From 325ae606ec3ba038915f9470590a01f02e7fddbf Mon Sep 17 00:00:00 2001 From: smartkeyerror Date: Fri, 20 Aug 2021 16:24:11 +0800 Subject: [PATCH] :sparkles: Write Process --- debug/articles/04-write-process/README.md | 113 ++++++++++++++++++ .../04-write-process/images/1628668662510.png | Bin 0 -> 29964 bytes .../04-write-process/images/1628755874963.png | Bin 0 -> 30745 bytes .../04-write-process/images/1628835101487.png | Bin 0 -> 133011 bytes table/filter_block.cc | 32 ++++- table/filter_block.h | 19 ++- 6 files changed, 153 insertions(+), 11 deletions(-) create mode 100644 debug/articles/04-write-process/README.md create mode 100755 debug/articles/04-write-process/images/1628668662510.png create mode 100755 debug/articles/04-write-process/images/1628755874963.png create mode 100755 debug/articles/04-write-process/images/1628835101487.png diff --git a/debug/articles/04-write-process/README.md b/debug/articles/04-write-process/README.md new file mode 100644 index 0000000..2bea105 --- /dev/null +++ b/debug/articles/04-write-process/README.md @@ -0,0 +1,113 @@ +## leveldb 的写入流程 + +在前面一节中我们讨论了 leveldb 中的 varint 以及 Key 格式,用户所传入的 User Key 将会被封装成 MemTable Key,再加上 User Value 封装成 MemTable Entry 写入到 MemTable 中: + +![Alt text](images/1628668662510.png) + +在这一节中,就来分析一下 K-V 是如何被写入到硬盘中的。 + +### 1. WriteBatch + +`include/leveldb/db.h` 中的 `DB` 类提供了 leveldb 对外的抽象 API,该类中所有的函数均为纯虚函数,也就是说,`DB` 类其实就是一个 Interface。不过,由于 C++ 语法的特殊性,`DB` 类也可以对纯虚函数进行实现,并且 leveldb 也给出了默认实现。 + +有两个类实现了 `DB` 接口,一个是位于 `db/db_test.cc` 文件中的 `ModelDB`,另一个则是位于 `db/db_impl.cc` 中的 `DBImpl`。很明显,`ModelDB` 主要用于测试,而 `DBImpl` 才是 leveldb 实现的核心类。在本节中,我们只需要关注 `DBImpl::Put()` 和 `DBImpl::Write()` 这两个方法即可。 + +首先来看 `Put()` 方法: + +```cpp +struct WriteOptions { + WriteOptions() = default; + bool sync = false; /* 写入 WAL 时是否同步调用 fsync() */ +}; + +/* DBImpl 实现的 Put() 方法,本质上还是调用了 leveldb 的默认实现 */ +Status DBImpl::Put(const WriteOptions& o, const Slice& key, const Slice& val) { + return DB::Put(o, key, val); +} + +/* 真正的 Put() 方法实现 */ +Status DB::Put(const WriteOptions& opt, const Slice& key, const Slice& value) { + WriteBatch batch; + batch.Put(key, value); + return Write(opt, &batch); +} +``` + +`WriteOptions` 中只有 `sync` 一个字段,用于控制当 leveldb 将数据写入到预写日志时,是否同步地调用 `fsync()` 将内核缓冲区中的数据 flush 到硬盘,其值默认为 `false`。在生产实践中,这个值一般为 `true`,尽管为 `false` 时能够提高 leveldb 的写入速度,但是会有数据丢失的风险。 + +在 `DB::Put()` 方法中,生成了一个 `WriteBatch` 对象。leveldb 为了提高写入效率,会将多个线程提交的 K-V 写操作打包成一个 `WriteBatch` 对象,然后进行一次性写入,是一种常见的批量写入优化手段。`WriteBatch` 的底层数据结构实际上就是一个 `std::string`,用于承载多个 K-V。 + +```cpp +class WriteBatch { +public: + WriteBatch(); + + void Put(const Slice& key, const Slice& value); /* 添加 K-V */ + void Delete(const Slice& key); /* 删除 K-V */ + void Append(const WriteBatch& source); /* Copy WriteBatch */ + + /* Iterate 方法将会遍历 rep_ 中的 K-V,并根据 K-V 中的 Value Type 来决定 + * 调用 handler->Put() 或 handler->Delete(),Handler 是一个抽象基类*/ + Status Iterate(Handler* handler) const; +private: + friend class WriteBatchInternal; + std::string rep_; +}; +``` + +因为 `DB::Put()` 调用了 `WriteBatch::Put()` 方法,所以现在来看下 `WriteBatch::Put()` 方法的具体实现: + +```cpp +/* 将 value.size() 进行 varint 编码,写入 dst 中,并且将 value 也追加到 dst 中 */ +void PutLengthPrefixedSlice(std::string* dst, const Slice& value) { + PutVarint32(dst, value.size()); + dst->append(value.data(), value.size()); +} + +/* 将 User Key、User Value 以及 Value Type 追加到 rep_ 中 */ +void WriteBatch::Put(const Slice& key, const Slice& value) { + WriteBatchInternal::SetCount(this, WriteBatchInternal::Count(this) + 1); + rep_.push_back(static_cast(kTypeValue)); + PutLengthPrefixedSlice(&rep_, key); + PutLengthPrefixedSlice(&rep_, value); +} +``` + +可以看到,`WriteBatch::Put` 只是简单地将 User Key、User Value 以及 Value Type 进行打包,得到一个打包后的“数组”,其内存布局如下图所示: + +![Alt text](images/1628755874963.png) + +需要注意的是在 `rep_` 的起始位置存在 12 字节的预留位,用于填充这些 K-V 的起始 Sequence Number 以及 Count 计数。将多个 K-V 打包完成以后,将会调用 `DBImpl::Write()` 方法,正式进入 leveldb 的写入流程。 + +### 2. 写入逻辑 + +在将数据写入预写日志文件之前,需要循环确认 leveldb 的状态,主要包括 MemTable 是否达到最大容量、Level-0 中的文件数是否已经达到某个阈值等。如果 MemTable 已经达到了最大容量,并且此时 Immutable MemTable 仍未 flush 到硬盘时,leveldb 将会等待后台线程完成其相关工作。这些判断均在 `MakeRoomForWrite()` 方法中进行: + +1. 如果当前 Level-0 层的 SSTable 数量已经达到了阈值 `kL0_SlowdownWritesTrigger`(默认为 8),则会调用 `std::this_thread::sleep_for()` 方法延迟 1 毫秒写入,该延迟写入只会进行 1 次。 +2. 如果当前 MemTable 容量没有达到最大大小(`write_buffer_size`,默认为 4MB)则允许写入,返回 OK 状态的 `Status`。 +3. 如果 MemTable 已达到最大容量,并且 Immutable MemTable 仍存在的话,就需要等待 Compaction 完成。 +4. 如果 Level-0 层的 SSTable 数量已经达到了阈值 `kL0_StopWritesTrigger`(默认为 12),同样需要等待 Compaction 完成。 +5. 最后,当 MemTable 以达到最大容量,并且 Immutable MemTable 不存在时,就需要将 Memtable 主动地变更为 Immutable MemTable,并初始化一个新的 MemTable 和日志文件,并主动地触发 Minor Compaction,可能会创建一个新的线程执行,同时允许写入。 + +当 `MakeRoomForWrite()` 方法调用返回时,不管之前发生了什么,现在的 MemTable 一定是有剩余容量并且 Level-0 的文件数量大概率小于 4,所以可以进行接下来的写入流程。 + +而后,我们需要获取最新的 Sequence Number,并将 `last_sequence + 1` 写入到 `write_batch` 的起始位置上,而后就可以写预写日志了。 + +在写完预写日志以后,会根据 `options.sync` 的值来决定是否调用 `fsync()` 进行刷盘,成功以后就会将 `write_batch` 中数据写入至 Skip List 所实现的 MemTable 中。 + +这个过程在 `WriteBatch::Iterate()` 方法中进行,遍历 `rep_` 中打包好的 K-V,逐一地调用 `MemTable::Add()` 方法: + +```cpp +void MemTable::Add(SequenceNumber s, ValueType type, const Slice& key, + const Slice& value); +``` + +可以看到,MemTable Entry 所需要的“四元素”均会由外部传入,在 `MemTable::Add()` 方法内部将会调用相关的编码函数将其打包成 MemTable Entry 的格式插入到 Skip List 中。MemTable Entry 格式具体可见本篇文章开头部分。 + +最后,leveldb 将会更新全局的 Sequence Number,其值就是原来的 Sequence Number 再加上此次 `write_batch` 写入的 K-V 数量。 + +整个写入流程可见下图: + +![Alt text](images/1628835101487.png) + +关于预写日志的格式与写入流程将在下篇描述。 diff --git a/debug/articles/04-write-process/images/1628668662510.png b/debug/articles/04-write-process/images/1628668662510.png new file mode 100755 index 0000000000000000000000000000000000000000..619cb9e02d82b06bacaa63b616906a2a170862c7 GIT binary patch literal 29964 zcmeFYRal)(uqKMTySuaSAi>=og1ZEF2=4Cg?vUW_?hq`vySqEH{ynqz?EG_iZq9Sg zaKXb`-`CY$UDEGcU4$#jOCZAG!GVB)AWBJ!DuaN42Lb=?gMkA6MlD5H1OXudkrEYB zb<;b`hICeyzz^AUlLVtcfk7c@!E|lLvat^(JCAK|_jCx|(W+=^7ja14xnLyC`SfyP z<8fGM$F8NA*O-)!MM=R;yhCtB-1Hh2WOhi>xYd14;*sKIO9Zu#e<_mwHV_UC!dVb#a z{d^^^`~EwER3v0TLqNdyBZ}_Z&#g`0&%v$UKo-k68aEFQqv04rVUjA{b}SAK4weP} zZyg@@mQln4MCw&KsOL$vDo}}3a>6qOV#W=+Rat+VTQnF1zOPJ?Ezwh}!Q3B{UTy1Y zyPrnADl7et&YAOKvpaSoosHaKXMhAgr?}WO>hAt=(|35bSVpPVNX&Y@4KpSt#_f6s 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zqyEoU2!6yiFH~JREVy%}YEU_d&^T+XhQ;786h*eD3MhAZ7kmk8=5O+Ez|xAq_NK*M0$~xBME6KR_4!(GGDFyQzq{<$Eh)@YOv=$LxgK_q?{^Wl|}XzcdcN?;eZA5^eX)GNxJ8tuFMKDgGH20gll zZK8_QHj8e3YhFTw{O}0&cYa26GR zq(c7Dns#0QrL*ly%=-;g{yqo0u>j*o1UF>)KMMAjmfMH}n~qoS1=IgH29F~}(H@P* m)6w*QfxZ6*|9?oz{{j9%eBB9d11KE;{$wN-B`U;>KKvhr3yB^8 literal 0 HcmV?d00001 diff --git a/table/filter_block.cc b/table/filter_block.cc index 09ec009..2c1372f 100644 --- a/table/filter_block.cc +++ b/table/filter_block.cc @@ -19,15 +19,24 @@ FilterBlockBuilder::FilterBlockBuilder(const FilterPolicy* policy) : policy_(policy) {} void FilterBlockBuilder::StartBlock(uint64_t block_offset) { + + /* block_offset 可以认为是 Data Block 的结束偏移量,kFilterBase 的值其实就是 2048, + * 即 2KB,filter_index 就表示需要创建多少个 Bloom Filter */ uint64_t filter_index = (block_offset / kFilterBase); + assert(filter_index >= filter_offsets_.size()); + + /* filter_offsets_ 用于保存每个 Bloom Filter 的起始偏移量,因为每一个 Bloom Filter 的 + * 长度可能是不同的。*/ while (filter_index > filter_offsets_.size()) { GenerateFilter(); } } +/* 此时的 key 为 InternalKey,也就是在尾部追加了 `Sequence Number | Value Type` 的结果 */ void FilterBlockBuilder::AddKey(const Slice& key) { Slice k = key; + /* 注意这里是 keys_.size(),而不是 key.size(),记录的是每一个 key 在 keys_ 中的起始地址 */ start_.push_back(keys_.size()); keys_.append(k.data(), k.size()); } @@ -37,19 +46,27 @@ Slice FilterBlockBuilder::Finish() { GenerateFilter(); } - // Append array of per-filter offsets const uint32_t array_offset = result_.size(); + /* 将所有的偏移量放到 result_ 尾部,偏移量为定长编码 */ for (size_t i = 0; i < filter_offsets_.size(); i++) { PutFixed32(&result_, filter_offsets_[i]); } + /* 将 Bloom Filters 的个数扔到 result_ 尾部*/ PutFixed32(&result_, array_offset); + /* 将 "base" 的大小放入,因为 kFilterBaseLg 可能会被修改 */ result_.push_back(kFilterBaseLg); // Save encoding parameter in result return Slice(result_); } +/* `GenerateFilter()` 方法用于构建一个具体的 Filter Block K,其内部将会调用 `policy_->CreateFilter` + * 创建一个布隆过滤器,并将结果位数组(string 实现)返回 */ void FilterBlockBuilder::GenerateFilter() { + + /* 获取 key 的数量 */ const size_t num_keys = start_.size(); + + /* 如果 num_keys 为 0 的话,直接压入 result_.size() 并返回 */ if (num_keys == 0) { // Fast path if there are no keys for this filter filter_offsets_.push_back(result_.size()); @@ -58,15 +75,20 @@ void FilterBlockBuilder::GenerateFilter() { // Make list of keys from flattened key structure start_.push_back(keys_.size()); // Simplify length computation + + /* tmp_keys_ 主要的作用就是作为 CreateFilter() 方法参数构建 Bloom Filter */ tmp_keys_.resize(num_keys); + + /* 逐一取出 keys_ 中的所有 InternalKey,并扔到 tmp_keys_ 中 */ for (size_t i = 0; i < num_keys; i++) { - const char* base = keys_.data() + start_[i]; - size_t length = start_[i + 1] - start_[i]; - tmp_keys_[i] = Slice(base, length); + const char* base = keys_.data() + start_[i]; /* 取得第 i 个 key 的起始地址 */ + size_t length = start_[i + 1] - start_[i]; /* 取得第 i 个 key 的长度 */ + tmp_keys_[i] = Slice(base, length); /* 通过 Slice 构造 key */ } - // Generate filter for current set of keys and append to result_. + /* result_.size() 的初始值为 0,所以 filter_offsets_ 记录的是 Bloom Filter 结果的起始偏移量 */ filter_offsets_.push_back(result_.size()); + /* 构建 Bloom Filter */ policy_->CreateFilter(&tmp_keys_[0], static_cast(num_keys), &result_); tmp_keys_.clear(); diff --git a/table/filter_block.h b/table/filter_block.h index 25ab75b..c09bb56 100644 --- a/table/filter_block.h +++ b/table/filter_block.h @@ -34,18 +34,25 @@ class FilterBlockBuilder { FilterBlockBuilder(const FilterBlockBuilder&) = delete; FilterBlockBuilder& operator=(const FilterBlockBuilder&) = delete; + /* 开始构建新的 Filter Block */ void StartBlock(uint64_t block_offset); + + /*添加一个新的 key,将在 `TableBuilder` 中被调用*/ void AddKey(const Slice& key); + + /*结束 Filter Block 的构建,并返回 Filter Block 的完整内容*/ Slice Finish(); private: - void GenerateFilter(); + void GenerateFilter(); /* 构建一个 Filter */ - const FilterPolicy* policy_; - std::string keys_; // Flattened key contents - std::vector start_; // Starting index in keys_ of each key - std::string result_; // Filter data computed so far - std::vector tmp_keys_; // policy_->CreateFilter() argument + const FilterPolicy* policy_; /* filter 类型,如 BloomFilterPolicy */ + std::string keys_; /* User Keys,全部塞到一个 string 中 */ + std::vector start_; /* 每一个 User Key 在 keys_ 中的起始位置 */ + std::string result_; /* keys_ 通过 policy_ 计算出来的 filtered data */ + std::vector tmp_keys_; /* policy_->CreateFilter() 的参数 */ + + /* filter 在 result_ 中的位置, filter_offsets_.size() 就是 Bloom Filter 的数量 */ std::vector filter_offsets_; };