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% Licensed under the Apache License, Version 2.0 (the "License"); you may not
% use this file except in compliance with the License. You may obtain a copy of
% the License at
% Unless required by applicable law or agreed to in writing, software
% distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
% WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
% License for the specific language governing permissions and limitations under
% the License.
% This is an implementation of an external N-way merge sort. It's primary
% purpose is to be used during database compaction as an optimization for
% managing the docid btree.
% Trunk currently writes the docid btree as its compacting the database but
% this is quite inneficient as its written out of order in the general case
% as writes are ordered by update_seq.
% The general design of this module is a very standard merge sort with one
% caveat due to append only files. This is described in more detail in the
% sorting phase.
% The basic algorithm is in two halves. The first half stores KV pairs to disk
% which is then followed by the actual sorting phase that streams KV's back
% to the client using a fold-like function. After some basic definitions we'll
% describe both phases.
% Key/Value apairs (aka, KV pairs, or KVs) are simply lists of two-tuples with
% a key as the first element and an arbitrary value as the second. The key of
% this pair is what used to determine the sort order based on native Erlang
% term comparison.
% Internally, KVs are stored as lists with a max size defined by
% #ems.chain_chunk. These lists are then chained together on disk using disk
% offsets as a poor man's linked list. The basic format of a list looks like
% {KVs, DiskOffset} where DiskOffset is either the atom nil which means "end
% of the list" or an integer that is a file position offset that is the
% location of another {KVs, DiskOffset} term. The head of each list is
% referred to with a single DiskOffset. The set of terms that extend from
% this initial DiskOffset to the last {KVs, nil} term is referred to in the
% code as a chain. Two important facts are that one call to couch_emsort:add/2
% creates a single chain, and that a chain is always sorted on disk (though its
% possible to be sorted in descending order which will be discussed later).
% The second major internal structure is the back bone. This is a list of
% chains that has a quite similar structure to chains but contains different
% data types and has no guarantee on ordering. The back bone is merely the
% list of all head DiskOffsets. The structure has the similar structure of
% {DiskOffsets, DiskOffset} that we use for chains, except that DiskOffsets is
% a list of integers that refer to the heads of chains. The maximum size of
% DiskOffsets is defined by #ems.bb_chunk. It is important to note that the
% backbone has no defined ordering. The other thing of note is that the RAM
% bounds are loosely defined as:
% #ems.bb_chunk * #ems.chain_chunk * avg_size(KV).
% Build Phase
% -----------
% As mentioned, each call to couch_emsort:add/2 creates a chain from the
% list of KVs that are passed in. This list is first sorted and then the
% chain is created by foldr-ing (note: r) across the list to build the
% chain on disk. It is important to note that the final chain is then
% sorted in ascending order on disk.
% Sort Phase
% ----------
% The sort phase is where the merge sort kicks in. This is generally your
% average merge sort with a caveat for append only storage. First the
% general outline.
% The general outline for this sort is that it iteratively merges chains
% in the backbone until less than #ems.bb_chunk chains exist. At this
% point it switches to the last merge sort phase where it just streams
% the sorted KVs back to the client using a fold function.
% The general chain merging is a pretty standard merge sort. You load up
% the initial KVs from each phase, pick the next one in sort order and
% then when you run out of KVs you're left with a single DiskOffset for
% the head of a single chain that represents the merge. These new
% DiskOffsets are used to build the new back bone.
% The one caveat here is that we're using append only storage. This is
% important because once we make a pass we've effectively reversed the
% sort order of each chain. Ie, the first merge results in chains that
% are ordered in descending order. Since, one pass reverses the list
% the trick is that each phase does two passes. The first phase picks
% the smallest KV to write next and the second phase picks the largest.
% In this manner each time we do a back bone merge we end up with chains
% that are always sorted in an ascending order.
% The one downfall is that in the interest of simplicity the sorting is
% restricted to Erlang's native term sorting. A possible extension would
% be to allow two comparison functions to be used, but this module is
% currently only used for docid sorting which is hardcoded to be raw
% Erlang ordering.
% Diagram
% -------
% If it helps, this is a general diagram of the internal structures. A
% couple points to note since this is ASCII art. The BB pointers across
% the top are lists of chains going down. Each BBN item is one of the
% {DiskOffsets, DiskOffset} structures discussed earlier. Going down,
% the CMN nodes are actually representing #ems.bb_chunk chains in parallel
% going off the back bone. It is important and not represented in this
% diagram that within these groups the chains don't have to be the same
% length. That's just a limitiationg of my ASCII artistic abilities.
% The BBN* node is marked with a * to denote that it is the only state
% that we store when writing headeres to disk as it has pointers that
% lead us to all data in the tree.
% BB1 <- BB2 <- BB3 <- BBN*
% | | | |
% v v v v
% CA1 CB1 CC1 CD1
% | | |
% v v v
% CA2 CC2 CD2
% | |
% v v
% CA3 CD3
-export([open/1, open/2, get_fd/1, get_state/1]).
-export([add/2, merge/1, sort/1, iter/1, next/1]).
-record(ems, {
bb_chunk = 10,
chain_chunk = 100
open(Fd) ->
{ok, #ems{fd=Fd}}.
open(Fd, Options) ->
{ok, set_options(#ems{fd=Fd}, Options)}.
set_options(Ems, []) ->
set_options(Ems, [{root, Root} | Rest]) ->
set_options(Ems#ems{root=Root}, Rest);
set_options(Ems, [{chain_chunk, Count} | Rest]) when is_integer(Count) ->
set_options(Ems#ems{chain_chunk=Count}, Rest);
set_options(Ems, [{back_bone_chunk, Count} | Rest]) when is_integer(Count) ->
set_options(Ems#ems{bb_chunk=Count}, Rest).
get_fd(#ems{fd=Fd}) ->
get_state(#ems{root=Root}) ->
add(Ems, []) ->
{ok, Ems};
add(Ems, KVs) ->
Pos = write_kvs(Ems, KVs),
{ok, add_bb_pos(Ems, Pos)}.
sort(#ems{}=Ems) ->
{ok, Ems1} = merge(Ems),
merge(#ems{root=undefined}=Ems) ->
{ok, Ems};
merge(#ems{}=Ems) ->
{ok, decimate(Ems)}.
iter(#ems{root=undefined}=Ems) ->
{ok, {Ems, []}};
iter(#ems{root={BB, nil}}=Ems) ->
Chains = init_chains(Ems, small, BB),
{ok, {Ems, Chains}};
iter(#ems{root={_, _}}) ->
{error, not_merged}.
next({_Ems, []}) ->
next({Ems, Chains}) ->
{KV, RestChains} = choose_kv(small, Ems, Chains),
{ok, KV, {Ems, RestChains}}.
add_bb_pos(#ems{root=undefined}=Ems, Pos) ->
Ems#ems{root={[Pos], nil}};
add_bb_pos(#ems{root={BB, Prev}}=Ems, Pos) ->
{NewBB, NewPrev} = append_item(Ems, {BB, Prev}, Pos, Ems#ems.bb_chunk),
Ems#ems{root={NewBB, NewPrev}}.
write_kvs(Ems, KVs) ->
% Write the list of KV's to disk in sorted order in chunks
% of 100. Also make sure that the order is so that they
% can be streamed in asscending order.
{LastKVs, LastPos} =
lists:foldr(fun(KV, Acc) ->
append_item(Ems, Acc, KV, Ems#ems.chain_chunk)
end, {[], nil}, lists:sort(KVs)),
{ok, Final, _} = couch_file:append_term(Ems#ems.fd, {LastKVs, LastPos}),
decimate(#ems{root={_BB, nil}}=Ems) ->
% We have less than bb_chunk backbone pointers so we're
% good to start streaming KV's back to the client.
decimate(#ems{root={BB, NextBB}}=Ems) ->
% To make sure we have a bounded amount of data in RAM
% at any given point we first need to decimate the data
% by performing the first couple iterations of a merge
% sort writing the intermediate results back to disk.
% The first pass gives us a sort with pointers linked from
% largest to smallest.
{RevBB, RevNextBB} = merge_back_bone(Ems, small, BB, NextBB),
% We have to run a second pass so that links are pointed
% back from smallest to largest.
{FwdBB, FwdNextBB} = merge_back_bone(Ems, big, RevBB, RevNextBB),
% Continue deicmating until we have an acceptable bound on
% the number of keys to use.
decimate(Ems#ems{root={FwdBB, FwdNextBB}}).
merge_back_bone(Ems, Choose, BB, NextBB) ->
BBPos = merge_chains(Ems, Choose, BB),
merge_rest_back_bone(Ems, Choose, NextBB, {[BBPos], nil}).
merge_rest_back_bone(_Ems, _Choose, nil, Acc) ->
merge_rest_back_bone(Ems, Choose, BBPos, Acc) ->
{ok, {BB, NextBB}} = couch_file:pread_term(Ems#ems.fd, BBPos),
NewPos = merge_chains(Ems, Choose, BB),
{NewBB, NewPrev} = append_item(Ems, Acc, NewPos, Ems#ems.bb_chunk),
merge_rest_back_bone(Ems, Choose, NextBB, {NewBB, NewPrev}).
merge_chains(Ems, Choose, BB) ->
Chains = init_chains(Ems, Choose, BB),
merge_chains(Ems, Choose, Chains, {[], nil}).
merge_chains(Ems, _Choose, [], ChainAcc) ->
{ok, CPos, _} = couch_file:append_term(Ems#ems.fd, ChainAcc),
merge_chains(#ems{chain_chunk=CC}=Ems, Choose, Chains, Acc) ->
{KV, RestChains} = choose_kv(Choose, Ems, Chains),
{NewKVs, NewPrev} = append_item(Ems, Acc, KV, CC),
merge_chains(Ems, Choose, RestChains, {NewKVs, NewPrev}).
init_chains(Ems, Choose, BB) ->
Chains = lists:map(fun(CPos) ->
{ok, {KVs, NextKVs}} = couch_file:pread_term(Ems#ems.fd, CPos),
{KVs, NextKVs}
end, BB),
order_chains(Choose, Chains).
order_chains(small, Chains) -> lists:sort(Chains);
order_chains(big, Chains) -> lists:reverse(lists:sort(Chains)).
choose_kv(_Choose, _Ems, [{[KV], nil} | Rest]) ->
{KV, Rest};
choose_kv(Choose, Ems, [{[KV], Pos} | RestChains]) ->
{ok, Chain} = couch_file:pread_term(Ems#ems.fd, Pos),
case Choose of
small -> {KV, ins_small_chain(RestChains, Chain, [])};
big -> {KV, ins_big_chain(RestChains, Chain, [])}
choose_kv(Choose, _Ems, [{[KV | RestKVs], Prev} | RestChains]) ->
case Choose of
small -> {KV, ins_small_chain(RestChains, {RestKVs, Prev}, [])};
big -> {KV, ins_big_chain(RestChains, {RestKVs, Prev}, [])}
ins_small_chain([{[{K1,_}|_],_}=C1|Rest], {[{K2,_}|_],_}=C2, Acc) when K1<K2 ->
ins_small_chain(Rest, C2, [C1 | Acc]);
ins_small_chain(Rest, Chain, Acc) ->
lists:reverse(Acc, [Chain | Rest]).
ins_big_chain([{[{K1,_}|_],_}=C1|Rest], {[{K2,_}|_],_}=C2, Acc) when K1>K2 ->
ins_big_chain(Rest, C2, [C1 | Acc]);
ins_big_chain(Rest, Chain, Acc) ->
lists:reverse(Acc, [Chain | Rest]).
append_item(Ems, {List, Prev}, Pos, Size) when length(List) >= Size ->
{ok, PrevList, _} = couch_file:append_term(Ems#ems.fd, {List, Prev}),
{[Pos], PrevList};
append_item(_Ems, {List, Prev}, Pos, _Size) ->
{[Pos | List], Prev}.