Time | Source | Dest | Description |
---|---|---|---|
12:01:54.6536050 | Redis-Server | Clients | TCP:Flags=…AP… |
12:01:54.6538580 | Redis-Server | Clients | TCP:Flags=…AP… |
12:01:54.6539770 | Redis-Server | Clients | TCP:Flags=…AP… |
12:01:54.6720580 | Redis-Server | Clients | TCP:Flags=…A..S.. |
12:01:54.6727200 | Redis-Server | Clients | TCP:Flags=…A…… |
12:01:54.6808480 | Redis-Server | Clients | TCP:Flags=…AP….. |
12:01:54.6910840 | Redis-Server | Clients | TCP:Flags=…A…S., |
12:01:54.6911950 | Redis-Server | Clients | TCP:Flags=…A…… |
… | … | … | … |
12:01:56.1181350 | Redis-Server | Clients | TCP:Flags=…AP…. |
12:01:54.6808480, Redis Server端向客户端发送了一个Push包,也就是对于查询请求的一个结果返回。后面的包都是在做连接处理,包括Ack包,Ack确认包,以及重置的RST包,紧接着下面一个Push包是在12:01:56.1181350发出的。中间的间隔是1.4372870秒。也就是说,在这1.4372870秒期间,Redis的服务器端,除了做一个查询,其他的操作都是在做建连,或拒绝连接。
客户端报错的前后逻辑已经清楚了,redis-server卡了1.43秒,client的connection pool被打满,疯狂新建连接,server的accept queue满,直接拒绝服务,client报错。开始怀疑client发送了特殊命令,这时需要确认一下client的最后几个命令是什么,找到redis-server卡死前的第一个包,装一个wireshark的redis插件,看到最后几个命令是简单的get,并且key-value都很小,不至于需要耗费1.43秒才能完成。服务端也没有slow log,此时排障再次陷入僵局。
【进一步分析】
为了了解这1.43秒之内,Redis Server在做什么事情,我们用pstack来抓取信息。Pstack本质上是gdb attach. 高频率的抓取会影响redis的吞吐。死循环0.5秒一次无脑抓,在redis-server卡死的时候抓到堆栈如下(过滤了没用的栈信息):
Thu May 31 11:29:18 CST 2018
Thread 1 (Thread 0x7ff2db6de720 (LWP 8378)):
#0 0x000000000048cec4 in ?? ()
#1 0x00000000004914a4 in je_arena_ralloc ()
#2 0x00000000004836a1 in je_realloc ()
#3 0x0000000000422cc5 in zrealloc ()
#4 0x00000000004213d7 in sdsRemoveFreeSpace ()
#5 0x000000000041ef3c in clientsCronResizeQueryBuffer ()
#6 0x00000000004205de in clientsCron ()
#7 0x0000000000420784 in serverCron ()
#8 0x0000000000418542 in aeProcessEvents ()
#9 0x000000000041873b in aeMain ()
#10 0x0000000000420fce in main ()
Thu May 31 11:29:19 CST 2018
Thread 1 (Thread 0x7ff2db6de720 (LWP 8378)):
#0 0x0000003729ee5407 in madvise () from /lib64/libc.so.6
#1 0x0000000000493a4e in je_pages_purge ()
#2 0x000000000048cf70 in ?? ()
#3 0x00000000004914a4 in je_arena_ralloc ()
#4 0x00000000004836a1 in je_realloc ()
#5 0x0000000000422cc5 in zrealloc ()
#6 0x00000000004213d7 in sdsRemoveFreeSpace ()
#7 0x000000000041ef3c in clientsCronResizeQueryBuffer ()
#8 0x00000000004205de in clientsCron ()
#9 0x0000000000420784 in serverCron ()
#10 0x0000000000418542 in aeProcessEvents ()
#11 0x000000000041873b in aeMain ()
#12 0x0000000000420fce in main ()
Thu May 31 11:29:19 CST 2018
Thread 1 (Thread 0x7ff2db6de720 (LWP 8378)):
#0 0x000000000048108c in je_malloc_usable_size ()
#1 0x0000000000422be6 in zmalloc ()
#2 0x00000000004220bc in sdsnewlen ()
#3 0x000000000042c409 in createStringObject ()
#4 0x000000000042918e in processMultibulkBuffer ()
#5 0x0000000000429662 in processInputBuffer ()
#6 0x0000000000429762 in readQueryFromClient ()
#7 0x000000000041847c in aeProcessEvents ()
#8 0x000000000041873b in aeMain ()
#9 0x0000000000420fce in main ()
Thu May 31 11:29:20 CST 2018
Thread 1 (Thread 0x7ff2db6de720 (LWP 8378)):
#0 0x000000372a60e7cd in write () from /lib64/libpthread.so.0
#1 0x0000000000428833 in sendReplyToClient ()
#2 0x0000000000418435 in aeProcessEvents ()
#3 0x000000000041873b in aeMain ()
#4 0x0000000000420fce in main ()
重复多次抓取后,从堆栈中发现可疑堆栈clientsCronResizeQueryBuffer位置,属于serverCron()函数下,这个redis-server内部的定时调度,并不在用户线程下,这个解释了为什么卡死的时候没有出现慢查询。
查看redis源码,确认到底redis-server在做什么:
clientsCron(server.h): #define CLIENTS_CRON_MIN_ITERATIONS 5 void clientsCron(void) { /* Make sure to process at least numclients/server.hz of clients * per call. Since this function is called server.hz times per second * we are sure that in the worst case we process all the clients in 1 * second. */ int numclients = listLength(server.clients); int iterations = numclients/server.hz; mstime_t now = mstime(); /* Process at least a few clients while we are at it, even if we need * to process less than CLIENTS_CRON_MIN_ITERATIONS to meet our contract * of processing each client once per second. */ if (iterations CLIENTS_CRON_MIN_ITERATIONS) iterations = (numclients CLIENTS_CRON_MIN_ITERATIONS) ? numclients : CLIENTS_CRON_MIN_ITERATIONS; while(listLength(server.clients) iterations--) { client *c; listNode *head; /* Rotate the list, take the current head, process. * This way if the client must be removed from the list it's the * first element and we don't incur into O(N) computation. */ listRotate(server.clients); head = listFirst(server.clients); c = listNodeValue(head); /* The following functions do different service checks on the client. * The protocol is that they return non-zero if the client was * terminated. */ if (clientsCronHandleTimeout(c,now)) continue; if (clientsCronResizeQueryBuffer(c)) continue; } }
clientsCron首先判断当前client的数量,用于控制一次清理连接的数量,生产服务器单实例的连接数量在5000不到,也就是一次清理的连接数是50个。
clientsCronResizeQueryBuffer(server.h): /* The client query buffer is an sds.c string that can end with a lot of * free space not used, this function reclaims space if needed. * * The function always returns 0 as it never terminates the client. */ int clientsCronResizeQueryBuffer(client *c) { size_t querybuf_size = sdsAllocSize(c->querybuf); time_t idletime = server.unixtime - c->lastinteraction; /* 只在以下两种情况下会Resize query buffer: * 1) Query buffer > BIG_ARG(在server.h 中定义#define PROTO_MBULK_BIG_ARG (1024*32)) 且这个Buffer的小于一段时间的客户端使用的峰值. * 2) 客户端空闲超过2s且Buffer size大于1k. */ if (((querybuf_size > PROTO_MBULK_BIG_ARG) (querybuf_size/(c->querybuf_peak+1)) > 2) || (querybuf_size > 1024 idletime > 2)) { /* Only resize the query buffer if it is actually wasting space. */ if (sdsavail(c->querybuf) > 1024) { c->querybuf = sdsRemoveFreeSpace(c->querybuf); } } /* Reset the peak again to capture the peak memory usage in the next * cycle. */ c->querybuf_peak = 0; return 0; }
如果redisClient对象的query buffer满足条件,那么就直接resize掉。满足条件的连接分成两种,一种是真的很大的,比该客户端一段时间内使用的峰值还大;还有一种是很闲(idle>2)的,这两种都要满足一个条件,就是buffer free的部分超过1k。那么redis-server卡住的原因就是正好有那么50个很大的或者空闲的并且free size超过了1k大小连接的同时循环做了resize,由于redis都属于单线程工作的程序,所以block了client。那么解决这个问题办法就很明朗了,让resize 的频率变低或者resize的执行速度变快。
既然问题出在query buffer上,我们先看一下这个东西被修改的位置:
readQueryFromClient(networking.c): redisClient *createClient(int fd) { redisClient *c = zmalloc(sizeof(redisClient)); /* passing -1 as fd it is possible to create a non connected client. * This is useful since all the Redis commands needs to be executed * in the context of a client. When commands are executed in other * contexts (for instance a Lua script) we need a non connected client. */ if (fd != -1) { anetNonBlock(NULL,fd); anetEnableTcpNoDelay(NULL,fd); if (server.tcpkeepalive) anetKeepAlive(NULL,fd,server.tcpkeepalive); if (aeCreateFileEvent(server.el,fd,AE_READABLE, readQueryFromClient, c) == AE_ERR) { close(fd); zfree(c); return NULL; } } selectDb(c,0); c->id = server.next_client_id++; c->fd = fd; c->name = NULL; c->bufpos = 0; c->querybuf = sdsempty(); 初始化是0 readQueryFromClient(networking.c): void readQueryFromClient(aeEventLoop *el, int fd, void *privdata, int mask) { redisClient *c = (redisClient*) privdata; int nread, readlen; size_t qblen; REDIS_NOTUSED(el); REDIS_NOTUSED(mask); server.current_client = c; readlen = REDIS_IOBUF_LEN; /* If this is a multi bulk request, and we are processing a bulk reply * that is large enough, try to maximize the probability that the query * buffer contains exactly the SDS string representing the object, even * at the risk of requiring more read(2) calls. This way the function * processMultiBulkBuffer() can avoid copying buffers to create the * Redis Object representing the argument. */ if (c->reqtype == REDIS_REQ_MULTIBULK c->multibulklen c->bulklen != -1 c->bulklen >= REDIS_MBULK_BIG_ARG) { int remaining = (unsigned)(c->bulklen+2)-sdslen(c->querybuf); if (remaining readlen) readlen = remaining; } qblen = sdslen(c->querybuf); if (c->querybuf_peak qblen) c->querybuf_peak = qblen; c->querybuf = sdsMakeRoomFor(c->querybuf, readlen); 在这里会被扩大
由此可见c->querybuf在连接第一次读取命令后的大小就会被分配至少1024*32,所以回过头再去看resize的清理逻辑就明显存在问题,每个被使用到的query buffer的大小至少就是1024*32,但是清理的时候判断条件是>1024,也就是说,所有的idle>2的被使用过的连接都会被resize掉,下次接收到请求的时候再重新分配到1024*32,这个其实是没有必要的,在访问比较频繁的群集,内存会被频繁得回收重分配,所以我们尝试将清理的判断条件改造为如下,就可以避免大部分没有必要的resize操作:
if (((querybuf_size > REDIS_MBULK_BIG_ARG) (querybuf_size/(c->querybuf_peak+1)) > 2) || (querybuf_size > 1024*32 idletime > 2)) { /* Only resize the query buffer if it is actually wasting space. */ if (sdsavail(c->querybuf) > 1024*32) { c->querybuf = sdsRemoveFreeSpace(c->querybuf); } }
这个改造的副作用是内存的开销,按照一个实例5k连接计算,5000*1024*32=160M,这点内存消耗对于上百G内存的服务器完全可以接受。
【问题重现】
在使用修改过源码的Redis server后,问题仍然重现了,客户端还是会报同类型的错误,且报错的时候,服务器内存依然会出现抖动。抓取内存堆栈信息如下:
Thu Jun 14 21:56:54 CST 2018
#3 0x0000003729ee893d in clone () from /lib64/libc.so.6
Thread 1 (Thread 0x7f2dc108d720 (LWP 27851)):
#0 0x0000003729ee5400 in madvise () from /lib64/libc.so.6
#1 0x0000000000493a1e in je_pages_purge ()
#2 0x000000000048cf40 in arena_purge ()
#3 0x00000000004a7dad in je_tcache_bin_flush_large ()
#4 0x00000000004a85e9 in je_tcache_event_hard ()
#5 0x000000000042c0b5 in decrRefCount ()
#6 0x000000000042744d in resetClient ()
#7 0x000000000042963b in processInputBuffer ()
#8 0x0000000000429762 in readQueryFromClient ()
#9 0x000000000041847c in aeProcessEvents ()
#10 0x000000000041873b in aeMain ()
#11 0x0000000000420fce in main ()
Thu Jun 14 21:56:54 CST 2018
Thread 1 (Thread 0x7f2dc108d720 (LWP 27851)):
#0 0x0000003729ee5400 in madvise () from /lib64/libc.so.6
#1 0x0000000000493a1e in je_pages_purge ()
#2 0x000000000048cf40 in arena_purge ()
#3 0x00000000004a7dad in je_tcache_bin_flush_large ()
#4 0x00000000004a85e9 in je_tcache_event_hard ()
#5 0x000000000042c0b5 in decrRefCount ()
#6 0x000000000042744d in resetClient ()
#7 0x000000000042963b in processInputBuffer ()
#8 0x0000000000429762 in readQueryFromClient ()
#9 0x000000000041847c in aeProcessEvents ()
#10 0x000000000041873b in aeMain ()
#11 0x0000000000420fce in main ()
显然,Querybuffer被频繁resize的问题已经得到了优化,但是还是会出现客户端报错。这就又陷入了僵局。难道还有其他因素导致query buffer resize变慢?我们再次抓取pstack。但这时,jemalloc引起了我们的注意。此时回想Redis的内存分配机制,Redis为避免libc内存不被释放导致大量内存碎片的问题,默认使用的是jemalloc用作内存分配管理,这次报错的堆栈信息中都是je_pages_purge () redis在调用jemalloc回收脏页。我们看下jemalloc做了些什么:
arena_purge(arena.c) static void arena_purge(arena_t *arena, bool all) { arena_chunk_t *chunk; size_t npurgatory; if (config_debug) { size_t ndirty = 0; arena_chunk_dirty_iter(arena->chunks_dirty, NULL, chunks_dirty_iter_cb, (void *)ndirty); assert(ndirty == arena->ndirty); } assert(arena->ndirty > arena->npurgatory || all); assert((arena->nactive >> opt_lg_dirty_mult) (arena->ndirty - arena->npurgatory) || all); if (config_stats) arena->stats.npurge++; npurgatory = arena_compute_npurgatory(arena, all); arena->npurgatory += npurgatory; while (npurgatory > 0) { size_t npurgeable, npurged, nunpurged; /* Get next chunk with dirty pages. */ chunk = arena_chunk_dirty_first(arena->chunks_dirty); if (chunk == NULL) { arena->npurgatory -= npurgatory; return; } npurgeable = chunk->ndirty; assert(npurgeable != 0); if (npurgeable > npurgatory chunk->nruns_adjac == 0) { arena->npurgatory += npurgeable - npurgatory; npurgatory = npurgeable; } arena->npurgatory -= npurgeable; npurgatory -= npurgeable; npurged = arena_chunk_purge(arena, chunk, all); nunpurged = npurgeable - npurged; arena->npurgatory += nunpurged; npurgatory += nunpurged; } }
Jemalloc每次回收都会判断所有实际应该清理的chunck并对清理做count,这个操作对于高响应要求的系统是很奢侈的,所以我们考虑通过升级jemalloc的版本来优化purge的性能。Redis 4.0版本发布后,性能有很大的改进,并可以通过命令回收内存,我们线上也正准备进行升级,跟随4.0发布的jemalloc版本为4.1,jemalloc的版本使用的在jemalloc的4.0之后版本的arena_purge()做了很多优化,去掉了计数器的调用,简化了很多判断逻辑,增加了arena_stash_dirty()方法合并了之前的计算和判断逻辑,增加了purge_runs_sentinel,用保持脏块在每个arena LRU中的方式替代之前的保持脏块在arena树的dirty-run-containing chunck中的方式,大幅度减少了脏块purge的体积,并且在内存回收过程中不再移动内存块。代码如下:
arena_purge(arena.c) static void arena_purge(arena_t *arena, bool all) { chunk_hooks_t chunk_hooks = chunk_hooks_get(arena); size_t npurge, npurgeable, npurged; arena_runs_dirty_link_t purge_runs_sentinel; extent_node_t purge_chunks_sentinel; arena->purging = true; /* * Calls to arena_dirty_count() are disabled even for debug builds * because overhead grows nonlinearly as memory usage increases. */ if (false config_debug) { size_t ndirty = arena_dirty_count(arena); assert(ndirty == arena->ndirty); } assert((arena->nactive >> arena->lg_dirty_mult) arena->ndirty || all); if (config_stats) arena->stats.npurge++; npurge = arena_compute_npurge(arena, all); qr_new(purge_runs_sentinel, rd_link); extent_node_dirty_linkage_init(purge_chunks_sentinel); npurgeable = arena_stash_dirty(arena, chunk_hooks, all, npurge, purge_runs_sentinel, purge_chunks_sentinel); assert(npurgeable >= npurge); npurged = arena_purge_stashed(arena, chunk_hooks, purge_runs_sentinel, purge_chunks_sentinel); assert(npurged == npurgeable); arena_unstash_purged(arena, chunk_hooks, purge_runs_sentinel, purge_chunks_sentinel); arena->purging = false; }
【解决问题】
实际上我们有多个选项。可以使用Google的tcmalloc来代替jemalloc,可以升级jemalloc的版本等等。我们根据上面的分析,尝试通过升级jemalloc版本,实际操作为升级Redis版本来解决。我们将Redis的版本升级到4.0.9之后观察,线上客户端连接超时这个棘手的问题得到了解决。
【问题总结】
Redis在生产环境中因其支持高并发,响应快,易操作被广泛使用,对于运维人员而言,其响应时间的要求带来了各种各样的问题,Redis的连接超时问题是其中比较典型的一种,从发现问题,客户端连接超时,到通过抓取客户端与服务端的网络包,内存堆栈定位问题,也被其中一些假象所迷惑,最终通过升级jemalloc(Redis)的版本解决问题,这次最值得总结和借鉴的是整个分析的思路。
总结
以上就是这篇文章的全部内容了,希望本文的内容对大家的学习或者工作具有一定的参考学习价值,如果有疑问大家可以留言交流,谢谢大家对脚本之家的支持。