From c715911135e21bd02243a0ed69f5c5e7f2602d37 Mon Sep 17 00:00:00 2001 From: stevenswong Date: Fri, 11 Dec 2015 17:26:08 +0800 Subject: [PATCH 1/7] add freshman talk about tera document --- doc/tera_understanding.md | 171 ++++++++++++++++++ .../images/understanding_master_startup.png | Bin 0 -> 116536 bytes .../images/understanding_tablet_merge.png | Bin 0 -> 71897 bytes .../images/understanding_tablet_read.png | Bin 0 -> 41588 bytes .../images/understanding_tablet_split.png | Bin 0 -> 206326 bytes resources/images/understanding_tera_arch.png | Bin 0 -> 53678 bytes 6 files changed, 171 insertions(+) create mode 100644 doc/tera_understanding.md create mode 100644 resources/images/understanding_master_startup.png create mode 100644 resources/images/understanding_tablet_merge.png create mode 100644 resources/images/understanding_tablet_read.png create mode 100644 resources/images/understanding_tablet_split.png create mode 100644 resources/images/understanding_tera_arch.png diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md new file mode 100644 index 000000000..5004f924b --- /dev/null +++ b/doc/tera_understanding.md @@ -0,0 +1,171 @@ +tera开发者黄俊辉的串讲文档 +=========================== +我最近转岗到openSeArch组,接手tera相关开发工作。这个文档整理自我在2015.12.3在组内做的串讲报告。 + +## tera 背景 +1. 基于hadoop的mapreduce实现的链接系统实效性比较差,不能做到实时、增量计算; +2. 内部的DDBS系统存在不能自动负载均衡、不支持scan操作等问题; + +## tera 架构 +![架构图](../resources/images/understanding_tera_arch.png) + +- tera系统包括5部分:一个master节点,若干个tablet server(TS),client(SDK),zookeeper集群,分布式文件系统; +- 在所有的TS节点里,有个除了管理普通的tablet外,还会被master选择加载meta表的数据; +- meta表是不能被分裂的,所以meta只会有一个tablet,也即是只有一个TS加载了meta表; + +## tera 架构特点 +- 强一致性、全局有序、列存储,以列族为单位可指定SSD/磁盘/内存; +- 自动负责均衡,记录多版本; +- CP系统,不是高可用系统; +- 目前支持单行事务,暂不支持跨行、跨表事务; +- 缺少类SQL查询语言,目前正在开发中; + +## tera 各模块的功能 +1. zookeeper + 1. 保证任何时候最多只有一个tera master; + 2. 保存meta table的地址信息; + 3. 保存当前在线的TS信息; + 4. 保证TS可以被踢走; +2. master + 1. 分配tablet给TS; + 2. 监控TS的加入和退出; + 3. 维持tera系统的负载均衡; + 4. 回收没有用的文件系统数据; + 5. 处理client对表格、快照的操作请求; +3. TS + 1. 管理一系列tablet,一般有10~1000个tablet; + 2. 处理client发过来的tablet读写请求; +4. client + 1. 通过zookeeper上的meta信息,找到meta table所有的TS地址; + 2. 读写请求直接与TS交互; + 3. 对表的创建、修改、删除,对快照的操作直接与master交互; +5. 分布式文件系统 + 1. 存储leveldb的sst文件、log文件等; + +## tera 数据模型与leveldb的数据模型的映射关系 +- tera是表格的数据模型,leveldb是kv的数据模型,tera是基于leveldb的存储系统,所以这里涉及到表格数据转换成kv结构的问题; +- tera的表格至少有一个LG,一个LG有一个或多个CF,一个LG对应一个leveldb的对象; +- leveldb的Key结构是|user\_key|sequence(7Bytes)|type(1Bytes)|,排序方法是首先user\_key升序,然后sequence降序,最后type降序。其中user_key是用户key序列化后值,sequence是操作的时间顺序,type是操作类型(写入或删除); +- 在tera里,有四种key类型: + - Readable: rowkey,qualifier都是字符串类型,没有\0; + - Binary: rowkey或qualifier包含\0字符,规定cf不能包含\0; + - TTLKv: 带过期时间的KV类型; + - GeneralKv: 没有过期时间的KV类型,leveldb原生支持这一类; +- 不同key类型,组成的user\_key也不同: + - Readable: [rowkey\0(rlen+1B)|column\0(clen+1B)+qualifier\0(qlen+1B)|type(1B)|timestamp(7B)] + - Binary: [rowkey(rlen)+column\0|qualifier|type|timestamp|rlen|qlen] + - TTLKv: [rowkey(rlen)|expire\_timestamp(4B)] + - GeneralKv: user\_key == key + - 写操作时,不同LG的数据是写到同一个log文件的,为提高恢复的效率,需要把LG的id和上面序列化生成的tera\_key一起写到日志中,具体格式是: + [9(4B)|//LG\_ID//|lg_id|klen|tera\_key] + +## tera meta 表结构 + - meta表是tera的核心表,维护了整个系统当前有哪些表,每个表的模式,每个tablet的信息,每个CF的访问控制信息; + - meta表不能被分裂,只有一个tablet,且只有一个LG,名字是lg\_meta,不带压缩,以内存为存储介质(数据持久化在NFS上); + - meta表实际上是一个KV结构类型,有3种类型的key,以key的首字符区分:'~'开头的是user相关的记录,'@'开头的是table相关的记录,大于'@'是tablet相关的记录,其它的无效; + - user 相关的记录,key的结构是'~'+用户名,value是以PB封装UserInfo的数据,TODO; + - table 相关的记录,key的结构是'@'+表名,value是以PB封装TableMeta的数据,TableMeta包含表名,状态,模式,快照,创建时间等信息; + - tablet 相关的记录,key的结构是表名+'#'+row\_key\_start,value是以PB封装TabletMeta的数据,TabletMeta包含表名字,tablet的路径,所属的ts地址,状态等信息; + +## master 启动流程 +![master启动](../resources/images/understanding_master_startup.png) + +1. MasterEntry::StartServer,调用MasterImpl::Init,初始化zk句柄,设置master的状态为kIsSecondary,步骤调用MasterImpl::InitAsync; + 1. MasterImpl::InitAsync,向zk获取正在工作的tabletnode列表,依次执行步骤2,3; + 2. MasterImpl::CollectAllTabletInfo,向所有在线的ts发送query请求,获取已经加载的tabletlist列表; + 3. MasterImpl::RestoreMetaTablet,遍历tabletlist,如果meta表被多个ts加载或加载不完全,则要求所有已经加载meta表的ts卸载。如果卸载失败,则强制踢出该ts。然后,选一个管理数据量最小的ts加载meta表,保存meta表的地址meta\_tablet\_addr。如果前面已经正确加载了meta表,则直接保存meta表的地址meta\_tablet\_addr;如果开启从本地文件恢复meta表的标记,则调用LoadMetaTableFromFile把meta表的数据加载到本地内存,同时,要求已经加载meta表的ts清空meta数据,然后用本地内存的数据同步到meta表里,操作结束则返回; 如果没有开启恢复meta表,则调用LoadMetaTable把meta表的数据同步到master的内存; + 4. MasterImpl::LoadMetaTable,通过向ts发送scan请求扫描meta表,如果row的首字符是'~',则是用户相关的元信息执行UserManager::LoadUserMeta,如果是'@',则是表格相关的元信息执行TabletManager::LoadTableMeta,如果是>@,则是tablet相关的元信息执行TabletManager::LoadTabletMeta; + 5. 在zk上更新meta表所有ts的地址; + 6. MasterImpl::RestoreUserTablet,遍历所有tablets,如果没有加载,则找一个ts加载;如果能正常工作的ts占比小于0.9阈值时,则master进入安全模式,否则启动定时任务:QueryTablet, TabletNodeGc, LoadBalance,并进入服务状态; +2. 注册RPC服务,并启动。 + +## tablet 分裂流程 +![tablet分裂](../resources/images/understanding_tablet_split.png) + +- master发现tablet的大小超过某阈值(默认512M),会触发split操作; +- master发现某tablet的负载超过平均负载的某阈值时,会触发split操作(目前未实现); +- 用户通过SDK主动触发split操作。 +- TS收到split请求后,需要在Tablet里找一个中间的key对其一分为二,具体找的方法是: + 1. 在Tablet里所有的LG中找一个数据量最大的,在这个LG里找待分裂的split\_key; + 2. 获取LG除level-0外的所有level的数据量大小总和size,确定分裂的数据量大小0.5*Size; + 3. 从level-1到level-6,找一个largest key最小sst,把这个sst的大小累加到split_size,如果达到要求则执行步骤4,否则继续这一步骤的流程; + 4. split\_key就是步骤3得到的最后一个largest key; + 5. 对于普通KV类型的key,上述split\_key就是最终所要的key,对于表格类型需要从中提取出rowkey作为split\_key; +- tablet的分裂只是修改了元信息,没有发生真实数据的移动,为实现这点leveldb需要做什么? + 1. 分裂后产生两个新的tablet,需要在它们的TabletMeta结构parent\_tablets里分别填写原tablet的tabletnum; + 2. TS收到需要Load命令后,如果parent\_tablets的个数为1,则认为这个tablet是分裂而来的,通过读取父tablet的CURRENT文件,在自己的DB目录下生成MANIFEST; + 3. 通过MANIFEST文件,tablet就知道自己有哪些sst文件,每个sst属于哪一级,以及每个sst的最小最大值; + 4. 对于原来的tablet的sst文件,leveldb是不会移动到自己DB目录下的,后面compact操作生成的新文件会保存到自己的DB目录; + 5. TS无法知道什么时候应该删除父tablet的sst文件,因为虽然自己没有用父tablet的sst文件了,但是可能由另一个分裂出来的tablet使用,这个只能由master定时向TS发送GC请求,收集需要删除的tablet文件。 + +## tablet 合并流程 +![tablet合并](../resources/images/understanding_tablet_merge.png) + +- master发现tablet的大小小于某阈值时,触发merge操作。 +- 同分裂一样,tablet的合并也只是修改了元信息,leveldb在load tablet时,如果parent\_tablets的个数为2,则认为这是merge过来的tablet,需要读取两父tablet的CURRENT文件,在自己的DB目录下生成MANIFEST。 +- 同样,删除父tablet没用的数据,也是由master负责的。 + +## tablet 读数据流程 +![tablet合并](../resources/images/understanding_tablet_read.png) + +1. 遍历Tablet请求的每一行,根据tablet_name,start\_key找到对应的TabletIO的句柄; +2. 调用TabletIO的ReadCells,并保存结果; + 1. 如果只是kv类型,调用DBTable的Get,即调用leveldb的Get,其它类型进入步骤2; + 2. 如果请求里有一个qualifier为空,即没有指定qualifier,则不能执行seek,即需要调用scan操作,否则调用seek; + - 新建一个迭代器,该迭代器内部包含了tablet里所有LG的迭代器; + - 指定row\_key, cf="", qualifier="", type=TKT\_FORSEEK, timestamp=INT64\_MAX, 把迭代器移动行首; + - 先遍历cf,然后在每个cf里遍历qualifier,调用ScanDrop检查是否需要跳过当前的记录,如果记录是有用的,则检查是否是原子操作,如果是就合并该key的所有value,如果不是原子操作,当前value就是最终需要的; + +3. 返回结果; + +## tablet 写数据流程 +1. 预处理待写的数据,生成由TabletIO到index的映射; +2. 对每一个TabletIO调用write方法; +3. 调用TabletWriter::Write,如果m\_stopped,则设置错误码为kAsyncNotRunning;如果active buffer缓存的数据量超过阈值MAX\_PENDING\_SIZE或tablet\_busy(只要tablet下面的某个LG对应leveldb的level-0的文件个数超过某阈值时则认为busy),则设置错误码为kTabletNodeIsBusy;否则把数据放到active buffer。检查active buffer数据是否超过阈值或请求是否是立即类型,如果是则触发写操作; + +## tablet 后台写线程 +1. 检查是否需要切换buffer,不需要则sleep,否则进入步骤2; +2. 调用TabletWriter::FlushToDiskBatch,调用BatchRequest把数据用leveldb::WriteBatch结构封装,BatchRequest函数对于表格类型的数据,把key封装成[lgidlen(4B)|//LG\_ID//|lg\_id(4B)|klen(4B)|tera\_key],其中"//LG\_ID//"是固定的字符串,lgidlen是接下来字符串的长度,值是9,lg\_id是后面的terakey属于哪一个LG;进入步骤3; + - 这里封装的key后面会直接写入到日志文件中; + - 封装的原因:原生的leveldb,一个db就有一个log文件。但是,在tera里一个tablet对应一个log文件,即多个db的数据会写到一log文件,所以需要在log文件里区分每个db的数据; +3. 调用TabletIO::WriteBatch,进入步骤4; +4. 调用DBTable::Write,使用RecordWriter封装数据,然后放到队列std::deque<RecordWriter\*>尾端,等待前面的数据写完成,然后依次执行如下步骤; + 1. 调用DBTable::GroupWriteBatch,把std::deque<RecordWriter*>里的数据追加到WriteBatch实例,这里不会追加需要同步的数据。同时,这里限制一次批量写的数据量,默认是1M; + 2. 检查是否需要切换日志文件; + 3. 数据追加到log文件里,如果写日志超时(默认5s),切换日志文件;写日志成功后,执行sync操作,等待sync操作完成,如果sync超时,切换日志文件,重新执行这一步骤; + 4. 对每个lg获取最后一次写的快照; + 5. 把步骤2生成的WriteBatch分到不同的lg上,对每一个lg调用DBImpl::Write操作,把数据写入memtable + +## tera compact的触发条件 +1. 用户通过SDK向TS发送compact请求; +2. 写数据时,memtable满了,变成immutable memtable,触发compact流程; +3. 读数据时,在memtable和immutable memtable都没有读到数据,且文件的allowed\_seeks小于等于0且file\_to\_compact为null时,触发compact流程; +4. 触发compact流程后,首先检查compact的分数是否大于等于1,如果是则进入compact操作,否则检查是否因seek导致compact,如果是则进入compact操作,其它情况不需要compact; + +## tera compact的流程 +1. 计算compact的分数的方法: + - 对level-0来说,score=当前level-0的文件个数/2; + - 对其它level来说,score=当前level的总字节大小/每个level配置的字节上限;(默认sst文件的大小是tera_tablet\_ldb\_sst\_size=8M,level-1上限是5*8M=40M,接下来的level是10倍的关系) +2. compact操作对磁盘IO的读写压力比较大,所以每个TS同时compact线程数是有限制的,默认tera\_tabletnode\_compact\_thread\_num=10。分数越高,compact的优先级越高; +3. compact的输入是level上的一个或若干没有重合的sst或者若干个有重合的sst文件(只有level为0时,才可能有多个重合的sst),以及level+1上的若干个与之有重合的sst文件,输出是level+1上多个新sst文件。具体参与compact文件的选择: + - level上参与compact的文件是轮询的(由compact\_pointer\_变量保存下一个需要compact的sst文件),轮询结束则从头开始选第一个文件; + - level+1上选sst落在[begin, end]范围内的文件,即是有重合的文件; + - 选定level+1上的文件后,基于level和level+1的merge后的[smallest, largest]范围在level上找出所有重叠的文件数,然后得到更新的范围[smallest, largest],拿这个范围在level+1上找出所有重叠的文件数,如果level+1在扩展前和扩展后的文件个数相同,则用扩展后的两组文件作为compact的输入,否则用上面两个步骤得到的两组文件作为compact输入; + - level+2上选sst落在level和level+1的merge后的[smallest, largest]范围内的文件,由std::vector grandparents_保存;这个变量主要是用来判断是否需要生成新sst、判断compact操作是否可以通过简单的move操作完成的依据; +4. 选定compact的文件后,就进入真正compact操作。如果不是人工触发的compact,且检查发现可以通过简单的move文件完成compact的,则直接调用LogAndApply在manifest文件里记录这种变化:level+1有这个文件,level没有这个文件;对其它情况,进入步骤5; +5. 对生成需要compact的两组sst文件,生成一个迭代器,遍历迭代器: + - 如果immutable memtable有数据,则优先dump当前immutable memtable的数据到level-0的sst上。这样做是为了避免因为compact,导致前端写数据堵塞; + - 如果把当前key写入sst文件,会导致level+1与level+2重叠的字节超过10*sst大小,则认为重叠部分过大,就停止对这个sst文件的写入,生成一个新sst文件; + - 检查当前的key是否需要丢弃,如果是,则next下一个key; + - 对于有用的key,如果key的类型是原子操作,则merge之前所有值,把merge的结果加到builder里,否则直接把key和value加到builder里; + - 检查当前sst的大小是否超过阈值,如果是,就关闭当前sst文件,生成新sst; + +## 业界的进展 +tera 是一个类似bigtable,hbase的NoSQL系统,所以可以借鉴这些系统的优点。目前hbase的进展主要是在可用性上。 + +- hbase基于timeline一致性为读操作提供高可用,99.9%提升到99.99%,[参考链接](http://hortonworks.com/blog/apache-hbase-high-availability-next-level/); +- facebook开发HydraBase进一步提供高可用,99.99%提升到99.999%,[参考链接](https://code.facebook.com/posts/321111638043166/hydrabase-the-evolution-of-hbase-facebook/); + - 解决跨机架、跨机房的容灾; + - 基于RAFT实现副本的一致性; + +另外,mongodb是近年比较流行的NoSQL系统,其功能越来越强大!在最新3.2版增加了内存存储引擎、数据加密、支持与第三方BI连接,[参考链接](https://www.mongodb.com/mongodb-3.2) \ No newline at end of file diff --git a/resources/images/understanding_master_startup.png b/resources/images/understanding_master_startup.png new file mode 100644 index 0000000000000000000000000000000000000000..bc4af48606c4a2cd004a7e53310c3c7d08135f43 GIT binary patch literal 116536 zcmd4(byQT*|27N*5<{1C4UIH|v~)L;LpMlwmqT}lv?8E%C@^#lQVL2+cc-K%@f`eq zfA{@+-?g6qAJ$?G9M0Ki@6X=*6W4WZ; zkW1hL*-Kkd26=9nGYt3z(_P8X3kiwU{P7!^Cc4cLxMA8s7wQdFR}-~%bK$nMakH}J 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zBPCdZ70;yf7nq`4vRbA2C>YgT5A5J?IXb@S@-_7{WqkPk#xsO)I-J#AMd+s|GgUS? z^aE^-P}OVeKHm`x>Yso!t*V9idfwDxZtFVvm` Date: Wed, 16 Dec 2015 15:50:54 +0800 Subject: [PATCH 2/7] update tablet merge diagram --- doc/tera_understanding.md | 2 +- .../images/understanding_tablet_merge.png | Bin 71897 -> 167775 bytes 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md index 5004f924b..811f7669d 100644 --- a/doc/tera_understanding.md +++ b/doc/tera_understanding.md @@ -96,7 +96,7 @@ tera开发者黄俊辉的串讲文档 2. TS收到需要Load命令后,如果parent\_tablets的个数为1,则认为这个tablet是分裂而来的,通过读取父tablet的CURRENT文件,在自己的DB目录下生成MANIFEST; 3. 通过MANIFEST文件,tablet就知道自己有哪些sst文件,每个sst属于哪一级,以及每个sst的最小最大值; 4. 对于原来的tablet的sst文件,leveldb是不会移动到自己DB目录下的,后面compact操作生成的新文件会保存到自己的DB目录; - 5. TS无法知道什么时候应该删除父tablet的sst文件,因为虽然自己没有用父tablet的sst文件了,但是可能由另一个分裂出来的tablet使用,这个只能由master定时向TS发送GC请求,收集需要删除的tablet文件。 + 5. TS无法知道什么时候应该删除父tablet的sst文件,因为虽然自己没有用父tablet的sst文件了,但是可能由另一个分裂出来的tablet使用,这个只能由master定时向TS发送GC请求,收集仍然在用的sst文件,同时,master通过list命令得到tablet下所有的文件,两者的diff就是需要删除的文件。 ## tablet 合并流程 ![tablet合并](../resources/images/understanding_tablet_merge.png) diff --git a/resources/images/understanding_tablet_merge.png b/resources/images/understanding_tablet_merge.png index e0614d0e9a7ac1a47341d1f660b46b6a7d352065..120816384bb7c5f10c09d79d0ef9d9bbdac64302 100644 GIT binary patch literal 167775 zcmeFZWmr{P+dry^0#a*%EI>LWq*1y{KuTZ(3+a|zbcd9blyq;9Q0eZF?(S}+VbT4c z%l$lV_w&5(Ip@Rqb`ICY#hQ~b#~kDC`*+`C1-+4%#C-hp@x6QZFr}r$mG0d`p1*ew z2@CZRaL0tK=>_oTzMYb!=>4fKMlaw3&00#`?%q9G9mMZ_k`Fazz<@E+KcMzdIayu< zm?g8GAxz(h+1b(>7=7;^zcVlJ(bC9XkKEbP!pe@v7;pfbyNay)`cji<6TRv(pP^n5_v5 z8xIc;3oAPdJ3A9Fg2~Rs%3jZz$;yuEuSxzrkGPSYfvu^vy(!F!95Jt+KFq;hfRYmN zqF+CMt<&Dr`0qDa+5KxFrKF#*8r+>fq zukrjWh>ib7h`*@(^DaPU!N>e8zgQ;txM+oa?%qA&d(z?}e>mUYnL|sqkW>*mcmQfUuk32Vy5;phTw#V}?=W0}PJRSPKc2VOX`v`+j 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zo(89|zmuiy^P&kJ^9ANBT&2gn@*U_%uuL>?!h){`^B)0LarOW3_k22W4h17QEp$i7!jHs$BQeCfPHx0_M)_oh0xEfpo%czPd1+b0(kI^ILjj;lnp)9G%y zexy5~5!>Ht_`CAkwxITa_j(Po&WwaX3)>1?OY*9B^xL%rFq2a9 z32Rc%vneND!NF5RxE|E0Xb&PgIV&Vaj0pzqYFq98`B91HO!fD~m0OFg-i2_s31E)d z?QGjy8rsi{rSzNTU5inW{=&hS&1+Oni^%;){m(iUHJSg{?SBYW}Ajdz+8?2 z`JpID1I#@8yJUA|NHu$91FftTmwpS6p&^|yEsWD$`&){3H_w(_2P@`5M`+cK^Xw;^ zSJoSE%U(Zj>Bf%V?b4anbA+lJ1Y<2peM=optt?Uls#kbp0_)h!oLlU63Lhj{g)9D9 z&PfX-S{{;^3%+oeIuWo{a~Uiu38N3Z_|zBvYS1M3viEJwe0}u8t>DS%h;L)E$sCv7 zx%J}PON%DRGXIwc&&(L5_RY(Ia$$z(iOsdaw$f-pWHKF+$` z5bW1f>KC|Tu(RvDHvgBt}nm<)czuAk#t<+iN|3ea( z#zQVaKgTH`ulDgLFP3o62K|fw7JDquMZCy2n^W%`-T*goR Date: Wed, 16 Dec 2015 22:45:57 +0800 Subject: [PATCH 3/7] update the process of tablet write --- doc/tera_understanding.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md index 811f7669d..12cd1e905 100644 --- a/doc/tera_understanding.md +++ b/doc/tera_understanding.md @@ -45,7 +45,7 @@ tera开发者黄俊辉的串讲文档 ## tera 数据模型与leveldb的数据模型的映射关系 - tera是表格的数据模型,leveldb是kv的数据模型,tera是基于leveldb的存储系统,所以这里涉及到表格数据转换成kv结构的问题; - tera的表格至少有一个LG,一个LG有一个或多个CF,一个LG对应一个leveldb的对象; -- leveldb的Key结构是|user\_key|sequence(7Bytes)|type(1Bytes)|,排序方法是首先user\_key升序,然后sequence降序,最后type降序。其中user_key是用户key序列化后值,sequence是操作的时间顺序,type是操作类型(写入或删除); +- leveldb的Key结构是|user\_key|sequence(7Bytes)|type(1Bytes)|,排序方法是首先user\_key升序,然后sequence降序,最后type降序。其中user\_key是用户key序列化后值,sequence是操作的时间顺序,type是操作类型(写入或删除); - 在tera里,有四种key类型: - Readable: rowkey,qualifier都是字符串类型,没有\0; - Binary: rowkey或qualifier包含\0字符,规定cf不能包含\0; @@ -57,7 +57,7 @@ tera开发者黄俊辉的串讲文档 - TTLKv: [rowkey(rlen)|expire\_timestamp(4B)] - GeneralKv: user\_key == key - 写操作时,不同LG的数据是写到同一个log文件的,为提高恢复的效率,需要把LG的id和上面序列化生成的tera\_key一起写到日志中,具体格式是: - [9(4B)|//LG\_ID//|lg_id|klen|tera\_key] + [9(4B)|//LG\_ID//|lg\_id|klen|tera\_key] ## tera meta 表结构 - meta表是tera的核心表,维护了整个系统当前有哪些表,每个表的模式,每个tablet的信息,每个CF的访问控制信息; @@ -88,7 +88,7 @@ tera开发者黄俊辉的串讲文档 - TS收到split请求后,需要在Tablet里找一个中间的key对其一分为二,具体找的方法是: 1. 在Tablet里所有的LG中找一个数据量最大的,在这个LG里找待分裂的split\_key; 2. 获取LG除level-0外的所有level的数据量大小总和size,确定分裂的数据量大小0.5*Size; - 3. 从level-1到level-6,找一个largest key最小sst,把这个sst的大小累加到split_size,如果达到要求则执行步骤4,否则继续这一步骤的流程; + 3. 从level-1到level-6,找一个largest key最小sst,把这个sst的大小累加到split\_size,如果达到要求则执行步骤4,否则继续这一步骤的流程; 4. split\_key就是步骤3得到的最后一个largest key; 5. 对于普通KV类型的key,上述split\_key就是最终所要的key,对于表格类型需要从中提取出rowkey作为split\_key; - tablet的分裂只是修改了元信息,没有发生真实数据的移动,为实现这点leveldb需要做什么? @@ -108,14 +108,13 @@ tera开发者黄俊辉的串讲文档 ## tablet 读数据流程 ![tablet合并](../resources/images/understanding_tablet_read.png) -1. 遍历Tablet请求的每一行,根据tablet_name,start\_key找到对应的TabletIO的句柄; +1. 遍历Tablet请求的每一行,根据tablet\_name,start\_key找到对应的TabletIO的句柄; 2. 调用TabletIO的ReadCells,并保存结果; 1. 如果只是kv类型,调用DBTable的Get,即调用leveldb的Get,其它类型进入步骤2; 2. 如果请求里有一个qualifier为空,即没有指定qualifier,则不能执行seek,即需要调用scan操作,否则调用seek; - 新建一个迭代器,该迭代器内部包含了tablet里所有LG的迭代器; - 指定row\_key, cf="", qualifier="", type=TKT\_FORSEEK, timestamp=INT64\_MAX, 把迭代器移动行首; - 先遍历cf,然后在每个cf里遍历qualifier,调用ScanDrop检查是否需要跳过当前的记录,如果记录是有用的,则检查是否是原子操作,如果是就合并该key的所有value,如果不是原子操作,当前value就是最终需要的; - 3. 返回结果; ## tablet 写数据流程 @@ -130,11 +129,12 @@ tera开发者黄俊辉的串讲文档 - 封装的原因:原生的leveldb,一个db就有一个log文件。但是,在tera里一个tablet对应一个log文件,即多个db的数据会写到一log文件,所以需要在log文件里区分每个db的数据; 3. 调用TabletIO::WriteBatch,进入步骤4; 4. 调用DBTable::Write,使用RecordWriter封装数据,然后放到队列std::deque<RecordWriter\*>尾端,等待前面的数据写完成,然后依次执行如下步骤; - 1. 调用DBTable::GroupWriteBatch,把std::deque<RecordWriter*>里的数据追加到WriteBatch实例,这里不会追加需要同步的数据。同时,这里限制一次批量写的数据量,默认是1M; - 2. 检查是否需要切换日志文件; - 3. 数据追加到log文件里,如果写日志超时(默认5s),切换日志文件;写日志成功后,执行sync操作,等待sync操作完成,如果sync超时,切换日志文件,重新执行这一步骤; - 4. 对每个lg获取最后一次写的快照; - 5. 把步骤2生成的WriteBatch分到不同的lg上,对每一个lg调用DBImpl::Write操作,把数据写入memtable + 1. 调用DBTable::GroupWriteBatch,把std::deque<RecordWriter*>里的数据追加到WriteBatch实例,如果队列头的数据是不需要sync日志的,但是当前的数据是需要sync,则结束追加数据。同时,这里限制一次批量写的数据量,默认是1M; + 2. 如果force\_switch\_log\_或日志大小超过阈值32M(tera\_tablet\_log\_file\_size),生成新日志文件; + 3. 数据追加到log文件里,如果写日志超时(默认5s,tera\_tablet\_write\_log\_time\_out),切换日志文件;写日志成功后,执行sync操作,等待sync操作完成,如果sync超时,切换日志文件,重新执行这一步骤; + 4. 对每个lg保存最后一次写的快照序; + 5. 把步骤2生成的WriteBatch分到不同的lg上,对每一个lg调用DBImpl::Write操作,把数据写入memtable; + 6. 释放最后一次提交的快照;更新最后一次提交的序号commit\_snapshot\_,这样读操作就能读到最新的数据; ## tera compact的触发条件 1. 用户通过SDK向TS发送compact请求; @@ -145,13 +145,13 @@ tera开发者黄俊辉的串讲文档 ## tera compact的流程 1. 计算compact的分数的方法: - 对level-0来说,score=当前level-0的文件个数/2; - - 对其它level来说,score=当前level的总字节大小/每个level配置的字节上限;(默认sst文件的大小是tera_tablet\_ldb\_sst\_size=8M,level-1上限是5*8M=40M,接下来的level是10倍的关系) + - 对其它level来说,score=当前level的总字节大小/每个level配置的字节上限;(默认sst文件的大小是tera\_tablet\_ldb\_sst\_size=8M,level-1上限是5*8M=40M,接下来的level是10倍的关系) 2. compact操作对磁盘IO的读写压力比较大,所以每个TS同时compact线程数是有限制的,默认tera\_tabletnode\_compact\_thread\_num=10。分数越高,compact的优先级越高; 3. compact的输入是level上的一个或若干没有重合的sst或者若干个有重合的sst文件(只有level为0时,才可能有多个重合的sst),以及level+1上的若干个与之有重合的sst文件,输出是level+1上多个新sst文件。具体参与compact文件的选择: - level上参与compact的文件是轮询的(由compact\_pointer\_变量保存下一个需要compact的sst文件),轮询结束则从头开始选第一个文件; - level+1上选sst落在[begin, end]范围内的文件,即是有重合的文件; - 选定level+1上的文件后,基于level和level+1的merge后的[smallest, largest]范围在level上找出所有重叠的文件数,然后得到更新的范围[smallest, largest],拿这个范围在level+1上找出所有重叠的文件数,如果level+1在扩展前和扩展后的文件个数相同,则用扩展后的两组文件作为compact的输入,否则用上面两个步骤得到的两组文件作为compact输入; - - level+2上选sst落在level和level+1的merge后的[smallest, largest]范围内的文件,由std::vector grandparents_保存;这个变量主要是用来判断是否需要生成新sst、判断compact操作是否可以通过简单的move操作完成的依据; + - level+2上选sst落在level和level+1的merge后的[smallest, largest]范围内的文件,由std::vector grandparents\_保存;这个变量主要是用来判断是否需要生成新sst、判断compact操作是否可以通过简单的move操作完成的依据; 4. 选定compact的文件后,就进入真正compact操作。如果不是人工触发的compact,且检查发现可以通过简单的move文件完成compact的,则直接调用LogAndApply在manifest文件里记录这种变化:level+1有这个文件,level没有这个文件;对其它情况,进入步骤5; 5. 对生成需要compact的两组sst文件,生成一个迭代器,遍历迭代器: - 如果immutable memtable有数据,则优先dump当前immutable memtable的数据到level-0的sst上。这样做是为了避免因为compact,导致前端写数据堵塞; From d4b2bea7c5c986580aed776fc0c9792b834f6678 Mon Sep 17 00:00:00 2001 From: stevenswong Date: Thu, 17 Dec 2015 16:41:01 +0800 Subject: [PATCH 4/7] add more detail for hbase timeline improvement --- doc/tera_understanding.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md index 12cd1e905..f943d3713 100644 --- a/doc/tera_understanding.md +++ b/doc/tera_understanding.md @@ -164,6 +164,9 @@ tera开发者黄俊辉的串讲文档 tera 是一个类似bigtable,hbase的NoSQL系统,所以可以借鉴这些系统的优点。目前hbase的进展主要是在可用性上。 - hbase基于timeline一致性为读操作提供高可用,99.9%提升到99.99%,[参考链接](http://hortonworks.com/blog/apache-hbase-high-availability-next-level/); + - 一个tablet只能由一个主ts写,但可以由多个从和主ts读; + - 从ts实时tail主ts更新的日志文件,更新自己的memtable数据; + - 客户端请求时通过指定一致性的要求(STRONG或TIMELINE)决定读请求到主ts还是从ts; - facebook开发HydraBase进一步提供高可用,99.99%提升到99.999%,[参考链接](https://code.facebook.com/posts/321111638043166/hydrabase-the-evolution-of-hbase-facebook/); - 解决跨机架、跨机房的容灾; - 基于RAFT实现副本的一致性; From 5b727a977601a0b82cfa2c4b803b15819d628410 Mon Sep 17 00:00:00 2001 From: stevenswong Date: Thu, 17 Dec 2015 17:30:13 +0800 Subject: [PATCH 5/7] update hbase timeline introduce --- doc/tera_understanding.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md index f943d3713..35c871e32 100644 --- a/doc/tera_understanding.md +++ b/doc/tera_understanding.md @@ -163,12 +163,13 @@ tera开发者黄俊辉的串讲文档 ## 业界的进展 tera 是一个类似bigtable,hbase的NoSQL系统,所以可以借鉴这些系统的优点。目前hbase的进展主要是在可用性上。 -- hbase基于timeline一致性为读操作提供高可用,99.9%提升到99.99%,[参考链接](http://hortonworks.com/blog/apache-hbase-high-availability-next-level/); +- hbase基于timeline一致性为读操作提供高可用,99.9%提升到99.99%,[hbase ha介绍](http://hortonworks.com/blog/apache-hbase-high-availability-next-level/),[hbase官方文档](https://hbase.apache.org/book.html#_timeline_consistency); - 一个tablet只能由一个主ts写,但可以由多个从和主ts读; - 从ts实时tail主ts更新的日志文件,更新自己的memtable数据; - - 客户端请求时通过指定一致性的要求(STRONG或TIMELINE)决定读请求到主ts还是从ts; -- facebook开发HydraBase进一步提供高可用,99.99%提升到99.999%,[参考链接](https://code.facebook.com/posts/321111638043166/hydrabase-the-evolution-of-hbase-facebook/); + - 客户端可以在读请求里指定一致性的要求:STRONG或TIMELINE,STRONG是hbase默认的行为读写都到主ts上,如果是TIMELINE,则sdk先请求主ts,如果超过阈值(默认10毫秒)没有返回,则向所有的从ts发送读请求,第一个响应结果作为最终的值; + - sdk提供Result.isStale()接口,表示结果是否来自主ts,即值为false,则是来自主ts的,数据肯定是最新的; +- facebook开发HydraBase进一步提供高可用,99.99%提升到99.999%,[facebook官方blog](https://code.facebook.com/posts/321111638043166/hydrabase-the-evolution-of-hbase-facebook/); - 解决跨机架、跨机房的容灾; - 基于RAFT实现副本的一致性; -另外,mongodb是近年比较流行的NoSQL系统,其功能越来越强大!在最新3.2版增加了内存存储引擎、数据加密、支持与第三方BI连接,[参考链接](https://www.mongodb.com/mongodb-3.2) \ No newline at end of file +另外,mongodb是近年比较流行的NoSQL系统,其功能越来越强大!在最新3.2版增加了内存存储引擎、数据加密、支持与第三方BI连接,[mongodb新功能介绍](https://www.mongodb.com/mongodb-3.2) \ No newline at end of file From 11bfe1f12b3cb15fea82d43715b010358040bf27 Mon Sep 17 00:00:00 2001 From: stevenswong Date: Sun, 20 Dec 2015 21:38:17 +0800 Subject: [PATCH 6/7] add introduction of iterator implement --- doc/tera_understanding.md | 38 ++++++++++++++++-- resources/images/understanding_db_iter.png | Bin 0 -> 9202 bytes resources/images/understanding_iter_class.png | Bin 0 -> 156821 bytes resources/images/understanding_merge_iter.png | Bin 0 -> 31451 bytes 4 files changed, 35 insertions(+), 3 deletions(-) create mode 100644 resources/images/understanding_db_iter.png create mode 100644 resources/images/understanding_iter_class.png create mode 100644 resources/images/understanding_merge_iter.png diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md index 35c871e32..be3dcb065 100644 --- a/doc/tera_understanding.md +++ b/doc/tera_understanding.md @@ -108,7 +108,7 @@ tera开发者黄俊辉的串讲文档 ## tablet 读数据流程 ![tablet合并](../resources/images/understanding_tablet_read.png) -1. 遍历Tablet请求的每一行,根据tablet\_name,start\_key找到对应的TabletIO的句柄; +1. 遍历Tablet请求的每一行(客户端可能一次请求读多行),根据tablet\_name,row\_key找到对应的TabletIO的句柄; 2. 调用TabletIO的ReadCells,并保存结果; 1. 如果只是kv类型,调用DBTable的Get,即调用leveldb的Get,其它类型进入步骤2; 2. 如果请求里有一个qualifier为空,即没有指定qualifier,则不能执行seek,即需要调用scan操作,否则调用seek; @@ -117,6 +117,35 @@ tera开发者黄俊辉的串讲文档 - 先遍历cf,然后在每个cf里遍历qualifier,调用ScanDrop检查是否需要跳过当前的记录,如果记录是有用的,则检查是否是原子操作,如果是就合并该key的所有value,如果不是原子操作,当前value就是最终需要的; 3. 返回结果; +## 迭代器的实现 +![迭代器类图](../resources/images/understanding_iter_class.png) + +1. ts读数据、扫描表格(同步扫描或流式扫描)、compaction的功能都需要借助迭代器实现; +2. 在tera系统里,一个tablet的数据由多个leveldb存储,一个leveldb又有多个level,而且数据又分版本,所以在读某行数据或扫描表格时,需要打开表格所有的lg,一个一个地遍历并检查是否有效。为简化代码实现,tera系统借鉴了原生leveldb的作法,对外只暴露一个迭代器,这个迭代器的seek,next,prev等操作由内部一组迭代器协同完成; +3. DBTable通过NewIterator函数完成所有lg的迭代器的初始化,然后调用NewMergingIterator函数得到MergingIterator,后面通过这个迭代器就能遍历记录; +4. lg迭代器的初始化则通过DBImpl::NewIterator完成,这个函数调用NewInternalIterator完成对memtable和immutable,以及TwoLevelIterator迭代器的初始化,这些迭代器由MergingIterator封装,然后赋值给DBIter; +5. TwoLevelIterator迭代器是遍历sst文件内部数据的,包含一个index_iter_和data_iter_, index_iter_是sst索引的迭代器,data_iter_初始为空,在seek到记录后,通过InitDataBlock初始化; + +## 迭代器遍历过程 +1. MergingIterator遍历过程 + 1. 图中总共有5个迭代器,绿色填充颜色的key表示当前每个迭代器的位置,其中current指向2|19|1(userkey|sequence|type),方向是forward,sequence number是全局唯一且递增,key的排序方式是按userkey升序,sequence降序,type降序; + 2. 求next: + - 如果当前的方向是forward,则current移动到下一个位置,即5|18|1,然后,在5个迭代器中找key最小值,在5|18|1,3|16|1,2|12|1,4|10|0,4|7|1中key最小的是2|12|1,所以把current指向2|12|1,然后返回;继续求next:移动current到下一个位置,因2|12|1是最后一个所以此时current指向null,在5|18|1,3|16|1,null,4|10|0,4|7|1中找key最小的,即3|16|1(null被认为是最大),如图中next-1和next-2分别指示的位置; + - 如果当前的方向是reverse,则需要把除current外的所有迭代器移动到key后面的一个位置,确保current指向的是当前所有迭代器中key最小值的位置,并修改方向为forward,接下来的步骤同上; + 3. 求prev: + - 假设当前的方向是forward,则需要把除current外的所有迭代器移动到key前面的一个位置,确保current指向的是当前所有迭代器中key最大值的位置,并修改为reverse,所以iter\_2根据key 2|19|1 seek到3|16|1,然后iter\_2取prev得null,iter\_3 找seek失败,因为没有>= 2|19|1的key,所以iter\_3移动到最后2|12|1的位置,iter\_4 seek到3|11|1取prev得null,iter\_5 seek到4|7|1取prev得null; + - 在上面得到的2|19|1,null,2|12|1,null,null(null被认为是最小)中找key最大的,即为2|19|1,移动iter\_1到前一个位置1|20|0,在1|20|0和2|12|1取最大值,即2|12|1;继续求prev:移动current到前一个位置2|13|1,在1|20|0和2|13|1取最大值,即2|13|1,如图中prev-1和prev-2指示的位置; + - 如果当前的方向是reverse,则current向后移动一个位置,然后在所有迭代器中找key最大的; +![合并迭代器](../resources/images/understanding_merge_iter.png) + +2. DBIter遍历过程 + 1. DBIter包含了一个MergingIterator,因此可以看成一个已经有序的遍历,DBIter需要做一些版本相关的处理,假设当前处于current 指向5|5|1位置,方向为forward; + 2. next: 方向为forward, 首先next到5|4|0, 由于这个数据的type是delete, 跳过所有user_key为5的节点,到6|12|1,该entry的type不是delete,即为next;如果当前是reverse,则检查当前迭代器是否有效,无效则把迭代器移到第一个key的位置,否则移动下一个,接下来流程同一前面; + 3. prev: 方向为reverse,首先prev到3|7|1,保存key值3,一直向前找到key值3的开头3|8|0,由于这个数据的type是delete,继续向前,key 2为delete也跳过,找到2|13|1,即为prev;如果当前forward,则一直取prev直到key发生变化,然后设置为reverse,接下来流程同前面,如果取prev过程中发现迭代器失效,则直接返回;**TODO** +![DBIter迭代器](../resources/images/understanding_db_iter.png) + +3. LowLevelScan遍历过程 + ## tablet 写数据流程 1. 预处理待写的数据,生成由TabletIO到index的映射; 2. 对每一个TabletIO调用write方法; @@ -169,7 +198,10 @@ tera 是一个类似bigtable,hbase的NoSQL系统,所以可以借鉴这些系 - 客户端可以在读请求里指定一致性的要求:STRONG或TIMELINE,STRONG是hbase默认的行为读写都到主ts上,如果是TIMELINE,则sdk先请求主ts,如果超过阈值(默认10毫秒)没有返回,则向所有的从ts发送读请求,第一个响应结果作为最终的值; - sdk提供Result.isStale()接口,表示结果是否来自主ts,即值为false,则是来自主ts的,数据肯定是最新的; - facebook开发HydraBase进一步提供高可用,99.99%提升到99.999%,[facebook官方blog](https://code.facebook.com/posts/321111638043166/hydrabase-the-evolution-of-hbase-facebook/); - - 解决跨机架、跨机房的容灾; - - 基于RAFT实现副本的一致性; + - 项目的目标是缩短ts节点挂掉后,tablet的恢复时间,支持跨机架、跨机房的容灾; + - 解决上面问题,没有采用master-slave的原因:(1)这种模式是基于集群为单位的故障转移(failover),意味着机器利用率不高;(2)在转移期间会导致服务中断;(3)如果集群是异步复制的,则有可能会导致数据丢失; + - 一个tablet由若干个ts管理,这些ts形成一个quorum,每个quorum有一个ts负责处理客户端的读写请求; + - 在一个含2F+1个节点的quorum,最多允许F个节点出现故障。quorum同步更新WAL,但只要绝大多数ts完成WAL的更新就能保证一致性; + - quorum的成员要么是active,要么witness模式,active模式的成员会写数据到HDFS,并且会执行数据持久化和compaction。witness成员只会参与复制WAL,但是当leader ts故障时winness可以升级了leader; 另外,mongodb是近年比较流行的NoSQL系统,其功能越来越强大!在最新3.2版增加了内存存储引擎、数据加密、支持与第三方BI连接,[mongodb新功能介绍](https://www.mongodb.com/mongodb-3.2) \ No newline at end of file diff --git a/resources/images/understanding_db_iter.png b/resources/images/understanding_db_iter.png new file mode 100644 index 0000000000000000000000000000000000000000..cf2aa9ac957a0f18255dfe2c05a875f4da93403d GIT binary patch literal 9202 zcmdUVbyVBUvp23qiWJu(!QI`8I}|TaiW33^2yVr_E$&jZ6n6>)C@yI!?ovpLOL4pD z)2F|C-}jvR|NDN=$v3-`+1Z_$o!$A&NsNw`G9C^k4iXX)o~nwX9ug7?38HO}g@Je* z>n|1~zL3Fs%JRtb!<@m04z`<$2^a~9)$;cTnI^K`3BfS!XkhGVtf?Vx0|IhezXH9q z<@N!(A-Iu{Bz?pYO`xr(HN6ke#T6{>BgOa+hd84Bo6N&V{|}3&vlOGTrVhOV$itRi zh+B}Gmr)vro}OOP%q& z=R$CBfqh*)t$ny$!B77P`71}!7Hs3;=;rANa;5(**ZL*M%TtPx@%KdkdH$@^)6wp~ zXL1Gqs}@2*p5MRl@Nx6<{6{uosN`>|xB>|1=3xs4Bjig9N&dt6f6)GWoMJL|CXig;b@D{`k&E!|Bn8jw14>}d44PYKMe85 z%KuOiW|qd0k zQp-a67R&k%nTHyN%K;DmyEAR@JpV-vCbaMnLP8XISu8AM2KxVBJF--AQ0J#-IzoSx zDZoOxCzt47n18#L{)u=ml~Hbs8I6(Q3%4Gp645`phAzlM8*r;uxSN%l1A(OGB_B!A zm~^Jp)GKljreF#S2~P5I+K-Pb$%MgzC&W`sF@kfvIEAWuu|sDRWz}gv#*|JXBo%1V zuSt*K@)chIEsxr_=ab6GWp?5IgNv`8PtrNV*F5Nb(jfKwaw0@aHG{U`&;C7Bj!(h8 znzG_8&^yo?GS)XdSk&f-VAi!)-Qk{p2cdOTPq%~*%cW&=S1ugrjO(6n7K!Z>fE6I- zBXtB{bKYm70@?sU-@(XvH;f<8TOV*P8tQ(zS3!z8BmLhHI*_8NrTLjLi!d|(c4fH- zS^!=CQr)>=Y@5)*r6B6V?7%7QWjtu#5|ph_?n4DzUR$BNPY@#SIt!;;wqIAn>6JqX zIa*t+r+_mC?SB*qk0~Q;rklJ%Ho_q$A(?1)6egT4bxJi71oFk#M8a$M3Ltfse1}Q$B+}(#M=rrfgwX&nt*S1z0R@AKmxI5yg`GuBl)3U3L*L!L>38)zg1&H3zyv@oEx{@vL4=G;sl3$Dh5^u=kAB}1b7`iD6!)- z(d@gBr%|k6Z`g=2DKNU%I_&$#ce$E1IQGV3dRwnzF`=~#fj3pAV5EWp`3k^g+j=^AF=~YYj}pr-5T{hO zkT1`nk@0G`$D5_WQwaG98-exg*p@JC=G0&AW(q$};~TIseINNA$!sAglqS9he;d4; zXd9RLgXW>$&>9nUy?6CddrjLp)-BuRICS+sKF+Q9c+B>ruL_v@8cH#AD&oVEi}nz> z9#kdL+3sRBHP4Ut{^9o#5&8&4`q2~BOxy5yD!Rc1tJbrA*?T-evRkZIX`}&WT?{(g zk#!_V4Mka*ox_y64xuKBm{WtbKiKjd`jxfIpCJB_k(Fj@lB;U>s1&H*WuVz$j zZ-$LiZ_sC_E5!TG*pD!Ea=!w_d(Ek_$XZt)xyniJEhVl=m-lpP+qpfu^&~}+rzb$k z-D7{W(!lG+gT5M5tK5p2AFl1TN__jY6xF7_?#09lv7v74!VRGSb`}O#n|(Qj0)E%D z+S6ow2*h)coR`XR71vfA$fFg06RGoF#`2j{W&y=V%tLKOzeCx}`A>U-Bn)Js@ylwt zTu{t=JfAPF5rLs$7&Vz6Khy46J%Qqvw$Q{k@1o}u)ZaJ0#{OY&85k|P2X@}b8|WBB z&uj8Xm3<6RACJ0K!##*6y}0|qWcE~@@;O)3?g}|4%k^ujnSro<+w7hPY~G8?1{?24 zy?rlp?f!3SDo=~WKR_xQqAW7b$%jM*UP{x>%%W7b1mBj)0TywGT}ynHz}&9_uA!=Qh*`2d^b67QZns$K7d_!=Kc3n~ce?NS_s0=M0eNQsHbKzMDUu)jT

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z^I9ISTy(h$Q8n~MZAzvbX0cESO~ZFp5b7sJcGs&b>u`;7)7E75%-w=->)0bjWDrIdozGej_t2f4wjXuBffWrPC$P^asEz6~7pF1}fx07^tV zu)6X*AD)O$+NvnaCDIengnNKA6AIoryVmJfByf1CFNWY4X{$BARh@l+7yzEmWeR6G zFr4MD;EtO*Oz4_6qsPY}VC|8EE{ZwOMbXn}7Z=W1Z1v+~)Pu&9C`xuDq#4o&@a14y z{0#&0pTLgtCWl0bXHe!>za2J^p4a0{ig`SQ_F0{Oabg0Y)SReHl%LPsQ4-YQgAjpp4LHOMx;*fp6VI@WE2w-gL&$z_ zfG}|C3gFzPmeAP0|DS?h>;LKZ0lp;a-ZtvXb!k-&8yd^q9d33RE-?bcU!w^*esb5( z#Fo|7u{HS?y~Hf%3UqHeggLN`6%9VWS{yy+Um`uU_$o5(nzNT`i$SB&X=L2lA2c|_ vT-5MyZ~Y0a{|U_g>BIk@pNb;G-6670QU|B Date: Wed, 23 Dec 2015 18:33:42 +0800 Subject: [PATCH 7/7] fix word wrong --- doc/tera_understanding.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/tera_understanding.md b/doc/tera_understanding.md index be3dcb065..a3240fc93 100644 --- a/doc/tera_understanding.md +++ b/doc/tera_understanding.md @@ -36,7 +36,7 @@ tera开发者黄俊辉的串讲文档 1. 管理一系列tablet,一般有10~1000个tablet; 2. 处理client发过来的tablet读写请求; 4. client - 1. 通过zookeeper上的meta信息,找到meta table所有的TS地址; + 1. 通过zookeeper上root_table节点,找到meta table所在的TS地址; 2. 读写请求直接与TS交互; 3. 对表的创建、修改、删除,对快照的操作直接与master交互; 5. 分布式文件系统 @@ -75,7 +75,7 @@ tera开发者黄俊辉的串讲文档 2. MasterImpl::CollectAllTabletInfo,向所有在线的ts发送query请求,获取已经加载的tabletlist列表; 3. MasterImpl::RestoreMetaTablet,遍历tabletlist,如果meta表被多个ts加载或加载不完全,则要求所有已经加载meta表的ts卸载。如果卸载失败,则强制踢出该ts。然后,选一个管理数据量最小的ts加载meta表,保存meta表的地址meta\_tablet\_addr。如果前面已经正确加载了meta表,则直接保存meta表的地址meta\_tablet\_addr;如果开启从本地文件恢复meta表的标记,则调用LoadMetaTableFromFile把meta表的数据加载到本地内存,同时,要求已经加载meta表的ts清空meta数据,然后用本地内存的数据同步到meta表里,操作结束则返回; 如果没有开启恢复meta表,则调用LoadMetaTable把meta表的数据同步到master的内存; 4. MasterImpl::LoadMetaTable,通过向ts发送scan请求扫描meta表,如果row的首字符是'~',则是用户相关的元信息执行UserManager::LoadUserMeta,如果是'@',则是表格相关的元信息执行TabletManager::LoadTableMeta,如果是>@,则是tablet相关的元信息执行TabletManager::LoadTabletMeta; - 5. 在zk上更新meta表所有ts的地址; + 5. 更新zk上root_table节点的值,即meta表所在ts的地址; 6. MasterImpl::RestoreUserTablet,遍历所有tablets,如果没有加载,则找一个ts加载;如果能正常工作的ts占比小于0.9阈值时,则master进入安全模式,否则启动定时任务:QueryTablet, TabletNodeGc, LoadBalance,并进入服务状态; 2. 注册RPC服务,并启动。