Research of Metadata Management Method of Hierarchical Storage System Based on HDFS

doi:10.3778/j.issn.1002-8331.2211-0230

Abstract

Abstract: With the continuous expansion of the scale of high-energy physics（HEP） experiments and the increase of experimental complexity, researchers are facing the challenge of big data storage. Considering the cost, energy consumption, storage cycle and maintenance management, the tape libraries with?large storage capacity and low cost have become an indispensable choice for mass storage systems in the field of HEP. However, HDFS heterogeneous storage doesn’t support tape library storage, it cannot meet the high cost performance requirements of storage system for the persistence and backup process of massive experimental data in Hadoop platform of HEP. In view of the above problems, in order to build an HDFS hierarchical storage system that supports disk-tape storage, the tape layer files can be seamlessly integrated in HDFS, and provide users with a unified file system namespace. In this research, it first overviews the existing methods of distributed file system metadata management, and further designs an improved one to realize the unified metadata management for HDFS hierarchical storage system. This method redesigns the file metadata structure in memory to build a unified memory directory tree, and implements access management and reliability assurance to achieve centralized and unified management of tape file metadata.?Test results show that the metadata server implements unified management of file metadata on heterogeneous resources and provides efficient metadata operation. The storage tiering system based on this method has high reliability. When reading and writing files of different sizes, the read and write throughput is better than that of the traditional storage tiering system EOSCTA in the HEP field.

Key words: Hadoop distributed file system（HDFS）, hierarchical storage system, metadata management in memory, unified namespace, persistence

摘要： 随着高能物理实验规模的不断扩大和实验复杂度的提高，研究人员正面临海量数据存储的挑战，考虑到成本、能耗、存储周期及运维管理等问题，具有存储容量大、成本低特点的磁带库成为高能物理领域海量存储系统中必不可少的选择。但HDFS现有异构存储研究不支持磁带库存储，无法满足高能物理Hadoop平台海量实验数据持久化和备份过程对于存储系统高性价比的需求。针对上述问题，为了构建支持磁盘-磁带存储的HDFS分级存储系统，使磁带层文件在HDFS中无缝融合，为用户提供统一的文件系统命名空间，调研了分布式文件系统元数据管理方法，在此基础上设计实现了HDFS分级存储系统中统一的元数据管理方法。该方法通过重新设计内存文件元数据结构，构建分级存储系统统一的内存目录树并实现其访问管理和可靠性保障，完成分级存储系统中不同层级文件元数据的集中统一管理。测试结果表明，该方法实现了分级存储系统异构资源上文件元数据的统一管理，提供了高效的元数据操作。基于该方法构建的分级存储系统可靠性高，在对不同规模大小的文件读写时，其读写吞吐量较优于高能物理领域传统分级存储系统EOSCTA。

关键词: HDFS分布式文件系统, 分级存储系统, 内存元数据管理, 统一命名空间, 持久化

LIU Xiaoyu, XIA Libin, JIANG Xiaowei, SUN Gongxing. Research of Metadata Management Method of Hierarchical Storage System Based on HDFS[J]. Computer Engineering and Applications, 2023, 59(17): 257-265.

刘晓宇, 夏立斌, 姜晓巍, 孙功星. HDFS分级存储系统元数据管理方法的研究[J]. 计算机工程与应用, 2023, 59(17): 257-265.

References

[1] 程耀东，石京燕，陈刚.高能物理计算环境概述[J].科研信息化技术与应用，2014，5（3）：3-10.
CHENG Y D，SHI J Y，CHEN G.A survey of high energy physics computing system[J].E-science Technology & App-lication，2014，5（3）：3-10.
[2] Apache Software Foundation.Apache Hadoop[EB/OL].[2021-12-27].https：//hadoop.apache.org/.
[3] ZAHARIA M，CHOWDHURY M，FRANKLIN，et al.Spark：cluster computing with working sets[C]//Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing（HotCloud’10），2010.
[4] 臧冬松，霍菁，梁栋，等.基于MapReduce的高能物理数据分析系统[J].计算机工程，2014，40（2）：1-5.
ZANG D S，HUO J，LIANG D，et al.High energy physics data analysis system based on MapReduce[J].Computer Engineering，2014，40（2）：1-5.
[5] 雷晓凤，李强，孙功星.基于HBase的高能物理数据存储及分析平台[J].计算机工程，2015，41（6）：49-55.
LEI X F，LI Q，SUN G X.HBase-based storage and analysis platform for high energy physics data[J].Computer Engineering，2015，41（6）：49-55.
[6] 李强，孙震宇，雷晓凤，等.基于磁盘I/O性能的Hadoop任务选择策略[J].计算机工程，2016，42（11）：76-82.
LI Q，SUN Z Y，LEI X F，et al.Hadoop task selection strategy based on disk I/O Performance[J].Computer Engineering，2016，42（11）：76-82.
[7] 尹乔，魏占辰，黄秋兰，等.Hadoop海量数据迁移系统开发及应用[J].计算机工程与应用，2019，55（13）：66-71.
YIN Q，WEI Z C，HUANG Q L，et al.Development and application of Hadoop massive data migration system[J].Computer Engineering and Applications，2019，55（13）：66-71.
[8] 魏占辰，刘晓宇，黄秋兰，等.Spark迭代密集型应用的优化方法研究[J].计算机工程与应用，2020，56（23）：68-73.
WEI Z C，LIU X Y，HUANG Q L，et al.Research on optimization for iteration-intensive applications on Spark[J].Computer Engineering and Applications，2020，56（23）：68-73.
[9] 李强.面向高能物理数据分析的Hadoop关键技术研究[D].北京：中国科学院大学，2017.
LI Q.Study of Hadoop key technologies for high energy physics data analysis[D].Beijing：University of Chinese Aca-demy of Sciences，2017.
[10] HUANG Q L，WEI Z C，SUN G X，et al.Using hadoop for high energy physics data analysis[C]//International Conference on Big Scientific Data Management（BigSDM 2018），Beijing，China.Cham：Springer，2018.
[11] 陈刚.高能物理实验中的数据与计算技术[J].中国科学：物理学、力学、天文学，2021，51（9）：14-23.
CHEN G.Data and computing for high energy physics experiments[J].Scientia Sinica-Physica，Mechanica & Astronomica，2021，51（9）：14-23.
[12] 李卫东，石京燕，汪璐，等.高能物理实验的离线计算[J].现代物理知识，2016，28（3）：38-45.
LI W D，SHI J Y，WANG L，et al.Off-line calculation for high energy physics experiments[J].Modern Physics，2016，28（3）：38-45.
[13] 刘爱贵.分布式文件系统元数据服务模型[EB/OL].[2021-12-20].https：//blog.csdn.net/liuaigui/article/details/6749188.
LIU A G.Distributed file system metadata service model[EB/OL].[2021-12-20].https：//blog.csdn.net/liuaigui/article/details/6749188.
[14] SHVACHKO K，KUANG H，RADIA S，et al.The hadoop distributed file system[C]//26th Symposium on Mass Storage Systems and Technologies（MSST），Incline Village，NV，USA，2010.
[15] GHEMAVAT S，GOBIOFF H，LEUNG S T.The Google file system[C]//19th ACM Symposium on Operating System Principles（SOSP），New York，NV，USA，2003.
[16] Apache Software Foundation.HDFS federation[EB/OL].[2021-12-26].https：//hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/Federation.html。
[17] Apache Software Foundation.HDFS high availability[EB/OL].[2021-12-26].https：//hadoop.apache.org/docs/r3.2.0/hadoop-project-dist/hadoop-hdfs/HDFSHighAvailabilityWithNFS.html.
[18] 汪璐.海量存储系统元数据服务设计及优化[D].北京：中国科学院大学，2011.
WANG L.The design and optimization of the metadata service in mass storage system[D].Beijing：University of Chinese Academy of Sciences，2011.
[19] DAI H，WANG Y，KENT K B，et al.The state of the art of metadata managements in large-scale distributed file systems scalability，performance and availability[J].IEEE Transactions on Parallel and Distributed Systems，2022，33（12）：3850-3869.
[20] SINGH H J，BAWA S.Scalable metadata management techniques for ultra-large distributed storage systems—a systematic review[J].ACM Computing Surveys，2019，51（4）：1-37.
[21] LI J W，HUANG S Y，REN Y J，et al.Enabling secure and space-efficient metadata management in encrypted deduplication[J].IEEE Transactions on Computers，2022，71（4）：959-970.
[22] Apache Software Foundation.Enable support for heterogeneous storages in HDFS[EB/OL].[2021-12-26].https：//issues.apache.org/jira/browse/HDFS-2832.
[23] Apache Software Foundation.Archival storage，SSD & memory[EB/OL].[2021-12-26].https：//hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/ArchivalStorage.html.
[24] KRISH K R，ANWAR A，BUTT A R.hatS：a heterogeneity-aware tiered storage for Hadoop[C]//14th IEEE/ACM International Symposium on Cluster，Cloud and Grid Computing，Chicago，IL，USA，2014.
[25] KAKOULLI E，HERODOTOU H.OctopusFS：a distributed file system with tiered storage management[C]//SIGMOD’17：2017 ACM International Conference on Management of Data，Illinois，Chicago，USA，2017.
[26] ISLAM N S，LU X Y，WASI-UR-RAHMAN M，et al.Triple-H：a hybrid approach to accelerate HDFS on HPC clusters with heterogeneous storage architecture[C]//15th IEEE/ACM International Symposium on Cluster，Cloud and Grid Computing（CCGRID），Shenzhen，China，2015.
[27] CERN.EOSCTA Docs[EB/OL].[2021-12-26].https：//eoscta.docs.cern.ch/.
[28] CERN.QuarkDB documentation[EB/OL].[2021-12-26].https：//quarkdb.web.cern.ch/quarkdb/docs/0.4.3/.
[29] Google Developers.Protocol buffers[EB/OL].[2021-12-27].https：//developers.google.cn/protocol-buffers?hl=zh-cn.
[30] tech2max.com.Mhvtl[EB/OL].[2022-12-02].http：//www.mhvtl.com/.
[31] PRESTI G L，BARRING O，EARL A，et al.CASTOR：a distributed storage resource facility for high performance data processing at CERN[C]//24th IEEE Conference on Mass Storage Systems and Technologies（MSST 2007），San Diego，CA，USA，2007.
[32] CANO E，BAHYL V，CAFFY C，et al.CERN tape archive：production status，migration from CASTOR and new features[C]//24th International Conference on Computing in High Energy and Nuclear Physics（CHEP 2019），Adelaide，Australia，2020.
[33] BAUER R.Evaluation of CTA for use at Fermilab[EB/OL].[2022-12-02].https：//lss.fnal.gov/archive/2022/slides/fermilab-slides-22-009-scd.pdf.
[34] Apache hadoop.HDFS erasure coding[EB/OL].[2021-12-27].https：//hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HDFSErasureCoding.html.