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leveled/test/end_to_end/tictac_SUITE.erl
Martin Sumner 0333604fd9 Change to cast in inker/iclerk interaction 
This allows for leveled_iclerk:clerk_stop to be a sync call, so that files will only be closed once the iclerk has stopped.  This is designed ot prevent iclerk crashes during shutdowns when files it is depnding on are closed mid shutdown.
2019-01-24 21:32:54 +00:00

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-module(tictac_SUITE).
-include_lib("common_test/include/ct.hrl").
-include("include/leveled.hrl").
-export([all/0]).
-export([
many_put_compare/1,
index_compare/1,
basic_headonly/1,
tuplebuckets_headonly/1
]).
all() -> [
many_put_compare,
index_compare,
basic_headonly,
tuplebuckets_headonly
].
-define(LMD_FORMAT, "~4..0w~2..0w~2..0w~2..0w~2..0w").
-define(V1_VERS, 1).
-define(MAGIC, 53). % riak_kv -> riak_object
many_put_compare(_Config) ->
TreeSize = small,
SegmentCount = 256 * 256,
% Test requires multiple different databases, so want to mount them all
% on individual file paths
RootPathA = testutil:reset_filestructure("testA"),
RootPathB = testutil:reset_filestructure("testB"),
RootPathC = testutil:reset_filestructure("testC"),
RootPathD = testutil:reset_filestructure("testD"),
% Start the first database, load a test object, close it, start it again
StartOpts1 = [{root_path, RootPathA},
{max_pencillercachesize, 16000},
{sync_strategy, riak_sync}],
{ok, Bookie1} = leveled_bookie:book_start(StartOpts1),
{B1, K1, V1, S1, MD} = {"Bucket",
"Key1.1.4567.4321",
"Value1",
[],
[{"MDK1", "MDV1"}]},
{TestObject, TestSpec} = testutil:generate_testobject(B1, K1, V1, S1, MD),
ok = testutil:book_riakput(Bookie1, TestObject, TestSpec),
testutil:check_forobject(Bookie1, TestObject),
ok = leveled_bookie:book_close(Bookie1),
StartOpts2 = [{root_path, RootPathA},
{max_journalsize, 500000000},
{max_pencillercachesize, 32000},
{sync_strategy, testutil:sync_strategy()}],
{ok, Bookie2} = leveled_bookie:book_start(StartOpts2),
testutil:check_forobject(Bookie2, TestObject),
% Generate 200K objects to be sued within the test, and load them into
% the first store (outputting the generated objects as a list of lists)
% to be used elsewhere
GenList = [2, 20002, 40002, 60002, 80002,
100002, 120002, 140002, 160002, 180002],
CLs = testutil:load_objects(20000,
GenList,
Bookie2,
TestObject,
fun testutil:generate_smallobjects/2,
20000),
% Start a new store, and load the same objects (except fot the original
% test object) into this store
StartOpts3 = [{root_path, RootPathB},
{max_journalsize, 200000000},
{max_pencillercachesize, 16000},
{sync_strategy, testutil:sync_strategy()}],
{ok, Bookie3} = leveled_bookie:book_start(StartOpts3),
lists:foreach(fun(ObjL) -> testutil:riakload(Bookie3, ObjL) end, CLs),
% Now run a tictac query against both stores to see the extent to which
% state between stores is consistent
TicTacQ = {tictactree_obj,
{o_rkv, "Bucket", null, null, true},
TreeSize,
fun(_B, _K) -> accumulate end},
{async, TreeAFolder} = leveled_bookie:book_returnfolder(Bookie2, TicTacQ),
{async, TreeBFolder} = leveled_bookie:book_returnfolder(Bookie3, TicTacQ),
SWA0 = os:timestamp(),
TreeA = TreeAFolder(),
io:format("Build tictac tree with 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWA0)]),
SWB0 = os:timestamp(),
TreeB = TreeBFolder(),
io:format("Build tictac tree with 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWB0)]),
SWC0 = os:timestamp(),
SegList0 = leveled_tictac:find_dirtyleaves(TreeA, TreeB),
io:format("Compare tictac trees with 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWC0)]),
io:format("Tree comparison shows ~w different leaves~n",
[length(SegList0)]),
AltList =
leveled_tictac:find_dirtyleaves(TreeA,
leveled_tictac:new_tree(0, TreeSize)),
io:format("Tree comparison shows ~w altered leaves~n",
[length(AltList)]),
true = length(SegList0) == 1,
% only the test object should be different
true = length(AltList) > 10000,
% check there are a significant number of differences from empty
WrongPartitionTicTacQ = {tictactree_obj,
{o_rkv, "Bucket", null, null, false},
TreeSize,
fun(_B, _K) -> pass end},
{async, TreeAFolder_WP} =
leveled_bookie:book_returnfolder(Bookie2, WrongPartitionTicTacQ),
TreeAWP = TreeAFolder_WP(),
DoubleEmpty =
leveled_tictac:find_dirtyleaves(TreeAWP,
leveled_tictac:new_tree(0, TreeSize)),
true = length(DoubleEmpty) == 0,
% Now run the same query by putting the tree-building responsibility onto
% the fold_objects_fun
ExtractClockFun =
fun(Key, Value) ->
{proxy_object, HeadBin, _Size, _FetchFun} = binary_to_term(Value),
<<?MAGIC:8/integer, ?V1_VERS:8/integer, VclockLen:32/integer,
VclockBin:VclockLen/binary, _Rest/binary>> = HeadBin,
case is_binary(Key) of
true ->
{Key,
lists:sort(binary_to_term(VclockBin))};
false ->
{term_to_binary(Key),
lists:sort(binary_to_term(VclockBin))}
end
end,
FoldObjectsFun =
fun(_Bucket, Key, Value, Acc) ->
leveled_tictac:add_kv(Acc, Key, Value, ExtractClockFun)
end,
FoldAccT = {FoldObjectsFun, leveled_tictac:new_tree(0, TreeSize)},
{async, TreeAObjFolder0} =
leveled_bookie:book_headfold(Bookie2,
o_rkv,
{range, "Bucket", all},
FoldAccT,
false,
true,
false),
SWB0Obj = os:timestamp(),
TreeAObj0 = TreeAObjFolder0(),
io:format("Build tictac tree via object fold with no "++
"presence check and 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWB0Obj)]),
true = length(leveled_tictac:find_dirtyleaves(TreeA, TreeAObj0)) == 0,
InitAccTree = leveled_tictac:new_tree(0, TreeSize),
{async, TreeAObjFolder1} =
leveled_bookie:book_headfold(Bookie2,
?RIAK_TAG,
{range, "Bucket", all},
{FoldObjectsFun,
InitAccTree},
true, true, false),
SWB1Obj = os:timestamp(),
TreeAObj1 = TreeAObjFolder1(),
io:format("Build tictac tree via object fold with "++
"presence check and 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWB1Obj)]),
true = length(leveled_tictac:find_dirtyleaves(TreeA, TreeAObj1)) == 0,
% For an exportable comparison, want hash to be based on something not
% coupled to erlang language - so use exportable query
AltExtractFun =
fun(K, V) ->
{proxy_object, HeadBin, _Size, _FetchFun} = binary_to_term(V),
<<?MAGIC:8/integer, ?V1_VERS:8/integer, VclockLen:32/integer,
VclockBin:VclockLen/binary, _Rest/binary>> = HeadBin,
{term_to_binary(K), VclockBin}
end,
AltFoldObjectsFun =
fun(_Bucket, Key, Value, Acc) ->
leveled_tictac:add_kv(Acc, Key, Value, AltExtractFun)
end,
{async, TreeAAltObjFolder0} =
leveled_bookie:book_headfold(Bookie2,
?RIAK_TAG,
{range, "Bucket", all},
{AltFoldObjectsFun,
InitAccTree},
false, true, false),
SWB2Obj = os:timestamp(),
TreeAAltObj = TreeAAltObjFolder0(),
io:format("Build tictac tree via object fold with no "++
"presence check and 200K objects and alt hash in ~w~n",
[timer:now_diff(os:timestamp(), SWB2Obj)]),
{async, TreeBAltObjFolder0} =
leveled_bookie:book_headfold(Bookie3,
?RIAK_TAG,
{range, "Bucket", all},
{AltFoldObjectsFun,
InitAccTree},
false, true, false),
SWB3Obj = os:timestamp(),
TreeBAltObj = TreeBAltObjFolder0(),
io:format("Build tictac tree via object fold with no "++
"presence check and 200K objects and alt hash in ~w~n",
[timer:now_diff(os:timestamp(), SWB3Obj)]),
DL_ExportFold =
length(leveled_tictac:find_dirtyleaves(TreeBAltObj, TreeAAltObj)),
io:format("Found dirty leaves with exportable comparison of ~w~n",
[DL_ExportFold]),
true = DL_ExportFold == 1,
%% Finding differing keys
FoldKeysFun =
fun(SegListToFind) ->
fun(_B, K, Acc) ->
Seg = get_segment(K, SegmentCount),
case lists:member(Seg, SegListToFind) of
true ->
[K|Acc];
false ->
Acc
end
end
end,
SegQuery = {keylist, o_rkv, "Bucket", {FoldKeysFun(SegList0), []}},
{async, SegKeyFinder} =
leveled_bookie:book_returnfolder(Bookie2, SegQuery),
SWSKL0 = os:timestamp(),
SegKeyList = SegKeyFinder(),
io:format("Finding ~w keys in ~w dirty segments in ~w~n",
[length(SegKeyList),
length(SegList0),
timer:now_diff(os:timestamp(), SWSKL0)]),
true = length(SegKeyList) >= 1,
true = length(SegKeyList) < 10,
true = lists:member("Key1.1.4567.4321", SegKeyList),
% Now remove the object which represents the difference between these
% stores and confirm that the tictac trees will now match
testutil:book_riakdelete(Bookie2, B1, K1, []),
{async, TreeAFolder0} = leveled_bookie:book_returnfolder(Bookie2, TicTacQ),
SWA1 = os:timestamp(),
TreeA0 = TreeAFolder0(),
io:format("Build tictac tree with 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWA1)]),
SegList1 = leveled_tictac:find_dirtyleaves(TreeA0, TreeB),
io:format("Tree comparison following delete shows ~w different leaves~n",
[length(SegList1)]),
true = length(SegList1) == 0,
% Removed test object so tictac trees should match
ok = testutil:book_riakput(Bookie3, TestObject, TestSpec),
{async, TreeBFolder0} = leveled_bookie:book_returnfolder(Bookie3, TicTacQ),
SWB1 = os:timestamp(),
TreeB0 = TreeBFolder0(),
io:format("Build tictac tree with 200K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWB1)]),
SegList2 = leveled_tictac:find_dirtyleaves(TreeA0, TreeB0),
true = SegList2 == SegList0,
% There is an identical difference now the difference is on Bookie3 not
% Bookie 2 (compared to it being in Bookie2 not Bookie3)
ok = leveled_bookie:book_close(Bookie3),
% Replace Bookie 3 with two stores Bookie 4 and Bookie 5 where the ojects
% have been randomly split between the stores
StartOpts4 = [{root_path, RootPathC},
{max_journalsize, 200000000},
{max_pencillercachesize, 24000},
{sync_strategy, testutil:sync_strategy()}],
{ok, Bookie4} = leveled_bookie:book_start(StartOpts4),
StartOpts5 = [{root_path, RootPathD},
{max_journalsize, 200000000},
{max_pencillercachesize, 24000},
{sync_strategy, testutil:sync_strategy()}],
{ok, Bookie5} = leveled_bookie:book_start(StartOpts5),
SplitFun =
fun(Obj) ->
case erlang:phash2(Obj) rem 2 of
0 ->
true;
1 ->
false
end
end,
lists:foreach(fun(ObjL) ->
{ObjLA, ObjLB} = lists:partition(SplitFun, ObjL),
testutil:riakload(Bookie4, ObjLA),
testutil:riakload(Bookie5, ObjLB)
end,
CLs),
% query both the stores, then merge the trees - the result should be the
% same as the result from the tree created aginst the store with both
% partitions
{async, TreeC0Folder} = leveled_bookie:book_returnfolder(Bookie4, TicTacQ),
{async, TreeC1Folder} = leveled_bookie:book_returnfolder(Bookie5, TicTacQ),
SWD0 = os:timestamp(),
TreeC0 = TreeC0Folder(),
io:format("Build tictac tree with 100K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWD0)]),
SWD1 = os:timestamp(),
TreeC1 = TreeC1Folder(),
io:format("Build tictac tree with 100K objects in ~w~n",
[timer:now_diff(os:timestamp(), SWD1)]),
TreeC2 = leveled_tictac:merge_trees(TreeC0, TreeC1),
SegList3 = leveled_tictac:find_dirtyleaves(TreeC2, TreeB),
io:format("Tree comparison following delete shows ~w different leaves~n",
[length(SegList3)]),
true = length(SegList3) == 0,
ok = leveled_bookie:book_close(Bookie2),
ok = leveled_bookie:book_close(Bookie4),
ok = leveled_bookie:book_close(Bookie5).
index_compare(_Config) ->
TreeSize = xxsmall,
LS = 2000,
JS = 50000000,
SS = testutil:sync_strategy(),
SegmentCount = 64 * 64,
% Test requires multiple different databases, so want to mount them all
% on individual file paths
RootPathA = testutil:reset_filestructure("testA"),
RootPathB = testutil:reset_filestructure("testB"),
RootPathC = testutil:reset_filestructure("testC"),
RootPathD = testutil:reset_filestructure("testD"),
% Book1A to get all objects
{ok, Book1A} = leveled_bookie:book_start(RootPathA, LS, JS, SS),
% Book1B/C/D will have objects partitioned across it
{ok, Book1B} = leveled_bookie:book_start(RootPathB, LS, JS, SS),
{ok, Book1C} = leveled_bookie:book_start(RootPathC, LS, JS, SS),
{ok, Book1D} = leveled_bookie:book_start(RootPathD, LS, JS, SS),
% Generate nine lists of objects
BucketBin = list_to_binary("Bucket"),
GenMapFun =
fun(_X) ->
V = testutil:get_compressiblevalue(),
Indexes = testutil:get_randomindexes_generator(8),
testutil:generate_objects(10000, binary_uuid, [], V, Indexes)
end,
ObjLists = lists:map(GenMapFun, lists:seq(1, 9)),
% Load all nine lists into Book1A
lists:foreach(fun(ObjL) -> testutil:riakload(Book1A, ObjL) end,
ObjLists),
% Split nine lists across Book1B to Book1D, three object lists in each
lists:foreach(fun(ObjL) -> testutil:riakload(Book1B, ObjL) end,
lists:sublist(ObjLists, 1, 3)),
lists:foreach(fun(ObjL) -> testutil:riakload(Book1C, ObjL) end,
lists:sublist(ObjLists, 4, 3)),
lists:foreach(fun(ObjL) -> testutil:riakload(Book1D, ObjL) end,
lists:sublist(ObjLists, 7, 3)),
GetTicTacTreeFun =
fun(X, Bookie) ->
SW = os:timestamp(),
ST = "!",
ET = "|",
Q = {tictactree_idx,
{BucketBin, "idx" ++ integer_to_list(X) ++ "_bin", ST, ET},
TreeSize,
fun(_B, _K) -> accumulate end},
{async, Folder} = leveled_bookie:book_returnfolder(Bookie, Q),
R = Folder(),
io:format("TicTac Tree for index ~w took " ++
"~w microseconds~n",
[X, timer:now_diff(os:timestamp(), SW)]),
R
end,
% Get a TicTac tree representing one of the indexes in Bucket A
TicTacTree1_Full = GetTicTacTreeFun(1, Book1A),
TicTacTree1_P1 = GetTicTacTreeFun(1, Book1B),
TicTacTree1_P2 = GetTicTacTreeFun(1, Book1C),
TicTacTree1_P3 = GetTicTacTreeFun(1, Book1D),
% Merge the tree across the partitions
TicTacTree1_Joined = lists:foldl(fun leveled_tictac:merge_trees/2,
TicTacTree1_P1,
[TicTacTree1_P2, TicTacTree1_P3]),
% Go compare! Also check we're not comparing empty trees
DL1_0 = leveled_tictac:find_dirtyleaves(TicTacTree1_Full,
TicTacTree1_Joined),
EmptyTree = leveled_tictac:new_tree(empty, TreeSize),
DL1_1 = leveled_tictac:find_dirtyleaves(TicTacTree1_Full, EmptyTree),
true = DL1_0 == [],
true = length(DL1_1) > 100,
ok = leveled_bookie:book_close(Book1A),
ok = leveled_bookie:book_close(Book1B),
ok = leveled_bookie:book_close(Book1C),
ok = leveled_bookie:book_close(Book1D),
% Double chekc all is well still after a restart
% Book1A to get all objects
{ok, Book2A} = leveled_bookie:book_start(RootPathA, LS, JS, SS),
% Book1B/C/D will have objects partitioned across it
{ok, Book2B} = leveled_bookie:book_start(RootPathB, LS, JS, SS),
{ok, Book2C} = leveled_bookie:book_start(RootPathC, LS, JS, SS),
{ok, Book2D} = leveled_bookie:book_start(RootPathD, LS, JS, SS),
% Get a TicTac tree representing one of the indexes in Bucket A
TicTacTree2_Full = GetTicTacTreeFun(2, Book2A),
TicTacTree2_P1 = GetTicTacTreeFun(2, Book2B),
TicTacTree2_P2 = GetTicTacTreeFun(2, Book2C),
TicTacTree2_P3 = GetTicTacTreeFun(2, Book2D),
% Merge the tree across the partitions
TicTacTree2_Joined = lists:foldl(fun leveled_tictac:merge_trees/2,
TicTacTree2_P1,
[TicTacTree2_P2, TicTacTree2_P3]),
% Go compare! Also check we're not comparing empty trees
DL2_0 = leveled_tictac:find_dirtyleaves(TicTacTree2_Full,
TicTacTree2_Joined),
EmptyTree = leveled_tictac:new_tree(empty, TreeSize),
DL2_1 = leveled_tictac:find_dirtyleaves(TicTacTree2_Full, EmptyTree),
true = DL2_0 == [],
true = length(DL2_1) > 100,
IdxSpc = {add, "idx2_bin", "zz999"},
{TestObj, TestSpc} = testutil:generate_testobject(BucketBin,
term_to_binary("K9.Z"),
"Value1",
[IdxSpc],
[{"MDK1", "MDV1"}]),
ok = testutil:book_riakput(Book2C, TestObj, TestSpc),
testutil:check_forobject(Book2C, TestObj),
TicTacTree3_Full = GetTicTacTreeFun(2, Book2A),
TicTacTree3_P1 = GetTicTacTreeFun(2, Book2B),
TicTacTree3_P2 = GetTicTacTreeFun(2, Book2C),
TicTacTree3_P3 = GetTicTacTreeFun(2, Book2D),
% Merge the tree across the partitions
TicTacTree3_Joined = lists:foldl(fun leveled_tictac:merge_trees/2,
TicTacTree3_P1,
[TicTacTree3_P2, TicTacTree3_P3]),
% Find all keys index, and then just the last key
IdxQ1 = {index_query,
BucketBin,
{fun testutil:foldkeysfun/3, []},
{"idx2_bin", "zz", "zz|"},
{true, undefined}},
{async, IdxFolder1} = leveled_bookie:book_returnfolder(Book2C, IdxQ1),
true = IdxFolder1() >= 1,
DL_3to2B = leveled_tictac:find_dirtyleaves(TicTacTree2_P1,
TicTacTree3_P1),
DL_3to2C = leveled_tictac:find_dirtyleaves(TicTacTree2_P2,
TicTacTree3_P2),
DL_3to2D = leveled_tictac:find_dirtyleaves(TicTacTree2_P3,
TicTacTree3_P3),
io:format("Individual tree comparison found dirty leaves of ~w ~w ~w~n",
[DL_3to2B, DL_3to2C, DL_3to2D]),
true = length(DL_3to2B) == 0,
true = length(DL_3to2C) == 1,
true = length(DL_3to2D) == 0,
% Go compare! Should find a difference in one leaf
DL3_0 = leveled_tictac:find_dirtyleaves(TicTacTree3_Full,
TicTacTree3_Joined),
io:format("Different leaves count ~w~n", [length(DL3_0)]),
true = length(DL3_0) == 1,
% Now we want to find for the {Term, Key} pairs that make up the segment
% diferrence (there should only be one)
%
% We want the database to filter on segment - so this doesn't have the
% overheads of key listing
FoldKeysIndexQFun =
fun(_Bucket, {Term, Key}, Acc) ->
Seg = get_segment(Key, SegmentCount),
case lists:member(Seg, DL3_0) of
true ->
[{Term, Key}|Acc];
false ->
Acc
end
end,
MismatchQ = {index_query,
BucketBin,
{FoldKeysIndexQFun, []},
{"idx2_bin", "!", "|"},
{true, undefined}},
{async, MMFldr_2A} = leveled_bookie:book_returnfolder(Book2A, MismatchQ),
{async, MMFldr_2B} = leveled_bookie:book_returnfolder(Book2B, MismatchQ),
{async, MMFldr_2C} = leveled_bookie:book_returnfolder(Book2C, MismatchQ),
{async, MMFldr_2D} = leveled_bookie:book_returnfolder(Book2D, MismatchQ),
SWSS = os:timestamp(),
SL_Joined = MMFldr_2B() ++ MMFldr_2C() ++ MMFldr_2D(),
SL_Full = MMFldr_2A(),
io:format("Segment search across both clusters took ~w~n",
[timer:now_diff(os:timestamp(), SWSS)]),
io:format("Joined SegList ~w~n", [SL_Joined]),
io:format("Full SegList ~w~n", [SL_Full]),
Diffs = lists:subtract(SL_Full, SL_Joined)
++ lists:subtract(SL_Joined, SL_Full),
io:format("Differences between lists ~w~n", [Diffs]),
% The actual difference is discovered
true = lists:member({"zz999", term_to_binary("K9.Z")}, Diffs),
% Without discovering too many others
true = length(Diffs) < 20,
ok = leveled_bookie:book_close(Book2A),
ok = leveled_bookie:book_close(Book2B),
ok = leveled_bookie:book_close(Book2C),
ok = leveled_bookie:book_close(Book2D).
tuplebuckets_headonly(_Config) ->
ObjectCount = 60000,
RootPathHO = testutil:reset_filestructure("testTBHO"),
StartOpts1 = [{root_path, RootPathHO},
{max_pencillercachesize, 16000},
{sync_strategy, none},
{head_only, with_lookup},
{max_journalsize, 500000}],
{ok, Bookie1} = leveled_bookie:book_start(StartOpts1),
ObjectSpecFun =
fun(Op) ->
fun(N) ->
Bucket = {<<"BucketType">>, <<"B", 0:4/integer, N:4/integer>>},
Key = <<"K", N:32/integer>>,
<<Hash:32/integer, _RestBN/bitstring>> =
crypto:hash(md5, <<N:32/integer>>),
{Op, Bucket, Key, null, Hash}
end
end,
ObjectSpecL = lists:map(ObjectSpecFun(add), lists:seq(1, ObjectCount)),
SW0 = os:timestamp(),
ok = load_objectspecs(ObjectSpecL, 32, Bookie1),
io:format("Loaded an object count of ~w in ~w ms~n",
[ObjectCount, timer:now_diff(os:timestamp(), SW0)/1000]),
CheckHeadFun =
fun({add, B, K, null, H}) ->
{ok, H} =
leveled_bookie:book_headonly(Bookie1, B, K, null)
end,
lists:foreach(CheckHeadFun, ObjectSpecL),
BucketList =
lists:map(fun(I) ->
{<<"BucketType">>, <<"B", 0:4/integer, I:4/integer>>}
end,
lists:seq(0, 15)),
FoldHeadFun =
fun(B, {K, null}, V, Acc) ->
[{add, B, K, null, V}|Acc]
end,
SW1 = os:timestamp(),
{async, HeadRunner1} =
leveled_bookie:book_headfold(Bookie1,
?HEAD_TAG,
{bucket_list, BucketList},
{FoldHeadFun, []},
false, false,
false),
ReturnedObjSpecL1 = lists:reverse(HeadRunner1()),
[FirstItem|_Rest] = ReturnedObjSpecL1,
LastItem = lists:last(ReturnedObjSpecL1),
io:format("Returned ~w objects with first ~w and last ~w in ~w ms~n",
[length(ReturnedObjSpecL1),
FirstItem, LastItem,
timer:now_diff(os:timestamp(), SW1)/1000]),
true = ReturnedObjSpecL1 == lists:sort(ObjectSpecL),
{add, {TB, B1}, K1, null, _H1} = FirstItem,
{add, {TB, BL}, KL, null, _HL} = LastItem,
SegList = [testutil:get_aae_segment({TB, B1}, K1),
testutil:get_aae_segment({TB, BL}, KL)],
SW2 = os:timestamp(),
{async, HeadRunner2} =
leveled_bookie:book_headfold(Bookie1,
?HEAD_TAG,
{bucket_list, BucketList},
{FoldHeadFun, []},
false, false,
SegList),
ReturnedObjSpecL2 = lists:reverse(HeadRunner2()),
io:format("Returned ~w objects using seglist in ~w ms~n",
[length(ReturnedObjSpecL2),
timer:now_diff(os:timestamp(), SW2)/1000]),
true = length(ReturnedObjSpecL2) < (ObjectCount/1000 + 2),
% Not too many false positives
true = lists:member(FirstItem, ReturnedObjSpecL2),
true = lists:member(LastItem, ReturnedObjSpecL2),
leveled_bookie:book_destroy(Bookie1).
basic_headonly(_Config) ->
ObjectCount = 200000,
RemoveCount = 100,
basic_headonly_test(ObjectCount, RemoveCount, with_lookup),
basic_headonly_test(ObjectCount, RemoveCount, no_lookup).
basic_headonly_test(ObjectCount, RemoveCount, HeadOnly) ->
% Load some AAE type objects into Leveled using the read_only mode. This
% should allow for the items to be added in batches. Confirm that the
% journal is garbage collected as expected, and that it is possible to
% perform a fold_heads style query
RootPathHO = testutil:reset_filestructure("testHO"),
StartOpts1 = [{root_path, RootPathHO},
{max_pencillercachesize, 16000},
{sync_strategy, sync},
{head_only, HeadOnly},
{max_journalsize, 500000}],
{ok, Bookie1} = leveled_bookie:book_start(StartOpts1),
{B1, K1, V1, S1, MD} = {"Bucket",
"Key1.1.4567.4321",
"Value1",
[],
[{"MDK1", "MDV1"}]},
{TestObject, TestSpec} = testutil:generate_testobject(B1, K1, V1, S1, MD),
{unsupported_message, put} =
testutil:book_riakput(Bookie1, TestObject, TestSpec),
ObjectSpecFun =
fun(Op) ->
fun(N) ->
Bucket = <<"B", N:8/integer>>,
Key = <<"K", N:32/integer>>,
<<SegmentID:20/integer, _RestBS/bitstring>> =
crypto:hash(md5, term_to_binary({Bucket, Key})),
<<Hash:32/integer, _RestBN/bitstring>> =
crypto:hash(md5, <<N:32/integer>>),
{Op, <<SegmentID:32/integer>>, Bucket, Key, Hash}
end
end,
ObjectSpecL = lists:map(ObjectSpecFun(add), lists:seq(1, ObjectCount)),
SW0 = os:timestamp(),
ok = load_objectspecs(ObjectSpecL, 32, Bookie1),
io:format("Loaded an object count of ~w in ~w microseconds with ~w~n",
[ObjectCount, timer:now_diff(os:timestamp(), SW0), HeadOnly]),
FoldFun =
fun(_B, _K, V, {HashAcc, CountAcc}) ->
{HashAcc bxor V, CountAcc + 1}
end,
InitAcc = {0, 0},
RunnerDefinition =
{foldheads_allkeys, h, {FoldFun, InitAcc},
false, false, false, false, false},
{async, Runner1} =
leveled_bookie:book_returnfolder(Bookie1, RunnerDefinition),
SW1 = os:timestamp(),
{AccH1, AccC1} = Runner1(),
io:format("AccH and AccC of ~w ~w in ~w microseconds~n",
[AccH1, AccC1, timer:now_diff(os:timestamp(), SW1)]),
true = AccC1 == ObjectCount,
JFP = RootPathHO ++ "/journal/journal_files",
{ok, FNs} = file:list_dir(JFP),
ok = leveled_bookie:book_trimjournal(Bookie1),
WaitForTrimFun =
fun(N, _Acc) ->
{ok, PollFNs} = file:list_dir(JFP),
case length(PollFNs) < length(FNs) of
true ->
true;
false ->
timer:sleep(N * 1000),
false
end
end,
true = lists:foldl(WaitForTrimFun, false, [1, 2, 3, 5, 8, 13]),
{ok, FinalFNs} = file:list_dir(JFP),
ok = leveled_bookie:book_trimjournal(Bookie1),
% CCheck a second trim is still OK
[{add, SegmentID0, Bucket0, Key0, Hash0}|_Rest] = ObjectSpecL,
case HeadOnly of
with_lookup ->
% If we allow HEAD_TAG to be suubject to a lookup, then test this
% here
{ok, Hash0} =
leveled_bookie:book_headonly(Bookie1,
SegmentID0,
Bucket0,
Key0),
CheckHeadFun =
fun({add, SegID, B, K, H}) ->
{ok, H} =
leveled_bookie:book_headonly(Bookie1, SegID, B, K)
end,
lists:foreach(CheckHeadFun, ObjectSpecL);
no_lookup ->
{unsupported_message, head} =
leveled_bookie:book_head(Bookie1,
SegmentID0,
{Bucket0, Key0},
h),
{unsupported_message, head} =
leveled_bookie:book_headonly(Bookie1,
SegmentID0,
Bucket0,
Key0)
end,
ok = leveled_bookie:book_close(Bookie1),
{ok, FinalJournals} = file:list_dir(JFP),
io:format("Trim has reduced journal count from " ++
"~w to ~w and ~w after restart~n",
[length(FNs), length(FinalFNs), length(FinalJournals)]),
{ok, Bookie2} = leveled_bookie:book_start(StartOpts1),
{async, Runner2} =
leveled_bookie:book_returnfolder(Bookie2, RunnerDefinition),
{AccH2, AccC2} = Runner2(),
true = AccC2 == ObjectCount,
case HeadOnly of
with_lookup ->
% If we allow HEAD_TAG to be suubject to a lookup, then test this
% here
{ok, Hash0} =
leveled_bookie:book_head(Bookie2,
SegmentID0,
{Bucket0, Key0},
h);
no_lookup ->
{unsupported_message, head} =
leveled_bookie:book_head(Bookie2,
SegmentID0,
{Bucket0, Key0},
h)
end,
RemoveSpecL0 = lists:sublist(ObjectSpecL, RemoveCount),
RemoveSpecL1 =
lists:map(fun(Spec) -> setelement(1, Spec, remove) end, RemoveSpecL0),
ok = load_objectspecs(RemoveSpecL1, 32, Bookie2),
{async, Runner3} =
leveled_bookie:book_returnfolder(Bookie2, RunnerDefinition),
{AccH3, AccC3} = Runner3(),
true = AccC3 == (ObjectCount - RemoveCount),
false = AccH3 == AccH2,
ok = leveled_bookie:book_close(Bookie2).
load_objectspecs([], _SliceSize, _Bookie) ->
ok;
load_objectspecs(ObjectSpecL, SliceSize, Bookie)
when length(ObjectSpecL) < SliceSize ->
load_objectspecs(ObjectSpecL, length(ObjectSpecL), Bookie);
load_objectspecs(ObjectSpecL, SliceSize, Bookie) ->
{Head, Tail} = lists:split(SliceSize, ObjectSpecL),
case leveled_bookie:book_mput(Bookie, Head) of
ok ->
load_objectspecs(Tail, SliceSize, Bookie);
pause ->
timer:sleep(10),
load_objectspecs(Tail, SliceSize, Bookie)
end.
get_segment(K, SegmentCount) ->
BinKey =
case is_binary(K) of
true ->
K;
false ->
term_to_binary(K)
end,
{SegmentID, ExtraHash} = leveled_codec:segment_hash(BinKey),
SegHash = (ExtraHash band 65535) bsl 16 + SegmentID,
leveled_tictac:get_segment(SegHash, SegmentCount).
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