunLength),
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Params),
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assess_candidates(Tail,
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MaxRunLength,
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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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MaxRunLength)).
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Params)).
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choose_best_assessment(RunToAssess, BestRun, 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, MaxRunLength),
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BestScore = score_run(BestRun, MaxRunLength),
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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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@ -432,18 +440,22 @@ choose_best_assessment(RunToAssess, BestRun, MaxRunLength) ->
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end
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end.
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score_run([], _MaxRunLength) ->
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score_run(Run, MaxRunLength) when is_integer(MaxRunLength) ->
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Params = {MaxRunLength,
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?MAXRUN_COMPACTION_TARGET,
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?SINGLEFILE_COMPACTION_TARGET},
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score_run(Run, Params);
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score_run([], _Params) ->
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0.0;
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score_run(Run, MaxRunLength) ->
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TargetIncr = case MaxRunLength of
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1 ->
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0.0;
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MaxRunSize ->
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(?MAXRUN_COMPACTION_TARGET
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- ?SINGLEFILE_COMPACTION_TARGET)
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/ (MaxRunSize - 1)
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end,
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Target = ?SINGLEFILE_COMPACTION_TARGET + TargetIncr * (length(Run) - 1),
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score_run(Run, {MaxRunLength, MR_CT, SF_CT}) ->
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TargetIncr =
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case MaxRunLength of
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1 ->
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0.0;
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MaxRunSize ->
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(MR_CT - SF_CT) / (MaxRunSize - 1)
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end,
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Target = SF_CT + TargetIncr * (length(Run) - 1),
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RunTotal = lists:foldl(fun(Cand, Acc) ->
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Acc + Cand#candidate.compaction_perc end,
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0.0,
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@ -645,7 +657,7 @@ simple_score_test() ->
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#candidate{compaction_perc = 75.0},
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#candidate{compaction_perc = 76.0},
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#candidate{compaction_perc = 70.0}],
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?assertMatch(-14.0, score_run(Run1, 4)),
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?assertMatch(-4.0, score_run(Run1, 4)),
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Run2 = [#candidate{compaction_perc = 75.0}],
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?assertMatch(-35.0, score_run(Run2, 4)),
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?assertMatch(0.0, score_run([], 4)),
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@ -657,10 +669,16 @@ score_compare_test() ->
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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(6.0, score_run(Run1, 4)),
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?assertMatch(16.0, score_run(Run1, 4)),
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Run2 = [#candidate{compaction_perc = 55.0}],
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?assertMatch(Run1, choose_best_assessment(Run1, Run2, 4)),
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?assertMatch(Run2, choose_best_assessment(Run1 ++ Run2, Run2, 4)).
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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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@ -683,6 +701,7 @@ find_bestrun_test() ->
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%% -define(SINGLEFILE_COMPACTION_TARGET, 40.0).
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%% -define(MAXRUN_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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@ -697,39 +716,39 @@ find_bestrun_test() ->
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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 = 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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CList0 = Block1 ++ Block2 ++ Block3 ++ Block4 ++ Block5,
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?assertMatch(Block4, assess_candidates(CList0, 4, [], [])),
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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, 4, [], [])),
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assess_candidates(CList1, Params, [], [])),
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CList2 = Block4 ++ Block3 ++ Block2 ++ Block1 ++ Block5,
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?assertMatch(Block4, assess_candidates(CList2, 4, [], [])),
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?assertMatch(Block4, assess_candidates(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, 4, [], [])),
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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, 4)),
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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, 5)),
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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, 6)).
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assess_candidates(CList0, setelement(1, Params, 6))).
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test_ledgerkey(Key) ->
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{o, "Bucket", Key, null}.
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@ -467,7 +467,7 @@ load_and_count_withdelete(_Config) ->
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space_clear_ondelete(_Config) ->
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RootPath = testutil:reset_filestructure(),
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StartOpts1 = [{root_path, RootPath},
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{max_journalsize, 20000000},
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{max_journalsize, 10000000},
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{sync_strategy, testutil:sync_strategy()}],
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{ok, Book1} = leveled_bookie:book_start(StartOpts1),
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G2 = fun testutil:generate_compressibleobjects/2,
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@ -586,7 +586,7 @@ space_clear_ondelete(_Config) ->
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io:format("FNsD - Bookie has ~w ledger files " ++
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"after second close~n", [length(FNsD_L)]),
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lists:foreach(fun(FN) ->
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io:format("FNsD - Ledger file if ~s~n", [FN])
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io:format("FNsD - Ledger file is ~s~n", [FN])
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end,
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FNsD_L),
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true = PointB_Journals < length(FNsA_J),
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