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Second, make use of your own ex-girlfriend latina brides or perhaps ex-wife’s label to start your bill. Thus giving your current account an aura that could be extremely tempting to on the net daters. Additionally, it facilitates as you will find it very difficult to become rejected in case you are not able to contact a friends or family paid members in advance.

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Do you wish to know how to connect with better half on-line? Certainly you have to do!

So that you can appeal to girls to the dating web site in which you are at present in search of I have to get future spouse internet, you should use the power of your mind. Take the time to loosen up together with meditate from time to time. Hold a positive mindset , nor find carried away from the thrills and even aspiration of actually finding normally the one.

Bear in mind the old saying: I have to locate long term spouse internet but it surely is much easier to meet these people online. Make sure that you keep profile neat plus clean without having in to unnecessary talks that just mistake the ladies. This could provide you with the assurance you need and give the possiblity to make an impression these ladies.

Be certain that the user profile shouldn’t point out anything phony about you. The account should claim what you would like it to say plus steer clear of offensive feedback. Pursue these basic rules together with soon you should 1000s of flourishing internet dating web-sites through your belt!

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如何利用软件定义简化网络运营维护工作

一、为什么是软件定义?

在这个“软件定义一切”的时代(有人戏称为“SDx”),网络自然也是其涵盖的范畴之一,应运而生的SDN便是一个方向,至于如何理解SDN,笔者更愿从广义的角度来看,而不单单是其狭义定义中的三个特点,即:控转分离、集中控制、开放可编程。或者说狭义的SDN是为广义的SDN来服务的,对底层网络的变革,带来的更大的好处是在后期网络运营时,对流量的可视化、流量调度、监控报警等方面带来更多的可操作性、可实施性及便捷性。

二、网络运营会面临哪些问题?

当一张网络在线运行之后,技术人员更多的时间和精力会投入到网络的优化、变更、监控、报警等,总结起来几个方面:
– 可视化
– 操作简化
– 实时监控
– 快速定位
– 可感知

面临大量维护工作的同时,还要总结网络是否满足日益繁杂的业务要求,是否需要调整,是否需要优化,如何优化,如何规避等问题,这是迭代过程中的必然过程,正所谓“打江山容易,守江山难”。

三、传统网络技术环境有哪些弊端?

面临网络运营问题,传统网络技术环境多为网络设备“堆砌”而成,“盒子”由设备厂商所有,很难看到一个全量的、透明的网络世界,因此在实现这些网络运营需求的过程中,技术人员只能通过标准协议将有限的MIB信息读取至各种或开源或商业化的网络信息平台,从而实现有限程度的运营需求,究其原因,笔者认为主要集中在以下几点:
– 厂商私有化严重,相对闭源。
– 诸如snmp、netconf等标准化协议的限制,无法更好的满足技术需求。
– 各种网络工具软件灵活性不够、功能性不足、适配性欠缺,需要定制或二次开发,部署难度大。

网络从业人员在面临上述问题时,往往为了网络运营这一刚需,会搭建大量相对无关联的网络信息平台、自定义监控脚本,费时费力的同时也只能满足部分监控指标,且这些平台本身也需要技术人员不定期维护。

四、如何给网络“松绑”?

当前网络技术发展处于开源与闭源的过渡期,之所以这么说,是因为以SDN为代表新型网络技术还没有完全落地,与之对应的硬件产品也在市场化过程中,且需要市场的考虑及技术的沉淀,而以传统网络厂商为代表的闭源产品,也正在融入开放、开源的理念在其产品中,很多网络设备已支持OpenFlow等南向协议、开放gRPC接口,甚至你可以把一台交换机视作一台服务器,可在其本地编写/运行脚本,这些改善有助于在网络技术迭代的过程中,完善、加强网络运营指标,更好服务于业务需求。笔者这里提供几点部署建议,供参考:
– 在保留既有网络投入及网络架构的前提下,引入网络开源协议或技术。
– 将新老技术混合应用在网络环境中,这并不冲突。
– 尝试“过渡式”的技术实现方案,新老技术并行,如:传统网络技术承载业务,开源网络方案介入网络管理。

五、网络平稳过渡很重要

网络平稳的重要性不言而喻,网络作为所有业务生产者/消费者的“传输媒介”,其稳定性将直接影响产品体验,新技术的引入、网络的迭代一定要与旧技术“并行”,在不影响当前业务的前提下,改善网络运营环境。示例如下图:

  1. 网络设备通过OpenFlow、gRPC等将管理流量引入SDN控制端,通过面向北向接口的各种平台多维度分析(所见即所得)。
  2. 对具备研发能力的网络人员可基于开源的SDN CONTROLLER(ODL/ONOS)定制网络模型、流量分类、数据标记等。
  3. 与三方的开源网络平台对接,如API等方式,摆脱传统的只能SNMP上报流量的方式。

六、小结

网络新型技术的出现必将改变传统的网络实现方式,并对“网络世界”的视角变得更加全面,流量见得大而全,数据才能分析的透,这对网络更全面的优化、了解流量更全面的特点、深耕业务的发展方向提供更有意义的指导和建议。

TIDB在伴鱼的实践

一、背景介绍

伴鱼少儿英语是目前飞速成长的互联网在线英语教育品牌之一,旗下包括伴鱼绘本、伴鱼少儿英语、伴鱼自然拼读和伴鱼精读课等系列产品。伴鱼使用的数据库种类有TiDB,MongoDB,MySQL,Codis。在伴鱼,目前生产环境共有10套TiDB集群,服务于在线教学、绘本、消息、交易和少儿等众多核心业务。

目前线上Tidb部署版本统一为2.1.15。TiDB server和PD混部,机器配置:64C64G500G SAS;Tikv单独部署,机器配置:64C64G1.5T NVME

下面从伴鱼为何选择TiDB,伴鱼TiDB架构、伴鱼在使用TiDB过程中遇到的问题、我们是如何解决这些问题的以及TiDB在伴鱼的后续使用计划等几个方面进行阐述。

二、伴鱼为何选择TiDB

伴鱼发展之初,选择了MongoDB作为数据存储,但随着业务的快速发展,逐渐暴露出一些问题,比如业务对事务的需求、单表数据极速增长导致的性能问题以及集群实例容量问题等。因此,我们对新存储的诉求有以下几点:

集群高可用
MongoDB副本集架构支持高可用,不需要通过第三方程序来支持高可用,可以减少依赖和运维成本。因此对于新存储,我们也需要它天然支持高可用。

支持事务
目前我们生产使用的MongoDB 3.2版本对事务的支持较弱,而业务的很多场景需要事务的支持。

大容量、高吞吐
MongoDB副本集主节点永远只有一个,当业务快速发展时,可能出现写瓶颈,如果这时想扩展写就非常痛苦了。同时,机器的磁盘空间有限,在数据增长到一定量级时,也会出现集群容量瓶颈的问题。

不分表
不管MySQL还是MongoDB数据库,表数据达到一定的量级,读写性能都会下降。通过分表可以提高读写性能,但是业务代码需要维护比较复杂的分表逻辑和路由。

基于以上这些需求,我们通过调研,选择了当前流行的TiDB分布式数据库,主要原因包括:TiDB天然支持以上几点需求;同时TiDB已经在很多公司使用起来,经历了众多业务场景的历练,而且我们的业务使用场景也比较类似。

三、伴鱼TiDB架构

TiDB 集群主要包括三个核心组件:TiDB Server,PD Server 和 TiKV Server。

TiDB Server
TiDB Server负责接收SQL请求,处理SQL相关的逻辑,并通过PD找到存储计算所需数据的TiKV地址,与TiKV交互获取数据,最终返回结果。TiDB Server是无状态的,其本身并不存储数据,只负责计算,可以无限水平扩展。

PD Server
Placement Driver是整个集群的管理模块,其主要工作有三个:一是存储集群的元信息(某个Key存储在哪个TiKV节点);二是对TiKV集群进行调度和负载均衡(如数据的迁移、Raft group leader的迁移等);三是分配全局唯一且递增的事务ID。
PD通过Raft协议保证数据的安全性。Raft的leader server负责处理所有操作,其余的PD server仅用于保证高可用。

TiKV Server
TiKV Server负责存储数据,从外部看TiKV是一个分布式的提供事务的Key-Value存储引擎。存储数据的基本单位是Region,每个Region负责存储一个Key Range(从 StartKey到EndKey的左闭右开区间)的数据,每个TiKV节点会负责多个Region。TiKV使用Raft协议做复制,保持数据的一致性和容灾。副本以Region为单位进行管理,不同节点上的多个Region构成一个Raft Group,互为副本。数据在多个TiKV之间的负载均衡由PD调度,这里也是以Region为单位进行调度。

伴鱼TiDB架构
目前伴鱼Tidb的业务接入方式主要通过SLB接入,当TiDB-Server节点宕机,10s可以被负载均衡识别,自动剔除故障节点。PD和Tikv数据默认三副本,组件自身的高可用由raft算法协议保证。

四、伴鱼在使用TiDB过程中遇到的问题
伴鱼在使用TiDB之初,我们统一了集群使用的TiDB版本,规范了版本的配置,这样可以避免维护多个版本带来的潜在问题,同时有利于增强我们对特定版本的把控程度。我们生产环境的TiDB版本为2.1.15,随着业务的快速发展,主要遇到以下几个问题:

优化器选择索引不准确
在生产环境中,我们碰到过以下几种现象
1)单表数据30W+,查询请求并发约10+,某次业务上线,新增一个索引后,导致原有的查询索引选择错误,tikv实例所在机器cpu迅速被打满,引发故障。
2)线上某张大表,请求量比较大,偶尔出现个别条件走不到索引,导致全表扫描,从而引发接口响应时间的抖动,影响业务。
3)线上某张14亿的大表,查询条件区分度很高,某天出现特定条件突然走不到索引,导致全表扫描,引发故障。后面经过pingcap同学排查,系bug导致。

数据库性能问题定位慢
生产环境发生过一次今人印象深刻的故障,持续时间约一天。其实现在回过头来看,问题可以简单描述为:一个sql由于走错索引,数据扫描比较大,同时加上高峰期并发比较大,导致tikv cpu被打满,导致整个集群响应时间变高,引发故障。但是这次问题的定位,我们付出了很大的代价。

大数据同步问题
许多公司都有数据分析的需求,我们把上游各Tidb集群的数据通过Pump/Drainer汇聚到一个TiDB集群供大数据分析使用。在使用过程中,遇到数据不一致以及数据同步慢等问题。

五、我们如何解决在使用TiDB过程中遇到的问题

对于优化器选择索引不准确问题
在开发使用TiDB的过程中,对于数据量大的表和请求量比较大的表,我们强烈要求开发使用强制索引,防止在特殊情况下,请求走不到索引。同时,我们SQL审核平台,对新建表的索引数量都有严格的限制,防止研发同学建过多的索引。索引越少,理论上可以降低优化器选错索引的概率。

对于数据库性能问题的快速定位
TiDB兼容了MySQL协议,运维方式上跟MySQL有很多相似之处。我们认为,数据库的性能问题,绝大部分原因都是由慢SQL导致的,当然像数据库bug、业务异常流量等情况,在伴鱼还是比较少见的。所以,我们如何准实时收集分布式TiDB的慢日志、如何快速的做分析统计以及如何及时的告警,对于快速解决线上问题,甚至将性能风险扼杀在摇篮里至关重要。

1)如何快速收集TiDB慢日志
我们采用了业界比较成熟的开源日志采集/分析/存储架构,很好的解决了我们对慢日志的分析统计需求。

日志采集架构中,其中filebeat负责增量收集TiDB产生的慢日志,由于filebeat比较轻量,对线上性能基本无影响;kafka负责接收从filebeat采集过来的慢日志;logstash负责读取kafka中的慢日志并进行解析,转换成我们想要的kv键值对;最后解析后的数据入到es,供kibana查询分析统计。

对于Tidb的慢日志,我们重点关注慢日志中的某些特定字段,比如:
Time:表示日志打印时间。
DB:表示执行语句时使用的database。
Query_time:表示执行这个语句花费的时间。
Total_keys:表示Coprocessor扫过的key的数量。
Process_keys:表示Coprocessor处理的key的数量。相比total_keys,processed_keys不包含 MVCC的旧版本。
SQL:执行的sql语句。

2)如何快速的分析统计
logstash解析的数据入到es,通过kibana,我们可以很方便的对数据进行分析统计,比如:
1)近5分钟内,查询时间超过1s的请求/倒序排列/按表统计
2)近5分钟内,Process_keys大于5000的请求/倒序排列/按表统计
3)按照业务db统计慢日志数量
等等,同时我们还开发了慢日志分析报表平台,从各个维度(库/表/操作类型/慢日志数量/总时间/平均响应时间等)对慢日志进行统计分析,及时发现性能风险。

3)如何及时的告警
在伴鱼,一个db对应一个服务,所以告警都是在特定db下设置规则。目前,我们告警粒度是一分钟,主要基于以下三类规则告警:某个db的慢日志达到一定数量则告警;某个db下的请求时间超过500ms且达到一定数量则告警;某个db下的查询Process_keys大于1000且达到一定数量则告警。当然告警规则的设置不是一蹴而就的,需要根据不同的业务场景,不断的调整,最终达到一个比较合理的阀值。

对于大数据同步问题
我们通过pump/drainer将上游Tidb的数据同步到大数据Tidb集群,供大数据分析统计。在使用过程中,我们遇到以下几个问题,并逐一解决。
1)某天业务反馈,大数据集群某些表的数据少于线上集群。通过排查发现,上游某张表数据乱码,drainer同步下游报错,导致服务重启,重启时同步的位点不正确,导致上下游数据不正确。最后通过修复上游数据,重新同步解决。
2)drainer消费数据很慢,通过加大txn-batch/worker-count数量解决。
3)TiDB大数据集群raft store cpu长期居高临下,我们通过开启开启Region Merge和调整raft-base-tick-interval解决,最终raft store cpu利用率从90%下降到50%。

六、TiDB在伴鱼的后续展望
目前,我们在保证生产环境TiDB-2.1.15现有版本的稳定的同时,也在调研Tidb-3.0.x新版本的一些特性。从官方介绍,我们重点关注以下特性:
1)3.0与2.1版本相比,提升了大规模集群的稳定性。优化Raft副本之间的心跳机制,按照Region的活跃程度调整心跳频率,减小冷数据对集群的负担;热点调度策略支持更多参数配置,采用更高优先级,并提升热点调度的准确性;优化PD调度流程,提供调度限流机制;新增分布式GC 功能,提升GC的性能,降低大集群GC时间。
2)TiDB 3.0版本采用多种优化手段提升查询计划的稳定性。如新增Fast Analyze功能,提升收集统计信息的速度,降低集群资源的消耗及对业务的影响;新增SQL Plan Management功能,支持在查询计划不准确时手动绑定查询计划。
3)3.0与2.1版本相比,在查询性能上有很大的提升。如TiDB持续优化SQL执行器;TiKV新增多线程Raftstore和Apply功能,提升单节点内并发处理能力和资源利用率,降低延时,大幅提升集群写入能力。
因此,我们计划在2020年上半年,陆续将生产Tidb版本从2.1升级到3.0版本。