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rocksdb/db/write_batch.cc

1398 lines
47 KiB

// Copyright (c) 2011-present, 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.
// This source code is also licensed under the GPLv2 license found in the
// COPYING file in the root directory of this source tree.
//
// 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.
//
// WriteBatch::rep_ :=
// sequence: fixed64
// count: fixed32
// data: record[count]
// record :=
// kTypeValue varstring varstring
// kTypeDeletion varstring
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
// kTypeSingleDeletion varstring
// kTypeMerge varstring varstring
// kTypeColumnFamilyValue varint32 varstring varstring
// kTypeColumnFamilyDeletion varint32 varstring varstring
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
// kTypeColumnFamilySingleDeletion varint32 varstring varstring
// kTypeColumnFamilyMerge varint32 varstring varstring
// kTypeBeginPrepareXID varstring
// kTypeEndPrepareXID
// kTypeCommitXID varstring
// kTypeRollbackXID varstring
// kTypeNoop
// varstring :=
// len: varint32
// data: uint8[len]
#include "rocksdb/write_batch.h"
#include <map>
#include <stack>
#include <stdexcept>
#include <type_traits>
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
#include <vector>
#include "db/column_family.h"
#include "db/db_impl.h"
#include "db/dbformat.h"
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
#include "db/flush_scheduler.h"
#include "db/memtable.h"
#include "db/merge_context.h"
#include "db/snapshot_impl.h"
#include "db/write_batch_internal.h"
#include "monitoring/perf_context_imp.h"
#include "monitoring/statistics.h"
#include "rocksdb/merge_operator.h"
#include "util/coding.h"
#include "util/string_util.h"
namespace rocksdb {
// anon namespace for file-local types
namespace {
enum ContentFlags : uint32_t {
DEFERRED = 1 << 0,
HAS_PUT = 1 << 1,
HAS_DELETE = 1 << 2,
HAS_SINGLE_DELETE = 1 << 3,
HAS_MERGE = 1 << 4,
HAS_BEGIN_PREPARE = 1 << 5,
HAS_END_PREPARE = 1 << 6,
HAS_COMMIT = 1 << 7,
HAS_ROLLBACK = 1 << 8,
HAS_DELETE_RANGE = 1 << 9,
};
struct BatchContentClassifier : public WriteBatch::Handler {
uint32_t content_flags = 0;
Status PutCF(uint32_t, const Slice&, const Slice&) override {
content_flags |= ContentFlags::HAS_PUT;
return Status::OK();
}
Status DeleteCF(uint32_t, const Slice&) override {
content_flags |= ContentFlags::HAS_DELETE;
return Status::OK();
}
Status SingleDeleteCF(uint32_t, const Slice&) override {
content_flags |= ContentFlags::HAS_SINGLE_DELETE;
return Status::OK();
}
Status DeleteRangeCF(uint32_t, const Slice&, const Slice&) override {
content_flags |= ContentFlags::HAS_DELETE_RANGE;
return Status::OK();
}
Status MergeCF(uint32_t, const Slice&, const Slice&) override {
content_flags |= ContentFlags::HAS_MERGE;
return Status::OK();
}
Status MarkBeginPrepare() override {
content_flags |= ContentFlags::HAS_BEGIN_PREPARE;
return Status::OK();
}
Status MarkEndPrepare(const Slice&) override {
content_flags |= ContentFlags::HAS_END_PREPARE;
return Status::OK();
}
Status MarkCommit(const Slice&) override {
content_flags |= ContentFlags::HAS_COMMIT;
return Status::OK();
}
Status MarkRollback(const Slice&) override {
content_flags |= ContentFlags::HAS_ROLLBACK;
return Status::OK();
}
};
} // anon namespace
struct SavePoints {
std::stack<SavePoint> stack;
};
WriteBatch::WriteBatch(size_t reserved_bytes, size_t max_bytes)
: save_points_(nullptr), content_flags_(0), max_bytes_(max_bytes), rep_() {
rep_.reserve((reserved_bytes > WriteBatchInternal::kHeader) ?
reserved_bytes : WriteBatchInternal::kHeader);
rep_.resize(WriteBatchInternal::kHeader);
}
WriteBatch::WriteBatch(const std::string& rep)
: save_points_(nullptr),
content_flags_(ContentFlags::DEFERRED),
max_bytes_(0),
rep_(rep) {}
WriteBatch::WriteBatch(const WriteBatch& src)
: save_points_(src.save_points_),
wal_term_point_(src.wal_term_point_),
content_flags_(src.content_flags_.load(std::memory_order_relaxed)),
max_bytes_(src.max_bytes_),
rep_(src.rep_) {}
WriteBatch::WriteBatch(WriteBatch&& src)
: save_points_(std::move(src.save_points_)),
wal_term_point_(std::move(src.wal_term_point_)),
content_flags_(src.content_flags_.load(std::memory_order_relaxed)),
max_bytes_(src.max_bytes_),
rep_(std::move(src.rep_)) {}
WriteBatch& WriteBatch::operator=(const WriteBatch& src) {
if (&src != this) {
this->~WriteBatch();
new (this) WriteBatch(src);
}
return *this;
}
WriteBatch& WriteBatch::operator=(WriteBatch&& src) {
if (&src != this) {
this->~WriteBatch();
new (this) WriteBatch(std::move(src));
}
return *this;
}
WriteBatch::~WriteBatch() { delete save_points_; }
WriteBatch::Handler::~Handler() { }
void WriteBatch::Handler::LogData(const Slice& blob) {
// If the user has not specified something to do with blobs, then we ignore
// them.
}
bool WriteBatch::Handler::Continue() {
return true;
}
void WriteBatch::Clear() {
rep_.clear();
rep_.resize(WriteBatchInternal::kHeader);
content_flags_.store(0, std::memory_order_relaxed);
if (save_points_ != nullptr) {
while (!save_points_->stack.empty()) {
save_points_->stack.pop();
}
}
wal_term_point_.clear();
}
int WriteBatch::Count() const {
return WriteBatchInternal::Count(this);
}
uint32_t WriteBatch::ComputeContentFlags() const {
auto rv = content_flags_.load(std::memory_order_relaxed);
if ((rv & ContentFlags::DEFERRED) != 0) {
BatchContentClassifier classifier;
Iterate(&classifier);
rv = classifier.content_flags;
// this method is conceptually const, because it is performing a lazy
// computation that doesn't affect the abstract state of the batch.
// content_flags_ is marked mutable so that we can perform the
// following assignment
content_flags_.store(rv, std::memory_order_relaxed);
}
return rv;
}
void WriteBatch::MarkWalTerminationPoint() {
wal_term_point_.size = GetDataSize();
wal_term_point_.count = Count();
wal_term_point_.content_flags = content_flags_;
}
bool WriteBatch::HasPut() const {
return (ComputeContentFlags() & ContentFlags::HAS_PUT) != 0;
}
bool WriteBatch::HasDelete() const {
return (ComputeContentFlags() & ContentFlags::HAS_DELETE) != 0;
}
bool WriteBatch::HasSingleDelete() const {
return (ComputeContentFlags() & ContentFlags::HAS_SINGLE_DELETE) != 0;
}
bool WriteBatch::HasDeleteRange() const {
return (ComputeContentFlags() & ContentFlags::HAS_DELETE_RANGE) != 0;
}
bool WriteBatch::HasMerge() const {
return (ComputeContentFlags() & ContentFlags::HAS_MERGE) != 0;
}
bool ReadKeyFromWriteBatchEntry(Slice* input, Slice* key, bool cf_record) {
assert(input != nullptr && key != nullptr);
// Skip tag byte
input->remove_prefix(1);
if (cf_record) {
// Skip column_family bytes
uint32_t cf;
if (!GetVarint32(input, &cf)) {
return false;
}
}
// Extract key
return GetLengthPrefixedSlice(input, key);
}
bool WriteBatch::HasBeginPrepare() const {
return (ComputeContentFlags() & ContentFlags::HAS_BEGIN_PREPARE) != 0;
}
bool WriteBatch::HasEndPrepare() const {
return (ComputeContentFlags() & ContentFlags::HAS_END_PREPARE) != 0;
}
bool WriteBatch::HasCommit() const {
return (ComputeContentFlags() & ContentFlags::HAS_COMMIT) != 0;
}
bool WriteBatch::HasRollback() const {
return (ComputeContentFlags() & ContentFlags::HAS_ROLLBACK) != 0;
}
Status ReadRecordFromWriteBatch(Slice* input, char* tag,
uint32_t* column_family, Slice* key,
Slice* value, Slice* blob, Slice* xid) {
assert(key != nullptr && value != nullptr);
*tag = (*input)[0];
input->remove_prefix(1);
*column_family = 0; // default
switch (*tag) {
case kTypeColumnFamilyValue:
if (!GetVarint32(input, column_family)) {
return Status::Corruption("bad WriteBatch Put");
}
// intentional fallthrough
case kTypeValue:
if (!GetLengthPrefixedSlice(input, key) ||
!GetLengthPrefixedSlice(input, value)) {
return Status::Corruption("bad WriteBatch Put");
}
break;
case kTypeColumnFamilyDeletion:
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
case kTypeColumnFamilySingleDeletion:
if (!GetVarint32(input, column_family)) {
return Status::Corruption("bad WriteBatch Delete");
}
// intentional fallthrough
case kTypeDeletion:
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
case kTypeSingleDeletion:
if (!GetLengthPrefixedSlice(input, key)) {
return Status::Corruption("bad WriteBatch Delete");
}
break;
case kTypeColumnFamilyRangeDeletion:
if (!GetVarint32(input, column_family)) {
return Status::Corruption("bad WriteBatch DeleteRange");
}
// intentional fallthrough
case kTypeRangeDeletion:
// for range delete, "key" is begin_key, "value" is end_key
if (!GetLengthPrefixedSlice(input, key) ||
!GetLengthPrefixedSlice(input, value)) {
return Status::Corruption("bad WriteBatch DeleteRange");
}
break;
case kTypeColumnFamilyMerge:
if (!GetVarint32(input, column_family)) {
return Status::Corruption("bad WriteBatch Merge");
}
// intentional fallthrough
case kTypeMerge:
if (!GetLengthPrefixedSlice(input, key) ||
!GetLengthPrefixedSlice(input, value)) {
return Status::Corruption("bad WriteBatch Merge");
}
break;
case kTypeLogData:
assert(blob != nullptr);
if (!GetLengthPrefixedSlice(input, blob)) {
return Status::Corruption("bad WriteBatch Blob");
}
break;
case kTypeNoop:
case kTypeBeginPrepareXID:
break;
case kTypeEndPrepareXID:
if (!GetLengthPrefixedSlice(input, xid)) {
return Status::Corruption("bad EndPrepare XID");
}
break;
case kTypeCommitXID:
if (!GetLengthPrefixedSlice(input, xid)) {
return Status::Corruption("bad Commit XID");
}
break;
case kTypeRollbackXID:
if (!GetLengthPrefixedSlice(input, xid)) {
return Status::Corruption("bad Rollback XID");
}
break;
default:
return Status::Corruption("unknown WriteBatch tag");
}
return Status::OK();
}
Status WriteBatch::Iterate(Handler* handler) const {
Slice input(rep_);
if (input.size() < WriteBatchInternal::kHeader) {
return Status::Corruption("malformed WriteBatch (too small)");
}
input.remove_prefix(WriteBatchInternal::kHeader);
Slice key, value, blob, xid;
int found = 0;
Status s;
while (s.ok() && !input.empty() && handler->Continue()) {
char tag = 0;
uint32_t column_family = 0; // default
s = ReadRecordFromWriteBatch(&input, &tag, &column_family, &key, &value,
&blob, &xid);
if (!s.ok()) {
return s;
}
switch (tag) {
case kTypeColumnFamilyValue:
case kTypeValue:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_PUT));
s = handler->PutCF(column_family, key, value);
found++;
break;
case kTypeColumnFamilyDeletion:
case kTypeDeletion:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_DELETE));
s = handler->DeleteCF(column_family, key);
found++;
break;
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
case kTypeColumnFamilySingleDeletion:
case kTypeSingleDeletion:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_SINGLE_DELETE));
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
s = handler->SingleDeleteCF(column_family, key);
found++;
break;
case kTypeColumnFamilyRangeDeletion:
case kTypeRangeDeletion:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_DELETE_RANGE));
s = handler->DeleteRangeCF(column_family, key, value);
found++;
break;
case kTypeColumnFamilyMerge:
case kTypeMerge:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_MERGE));
s = handler->MergeCF(column_family, key, value);
found++;
break;
case kTypeLogData:
handler->LogData(blob);
break;
case kTypeBeginPrepareXID:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_BEGIN_PREPARE));
handler->MarkBeginPrepare();
break;
case kTypeEndPrepareXID:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_END_PREPARE));
handler->MarkEndPrepare(xid);
break;
case kTypeCommitXID:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_COMMIT));
handler->MarkCommit(xid);
break;
case kTypeRollbackXID:
assert(content_flags_.load(std::memory_order_relaxed) &
(ContentFlags::DEFERRED | ContentFlags::HAS_ROLLBACK));
handler->MarkRollback(xid);
break;
case kTypeNoop:
break;
default:
return Status::Corruption("unknown WriteBatch tag");
}
}
if (!s.ok()) {
return s;
}
if (found != WriteBatchInternal::Count(this)) {
return Status::Corruption("WriteBatch has wrong count");
} else {
return Status::OK();
}
}
int WriteBatchInternal::Count(const WriteBatch* b) {
return DecodeFixed32(b->rep_.data() + 8);
}
void WriteBatchInternal::SetCount(WriteBatch* b, int n) {
EncodeFixed32(&b->rep_[8], n);
}
SequenceNumber WriteBatchInternal::Sequence(const WriteBatch* b) {
return SequenceNumber(DecodeFixed64(b->rep_.data()));
}
void WriteBatchInternal::SetSequence(WriteBatch* b, SequenceNumber seq) {
EncodeFixed64(&b->rep_[0], seq);
}
size_t WriteBatchInternal::GetFirstOffset(WriteBatch* b) {
return WriteBatchInternal::kHeader;
}
Status WriteBatchInternal::Put(WriteBatch* b, uint32_t column_family_id,
const Slice& key, const Slice& value) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeValue));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyValue));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSlice(&b->rep_, key);
PutLengthPrefixedSlice(&b->rep_, value);
b->content_flags_.store(
b->content_flags_.load(std::memory_order_relaxed) | ContentFlags::HAS_PUT,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::Put(ColumnFamilyHandle* column_family, const Slice& key,
const Slice& value) {
return WriteBatchInternal::Put(this, GetColumnFamilyID(column_family), key,
value);
}
Status WriteBatchInternal::Put(WriteBatch* b, uint32_t column_family_id,
const SliceParts& key, const SliceParts& value) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeValue));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyValue));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSliceParts(&b->rep_, key);
PutLengthPrefixedSliceParts(&b->rep_, value);
b->content_flags_.store(
b->content_flags_.load(std::memory_order_relaxed) | ContentFlags::HAS_PUT,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::Put(ColumnFamilyHandle* column_family, const SliceParts& key,
const SliceParts& value) {
return WriteBatchInternal::Put(this, GetColumnFamilyID(column_family), key,
value);
}
Status WriteBatchInternal::InsertNoop(WriteBatch* b) {
b->rep_.push_back(static_cast<char>(kTypeNoop));
return Status::OK();
}
Status WriteBatchInternal::MarkEndPrepare(WriteBatch* b, const Slice& xid) {
// a manually constructed batch can only contain one prepare section
assert(b->rep_[12] == static_cast<char>(kTypeNoop));
// all savepoints up to this point are cleared
if (b->save_points_ != nullptr) {
while (!b->save_points_->stack.empty()) {
b->save_points_->stack.pop();
}
}
// rewrite noop as begin marker
b->rep_[12] = static_cast<char>(kTypeBeginPrepareXID);
b->rep_.push_back(static_cast<char>(kTypeEndPrepareXID));
PutLengthPrefixedSlice(&b->rep_, xid);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_END_PREPARE |
ContentFlags::HAS_BEGIN_PREPARE,
std::memory_order_relaxed);
return Status::OK();
}
Status WriteBatchInternal::MarkCommit(WriteBatch* b, const Slice& xid) {
b->rep_.push_back(static_cast<char>(kTypeCommitXID));
PutLengthPrefixedSlice(&b->rep_, xid);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_COMMIT,
std::memory_order_relaxed);
return Status::OK();
}
Status WriteBatchInternal::MarkRollback(WriteBatch* b, const Slice& xid) {
b->rep_.push_back(static_cast<char>(kTypeRollbackXID));
PutLengthPrefixedSlice(&b->rep_, xid);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_ROLLBACK,
std::memory_order_relaxed);
return Status::OK();
}
Status WriteBatchInternal::Delete(WriteBatch* b, uint32_t column_family_id,
const Slice& key) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeDeletion));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyDeletion));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSlice(&b->rep_, key);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_DELETE,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::Delete(ColumnFamilyHandle* column_family, const Slice& key) {
return WriteBatchInternal::Delete(this, GetColumnFamilyID(column_family),
key);
}
Status WriteBatchInternal::Delete(WriteBatch* b, uint32_t column_family_id,
const SliceParts& key) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeDeletion));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyDeletion));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSliceParts(&b->rep_, key);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_DELETE,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::Delete(ColumnFamilyHandle* column_family,
const SliceParts& key) {
return WriteBatchInternal::Delete(this, GetColumnFamilyID(column_family),
key);
}
Status WriteBatchInternal::SingleDelete(WriteBatch* b,
uint32_t column_family_id,
const Slice& key) {
LocalSavePoint save(b);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeSingleDeletion));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilySingleDeletion));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSlice(&b->rep_, key);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_SINGLE_DELETE,
std::memory_order_relaxed);
return save.commit();
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
}
Status WriteBatch::SingleDelete(ColumnFamilyHandle* column_family,
const Slice& key) {
return WriteBatchInternal::SingleDelete(
this, GetColumnFamilyID(column_family), key);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
}
Status WriteBatchInternal::SingleDelete(WriteBatch* b,
uint32_t column_family_id,
const SliceParts& key) {
LocalSavePoint save(b);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeSingleDeletion));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilySingleDeletion));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSliceParts(&b->rep_, key);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_SINGLE_DELETE,
std::memory_order_relaxed);
return save.commit();
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
}
Status WriteBatch::SingleDelete(ColumnFamilyHandle* column_family,
const SliceParts& key) {
return WriteBatchInternal::SingleDelete(
this, GetColumnFamilyID(column_family), key);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
}
Status WriteBatchInternal::DeleteRange(WriteBatch* b, uint32_t column_family_id,
const Slice& begin_key,
const Slice& end_key) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeRangeDeletion));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyRangeDeletion));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSlice(&b->rep_, begin_key);
PutLengthPrefixedSlice(&b->rep_, end_key);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_DELETE_RANGE,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::DeleteRange(ColumnFamilyHandle* column_family,
const Slice& begin_key, const Slice& end_key) {
return WriteBatchInternal::DeleteRange(this, GetColumnFamilyID(column_family),
begin_key, end_key);
}
Status WriteBatchInternal::DeleteRange(WriteBatch* b, uint32_t column_family_id,
const SliceParts& begin_key,
const SliceParts& end_key) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeRangeDeletion));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyRangeDeletion));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSliceParts(&b->rep_, begin_key);
PutLengthPrefixedSliceParts(&b->rep_, end_key);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_DELETE_RANGE,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::DeleteRange(ColumnFamilyHandle* column_family,
const SliceParts& begin_key,
const SliceParts& end_key) {
return WriteBatchInternal::DeleteRange(this, GetColumnFamilyID(column_family),
begin_key, end_key);
}
Status WriteBatchInternal::Merge(WriteBatch* b, uint32_t column_family_id,
const Slice& key, const Slice& value) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeMerge));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyMerge));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSlice(&b->rep_, key);
PutLengthPrefixedSlice(&b->rep_, value);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_MERGE,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::Merge(ColumnFamilyHandle* column_family, const Slice& key,
const Slice& value) {
return WriteBatchInternal::Merge(this, GetColumnFamilyID(column_family), key,
value);
}
Status WriteBatchInternal::Merge(WriteBatch* b, uint32_t column_family_id,
const SliceParts& key,
const SliceParts& value) {
LocalSavePoint save(b);
WriteBatchInternal::SetCount(b, WriteBatchInternal::Count(b) + 1);
if (column_family_id == 0) {
b->rep_.push_back(static_cast<char>(kTypeMerge));
} else {
b->rep_.push_back(static_cast<char>(kTypeColumnFamilyMerge));
PutVarint32(&b->rep_, column_family_id);
}
PutLengthPrefixedSliceParts(&b->rep_, key);
PutLengthPrefixedSliceParts(&b->rep_, value);
b->content_flags_.store(b->content_flags_.load(std::memory_order_relaxed) |
ContentFlags::HAS_MERGE,
std::memory_order_relaxed);
return save.commit();
}
Status WriteBatch::Merge(ColumnFamilyHandle* column_family,
const SliceParts& key, const SliceParts& value) {
return WriteBatchInternal::Merge(this, GetColumnFamilyID(column_family), key,
value);
}
Status WriteBatch::PutLogData(const Slice& blob) {
LocalSavePoint save(this);
rep_.push_back(static_cast<char>(kTypeLogData));
PutLengthPrefixedSlice(&rep_, blob);
return save.commit();
}
void WriteBatch::SetSavePoint() {
if (save_points_ == nullptr) {
save_points_ = new SavePoints();
}
// Record length and count of current batch of writes.
save_points_->stack.push(SavePoint(
GetDataSize(), Count(), content_flags_.load(std::memory_order_relaxed)));
}
Status WriteBatch::RollbackToSavePoint() {
if (save_points_ == nullptr || save_points_->stack.size() == 0) {
return Status::NotFound();
}
// Pop the most recent savepoint off the stack
SavePoint savepoint = save_points_->stack.top();
save_points_->stack.pop();
assert(savepoint.size <= rep_.size());
assert(savepoint.count <= Count());
if (savepoint.size == rep_.size()) {
// No changes to rollback
} else if (savepoint.size == 0) {
// Rollback everything
Clear();
} else {
rep_.resize(savepoint.size);
WriteBatchInternal::SetCount(this, savepoint.count);
content_flags_.store(savepoint.content_flags, std::memory_order_relaxed);
}
return Status::OK();
}
Status WriteBatch::PopSavePoint() {
if (save_points_ == nullptr || save_points_->stack.size() == 0) {
return Status::NotFound();
}
// Pop the most recent savepoint off the stack
save_points_->stack.pop();
return Status::OK();
}
class MemTableInserter : public WriteBatch::Handler {
SequenceNumber sequence_;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
ColumnFamilyMemTables* const cf_mems_;
FlushScheduler* const flush_scheduler_;
const bool ignore_missing_column_families_;
const uint64_t recovering_log_number_;
// log number that all Memtables inserted into should reference
uint64_t log_number_ref_;
DBImpl* db_;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
const bool concurrent_memtable_writes_;
bool post_info_created_;
bool* has_valid_writes_;
// On some (!) platforms just default creating
// a map is too expensive in the Write() path as they
// cause memory allocations though unused.
// Make creation optional but do not incur
// unique_ptr additional allocation
using
MemPostInfoMap = std::map<MemTable*, MemTablePostProcessInfo>;
using
PostMapType = std::aligned_storage<sizeof(MemPostInfoMap)>::type;
PostMapType mem_post_info_map_;
// current recovered transaction we are rebuilding (recovery)
WriteBatch* rebuilding_trx_;
MemPostInfoMap& GetPostMap() {
assert(concurrent_memtable_writes_);
if(!post_info_created_) {
new (&mem_post_info_map_) MemPostInfoMap();
post_info_created_ = true;
}
return *reinterpret_cast<MemPostInfoMap*>(&mem_post_info_map_);
}
public:
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
// cf_mems should not be shared with concurrent inserters
MemTableInserter(SequenceNumber sequence, ColumnFamilyMemTables* cf_mems,
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
FlushScheduler* flush_scheduler,
bool ignore_missing_column_families,
uint64_t recovering_log_number, DB* db,
bool concurrent_memtable_writes,
bool* has_valid_writes = nullptr)
: sequence_(sequence),
cf_mems_(cf_mems),
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
flush_scheduler_(flush_scheduler),
ignore_missing_column_families_(ignore_missing_column_families),
recovering_log_number_(recovering_log_number),
log_number_ref_(0),
db_(reinterpret_cast<DBImpl*>(db)),
concurrent_memtable_writes_(concurrent_memtable_writes),
post_info_created_(false),
has_valid_writes_(has_valid_writes),
rebuilding_trx_(nullptr) {
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
assert(cf_mems_);
}
~MemTableInserter() {
if (post_info_created_) {
reinterpret_cast<MemPostInfoMap*>
(&mem_post_info_map_)->~MemPostInfoMap();
}
}
MemTableInserter(const MemTableInserter&) = delete;
MemTableInserter& operator=(const MemTableInserter&) = delete;
void set_log_number_ref(uint64_t log) { log_number_ref_ = log; }
SequenceNumber get_final_sequence() const { return sequence_; }
void PostProcess() {
assert(concurrent_memtable_writes_);
// If post info was not created there is nothing
// to process and no need to create on demand
if(post_info_created_) {
for (auto& pair : GetPostMap()) {
pair.first->BatchPostProcess(pair.second);
}
}
}
bool SeekToColumnFamily(uint32_t column_family_id, Status* s) {
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
// If we are in a concurrent mode, it is the caller's responsibility
// to clone the original ColumnFamilyMemTables so that each thread
// has its own instance. Otherwise, it must be guaranteed that there
// is no concurrent access
bool found = cf_mems_->Seek(column_family_id);
if (!found) {
if (ignore_missing_column_families_) {
*s = Status::OK();
} else {
*s = Status::InvalidArgument(
"Invalid column family specified in write batch");
}
return false;
}
if (recovering_log_number_ != 0 &&
recovering_log_number_ < cf_mems_->GetLogNumber()) {
// This is true only in recovery environment (recovering_log_number_ is
// always 0 in
// non-recovery, regular write code-path)
// * If recovering_log_number_ < cf_mems_->GetLogNumber(), this means that
// column
// family already contains updates from this log. We can't apply updates
// twice because of update-in-place or merge workloads -- ignore the
// update
*s = Status::OK();
return false;
}
if (has_valid_writes_ != nullptr) {
*has_valid_writes_ = true;
}
if (log_number_ref_ > 0) {
cf_mems_->GetMemTable()->RefLogContainingPrepSection(log_number_ref_);
}
return true;
}
virtual Status PutCF(uint32_t column_family_id, const Slice& key,
const Slice& value) override {
if (rebuilding_trx_ != nullptr) {
WriteBatchInternal::Put(rebuilding_trx_, column_family_id, key, value);
return Status::OK();
}
Status seek_status;
if (!SeekToColumnFamily(column_family_id, &seek_status)) {
++sequence_;
return seek_status;
}
MemTable* mem = cf_mems_->GetMemTable();
auto* moptions = mem->GetMemTableOptions();
if (!moptions->inplace_update_support) {
mem->Add(sequence_, kTypeValue, key, value, concurrent_memtable_writes_,
get_post_process_info(mem));
} else if (moptions->inplace_callback == nullptr) {
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
assert(!concurrent_memtable_writes_);
mem->Update(sequence_, key, value);
RecordTick(moptions->statistics, NUMBER_KEYS_UPDATED);
} else {
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
assert(!concurrent_memtable_writes_);
if (mem->UpdateCallback(sequence_, key, value)) {
} else {
// key not found in memtable. Do sst get, update, add
SnapshotImpl read_from_snapshot;
read_from_snapshot.number_ = sequence_;
ReadOptions ropts;
ropts.snapshot = &read_from_snapshot;
std::string prev_value;
std::string merged_value;
auto cf_handle = cf_mems_->GetColumnFamilyHandle();
Status s = Status::NotSupported();
if (db_ != nullptr && recovering_log_number_ == 0) {
if (cf_handle == nullptr) {
cf_handle = db_->DefaultColumnFamily();
}
s = db_->Get(ropts, cf_handle, key, &prev_value);
}
char* prev_buffer = const_cast<char*>(prev_value.c_str());
uint32_t prev_size = static_cast<uint32_t>(prev_value.size());
auto status = moptions->inplace_callback(s.ok() ? prev_buffer : nullptr,
s.ok() ? &prev_size : nullptr,
value, &merged_value);
if (status == UpdateStatus::UPDATED_INPLACE) {
// prev_value is updated in-place with final value.
mem->Add(sequence_, kTypeValue, key, Slice(prev_buffer, prev_size));
RecordTick(moptions->statistics, NUMBER_KEYS_WRITTEN);
} else if (status == UpdateStatus::UPDATED) {
// merged_value contains the final value.
mem->Add(sequence_, kTypeValue, key, Slice(merged_value));
RecordTick(moptions->statistics, NUMBER_KEYS_WRITTEN);
}
}
}
// Since all Puts are logged in trasaction logs (if enabled), always bump
// sequence number. Even if the update eventually fails and does not result
// in memtable add/update.
sequence_++;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
CheckMemtableFull();
return Status::OK();
}
Status DeleteImpl(uint32_t column_family_id, const Slice& key,
const Slice& value, ValueType delete_type) {
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
MemTable* mem = cf_mems_->GetMemTable();
mem->Add(sequence_, delete_type, key, value, concurrent_memtable_writes_,
get_post_process_info(mem));
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
sequence_++;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
CheckMemtableFull();
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
return Status::OK();
}
virtual Status DeleteCF(uint32_t column_family_id,
const Slice& key) override {
if (rebuilding_trx_ != nullptr) {
WriteBatchInternal::Delete(rebuilding_trx_, column_family_id, key);
return Status::OK();
}
Status seek_status;
if (!SeekToColumnFamily(column_family_id, &seek_status)) {
++sequence_;
return seek_status;
}
return DeleteImpl(column_family_id, key, Slice(), kTypeDeletion);
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
virtual Status SingleDeleteCF(uint32_t column_family_id,
const Slice& key) override {
if (rebuilding_trx_ != nullptr) {
WriteBatchInternal::SingleDelete(rebuilding_trx_, column_family_id, key);
return Status::OK();
}
Status seek_status;
if (!SeekToColumnFamily(column_family_id, &seek_status)) {
++sequence_;
return seek_status;
}
return DeleteImpl(column_family_id, key, Slice(), kTypeSingleDeletion);
}
virtual Status DeleteRangeCF(uint32_t column_family_id,
const Slice& begin_key,
const Slice& end_key) override {
if (rebuilding_trx_ != nullptr) {
WriteBatchInternal::DeleteRange(rebuilding_trx_, column_family_id,
begin_key, end_key);
return Status::OK();
}
Status seek_status;
if (!SeekToColumnFamily(column_family_id, &seek_status)) {
++sequence_;
return seek_status;
}
if (db_ != nullptr) {
auto cf_handle = cf_mems_->GetColumnFamilyHandle();
if (cf_handle == nullptr) {
cf_handle = db_->DefaultColumnFamily();
}
auto* cfd = reinterpret_cast<ColumnFamilyHandleImpl*>(cf_handle)->cfd();
if (!cfd->is_delete_range_supported()) {
return Status::NotSupported(
std::string("DeleteRange not supported for table type ") +
cfd->ioptions()->table_factory->Name() + " in CF " +
cfd->GetName());
}
}
return DeleteImpl(column_family_id, begin_key, end_key, kTypeRangeDeletion);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
}
virtual Status MergeCF(uint32_t column_family_id, const Slice& key,
const Slice& value) override {
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
assert(!concurrent_memtable_writes_);
if (rebuilding_trx_ != nullptr) {
WriteBatchInternal::Merge(rebuilding_trx_, column_family_id, key, value);
return Status::OK();
}
Status seek_status;
if (!SeekToColumnFamily(column_family_id, &seek_status)) {
++sequence_;
return seek_status;
}
MemTable* mem = cf_mems_->GetMemTable();
auto* moptions = mem->GetMemTableOptions();
bool perform_merge = false;
// If we pass DB through and options.max_successive_merges is hit
// during recovery, Get() will be issued which will try to acquire
// DB mutex and cause deadlock, as DB mutex is already held.
// So we disable merge in recovery
if (moptions->max_successive_merges > 0 && db_ != nullptr &&
recovering_log_number_ == 0) {
LookupKey lkey(key, sequence_);
// Count the number of successive merges at the head
// of the key in the memtable
size_t num_merges = mem->CountSuccessiveMergeEntries(lkey);
if (num_merges >= moptions->max_successive_merges) {
perform_merge = true;
}
}
if (perform_merge) {
// 1) Get the existing value
std::string get_value;
// Pass in the sequence number so that we also include previous merge
// operations in the same batch.
SnapshotImpl read_from_snapshot;
read_from_snapshot.number_ = sequence_;
ReadOptions read_options;
read_options.snapshot = &read_from_snapshot;
auto cf_handle = cf_mems_->GetColumnFamilyHandle();
if (cf_handle == nullptr) {
cf_handle = db_->DefaultColumnFamily();
}
db_->Get(read_options, cf_handle, key, &get_value);
Slice get_value_slice = Slice(get_value);
// 2) Apply this merge
auto merge_operator = moptions->merge_operator;
assert(merge_operator);
std::string new_value;
Status merge_status = MergeHelper::TimedFullMerge(
Introduce FullMergeV2 (eliminate memcpy from merge operators) Summary: This diff update the code to pin the merge operator operands while the merge operation is done, so that we can eliminate the memcpy cost, to do that we need a new public API for FullMerge that replace the std::deque<std::string> with std::vector<Slice> This diff is stacked on top of D56493 and D56511 In this diff we - Update FullMergeV2 arguments to be encapsulated in MergeOperationInput and MergeOperationOutput which will make it easier to add new arguments in the future - Replace std::deque<std::string> with std::vector<Slice> to pass operands - Replace MergeContext std::deque with std::vector (based on a simple benchmark I ran https://gist.github.com/IslamAbdelRahman/78fc86c9ab9f52b1df791e58943fb187) - Allow FullMergeV2 output to be an existing operand ``` [Everything in Memtable | 10K operands | 10 KB each | 1 operand per key] DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="mergerandom,readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --merge_keys=10000 --num=10000 --disable_auto_compactions --value_size=10240 --write_buffer_size=1000000000 [FullMergeV2] readseq : 0.607 micros/op 1648235 ops/sec; 16121.2 MB/s readseq : 0.478 micros/op 2091546 ops/sec; 20457.2 MB/s readseq : 0.252 micros/op 3972081 ops/sec; 38850.5 MB/s readseq : 0.237 micros/op 4218328 ops/sec; 41259.0 MB/s readseq : 0.247 micros/op 4043927 ops/sec; 39553.2 MB/s [master] readseq : 3.935 micros/op 254140 ops/sec; 2485.7 MB/s readseq : 3.722 micros/op 268657 ops/sec; 2627.7 MB/s readseq : 3.149 micros/op 317605 ops/sec; 3106.5 MB/s readseq : 3.125 micros/op 320024 ops/sec; 3130.1 MB/s readseq : 4.075 micros/op 245374 ops/sec; 2400.0 MB/s ``` ``` [Everything in Memtable | 10K operands | 10 KB each | 10 operand per key] DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="mergerandom,readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --merge_keys=1000 --num=10000 --disable_auto_compactions --value_size=10240 --write_buffer_size=1000000000 [FullMergeV2] readseq : 3.472 micros/op 288018 ops/sec; 2817.1 MB/s readseq : 2.304 micros/op 434027 ops/sec; 4245.2 MB/s readseq : 1.163 micros/op 859845 ops/sec; 8410.0 MB/s readseq : 1.192 micros/op 838926 ops/sec; 8205.4 MB/s readseq : 1.250 micros/op 800000 ops/sec; 7824.7 MB/s [master] readseq : 24.025 micros/op 41623 ops/sec; 407.1 MB/s readseq : 18.489 micros/op 54086 ops/sec; 529.0 MB/s readseq : 18.693 micros/op 53495 ops/sec; 523.2 MB/s readseq : 23.621 micros/op 42335 ops/sec; 414.1 MB/s readseq : 18.775 micros/op 53262 ops/sec; 521.0 MB/s ``` ``` [Everything in Block cache | 10K operands | 10 KB each | 1 operand per key] [FullMergeV2] $ DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --num=100000 --db="/dev/shm/merge-random-10K-10KB" --cache_size=1000000000 --use_existing_db --disable_auto_compactions readseq : 14.741 micros/op 67837 ops/sec; 663.5 MB/s readseq : 1.029 micros/op 971446 ops/sec; 9501.6 MB/s readseq : 0.974 micros/op 1026229 ops/sec; 10037.4 MB/s readseq : 0.965 micros/op 1036080 ops/sec; 10133.8 MB/s readseq : 0.943 micros/op 1060657 ops/sec; 10374.2 MB/s [master] readseq : 16.735 micros/op 59755 ops/sec; 584.5 MB/s readseq : 3.029 micros/op 330151 ops/sec; 3229.2 MB/s readseq : 3.136 micros/op 318883 ops/sec; 3119.0 MB/s readseq : 3.065 micros/op 326245 ops/sec; 3191.0 MB/s readseq : 3.014 micros/op 331813 ops/sec; 3245.4 MB/s ``` ``` [Everything in Block cache | 10K operands | 10 KB each | 10 operand per key] DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --num=100000 --db="/dev/shm/merge-random-10-operands-10K-10KB" --cache_size=1000000000 --use_existing_db --disable_auto_compactions [FullMergeV2] readseq : 24.325 micros/op 41109 ops/sec; 402.1 MB/s readseq : 1.470 micros/op 680272 ops/sec; 6653.7 MB/s readseq : 1.231 micros/op 812347 ops/sec; 7945.5 MB/s readseq : 1.091 micros/op 916590 ops/sec; 8965.1 MB/s readseq : 1.109 micros/op 901713 ops/sec; 8819.6 MB/s [master] readseq : 27.257 micros/op 36687 ops/sec; 358.8 MB/s readseq : 4.443 micros/op 225073 ops/sec; 2201.4 MB/s readseq : 5.830 micros/op 171526 ops/sec; 1677.7 MB/s readseq : 4.173 micros/op 239635 ops/sec; 2343.8 MB/s readseq : 4.150 micros/op 240963 ops/sec; 2356.8 MB/s ``` Test Plan: COMPILE_WITH_ASAN=1 make check -j64 Reviewers: yhchiang, andrewkr, sdong Reviewed By: sdong Subscribers: lovro, andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D57075
8 years ago
merge_operator, key, &get_value_slice, {value}, &new_value,
moptions->info_log, moptions->statistics, Env::Default());
if (!merge_status.ok()) {
// Failed to merge!
// Store the delta in memtable
perform_merge = false;
} else {
// 3) Add value to memtable
mem->Add(sequence_, kTypeValue, key, new_value);
}
}
if (!perform_merge) {
// Add merge operator to memtable
mem->Add(sequence_, kTypeMerge, key, value);
}
sequence_++;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
CheckMemtableFull();
return Status::OK();
}
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
void CheckMemtableFull() {
if (flush_scheduler_ != nullptr) {
auto* cfd = cf_mems_->current();
assert(cfd != nullptr);
if (cfd->mem()->ShouldScheduleFlush() &&
cfd->mem()->MarkFlushScheduled()) {
// MarkFlushScheduled only returns true if we are the one that
// should take action, so no need to dedup further
flush_scheduler_->ScheduleFlush(cfd);
}
}
}
Status MarkBeginPrepare() override {
assert(rebuilding_trx_ == nullptr);
assert(db_);
if (recovering_log_number_ != 0) {
// during recovery we rebuild a hollow transaction
// from all encountered prepare sections of the wal
if (db_->allow_2pc() == false) {
return Status::NotSupported(
"WAL contains prepared transactions. Open with "
"TransactionDB::Open().");
}
// we are now iterating through a prepared section
rebuilding_trx_ = new WriteBatch();
if (has_valid_writes_ != nullptr) {
*has_valid_writes_ = true;
}
} else {
// in non-recovery we ignore prepare markers
// and insert the values directly. making sure we have a
// log for each insertion to reference.
assert(log_number_ref_ > 0);
}
return Status::OK();
}
Status MarkEndPrepare(const Slice& name) override {
assert(db_);
assert((rebuilding_trx_ != nullptr) == (recovering_log_number_ != 0));
if (recovering_log_number_ != 0) {
assert(db_->allow_2pc());
db_->InsertRecoveredTransaction(recovering_log_number_, name.ToString(),
rebuilding_trx_);
rebuilding_trx_ = nullptr;
} else {
assert(rebuilding_trx_ == nullptr);
assert(log_number_ref_ > 0);
}
return Status::OK();
}
Status MarkCommit(const Slice& name) override {
assert(db_);
Status s;
if (recovering_log_number_ != 0) {
// in recovery when we encounter a commit marker
// we lookup this transaction in our set of rebuilt transactions
// and commit.
auto trx = db_->GetRecoveredTransaction(name.ToString());
// the log contaiting the prepared section may have
// been released in the last incarnation because the
// data was flushed to L0
if (trx != nullptr) {
// at this point individual CF lognumbers will prevent
// duplicate re-insertion of values.
assert(log_number_ref_ == 0);
// all insertes must reference this trx log number
log_number_ref_ = trx->log_number_;
s = trx->batch_->Iterate(this);
log_number_ref_ = 0;
if (s.ok()) {
db_->DeleteRecoveredTransaction(name.ToString());
}
if (has_valid_writes_ != nullptr) {
*has_valid_writes_ = true;
}
}
} else {
// in non recovery we simply ignore this tag
}
return s;
}
Status MarkRollback(const Slice& name) override {
assert(db_);
if (recovering_log_number_ != 0) {
auto trx = db_->GetRecoveredTransaction(name.ToString());
// the log containing the transactions prep section
// may have been released in the previous incarnation
// because we knew it had been rolled back
if (trx != nullptr) {
db_->DeleteRecoveredTransaction(name.ToString());
}
} else {
// in non recovery we simply ignore this tag
}
return Status::OK();
}
private:
MemTablePostProcessInfo* get_post_process_info(MemTable* mem) {
if (!concurrent_memtable_writes_) {
// No need to batch counters locally if we don't use concurrent mode.
return nullptr;
}
return &GetPostMap()[mem];
}
};
// This function can only be called in these conditions:
// 1) During Recovery()
// 2) During Write(), in a single-threaded write thread
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
// 3) During Write(), in a concurrent context where memtables has been cloned
// The reason is that it calls memtables->Seek(), which has a stateful cache
Status WriteBatchInternal::InsertInto(WriteThread::WriteGroup& write_group,
SequenceNumber sequence,
ColumnFamilyMemTables* memtables,
FlushScheduler* flush_scheduler,
bool ignore_missing_column_families,
uint64_t recovery_log_number, DB* db,
bool concurrent_memtable_writes) {
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
MemTableInserter inserter(sequence, memtables, flush_scheduler,
ignore_missing_column_families, recovery_log_number,
db, concurrent_memtable_writes);
for (auto w : write_group) {
if (!w->ShouldWriteToMemtable()) {
continue;
}
inserter.set_log_number_ref(w->log_ref);
w->status = w->batch->Iterate(&inserter);
if (!w->status.ok()) {
return w->status;
}
}
return Status::OK();
}
Status WriteBatchInternal::InsertInto(WriteThread::Writer* writer,
ColumnFamilyMemTables* memtables,
FlushScheduler* flush_scheduler,
bool ignore_missing_column_families,
uint64_t log_number, DB* db,
bool concurrent_memtable_writes) {
MemTableInserter inserter(WriteBatchInternal::Sequence(writer->batch),
memtables, flush_scheduler,
ignore_missing_column_families, log_number, db,
concurrent_memtable_writes);
assert(writer->ShouldWriteToMemtable());
inserter.set_log_number_ref(writer->log_ref);
Status s = writer->batch->Iterate(&inserter);
if (concurrent_memtable_writes) {
inserter.PostProcess();
}
return s;
}
Status WriteBatchInternal::InsertInto(
const WriteBatch* batch, ColumnFamilyMemTables* memtables,
FlushScheduler* flush_scheduler, bool ignore_missing_column_families,
uint64_t log_number, DB* db, bool concurrent_memtable_writes,
SequenceNumber* last_seq_used, bool* has_valid_writes) {
MemTableInserter inserter(WriteBatchInternal::Sequence(batch), memtables,
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
flush_scheduler, ignore_missing_column_families,
log_number, db, concurrent_memtable_writes,
has_valid_writes);
Status s = batch->Iterate(&inserter);
if (last_seq_used != nullptr) {
*last_seq_used = inserter.get_final_sequence();
}
if (concurrent_memtable_writes) {
inserter.PostProcess();
}
return s;
}
Status WriteBatchInternal::SetContents(WriteBatch* b, const Slice& contents) {
assert(contents.size() >= WriteBatchInternal::kHeader);
b->rep_.assign(contents.data(), contents.size());
b->content_flags_.store(ContentFlags::DEFERRED, std::memory_order_relaxed);
return Status::OK();
}
Status WriteBatchInternal::Append(WriteBatch* dst, const WriteBatch* src,
const bool wal_only) {
size_t src_len;
int src_count;
uint32_t src_flags;
const SavePoint& batch_end = src->GetWalTerminationPoint();
if (wal_only && !batch_end.is_cleared()) {
src_len = batch_end.size - WriteBatchInternal::kHeader;
src_count = batch_end.count;
src_flags = batch_end.content_flags;
} else {
src_len = src->rep_.size() - WriteBatchInternal::kHeader;
src_count = Count(src);
src_flags = src->content_flags_.load(std::memory_order_relaxed);
}
SetCount(dst, Count(dst) + src_count);
assert(src->rep_.size() >= WriteBatchInternal::kHeader);
dst->rep_.append(src->rep_.data() + WriteBatchInternal::kHeader, src_len);
dst->content_flags_.store(
dst->content_flags_.load(std::memory_order_relaxed) | src_flags,
std::memory_order_relaxed);
return Status::OK();
}
size_t WriteBatchInternal::AppendedByteSize(size_t leftByteSize,
size_t rightByteSize) {
if (leftByteSize == 0 || rightByteSize == 0) {
return leftByteSize + rightByteSize;
} else {
return leftByteSize + rightByteSize - WriteBatchInternal::kHeader;
}
}
} // namespace rocksdb