Ceph 之RGW Cache

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发表文章数:3062

Java学习笔记(二) 面向对象—构造函数

Overview

缓存是为达到系统快速响应的一项关键技术,Ceph 作为一个复杂的分布式存储系统,有多种、多级缓存存在。缓存按照位置分为:

  • 客户端缓存
  • 服务端缓存
  • 网络中缓存

按照部署方式分为:

  • 单体缓存
  • 缓存集群
  • 分布式缓存

而Rados 网关缓存,也即RGW Cache 按照位置:作为Ceph client 可以归为客户端缓存,作为上层应用的服务端可以归为服务端缓存。而按照部署方式则为分布式缓存,因为Ceph 集群通常会存在多个RGW 实例,分布式缓存会涉及到缓存同步等问题。

RGW Cache 将对象存储的相关元数据存储在内部缓存中,用于提升性能。

RGW Cache 执行路径

前面已经提到,目前Ceph 中涉及RGW Cache 的配置参数有三个:

  • rgw_cache_enabled: RGW Cache 开关,默认为true,即开启。
  • rgw_cache_expiry_interval: 缓存数据的过期时间,默认900秒。
  • rgw_cache_lru_size: RGW 缓存entries的最大数量,当缓存满后会根据LRU算法做缓存entries替换,entries size默认为10000。读请求较多的场景,适当大的参数配置可以带来更好的性能。

查看RGW cache 命中率:

[[email protected] build]# bin/ceph daemon out/radosgw.8000.asok perf dump|grep cache
        "cache_hit": 336,
        "cache_miss": 135,

ceph.conf 中配置参数rgw_cache_enabled。

rgw_main.cc 中,获得RGWRados *store:

int main() {
  RGWRados *store =
    RGWStoreManager::get_storage(g_ceph_context,
                 g_conf()->rgw_enable_gc_threads,
                 g_conf()->rgw_enable_lc_threads,
                 g_conf()->rgw_enable_bl_threads,
                 g_conf()->rgw_enable_quota_threads,
                 g_conf()->rgw_run_sync_thread,
                 g_conf().get_val<bool>("rgw_dynamic_resharding"),
                 g_conf()->rgw_cache_enabled); // 获取rgw_cache_enabled 的配置,决定是否开启缓存
}

调用路径如下:

RGWRados RGWStoreManager::RGWStoreManager::get_storage() ==> RGWRados RGWStoreManager::init_storage_provider() ==> int RGWRados::initialize(CephContext *_cct) ==> int RGWRados::initialize()

/** 
 * Initialize the RADOS instance and prepare to do other ops
 * Returns 0 on success, -ERR# on failure.
 */
int RGWRados::initialize()
{
  int ret;

  inject_notify_timeout_probability =
    cct->_conf.get_val<double>("rgw_inject_notify_timeout_probability");
  max_notify_retries = cct->_conf.get_val<uint64_t>("rgw_max_notify_retries");

  ret = init_svc(false); // 初始化包含svc_sysobj, sysobj_cache, svc_notify等的RGW Services
  if (ret < 0) {
    ldout(cct, 0) << "ERROR: failed to init services (ret=" << cpp_strerror(-ret) << ")" << dendl;
    return ret;
  }

  host_id = svc.zone_utils->gen_host_id();

  ret = init_rados(); //rados 相关上下文初始化
  if (ret < 0)
    return ret;

  return init_complete(); // 初始化gc,lc,reshard 等线程
}

RGWRados *store的初始化中初始化RGW 服务:

int RGWRados::init_svc(bool raw) raw=false ==> int RGWServices::init(CephContext cct, bool have_cache) ==> int RGWServices::do_init(CephContext cct, bool have_cache, false) ==> int RGWServices_Def::init(CephContext *cct, bool have_cache, false)

int RGWServices_Def::init(CephContext *cct,
              bool have_cache,
                          bool raw)
{
  finisher = std::make_unique<RGWSI_Finisher>(cct);
  notify = std::make_unique<RGWSI_Notify>(cct);
  rados = std::make_unique<RGWSI_RADOS>(cct);
  zone = std::make_unique<RGWSI_Zone>(cct);
  zone_utils = std::make_unique<RGWSI_ZoneUtils>(cct);
  quota = std::make_unique<RGWSI_Quota>(cct);
  sync_modules = std::make_unique<RGWSI_SyncModules>(cct);
  sysobj = std::make_unique<RGWSI_SysObj>(cct);
  sysobj_core = std::make_unique<RGWSI_SysObj_Core>(cct);

  if (have_cache) {
    sysobj_cache = std::make_unique<RGWSI_SysObj_Cache>(cct);
  }

  ...
  // 各类服务初始化
  sysobj_core->core_init(rados.get(), zone.get());
  if (have_cache) {
    sysobj_cache->init(rados.get(), zone.get(), notify.get());
    sysobj->init(rados.get(), sysobj_cache.get());
  } else {
    sysobj->init(rados.get(), sysobj_core.get());
  }
  ...
  //启动notify 服务
  if (!raw) {
    r = notify->start();
    if (r < 0) {
      ldout(cct, 0) << "ERROR: failed to start notify service (" << cpp_strerror(-r) << dendl;
      return r;
    }
  }
  ...
  // 启动sysobj_core 服务
  r = sysobj_core->start();
  if (r < 0) {
    ldout(cct, 0) << "ERROR: failed to start sysobj_core service (" << cpp_strerror(-r) << dendl;
    return r;
  }
  // 根据参数配置选择是否启动sysobj_cache 服务
  if (have_cache) {
    r = sysobj_cache->start();
    if (r < 0) {
      ldout(cct, 0) << "ERROR: failed to start sysobj_cache service (" << cpp_strerror(-r) << dendl;
      return r;
    }
  }
  // 启动sysobj 服务
  r = sysobj->start();
  if (r < 0) {
    ldout(cct, 0) << "ERROR: failed to start sysobj service (" << cpp_strerror(-r) << dendl;
    return r;
  }
  /* cache or core services will be started by sysobj */
  return 0;
}

CacheProovider RGWSI_SysObj_Cache继承自RGWSI_SysObj_Core,而RGWSI_SysObj_Core 又是RGWServiceInstance的子类。
最终启动RGWSI_SysObj_Cache 服务。

int RGWServiceInstance::start() ==> virtual int RGWServiceInstance::do_start() ==> int RGWSI_SysObj_Cache::do_start()

子类RGWSI_SysObj_Cache::do_start()中

int RGWSI_SysObj_Cache::do_start()
{
  int r = RGWSI_SysObj_Core::do_start(); // 目前并没做什么,return 0
  if (r < 0) {
    return r;
  }
  // 启动notify 服务,为了后面的不同实例间的缓存分发
  r = notify_svc->start();
  if (r < 0) {
    return r;
  }

  assert(notify_svc->is_started());

  cb.reset(new RGWSI_SysObj_Cache_CB(this)); // 初始化回调对象

  // 注册包含回调函数的对象至notify_svc
  // 通过notify_svc 的watch/notify 机制调用到已注册的回调函数 int RGWSI_SysObj_Cache::watch_cb()
  notify_svc->register_watch_cb(cb.get());

  return 0;
}

watch_cb()的调用路径是:

int RGWSI_Notify::watch_cb() ==> int RGWSI_SysObj_Cache_CB::watch_cb() ==> int RGWSI_SysObj_Cache::watch_cb()

RGW Cache 组织架构

一般的Cache 系统会有以下四个重要的概念:

  • CachingProvider:定义了创建、配置、获取、管理和控制一个或多个CacheManager。一个应用可以访问多个CachingProvider。
  • CacheManager:定义了创建、配置、获取、管理和控制一个或多个唯一命名的Cache,这些Cache 存在于CacheManager的上下文中。一个CacheManager仅被一个CachingProvider拥有。
  • Cache:是一个类似于Map 的数据结构并临时存储以key 为索引的值。一个Cache 仅被一个CacheManager 拥有。
  • Entry:是一个存储在Cache 中的key-value 对。

CachingProvider <>-----> CacheManager <>-----> Cache <>-----> Entry

RGW Cache 主要在以下源文件中实现:

  • rgw_cache.h
  • rgw_cache.cc
  • svc_sys_obj_cache.h
  • svc_sys_obj_cache.cc

类图结构如下:
Ceph 之RGW Cache

根据各部分起到的作用,其中

  • ObjectCache 就是CacheManager 的角色,管理一个Cache(Map)(即std::unordered_map<string, ObjectCacheEntry> cache_map)。
  • RGWSI_SysObj_Cache 相当于CachingProvider,管理一个CacheManager(即ObjectCache cache)。
  • ObjectCacheEntry 当然就是Entry 的角色。

CachingProvider RGWSI_SysObj_Cache:

class RGWSI_SysObj_Cache : public RGWSI_SysObj_Core
{
    //......
    RGWSI_Notify *notify_svc{nullptr};
    ObjectCache cache; //

    std::shared_ptr<RGWSI_SysObj_Cache_CB> cb;
};

关于Entry ObjectCacheEntry

struct ObjectCacheEntry {
  ObjectCacheInfo info; //包含缓存对象data、metadata及xattr
  std::list<string>::iterator lru_iter;
  uint64_t lru_promotion_ts;
  uint64_t gen; //entry 的版本,初始为0,每次更新后加一
  std::vector<pair<RGWChainedCache *, string> > chained_entries; //

  ObjectCacheEntry() : lru_promotion_ts(0), gen(0) {}
};

每个Entry 中包含对应Object 的缓存数据及相关信息,LRU信息,版本信息,chained_entries 等。

struct ObjectCacheInfo {
  int status = 0;
  uint32_t flags = 0; //?
  uint64_t epoch = 0; //?
  bufferlist data;
  map<string, bufferlist> xattrs;
  map<string, bufferlist> rm_xattrs; // 待移除xattrs
  ObjectMetaInfo meta;
  obj_version version = {};
  ceph::coarse_mono_time time_added; //加入缓存的时间, 重新加入缓存的对象需要更新该时间
......
};

可以看到Cache 中包含了数据、元数据以及xattr等信息。

缓存管理

前面提到ObjectCache充当了CacheManager的角色,而RGWSI_SysObj_Cache相当于CachingProvider

图像形态学提取边界和区域填充

基于LRU 的淘汰算法

LRU 是一类常见的缓存淘汰算法,在Ehcache,Redis等很多系统中都有实现或改进实现。
LRU(Least recently used,最近最少使用)算法根据数据的历访问记录来进行数据淘汰,其核心思想是:如果数据最近被访问过,那么将来被访问到的概率也很高。

  • 而最近很少被使用的数据,很大概率下一次不再用到。
  • 当缓存容量的满时候,优先淘汰最近很少使用的数据。

Ceph 之RGW Cache

LRU 操作总结:

  • 新数据直接插入到列表头部
  • 缓存数据被命中,将数据移动到列表头部
  • 缓存已满的时候,移除列表尾部数据。

CachingProvider

RGWSI_SysObj_Cache 作为CachingProvider,它负责对CacheManager ObjectCache的管理。
新的系统对象服务(system objects service)通过sysobj_core 用于核心的操作,这样可以在system objects service 上扩展cache service,以实现object cache,其在PR 24014中引入。
RGWSI_SysObj_Core 是系统对象的基本抽象:属性和方法,RGWSI_SysObj_Cache 继承自RGWSI_SysObj_Core,实现cache service 的扩展。

class RGWSI_SysObj_Cache : public RGWSI_SysObj_Core
{
    //......
    RGWSI_Notify *notify_svc{nullptr};
    ObjectCache cache; //

    std::shared_ptr<RGWSI_SysObj_Cache_CB> cb;
protected:
  void init(RGWSI_RADOS *_rados_svc,
            RGWSI_Zone *_zone_svc,
            RGWSI_Notify *_notify_svc) {
    core_init(_rados_svc, _zone_svc);
    notify_svc = _notify_svc;
  }

  int do_start() override;

  int raw_stat(const rgw_raw_obj& obj, uint64_t *psize, real_time *pmtime, uint64_t *epoch,
               map<string, bufferlist> *attrs, bufferlist *first_chunk,
               RGWObjVersionTracker *objv_tracker) override;

  int read(); //读操作
  int get_attr(); // 获取xattr
  int set_attrs(); // 设置xattr
  int remove(); //移除缓存
  int write();
  int write_data(); //
  int distribute_cache(); // 分发缓存,因为通常会有多个RGW 实例,需要将缓存在多个RGW 实例间同步,保证数据一致性。
  int watch_cb(); // watch 回调函数
  void set_enabled(bool status); // watch/notify 开关,用于分布式多RGW 实例的缓存同步
public:
  // chain cache
  bool chain_cache_entry(std::initializer_list<rgw_cache_entry_info *> cache_info_entries,
                         RGWChainedCache::Entry *chained_entry);
  ......
};

移除缓存remove()

int RGWSI_SysObj_Cache::remove(RGWSysObjectCtxBase& obj_ctx,
                               RGWObjVersionTracker *objv_tracker,
                               const rgw_raw_obj& obj)

{
  rgw_pool pool;
  string oid;
  normalize_pool_and_obj(obj.pool, obj.oid, pool, oid);

  string name = normal_name(pool, oid);
  // 根据前面构成的标准cache name,调用CacheManager的bool ObjectCache::remove(const string& name) 执行缓存删除
  cache.remove(name);

  ObjectCacheInfo info;
  // 向分布式系统中的其他RGW 实例分发缓存操作
  int r = distribute_cache(name, obj, info, REMOVE_OBJ);
  if (r < 0) {
    ldout(cct, 0) << "ERROR: " << __func__ << "(): failed to distribute cache: r=" << r << dendl;
  }
  // 删除sysobj_core 对象
  return RGWSI_SysObj_Core::remove(obj_ctx, objv_tracker, obj);
}

具体的缓存删除操作由CacheManager ObjectCache 执行

bool ObjectCache::remove(const string& name)
{
  RWLock::WLocker l(lock); // 第一步:获取写锁

  if (!enabled) {
    return false;
  }

  // 在cache map中找到指定缓存
  auto iter = cache_map.find(name);
  if (iter == cache_map.end())
    return false;

  ldout(cct, 10) << "removing " << name << " from cache" << dendl;
  ObjectCacheEntry& entry = iter->second;

  // 移除指定ObjectCacheEntry 关联的所有 chained_entries
  for (auto& kv : entry.chained_entries) {
    kv.first->invalidate(kv.second);
  }

  remove_lru(name, iter->second.lru_iter); // 更新lru 
  cache_map.erase(iter); // cache map 中移除该对象缓存
  return true;
}

以缓存中最常见、最重要的操作read()为例分析:

int RGWSI_SysObj_Cache::read(RGWSysObjectCtxBase& obj_ctx,
                             GetObjState& read_state,
                             RGWObjVersionTracker *objv_tracker,
                             const rgw_raw_obj& obj,
                             bufferlist *obl, off_t ofs, off_t end,
                             map<string, bufferlist> *attrs,
                             bool raw_attrs,
                             rgw_cache_entry_info *cache_info,
                             boost::optional<obj_version> refresh_version)
{
  rgw_pool pool;
  string oid;
  // 若指定非开始处的offset 读取,则直接读取sysobj_core 对象
  if (ofs != 0) {
    return RGWSI_SysObj_Core::read(obj_ctx, read_state, objv_tracker,
                          obj, obl, ofs, end, attrs, raw_attrs,
                          cache_info, refresh_version);
  }

  normalize_pool_and_obj(obj.pool, obj.oid, pool, oid);
  string name = normal_name(pool, oid);

  ObjectCacheInfo info;

  uint32_t flags = (end != 0 ? CACHE_FLAG_DATA : 0);
  if (objv_tracker)
    flags |= CACHE_FLAG_OBJV;
  if (attrs)
    flags |= CACHE_FLAG_XATTRS;

  // 获取指定name 的cache
  if ((cache.get(name, info, flags, cache_info) == 0) &&
      (!refresh_version || !info.version.compare(&(*refresh_version)))) {
    if (info.status < 0)
      return info.status;

    bufferlist& bl = info.data;

    bufferlist::iterator i = bl.begin();

    obl->clear();

    i.copy_all(*obl);
    if (objv_tracker)
      objv_tracker->read_version = info.version;
    if (attrs) {
      if (raw_attrs) {
        *attrs = info.xattrs;
      } else {
        rgw_filter_attrset(info.xattrs, RGW_ATTR_PREFIX, attrs);
      }
    }
    return obl->length();
  }

  map<string, bufferlist> unfiltered_attrset;
  int r = RGWSI_SysObj_Core::read(obj_ctx, read_state, objv_tracker,
                         obj, obl, ofs, end,
                         (attrs ? &unfiltered_attrset : nullptr),
                         true, /* cache unfiltered attrs */
                         cache_info,
                         refresh_version);
  if (r < 0) {
    // 未读到该对象时,将该对象加入cache
    if (r == -ENOENT) { // only update ENOENT, we'd rather retry other errors
      info.status = r;
      cache.put(name, info, cache_info);
    }
    return r;
  }

  if (obl->length() == end + 1) {
    /* in this case, most likely object contains more data, we can't cache it */
    flags &= ~CACHE_FLAG_DATA;
  } else {
    bufferptr p(r);
    bufferlist& bl = info.data;
    bl.clear();
    bufferlist::iterator o = obl->begin();
    o.copy_all(bl);
  }

  info.status = 0;
  info.flags = flags;
  if (objv_tracker) {
    info.version = objv_tracker->read_version;
  }
  if (attrs) {
    info.xattrs = std::move(unfiltered_attrset);
    if (raw_attrs) {
      *attrs = info.xattrs;
    } else {
      rgw_filter_attrset(info.xattrs, RGW_ATTR_PREFIX, attrs);
    }
  }
  cache.put(name, info, cache_info);
  return r;
}

CacheManager

CacheManager ObjectCache 负责具体Cache Entries的管理:缓存获取,缓存移除,LRU 管理

class ObjectCache {
  std::unordered_map<string, ObjectCacheEntry> cache_map;
  std::list<string> lru; // LRU 列表
  unsigned long lru_size; // LRU 表的大小
  unsigned long lru_counter; // 当前LRU 数
  unsigned long lru_window; // rgw_cache_lru_size 的一半大小
  RWLock lock;
  CephContext *cct;

  vector<RGWChainedCache *> chained_cache;

  bool enabled; // watch/notify 的开关
  ceph::timespan expiry; // 缓存过期时间大小
};
缓存获取
int ObjectCache::get(const string& name, ObjectCacheInfo& info, uint32_t mask, rgw_cache_entry_info *cache_info)
{
  RWLock::RLocker l(lock); // 第一步,先获取读锁

  if (!enabled) {
    return -ENOENT;
  }
  // 获取指定缓存
  auto iter = cache_map.find(name);
  if (iter == cache_map.end()) {
    ldout(cct, 10) << "cache get: name=" << name << " : miss" << dendl;
    if (perfcounter)
      perfcounter->inc(l_rgw_cache_miss);
    return -ENOENT;
  }
  // 缓存是否已经过期
  // 过期缓存需要从cache map中移除,从LRU 表中移除
  if (expiry.count() &&
       (ceph::coarse_mono_clock::now() - iter->second.info.time_added) > expiry) {
    ldout(cct, 10) << "cache get: name=" << name << " : expiry miss" << dendl;
    lock.unlock();
    lock.get_write(); // 由读锁转为写锁
    // check that wasn't already removed by other thread
    iter = cache_map.find(name);
    if (iter != cache_map.end()) {
      for (auto &kv : iter->second.chained_entries)
        kv.first->invalidate(kv.second);
      remove_lru(name, iter->second.lru_iter);
      cache_map.erase(iter);
    }
    if(perfcounter)
      perfcounter->inc(l_rgw_cache_miss);
    return -ENOENT;
  }

  ObjectCacheEntry *entry = &iter->second;

  // 当前entry 计数距离总计数lru_counter超过LRU 窗口大小,即当前entry 已经落在LRU 表后半段,这时才去更新entry LRU表
  // [lru window](https://github.com/ceph/ceph/commit/a84cf15f64211c00bc6c95687ff4509d16b1f909)
  if (lru_counter - entry->lru_promotion_ts > lru_window) {
    ldout(cct, 20) << "cache get: touching lru, lru_counter=" << lru_counter
                   << " promotion_ts=" << entry->lru_promotion_ts << dendl;
    lock.unlock();
    lock.get_write(); /* promote lock to writer */

    /* need to redo this because entry might have dropped off the cache */
    iter = cache_map.find(name);
    if (iter == cache_map.end()) {
      ldout(cct, 10) << "lost race! cache get: name=" << name << " : miss" << dendl;
      if(perfcounter) perfcounter->inc(l_rgw_cache_miss);
      return -ENOENT;
    }

    entry = &iter->second;
    /* check again, we might have lost a race here */
    if (lru_counter - entry->lru_promotion_ts > lru_window) {
      touch_lru(name, *entry, iter->second.lru_iter); // 更新缓存LRU
    }
  }

  ObjectCacheInfo& src = iter->second.info;
  if ((src.flags & mask) != mask) {
    ldout(cct, 10) << "cache get: name=" << name << " : type miss (requested=0x"
                   << std::hex << mask << ", cached=0x" << src.flags
                   << std::dec << ")" << dendl;
    if(perfcounter) perfcounter->inc(l_rgw_cache_miss);
    return -ENOENT;
  }
  ldout(cct, 10) << "cache get: name=" << name << " : hit (requested=0x"
                 << std::hex << mask << ", cached=0x" << src.flags
                 << std::dec << ")" << dendl;

  info = src;
  if (cache_info) {
    cache_info->cache_locator = name;
    cache_info->gen = entry->gen;
  }
  if(perfcounter) perfcounter->inc(l_rgw_cache_hit);

  return 0;
}
缓存添加
void ObjectCache::put(const string& name, ObjectCacheInfo& info, rgw_cache_entry_info *cache_info)
{
  RWLock::WLocker l(lock);

  if (!enabled) {
    return;
  }

  ldout(cct, 10) << "cache put: name=" << name << " info.flags=0x"
                 << std::hex << info.flags << std::dec << dendl;

  auto [iter, inserted] = cache_map.emplace(name, ObjectCacheEntry{});
  ObjectCacheEntry& entry = iter->second;
  entry.info.time_added = ceph::coarse_mono_clock::now();
  if (inserted) {
    entry.lru_iter = lru.end();
  }
  ObjectCacheInfo& target = entry.info;

  invalidate_lru(entry);

  entry.chained_entries.clear();
  entry.gen++;

  touch_lru(name, entry, entry.lru_iter);

  target.status = info.status;

  if (info.status < 0) {
    target.flags = 0;
    target.xattrs.clear();
    target.data.clear();
    return;
  }

  if (cache_info) {
    cache_info->cache_locator = name;
    cache_info->gen = entry.gen;
  }

  target.flags |= info.flags;

  if (info.flags & CACHE_FLAG_META)
    target.meta = info.meta;
  else if (!(info.flags & CACHE_FLAG_MODIFY_XATTRS))
    target.flags &= ~CACHE_FLAG_META; // non-meta change should reset meta

  if (info.flags & CACHE_FLAG_XATTRS) {
    target.xattrs = info.xattrs;
    map<string, bufferlist>::iterator iter;
    for (iter = target.xattrs.begin(); iter != target.xattrs.end(); ++iter) {
      ldout(cct, 10) << "updating xattr: name=" << iter->first << " bl.length()=" << iter->second.length() << dendl;
    }
  } else if (info.flags & CACHE_FLAG_MODIFY_XATTRS) {
    map<string, bufferlist>::iterator iter;
    for (iter = info.rm_xattrs.begin(); iter != info.rm_xattrs.end(); ++iter) {
      ldout(cct, 10) << "removing xattr: name=" << iter->first << dendl;
      target.xattrs.erase(iter->first);
    }
    for (iter = info.xattrs.begin(); iter != info.xattrs.end(); ++iter) {
      ldout(cct, 10) << "appending xattr: name=" << iter->first << " bl.length()=" << iter->second.length() << dendl;
      target.xattrs[iter->first] = iter->second;
    }
  }

  if (info.flags & CACHE_FLAG_DATA)
    target.data = info.data;

  if (info.flags & CACHE_FLAG_OBJV)
    target.version = info.version;
}
缓存移除
bool ObjectCache::remove(const string& name)
{
  RWLock::WLocker l(lock); // 第一步,获取写锁

  if (!enabled) {
    return false;
  }

  auto iter = cache_map.find(name);
  if (iter == cache_map.end())
    return false;

  ldout(cct, 10) << "removing " << name << " from cache" << dendl;
  ObjectCacheEntry& entry = iter->second;
  // 移除跟cache entry 关联的所有chained entries 
  for (auto& kv : entry.chained_entries) {
    kv.first->invalidate(kv.second);
  }
  // 移除LRU 表中的cache object对应项
  remove_lru(name, iter->second.lru_iter);
  cache_map.erase(iter);
  return true;
}
LRU 更新

LRU 表是一个双向列表 std:list<>,可支持表头插入、表尾插入。RGW Cache 实现在LRU 表头

std::list<string> lru;

LRU 移除

void ObjectCache::remove_lru(const string& name,
                 std::list<string>::iterator& lru_iter)
{
  if (lru_iter == lru.end())//确定是否在LRU 表中
    return;

  lru.erase(lru_iter);// 移除该项
  lru_size--; // LRU 当前size 减一
  lru_iter = lru.end(); //将当前iter 置为无效
}

touch_lru 负责更新缓存项至LRU 表:

void ObjectCache::touch_lru(const string& name, ObjectCacheEntry& entry,
                std::list<string>::iterator& lru_iter)
{
  // 当前lru size 超过预设值rgw_cache_lru_size,需要先删除LRU 头
  while (lru_size > (size_t)cct->_conf->rgw_cache_lru_size) {
    auto iter = lru.begin(); // LRU 表尾项
    if ((*iter).compare(name) == 0) { // 如果当前对象是LRU 是LRU 表尾项,不用立马显式删除,LRU 会根据rgw_cache_lru_size 自动不包含该项
      /*
       * if the entry we're touching happens to be at the lru end, don't remove it,
       * lru shrinking can wait for next time
       */
      break;
    }
    // 移除LRU 表尾项对应的对象缓存
    auto map_iter = cache_map.find(*iter);
    ldout(cct, 10) << "removing entry: name=" << *iter << " from cache LRU" << dendl;
    if (map_iter != cache_map.end()) {
      ObjectCacheEntry& entry = map_iter->second;
      invalidate_lru(entry);
      cache_map.erase(map_iter);
    }
    // 删除LRU 表尾项,并将当前LRU size 减一
    lru.pop_front();
    lru_size--;
  }

  if (lru_iter == lru.end()) { // lru_iter不在LRU 表中:插入当前项至LRU 表头(list 尾)
    lru.push_back(name);
    lru_size++;
    lru_iter--;
    ldout(cct, 10) << "adding " << name << " to cache LRU end" << dendl;
  } else { // lru_iter在LRU 表中:移动至当前项至LRU 表头(list 尾)
    ldout(cct, 10) << "moving " << name << " to cache LRU end" << dendl;
    lru.erase(lru_iter);
    lru.push_back(name);
    lru_iter = lru.end();
    --lru_iter;
  }

  lru_counter++;
  entry.lru_promotion_ts = lru_counter; // 
}

缓存一致性

RGW Cache 属于分布式缓存,通常会有多个RGW 实例,缓存需要在各个RGW 实例间分发,且需要保证缓存一致性。
RGW Cache的调用路径中已经给出,CachingProvider RGWSI_SysObj_Cache 会在服务启动do_start() 中start notify_svc,并注册watch_cb 函数。
notify_svc 这个服务的作用就是提供一种watch/notify 机制,以确保缓存一致性。
watch/notify 机制由librados提供。其中,notify rados object 存在default.rgw.control 池中。

[[email protected] build]# bin/rados ls -p default.rgw.control
notify.1
notify.6
notify.3
notify.7
notify.2
notify.4
notify.5
notify.0

[[email protected] build]# bin/rados -p default.rgw.control stat notify.1
default.rgw.control/notify.1 mtime 2020-01-10 18:59:13.000000, size 0

[[email protected] build]# bin/rados -p default.rgw.control stat notify.7
default.rgw.control/notify.7 mtime 2020-01-10 18:59:14.000000, size 0

notify_svc 服务的启动路径跟cache_svc 类似:

int RGWServiceInstance::start() ==> virtual int RGWServiceInstance::do_start() ==> int RGWSI_Notify::do_start()

do_start() 会初始化watch:

int RGWSI_Notify::init_watch()
{
  num_watchers = cct->_conf->rgw_num_control_oids; // 有参数rgw_num_control_oids 配置,默认8个 watcher
  bool compat_oid = (num_watchers == 0);

  if (num_watchers <= 0)
    num_watchers = 1;

  watchers = new RGWWatcher *[num_watchers];
  ......
}

在cache op 之后,会执行cache 分发操作distribute_cache():

int RGWSI_SysObj_Cache::distribute_cache(const string& normal_name, const rgw_raw_obj& obj, ObjectCacheInfo& obj_info, int op)
{
  RGWCacheNotifyInfo info;

  info.op = op;

  info.obj_info = obj_info;
  info.obj = obj;
  bufferlist bl;
  encode(info, bl);
  return notify_svc->distribute(normal_name, bl); // 利用notify_svc 分发
}

分发过程:

int RGWSI_Notify::distribute(const string& key, bufferlist& bl)
{
  // 选择一个notify obj
  RGWSI_RADOS::Obj notify_obj = pick_control_obj(key);

  ldout(cct, 10) << "distributing notification oid=" << notify_obj.get_ref().obj
      << " bl.length()=" << bl.length() << dendl;
  // 执行分发
  return robust_notify(notify_obj, bl);
}

分发细节会在RGW Services — Notify Service 中说明。

另外,在notify_svc 服务的watcher 的handle_notify()中调用已注册的回调函数。
watcher 收到notify的更新通知后,会更新本地缓存。

void RGWWatcher::handle_notify()
{
......
    // 调用cache_svc 服务注册的回调函数
    svc->watch_cb(notify_id, cookie, notifier_id, bl);
    // 向通知者发送确认消息
    bufferlist reply_bl; // empty reply payload
    obj.notify_ack(notify_id, cookie, reply_bl);
......
}

回调函数中根据操作类型,利用CacheManager 完成cache 更新或移除:

int RGWSI_SysObj_Cache::watch_cb(uint64_t notify_id,
                                 uint64_t cookie,
                                 uint64_t notifier_id,
                                 bufferlist& bl)
{
  RGWCacheNotifyInfo info; //cache notify 信息,包含:操作、rgw raw object、obj cache info、offset等

  try {
    auto iter = bl.cbegin();
    decode(info, iter);
  } catch (buffer::end_of_buffer& err) {
    ldout(cct, 0) << "ERROR: got bad notification" << dendl;
    return -EIO;
  } catch (buffer::error& err) {
    ldout(cct, 0) << "ERROR: buffer::error" << dendl;
    return -EIO;
  }

  rgw_pool pool;
  string oid;
  normalize_pool_and_obj(info.obj.pool, info.obj.oid, pool, oid);
  string name = normal_name(pool, oid);
  
  switch (info.op) {
  case UPDATE_OBJ: //利用CacheManager 更新缓存
    cache.put(name, info.obj_info, NULL);
    break;
  case REMOVE_OBJ: //利用CacheManager 移除缓存
    cache.remove(name);
    break;
  default:
    ldout(cct, 0) << "WARNING: got unknown notification op: " << info.op << dendl;
    return -EINVAL;
  }

  return 0;
}

Chained cache

Chained cache 让user info,bucket info 可以通过链接原生缓存,得以开启缓存。

Basically chains bucket info and user info caches to the raw metadata object cache.

binfo_cache = new RGWChainedCacheImpl<bucket_info_entry>;
static RGWChainedCacheImpl<user_info_entry> uinfo_cache;

以user cache 为例,在开启RGW Cache后,优先从缓存中获取:

void rgw_user_init(RGWRados *store)
{
  uinfo_cache.init(store->svc.cache);

  user_meta_handler = new RGWUserMetadataHandler;
  store->meta_mgr->register_handler(user_meta_handler);
}

int rgw_get_user_info_from_index(RGWRados * const store,
                                 const string& key,
                                 const rgw_pool& pool,
                                 RGWUserInfo& info,
                                 RGWObjVersionTracker * const objv_tracker,
                                 real_time * const pmtime)
{
  // 首选尝试获取缓存
  if (auto e = uinfo_cache.find(key)) {
    info = e->info;
    if (objv_tracker)
      *objv_tracker = e->objv_tracker;
    if (pmtime)
      *pmtime = e->mtime;
    return 0;
  }
 ......
 // 未能从缓存中获取,直接从RADOS 集群中获取
 // 获取到之后,更新uinfo 缓存
 uinfo_cache.put(store->svc.cache, key, &e, { &cache_info });
 .......
class RGWChainedCache {
public:
  ......
  struct Entry {
    RGWChainedCache *cache; // 关联cache
    const string& key; // email/swift_name/access_key/bucket name 
    void *data; // 指向bucket_info_entry或user_info_entry

    Entry(RGWChainedCache *_c, const string& _k, void *_d) : cache(_c), key(_k), data(_d) {}
  };
};

通过sysobj_cache_svc 服务提供chain cache:
将chain_entry添加到chained cache,并和cache_info_entries 指向的ObjectCacheEntry相关联。

  bool RGWChainedCache::put(RGWSI_SysObj_Cache *svc, const string& key, T *entry,
       std::initializer_list<rgw_cache_entry_info *> cache_info_entries) {
    if (!svc) {
      return false;
    }

    Entry chain_entry(this, key, entry);

    /* we need the svc cache to call us under its lock to maintain lock ordering */
    return svc->chain_cache_entry(cache_info_entries, &chain_entry);
  }

bool ObjectCache::chain_cache_entry(std::initializer_list<rgw_cache_entry_info*> cache_info_entries, RGWChainedCache::Entry *chained_entry)
{
  // 确认所有有效ObjectCacheEntry
......
  // 将待添加entry添加到对应chain cache中
  chained_entry->cache->chain_cb(chained_entry->key, chained_entry->data);

  // 将chained entry关联到指定的所有有效的ObjectCacheEntry
  for (auto entry : entries) {
    entry->chained_entries.push_back(make_pair(chained_entry->cache,
                           chained_entry->key));
  }
......
}

chained cache 依赖于ObjectCache,

更新ObjectCache的成员 vector<RGWChainedCache *> chained_cache:

void ObjectCache::chain_cache(RGWChainedCache *cache);
void ObjectCache::unchain_cache(RGWChainedCache *cache);

RGW Cache 优化方向

前面的测试系统的cache 命中率:”cache_hit”: 336,”cache_miss”: 135, 336/(336+135)*100% = 71%
缓存系统适合读多写少的场景。如何在这种场景下,提高RGW Cache 的命中率,以下方向可以考虑:

  • 将缓存粒度设计的更细?
  • 增大缓存容量(这个已经可以根据实际配置)

References

常用控件

未经允许不得转载:www.xssyun.com作者:站长, 转载或复制请以 超链接形式 并注明出处 xss云之家-资源网,新人技术交流平台,一个湖北娃的个人博客
原文地址:《Ceph 之RGW Cache》 发布于2020-01-31

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