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rocksdb/table/merger.cc

327 lines
9.2 KiB

// Copyright (c) 2013, Facebook, Inc. All rights reserved.
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree. An additional grant
// of patent rights can be found in the PATENTS file in the same directory.
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#include "table/merger.h"
#include <vector>
#include "rocksdb/comparator.h"
#include "rocksdb/iterator.h"
#include "rocksdb/options.h"
#include "table/iter_heap.h"
#include "table/iterator_wrapper.h"
#include "util/arena.h"
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
#include "util/heap.h"
#include "util/stop_watch.h"
#include "util/perf_context_imp.h"
#include "util/autovector.h"
namespace rocksdb {
// Without anonymous namespace here, we fail the warning -Wmissing-prototypes
namespace {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
typedef BinaryHeap<IteratorWrapper*, MaxIteratorComparator> MergerMaxIterHeap;
typedef BinaryHeap<IteratorWrapper*, MinIteratorComparator> MergerMinIterHeap;
} // namespace
const size_t kNumIterReserve = 4;
class MergingIterator : public Iterator {
public:
MergingIterator(const Comparator* comparator, Iterator** children, int n,
bool is_arena_mode)
: is_arena_mode_(is_arena_mode),
comparator_(comparator),
current_(nullptr),
direction_(kForward),
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
minHeap_(comparator_) {
children_.resize(n);
for (int i = 0; i < n; i++) {
children_[i].Set(children[i]);
}
for (auto& child : children_) {
if (child.Valid()) {
minHeap_.push(&child);
}
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentForward();
}
virtual void AddIterator(Iterator* iter) {
assert(direction_ == kForward);
children_.emplace_back(iter);
auto new_wrapper = children_.back();
if (new_wrapper.Valid()) {
minHeap_.push(&new_wrapper);
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentForward();
}
}
virtual ~MergingIterator() {
for (auto& child : children_) {
child.DeleteIter(is_arena_mode_);
}
}
virtual bool Valid() const override { return (current_ != nullptr); }
virtual void SeekToFirst() override {
ClearHeaps();
for (auto& child : children_) {
child.SeekToFirst();
if (child.Valid()) {
minHeap_.push(&child);
}
}
direction_ = kForward;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentForward();
}
virtual void SeekToLast() override {
ClearHeaps();
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
InitMaxHeap();
for (auto& child : children_) {
child.SeekToLast();
if (child.Valid()) {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
maxHeap_->push(&child);
}
}
direction_ = kReverse;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentReverse();
}
virtual void Seek(const Slice& target) override {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
ClearHeaps();
for (auto& child : children_) {
{
PERF_TIMER_GUARD(seek_child_seek_time);
child.Seek(target);
}
PERF_COUNTER_ADD(seek_child_seek_count, 1);
if (child.Valid()) {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
PERF_TIMER_GUARD(seek_min_heap_time);
minHeap_.push(&child);
}
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
direction_ = kForward;
{
PERF_TIMER_GUARD(seek_min_heap_time);
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentForward();
}
}
virtual void Next() override {
assert(Valid());
// Ensure that all children are positioned after key().
// If we are moving in the forward direction, it is already
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// true for all of the non-current children since current_ is
// the smallest child and key() == current_->key().
if (direction_ != kForward) {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// Otherwise, advance the non-current children. We advance current_
// just after the if-block.
ClearHeaps();
for (auto& child : children_) {
if (&child != current_) {
child.Seek(key());
if (child.Valid() &&
comparator_->Compare(key(), child.key()) == 0) {
child.Next();
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
}
if (child.Valid()) {
minHeap_.push(&child);
}
}
direction_ = kForward;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// The loop advanced all non-current children to be > key() so current_
// should still be strictly the smallest key.
assert(current_ == CurrentForward());
}
// as the current points to the current record. move the iterator forward.
current_->Next();
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
if (current_->Valid()) {
// current is still valid after the Next() call above. Call
// replace_top() to restore the heap property. When the same child
// iterator yields a sequence of keys, this is cheap.
minHeap_.replace_top(current_);
} else {
// current stopped being valid, remove it from the heap.
minHeap_.pop();
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentForward();
}
virtual void Prev() override {
assert(Valid());
// Ensure that all children are positioned before key().
// If we are moving in the reverse direction, it is already
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// true for all of the non-current children since current_ is
// the largest child and key() == current_->key().
if (direction_ != kReverse) {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// Otherwise, retreat the non-current children. We retreat current_
// just after the if-block.
ClearHeaps();
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
InitMaxHeap();
for (auto& child : children_) {
if (&child != current_) {
child.Seek(key());
if (child.Valid()) {
// Child is at first entry >= key(). Step back one to be < key()
child.Prev();
} else {
// Child has no entries >= key(). Position at last entry.
child.SeekToLast();
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
}
if (child.Valid()) {
maxHeap_->push(&child);
}
}
direction_ = kReverse;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// The loop retreated all non-current children to be < key() so current_
// should still be strictly the largest key.
assert(current_ == CurrentReverse());
}
current_->Prev();
if (current_->Valid()) {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// current is still valid after the Prev() call above. Call
// replace_top() to restore the heap property. When the same child
// iterator yields a sequence of keys, this is cheap.
maxHeap_->replace_top(current_);
} else {
// current stopped being valid, remove it from the heap.
maxHeap_->pop();
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
current_ = CurrentReverse();
}
virtual Slice key() const override {
assert(Valid());
return current_->key();
}
virtual Slice value() const override {
assert(Valid());
return current_->value();
}
virtual Status status() const override {
Status s;
for (auto& child : children_) {
s = child.status();
if (!s.ok()) {
break;
}
}
return s;
}
private:
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// Clears heaps for both directions, used when changing direction or seeking
void ClearHeaps();
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// Ensures that maxHeap_ is initialized when starting to go in the reverse
// direction
void InitMaxHeap();
bool is_arena_mode_;
const Comparator* comparator_;
autovector<IteratorWrapper, kNumIterReserve> children_;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// Cached pointer to child iterator with the current key, or nullptr if no
// child iterators are valid. This is the top of minHeap_ or maxHeap_
// depending on the direction.
IteratorWrapper* current_;
// Which direction is the iterator moving?
enum Direction {
kForward,
kReverse
};
Direction direction_;
MergerMinIterHeap minHeap_;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
// Max heap is used for reverse iteration, which is way less common than
// forward. Lazily initialize it to save memory.
std::unique_ptr<MergerMaxIterHeap> maxHeap_;
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
IteratorWrapper* CurrentForward() const {
assert(direction_ == kForward);
return !minHeap_.empty() ? minHeap_.top() : nullptr;
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
IteratorWrapper* CurrentReverse() const {
assert(direction_ == kReverse);
assert(maxHeap_);
return !maxHeap_->empty() ? maxHeap_->top() : nullptr;
}
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
};
void MergingIterator::ClearHeaps() {
Replace std::priority_queue in MergingIterator with custom heap Summary: While profiling compaction in our service I noticed a lot of CPU (~15% of compaction) being spent in MergingIterator and key comparison. Looking at the code I found MergingIterator was (understandably) using std::priority_queue for the multiway merge. Keys in our dataset include sequence numbers that increase with time. Adjacent keys in an L0 file are very likely to be adjacent in the full database. Consequently, compaction will often pick a chunk of rows from the same L0 file before switching to another one. It would be great to avoid the O(log K) operation per row while compacting. This diff replaces std::priority_queue with a custom binary heap implementation. It has a "replace top" operation that is cheap when the new top is the same as the old one (i.e. the priority of the top entry is decreased but it still stays on top). Test Plan: make check To test the effect on performance, I generated databases with data patterns that mimic what I describe in the summary (rows have a mostly increasing sequence number). I see a 10-15% CPU decrease for compaction (and a matching throughput improvement on tmpfs). The exact improvement depends on the number of L0 files and the amount of locality. Performance on randomly distributed keys seems on par with the old code. Reviewers: kailiu, sdong, igor Reviewed By: igor Subscribers: yoshinorim, dhruba, tnovak Differential Revision: https://reviews.facebook.net/D29133
9 years ago
minHeap_.clear();
if (maxHeap_) {
maxHeap_->clear();
}
}
void MergingIterator::InitMaxHeap() {
if (!maxHeap_) {
maxHeap_.reset(new MergerMaxIterHeap(comparator_));
}
}
Iterator* NewMergingIterator(const Comparator* cmp, Iterator** list, int n,
Arena* arena) {
assert(n >= 0);
if (n == 0) {
return NewEmptyIterator(arena);
} else if (n == 1) {
return list[0];
} else {
if (arena == nullptr) {
return new MergingIterator(cmp, list, n, false);
} else {
auto mem = arena->AllocateAligned(sizeof(MergingIterator));
return new (mem) MergingIterator(cmp, list, n, true);
}
}
}
MergeIteratorBuilder::MergeIteratorBuilder(const Comparator* comparator,
Arena* a)
: first_iter(nullptr), use_merging_iter(false), arena(a) {
auto mem = arena->AllocateAligned(sizeof(MergingIterator));
merge_iter = new (mem) MergingIterator(comparator, nullptr, 0, true);
}
void MergeIteratorBuilder::AddIterator(Iterator* iter) {
if (!use_merging_iter && first_iter != nullptr) {
merge_iter->AddIterator(first_iter);
use_merging_iter = true;
}
if (use_merging_iter) {
merge_iter->AddIterator(iter);
} else {
first_iter = iter;
}
}
Iterator* MergeIteratorBuilder::Finish() {
if (!use_merging_iter) {
return first_iter;
} else {
auto ret = merge_iter;
merge_iter = nullptr;
return ret;
}
}
} // namespace rocksdb