From f3ff30fd5ed7b509139cdb219885efa90d47debf Mon Sep 17 00:00:00 2001 From: xhguo <10211900416@stu.ecnu.edu.cn> Date: Thu, 7 Nov 2024 11:20:11 +0800 Subject: [PATCH] finish report --- README.md | 4 +- assets/image-20241107080532982.png | Bin 0 -> 422801 bytes assets/image-20241107082315854.png | Bin 0 -> 78287 bytes assets/key_value_pair.jpg | Bin 0 -> 13856 bytes report.md | 461 +++++++++++++++++++++++++++++++++++++ test/ttl_test.cc | 2 +- 6 files changed, 465 insertions(+), 2 deletions(-) create mode 100644 assets/image-20241107080532982.png create mode 100644 assets/image-20241107082315854.png create mode 100644 assets/key_value_pair.jpg create mode 100644 report.md diff --git a/README.md b/README.md index 099d1f5..db4f243 100644 --- a/README.md +++ b/README.md @@ -6,4 +6,6 @@ LevelDB project 1 10211900416 郭夏辉 -如果出现open db failed,可能是运行地太慢了,请酌情调高ttl_test.cc中ttl的值。 \ No newline at end of file +如果出现open db failed,可能是运行地太慢了,请酌情调高ttl_test.cc中ReadTTL的ttl的值。 + +报告中见report.md \ No newline 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+最开始,我们拓展了`DBImpl::Put`使得它支持TTL。从前到后的逻辑很简单: + +1. 创建一个空的 `std::string val_with_ts`,存储要写入的带过期时间的value,并且为其预留足够的空间(value和时间戳的长度之和) +2. 利用`env_`提供的`NowMicros()`方法,以微秒为单位计算过期时间(`当前时间+TTL*1000000`)这里我们尽量将所有底层操作交给`env_`完成,而DBImpl有该对象可以调用相关查找时间的函数 +3. 利用`EncodeFixed64`编码函数将`expiration_time`编码为一个固定长度的字符串,并存储在`ts_string`中。利用LevelDB自带的而不是自己手写的编码函数可以提升一些运行效率。 +4. 追加原始`value`到`val_with_ts`以及将`expiration_time` 追加到 `val_with_ts`只需要使用字符串的`append`方法从前往后拼接即可。 +5. 最后将刚刚处理得到的`val_with_ts`打包成一个`Slice`,然后再调用原始的`DB::Put`方法。 + +为了适配之后的实验,提升兼容性,我们将上面这套逻辑重复了一遍,也完成了新的`Status DB::Put(const WriteOptions& options, const Slice& key, const Slice& value, uint64_t ttl) `,使得它支持TTL。 + +```cpp +Status DB::Put(const WriteOptions& options, const Slice& key, + const Slice& value, uint64_t ttl) { + // 将 value 和 expiration_time 合并到一起,形成带 TTL 的 value + std::string val_with_ts; + + val_with_ts.reserve(value.size() + sizeof(uint64_t)); + + uint64_t expiration_time = std::chrono::duration_cast( + std::chrono::system_clock::now().time_since_epoch()) + .count() + ttl * 1000; + + // 追加原始 value 到 val_with_ts + val_with_ts.append(value.data(), value.size()); + + // 将 expiration_time 追加到 val_with_ts + val_with_ts.append(reinterpret_cast(&expiration_time), sizeof(expiration_time)); + + WriteBatch batch; + batch.Put(key, Slice(val_with_ts)); + return Write(options, &batch); +} +``` + +只不过在这里计算当前时间不能再使用`env_`的相关方法了,使用的是`std::chrono::system_clock::now()`获取当前时间,其他的逻辑是一样的。 + +实现很简单,就是将TTL值转为到期的时间戳,加入value尾部,这样可以尽可能降低整个项目的耦合程度,我们无需对ExtractUserkey(提取具体key值相关的函数),FindShortSeperator(简化key长度,这里还可能导致时间戳被修改)等函数进行复杂的重写。此外,还可以节约空间。 + +这里基于原先的项目举几个例子来说明为什么不讲时间戳作为key的一部分写入,原先的index_block就是为了尽量缩短key的长度,而如果将时间戳加入key的尾部,其时间戳本身的长度就会大于key的长度。并且由于其内部的时间戳与key不同,是无序存储的,因此很难使用重启点来减少存储时间戳的空间开销。 + +![image-20241107080532982](assets/image-20241107080532982.png) + +### 4.2支持TTL的Get方法 + +Get操作的核心便是检查数据是否过期,只要实现这里就能让读取顺利进行了。 + +```cpp +uint64_t DBImpl::GetTS(std::string* val) { + auto expiration_time = DecodeFixed64(val->data() + val->size() - kTSLength); + val->resize(val->size() - kTSLength);//保证返回值没有ttl + return expiration_time; +} + +Status DBImpl::CheckIsExpire(std::string* value) { + //debug 用 + auto a = env_->NowMicros(); + auto b = GetTS(value); + if(a > b){ + return Status::Expire("Expire",Slice()); + } + return Status(); +} + +Status DBImpl::Get(const ReadOptions& options, const Slice& key, + std::string* value) { + // other code... + // Unlock while reading from files and memtables + // Unlock while reading from files and memtables + { + mutex_.Unlock(); + // First look in the memtable, then in the immutable memtable (if any). + LookupKey lkey(key, snapshot); + if (mem->Get(lkey, value, &s)) { + // Done + } else if (imm != nullptr && imm->Get(lkey, value, &s)) { + // Done + } else { + stats.now_ts = this->env_->NowMicros(); + s = current->Get(options, lkey, value, &stats); + have_stat_update = true; + } + mutex_.Lock(); + } + if(s.ok()){ + s = CheckIsExpire(value); + } + if (have_stat_update && current->UpdateStats(stats,s.IsExpire())) { + MaybeScheduleCompaction();//有可能寻址过多,导致allow_seek为0,触发合并。 + } + //...... +} + +bool Version::UpdateStats(const GetStats& stats,bool is_expire) { + FileMetaData* f = stats.seek_file; + if (f != nullptr) { + f->allowed_seeks--; + if(is_expire)f->allowed_seeks--; + if (f->allowed_seeks <= 0 && file_to_compact_ == nullptr) { + file_to_compact_ = f; + file_to_compact_level_ = stats.seek_file_level; + return true; + } + } + return false; +} +``` + +与编码时候的`EncodeFixed64`相对应,我们使用`DecodeFixed64`将过期时间戳从value里面提取出来,并实现了`DBImpl::GetTS`方法。在此基础上我们检查value是否应该被丢弃了,实现了`DBImpl::CheckIsExpire`方法,如果当前时间已经超过了时间戳,那么直接返回过期状态`Status::Expire`。(其实这里也纠结了一下要不要按照实验文档中要求的那样返回状态`Status::NotFound`,经过测试两者都是可以的,然后严谨一些,实际情况是key已经存在,但由于某种原因不可再访问,所以最终还是使用了`Status::Expire`) + +在此我们进行一些时间分析,对于涉及内存(memtable内,缓存命中)的查找速度很快;而这里可能会想到,有许多过期数据都在缓存池内要怎么办,实际上,只要是在缓存池内的数据,即使过期了,它也是有信息价值的(提供了过期这一信息)。因此,我们实际需要解决的是,如何降低IO层面的对于过期数据的查找。这里我们在基于leveldb采用的自动合并优化上(即一个文件访问多次后,它将会作为input传给compaction,然后后续过滤过期数据),对于在缓存池中,读取到的所过期的数据所在的文件,其`allowed_seek`次数再次减一(也可以基于需求减去其他值),这样的目的是,期望尽快对于其key值所在的文件触发合并,减少底层过期的数据量。 + +当然,这样做对于此次的查找帮助不大,主要是为了以后再次查找时,能加快扫描速度。接下里我们可能会想,如何减少cache内减少IO操作的次数。先回忆整个cache的读取流程:获得布隆过滤器,若key值存在,从硬盘中读取IndexBlock,然后再从硬盘读取DataBlock。其中IndexBlock的设计在之前已经提到,为了节约空间开销,不能添加时间戳。因此我们只能依靠于bloomfilter类似的功能。这里我们以manifest的元数据作为过滤的基础。 + +我们为每个文件的元数据增加新字段 + +```cpp +struct FileMetaData { + FileMetaData() : refs(0), allowed_seeks(1 << 30), file_size(0),oldest_ts(UINT64_MAX),newer_ts(0) {} + + int refs; + int allowed_seeks; // Seeks allowed until compaction + uint64_t number; + uint64_t file_size; // File size in bytes + InternalKey smallest; // Smallest internal key served by table + InternalKey largest; // Largest internal key served by table + TIMESTAMP oldest_ts; //新字段:该文件内最小的时间戳, + TIMESTAMP newer_ts; //新字段:该文件内最大的时间戳 +}; +``` + +接下来就是对相关的MANIFEST文件的元数据写入进行修改以及在写入key,value对时,对时间戳更新。包括`VersionEdit::AddFile`,`VersionEdit::EncodeTo`Status `VersionEdit::DecodeFrom`,`builder.h:Status BuildTable`这里不在报告中举出。然后最后的过滤操作,则在`Status Version::Get`内的`match`函数 + +```cpp +//1.调用Status Version::Get(const ReadOptions& options, const LookupKey& k, +// std::string* value, GetStats* stats)接口,进入非memtable部分查找 +//2调用void Version::ForEachOverlapping(Slice user_key, Slice internal_key, void* arg, +// bool (*func)(void*, int, FileMetaData*))查找涉及到的key以及对应的文件元数据 +//3.调用match函数,寻址 + +static bool Match(void* arg, int level, FileMetaData* f) { + State* state = reinterpret_cast(arg); + if (state->stats->seek_file == nullptr && state->last_file_read != nullptr) { + // We have had more than one seek for this read. Charge the 1st file. + state->stats->seek_file = state->last_file_read; + state->stats->seek_file_level = state->last_file_read_level; + } + state->last_file_read = f; + state->last_file_read_level = level; + if(state->stats->now_ts > f->newer_ts)return false;//在访问缓存池前,判断是否过期 + state->s = state->vset->table_cache_->Get(*state->options, f->number, f->file_size, state->ikey,&state->saver, SaveValue); + // other code... +} +``` + +这里有尝试`if(state->stats->now_ts > f->newer_ts)return false;`根据文件内的最新时间戳来进行过滤。但是实际上,对于多次的批量插入以及一个batch内不同的TTL,在合并后,我们无法很好的预测出一个文件的新旧情况,即每一个文件的元数据的new_ts有可能都十分大,最终还是要进入cache内查找。 + + + +**经过刚刚的修改,我们初步完成了读取功能,第一个测试点经过一些修改也能顺利通过了。** + +在这时曾发现ttl_test启动后,无论如何修改ReadTTL都显示 failed,经过助教提醒发现是随机种子的问题,如果采用`static_cast(time(0))`根据当前的时间戳生成随机数,那么`InsertData`插入数据的 key 和查找时所使用的 key 不一致导致读取失败。解决的办法就是`srand(0)`固定随机种子。 + +ReadTTL之后,CompactionTTL还未开始,直接显示 open DB failed,出现这个原因是ReadTTL之后打开的数据库没有清除,只需要在ReadTTL的末尾加上`delete db;`就解决了问题。 + +还有就是`delete db;`之后需要睡眠一秒,否则还是会出现read DB failed。这里的另外一个问题就是ReadTTL测试用例在我的电脑上一直出现open db fail,但是在另一位队友的电脑上就不会,这是因为在我这里程序跑的太慢了,超过它所设定的20s,这里酌情提高之后就能解决了。 + +### 4.3支持TTL的Compaction + +在数据Compaction过程中,需要删除过期的数据。大合并操作都调用`DoCompactionWork`进行,结合之前的授课我们可以知道它本身就会检查数据是否有效,因此我们只需要在此多加入一些关于TTL判断的逻辑就行。比较巧的是,`DoCompactionWork`自带drop变量来标记数据是否需要被丢弃,我们只需要利用好它就行。 + +```cpp +Status DBImpl::DoCompactionWork(CompactionState* compact) { + // other code... + while (input->Valid() && !shutting_down_.load(std::memory_order_acquire)) { + // other code... + if (!ParseInternalKey(key, &ikey)) { + // other code... + } else { + if (!has_current_user_key || + user_comparator()->Compare(ikey.user_key, Slice(current_user_key)) != + 0) { + // other code... + } + if (last_sequence_for_key <= compact->smallest_snapshot) { + // other code... + } else if (ikey.type == kTypeDeletion && + ikey.sequence <= compact->smallest_snapshot && + compact->compaction->IsBaseLevelForKey(ikey.user_key)) { + // other code... + }else if((ts = DecodeFixed64(input->value().data() + input->value().size() - kTSLength)) < env_->NowMicros()){ + // TTL + drop = true; + } + + last_sequence_for_key = ikey.sequence; + } +// other code... + return status; +} +``` + +其实判断的逻辑和之前`Get`和`Put`时候是一样的,如果当前时间超出了过期时间,则将 drop 标记为 true,表示丢弃该数据;若当前时间尚未超出过期时间则将数据视为有效的。 + +这里有一个小问题是TEST中无法完全删除所有过期数据。具体的例子如下,未触发手动合并前:level0文件数量:31个;level1数量:1;level2数量:1个;然后手动合并后,它会先尝试合并level0数据,然后在合并的过程中,它会将level0和level1的所有数据都当做input,放入一个mergeIterator。因为时间过期了,它会将这些文件全删掉,只剩下level2的一个文件。然后因为此时我们最大的遍历层只能到level1,level2的数据删除依赖于level1在合并时,其key的范围覆盖到level2,然后删除。此外,即使level1还剩下一个文件,也需要其所有。对于这个问题的解决,我们最开始是修改`kL0_CompactionTrigger `等参数,加大level0的文件数量,使得所有数据都放在level0中,这样可以确保所有数据都在compaction中作为input,但这会导致在今后正常运行时(非测试情况),其IO开销较大(因为level0层会覆盖很多key)。 + +在这里经过助教提示发现了一个小问题,就是手动合并可能无法保证合并所有数据,导致无法完全丢弃过期数据。解决方法就是修改 CompactRange 的流程使得所有数据得到合并。原始代码并没有去合并最大的含有文件的level(max_level_with_files)的文件,只合并了在此之前的level,我们的修改是这样的: + +```cpp +void DBImpl::CompactRange(const Slice* begin, const Slice* end) { + // other code... + TEST_CompactMemTable(); // TODO(sanjay): Skip if memtable does not overlap + for (int level = 0; level < max_level_with_files; level++) { + TEST_CompactRange(level, begin, end); + } + TEST_CompactRange(max_level_with_files, begin, end); +} +``` + + + +## 5.测试和结果 + +### 5.1测试用例 + +```cpp +#include "gtest/gtest.h" +#include "leveldb/env.h" +#include "leveldb/db.h" + +using namespace leveldb; + +constexpr int value_size = 2048; +constexpr int data_size = 128 << 20; + +Status OpenDB(std::string dbName, DB **db) { + Options options; + options.create_if_missing = true; + return DB::Open(options, dbName, db); +} + +void InsertData(DB *db, uint64_t ttl/* second */) { + WriteOptions writeOptions; + int key_num = data_size / value_size; + srand(0); + + for (int i = 0; i < key_num; i++) { + int key_ = rand() % key_num+1; + std::string key = std::to_string(key_); + std::string value(value_size, 'a'); + db->Put(writeOptions, key, value, ttl); + } +} + +void GetData(DB *db, int size = (1 << 30)) { + ReadOptions readOptions; + int key_num = data_size / value_size; + + // 点查 + srand(0); + for (int i = 0; i < 100; i++) { + int key_ = rand() % key_num+1; + std::string key = std::to_string(key_); + std::string value; + db->Get(readOptions, key, &value); + } +} +// +TEST(TestTTL, ReadTTL) { + DB *db; + if(OpenDB("testdb", &db).ok() == false) { + std::cerr << "open db failed" << std::endl; + abort(); + } + // 如果出现open db fail,请酌情提高这里 + uint64_t ttl = 20; + + InsertData(db, ttl); + + ReadOptions readOptions; + Status status; + int key_num = data_size / value_size; + srand(0); + for (int i = 0; i < 100; i++) { + int key_ = rand() % key_num+1; + std::string key = std::to_string(key_); + std::string value; + status = db->Get(readOptions, key, &value); + ASSERT_TRUE(status.ok()); + } + + Env::Default()->SleepForMicroseconds(ttl * 1000000); + + for (int i = 0; i < 100; i++) { + int key_ = rand() % key_num+1; + std::string key = std::to_string(key_); + std::string value; + status = db->Get(readOptions, key, &value); + ASSERT_FALSE(status.ok()); + } + delete db; + Env::Default()->SleepForMicroseconds( 1000); +} + +TEST(TestTTL, CompactionTTL) { + DestroyDB("testdb", Options()); + DB *db; + if(OpenDB("testdb", &db).ok() == false) { + std::cerr << "open db failed" << std::endl; + abort(); + } + + uint64_t ttl = 20; + InsertData(db, ttl); + + leveldb::Range ranges[1]; + ranges[0] = leveldb::Range("-", "A"); + uint64_t sizes[1]; + db->GetApproximateSizes(ranges, 1, sizes); + ASSERT_GT(sizes[0], 0); + + Env::Default()->SleepForMicroseconds(ttl * 1000000); + + db->CompactRange(nullptr, nullptr); + + leveldb::Range ranges2[1]; + ranges2[0] = leveldb::Range("-", "A"); + uint64_t sizes2[1]; + db->GetApproximateSizes(ranges2, 1, sizes2); + ASSERT_EQ(sizes2[0], 0); +} + + +int main(int argc, char** argv) { + // All tests currently run with the same read-only file limits. + testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} +``` + +### 5.2实验结果 + +在我的电脑上,我适当调大了ReadTTL中TTL之后(调整到了50),结果如下所示: + +![image-20241107082315854](assets/image-20241107082315854.png) + +## 5.3其他可能出现的问题: + +可能是因为test文件内的数据准备以及硬件的运行速度,有些问题有可能会发生。 + +```cpp +inline bool DBIter::ParseKey(ParsedInternalKey* ikey) { + Slice k = iter_->key(); + + size_t bytes_read = k.size() + iter_->value().size(); + while (bytes_until_read_sampling_ < bytes_read) { + bytes_until_read_sampling_ += RandomCompactionPeriod(); + db_->RecordReadSample(k); + } + + //...... + void DBImpl::RecordReadSample(Slice key) { + MutexLock l(&mutex_); + if (versions_->current()->RecordReadSample(key)) { + MaybeScheduleCompaction(); + } +} +``` + +上述过程是在读取的过程中发生的,当发现key跨文件存在后,它也会触发合并。这就有可能导致,对于testRead文件,如果ttl设置的比较小,在读取key1(file1内)时,发现它跨文件存在于file2,然后触发合并,但是值key2也在file2内,此时有可能过期。这就导致在读取key2前,就已经触发合并,将过期数据删除,这样会导致test报错。 + + + +## 6.本实验ttl实现与已有ttl实现的比较 + + **RocksDB** : + +本身支持一种数据过期淘汰方案,该方案是通过特定的 API 打开 DB,对写入该 DB 的全部 key 都遵循一个 TTL 过期策略,例如 TTL 为 3 天,那么写入该 DB 的 key 都会在写入的三天后自动过期。但是该方案不灵活,无法针对每一条 key 设置 TTL。 + +此外,rocksdb该方案底层也是通过 compaction filter 实现的,也就是说过期数据虽然对用户不可见,但是磁盘空间并不会及时回收,根本原因就是数据堆积在某一层,而该层没有触发 compaction,那么我们可以手动调用 RocksDB 的 CompactionRange 函数,来触发 compaction filter,达到快速回收磁盘空间的目的。但是主动调用 CompactionRange 会导致 RocksDB 自身的 compaction 暂停,这会触发 Write Stall,造成非常严重的后果。 + +**Parker**: + +采用`Periodic compaction + dynamic compaction`。Periodic compaction 的主要原理是增加一个 periodic_compaction_seconds 参数,并记录每个SST 文件的创建时间,每隔 periodic_compaction_seconds 秒,主动对这个 SST 文件进行 compaction 操作,从而回收沉底的 SST 文件;而 dynamic compaction 则是通过设置level_compaction_dynamic_level_bytes 为 true,进行动态合并,而不是按 level 的顺序合并到下一层,这使得 compaction 更加频繁。 + +**业内有赞 KV :** + +具体做法是,保持现有存储列族不变,另外增加一个列族,专门存储 key 的 TTL,然后通过一个 goroutine 根据当前时间戳,按照过期数据删除策略可以是定时触发,例如凌晨1点,或者每个一段时间触发等。该列族中 key 的编码规则如下: + +- key:时间戳 + key 的类型 + key 值 +- value:存储一个字节,代表不同数据类型 + +这个方案优点是回收速度快且回收时间可控,但是缺点就是实现复杂,极端情况下会降低 50% 的TPS。 + + + +**本实验的ttl** + +在数据的 value 的尾部添加八个字节的 TTL,实现灵活。 + +![屏幕截图 2024-11-07 110211](./assets/key_value_pair.jpg) + +基于ttl过期的case,显著降低allowed_seek次数,使得对于指定的过期文件,能够加快合并速度。 + +## 7.实验总结和思考 + +通过本次实验,我们对LevelDB有了更深入的理解。超脱于之前的理论讲解,我们透过代码掌握了各个部分是如何实现的,也对整体样貌再次有了一个概览,为日后的数据管理系统的学习打下了坚实基础。 \ No newline at end of file diff --git a/test/ttl_test.cc b/test/ttl_test.cc index 521cae8..abfa25b 100644 --- a/test/ttl_test.cc +++ b/test/ttl_test.cc @@ -47,7 +47,7 @@ TEST(TestTTL, ReadTTL) { abort(); } // 如果出现open db fail,请酌情提高这里 - uint64_t ttl = 20; + uint64_t ttl = 50; InsertData(db, ttl);