【摘要】：數(shù)據(jù)已成為當(dāng)代互聯(lián)網(wǎng)企業(yè)核心競爭力,而高效的作業(yè)調(diào)度系統(tǒng)是離線海量數(shù)據(jù)管理的重要工具,誰能有效管理這些海量數(shù)據(jù),并能有效挖掘其中有價(jià)值信息,誰就站在了戰(zhàn)略至高點(diǎn)。ETL作業(yè)是數(shù)據(jù)倉庫日常工作的核心內(nèi)容,海量具有復(fù)雜關(guān)系的作業(yè)只有在作業(yè)調(diào)度系統(tǒng)的調(diào)度管理下才能高效有序進(jìn)行。在當(dāng)前以數(shù)據(jù)為生產(chǎn)力的信息經(jīng)濟(jì)時(shí)代,電商數(shù)據(jù)倉庫日常工作已不是簡單的數(shù)據(jù)備份和日志拉取,任何能夠關(guān)聯(lián)的數(shù)據(jù)都有可能產(chǎn)生新的火花。由此,作業(yè)調(diào)度系統(tǒng)不僅要保證高效和穩(wěn)定地觸發(fā)作業(yè),又要兼顧各作業(yè)之間的依賴關(guān)系,最后以作業(yè)鏈的形式有序觸發(fā)所有作業(yè)。這些要求是作業(yè)調(diào)度系統(tǒng)建設(shè)將要面臨的新的挑戰(zhàn)。隨著大數(shù)據(jù)時(shí)代的到來,以Hadoop生態(tài)系統(tǒng)為基礎(chǔ)的大數(shù)據(jù)處理工具得到了市場的廣泛認(rèn)可。而Hive數(shù)據(jù)庫的誕生正迎合了大數(shù)據(jù)時(shí)代的需要。本系統(tǒng)將對Hive數(shù)據(jù)處理的支持納入數(shù)據(jù)倉庫重要部分,充分利用了hadoop集群穩(wěn)定高擴(kuò)展性優(yōu)勢,采用分布式集群滿足電子商務(wù)企業(yè)對數(shù)據(jù)倉庫的穩(wěn)定/高效/經(jīng)濟(jì)的需求。由此新的作業(yè)調(diào)度系統(tǒng)不僅支持常規(guī)關(guān)系型數(shù)據(jù)庫處理,還能兼容HIVE數(shù)據(jù)處理功能。目前,國內(nèi)外各大企業(yè)數(shù)據(jù)倉庫作業(yè)調(diào)度系統(tǒng)多以自主建設(shè)為主,也有些優(yōu)秀的開源作業(yè)調(diào)度系統(tǒng)(如OOZIE)和一些優(yōu)秀的作業(yè)調(diào)度系統(tǒng)框架(如quartz),但是在使用場景和功能上與企業(yè)當(dāng)前發(fā)展階段需求不符。本文通過總結(jié)日常工作中的調(diào)度需求,為企業(yè)設(shè)計(jì)開發(fā)了一套符合當(dāng)前發(fā)展階段的定制化的電商數(shù)據(jù)倉庫作業(yè)調(diào)度引擎,數(shù)據(jù)開發(fā)人員能夠方便地在任意作業(yè)機(jī)部署自己的作業(yè),并提供按周期調(diào)起,靈活添加依賴,負(fù)載均衡,日志記錄,監(jiān)控報(bào)警等一系列的統(tǒng)一高效管理。
[Abstract]:Data has become the core competitiveness of contemporary Internet enterprises, and efficient job scheduling system is an important tool for offline mass data management. Who can effectively manage these massive data and effectively mine valuable information. The ETL job is the core of the daily work of data warehouse. A large number of jobs with complex relationships can only be carried out efficiently and orderly under the scheduling management of the job scheduling system. In the current era of information economy with data as productivity, the daily work of e-commerce data warehouse is no longer a simple data backup and log pull, any data can be associated with the possibility of a new spark. Therefore, the job scheduling system should not only guarantee the efficient and stable triggering of jobs, but also take into account the dependencies among the jobs. Finally, all jobs will be triggered in an orderly manner in the form of job chains. These requirements are the new challenges to the construction of job scheduling system. With the arrival of big data era, big data processing tools based on Hadoop ecosystem have been widely accepted by the market. The birth of Hive database is to meet the needs of big data era. In this system, the support of Hive data processing is brought into the important part of data warehouse, and the stable and high expansibility advantage of hadoop cluster is fully utilized, and the distributed cluster is adopted to meet the stable / efficient / economical demand of electronic commerce enterprises for data warehouse. The new job scheduling system not only supports conventional relational database processing, but also can be compatible with HIVE data processing function. At present, most of the job scheduling systems in domestic and foreign enterprises are mainly self-built, and there are some excellent open source job scheduling systems (such as OOZIE) and some excellent job scheduling system frameworks (such as quartz),). However, in the use of scenarios and functions with the current stage of enterprise development requirements. Through summing up the scheduling requirements of daily work, this paper designs and develops a set of customized scheduling engine of e-commerce data warehouse in accordance with the current development stage for the enterprise. Data developers can easily deploy their jobs on any job machine and provide a series of unified and efficient management such as cycle adjustment, flexible addition of dependencies, load balancing, logging, monitoring and alarm, and so on.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

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