HadoopXML                                                                              A Suite for Parallel Processing of ...
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A poster version of HadoopXML


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This poster is made for our demonstration at CIKM'12

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  • can u suggest me any poster like this on the working of hadoop with cloud computing technology in it??

    atleast working of hadoop flavours like map reduce pig hive hbase etc etc...
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Transcript of "A poster version of HadoopXML"

  1. 1. HadoopXML A Suite for Parallel Processing of Massive XML Data with Multiple Twig Pattern Queries 1Computer Hyebong Choi1, Kyong-Ha Lee1, Soo-Hyong Kim1, Yoon-Joon Lee1 and Bongki Moon2 Science Department, KAIST, Korea 2Computer Science Department, University of Arizona, USA hbchoi@dbserver.kaist.ac.kr bart7449@gmail.com kimsh@dbserver.kaist.ac.kr yoonjoon.lee@kaist.ac.kr bkmoon@cs.arizona.edu Motivation System Architecture Performance Twig pattern  Experimental environment Big data in XML join Mappers Tagging Reducers Hadoop CentOS 6.2 1Gb switching hub A large 0.21.0 [1] ▶ More than 100GB of protein sequences and their  XML  Reducer ID Holistic Final XML file AMD Athlon II x4 620  8GB memory Pre‐ blocks Path Final twig join answers 1 master 1st M/R job 2nd M/R job Tagging 4‐cores 7200 RPM HDD XPath processing Query Solutions Answers Path functional information are provided in XML format  queries index solutions Reducer ID Holistic Final 8 slaves i5‐2500k processor 4‐cores 8GB memory 7200 RPM HDD Tagging twig join answers and also updated in every four weeks [2] Reducer ID Shuffle by ReducerId XML dataset statistics Loading time ▶ Conventional XML tools like single‐site XML DBMSes Size information for path solutions Distributed cache Filename UniRef100 UniParc UniProtKB XMark1000 File size (MB) 24,500 37,436 105,745 114,414 and XML pub/sub systems failed to process that size of  Relationship Multi query # of elements 335M  360M  2,110M 1,670M btw. path patterns & XML data Path query  twig patterns optimizer # of attributes Depth in avg. 589M 4.5649 1,215M 2,783M 3.7753 4.3326 383M 4.7375 Query index  Query  XML DB eXist [9] BaseX [10] builder index processing Max depth 6 5 7 12 Mappers Reducers XML Label # distinct paths 30 24 149 548 Query processing Query processing HDFS Path Path  Data size Loading time Loading time Path  Counting Path  w/ 4000 twig queries w/ 4000 twig queries XPath Query  block1 filtering solutions patterns solutions solutions Overall execution time queries Decomposition XML Label XML Label 1GB 5m 54s failed 2m 1s 2h 48m 7s Relationship Path Path block1 block1 block2 btw. paths  filtering solutions Counting Path  … 10GB 1h 5m 21s failed 19m 36s 30h 11m 34s Copy to  XML Label Synthetic dataset Real‐world dataset and twigs Solutions solutions HDFS block2 block2 XML Label 100GB failed ‐ failed ‐ Path Path … … A large Partitioning Label blocks blockn filtering solutions <Path ID, a list of labels> Yfilter [5] XML file & Labeling XML Label <Path ID, label> XML blocks blockn blockn Query index Data size Filtering time Postprocessing time (twig  pattern join) Size information  Block collocation Distributed cache for path solutions 1MB 2m 4s  0.264s 10MB 20s 14s 16s 100MB 3h 22m 6s 1h 1m 37s 1GB failed ‐ Working Example Effect of converting paths  to distinct paths Effect of block collocation Label_1 <region> block_1 / <region> Example.xml <Africa> 1, 24, 1 HadoopXML <Africa> <item id=“item0”> <quantity>1</quantity> <payment>Creditcard</payment> <item id=“item0”> <quantity>1</quantity> <payment>Creditcard</payment> </item> 2, 15, 2 3, 8, 3 4, 5, 4 6, 7, 4 Path offset Path query  Path solution ID 1.1 3, 8, 3 </item>▶ It efficiently processes many twig pattern queries for  <item id=“item1”> block_2 Label_2 9, 14, 3 <item id=“item1”> <quantity>1</quantity> <quantity>1</quantity> /region/Africa 1.2 4, 5, 4 Twig query ID Path solution <payment>Money order</payment> Preprocessing <payment>Money order</payment> 9, 14, 3 10, 11, 4  2nd M/R 1 6, 7, 4 </item> Partitioning  </item> 10, 11, 4 1st M/R Twig pattern  Effect of multi query optimization a massive volume of XML data in parallel </Africa> <Asia> & labeling </Africa> <Asia> 12, 13, 4 16, 23, 2 Path filtering 1.3 6, 7, 4 12, 13, 4 join 2 12, 13, 4 17, 22, 3 <item id="item135"> <item id="item135"> 2 17, 22, 3 <quantity>2</quantity> block_3 Label_3 ‐ Block partitioning with no loss of structural information <payment>Personal Check</payment> <quantity>2</quantity> /region/Asia <payment>Personal Check</payment> 17, 22, 3 </item> </item> Path query  Count 18, 19, 4 </Asia> </Asia> ID ‐ Path filtering with NFA‐style query indexes [5] </region> </region> 20, 21, 4 1.1 2 Multi query Path query ID  Path query 1.2 2 optimizer ‐ I/O optimal Holistic twig pattern joins [3]  Twig query ID 1 Twig query /region/Africa/item[quantity]/payment Query decomposition 1.1 /region/Africa/item 1.3 2 1.2 /region/Africa/item/quantity A  query index 2 1 2 //Asia/item & Converting to ▶ Simultaneous processing of multiple twig pattern  1.3 /region/Africa/item/payment …  .  . . .  . root‐to‐leaf paths 2 /region/Asia/item queries Load Balancing &  ‐ Many twig pattern joins are distributed across nodes and  Path Filtering Multi Query Optimization References <item id=“item1”> <quantity>1</quantity> block_2 /region/Africa Label_2 ▶ Twig pattern join, a specialized multi‐way join that reads multiple  [1] Hadoop. http://hadoop.apache.org, Apache Software Foundation. executed in parallel <payment>Money order</payment> 9, 14, 3 path solutions  [2] A. Bairoch et al. The universal protein resource (uniprot). Nucleic acids  </item> 10, 11, 4 12, 13, 4 ‐ With static one‐to‐one shuffling scheme, i.e. given partitioned path solutions, reducers  research, 33(suppl 1):D154–D159, 2005.▶ Optimization of the I/O cost in MapReduce jobs </Africa> <Asia> 16, 23, 2 generate incomplete join results [3] N. Bruno et al. Holistic twig joins: optimal xml pattern matching. In  Reducer1 Missing results! Q1: A1 join B1 join C1 A1 join B2 join C2 Proceedings of ACM SIGMOD, pages 310–321. ACM, 2002. ‐ Sharing input scans and intermediate path solutions startElement(“region”) A1 Q2: A1 join C1 join D1 A2 join B1 join C2 startElement(“Africa”) [4] J. Dean et al. Mapreduce: Simplified data processing on large clusters.  & SAX events from block_2 B1 Q3: A1 join B1 join D1 ‐ Converting redundant path patterns  with {//, *} to a few  A2 … Communications of the ACM, 51(1):107–113, 2008. C1 NFA style Path solutions  A B2 D1 Reducer2 Input queries [5] Y. Diao et al. Path sharing and predicate evaluation for high‐performance xml  distinct root‐to‐leaf paths Query index region 1st Mapper B C2 Q1: A2 join B2 join C2 Q1: A join B join C filtering. ACM Transactions on Database Systems, 28(4):467–516, 2003.  &1 D2 Q2: A2 join C2 join D2 Q2: A join C join D Africa C Q3: A2 join B2 join D2 Q3: A join B join D [6] K. Lee et al. Parallel data processing with MapReduce: a survey. ACM  ‐ Collocation of XML blocks and corresponding label blocks Asia D SIGMOD Record, 40(4):11–20, 2011. &2 &3 item ▶ Runtime one‐to‐many data shuffling [7] Q. Li et al. Indexing and querying xml data for regular path expressions. In ▶ Runtime load balancing & multi query optimization 1.1 item ‐ It distributes both queries and data at runtime Proceedings of VLDB, pages 361–370, 2001. &4 &5 quantity payment ‐ Path solutions can be redundantly copied to reducers, involving redundant I/Os 2 [8] T. Nykiel et al. MRshare: Sharing across multiple queries in MapReduce.  ‐ XML twig queries may share path patterns each other Runtime stack ‐ a straggling reduce task dominates the overall performance of M/R jobs Proceedings of the VLDB Endowment, 3(1‐2):494–505, 2010 ‐ Optimization problem : find  the optimal way that distributes queries and path solutions across  &6 &7 reducers  so that every reducer is assigned even workload ‐ For I/O reduction and workload balance, twig pattern  [9] W. Meier. eXist: An open source native XML database. Web, Web‐Services,  1.2 1.3 Reducer1 30 Q1: A join B join C and Database Systems 2002, LNCS 2593, Springer, Berlin (2002), pp. 169–183 queries that share path patterns are grouped together Path solution  Path solutions A 80 cost = |A|+|B|+|C| = 200 Input queries [10] C. Grün et al. BaseX ‐ Processing and Visualizing XML with a native XML  For block_2 Q1: A join B join C Database, http://www.basex.org/, 2010. 90 Reducer2 Path query ID Path solution Q2: A join C join D ‐ The twig query groups are assigned to reducers at  1.1 9, 14, 3 B Q2: A join C join D Q3: A join B join D Q3: A join B Join D 1.2 10, 11, 4  C runtime such that every reducer has the same overall  5 cost = |A|+|C|+|D| + 1.3 12, 13, 4 D |A|+|B|+|D| = 240  cost of join operations This work was partly supported by NRF grant funded by the Korea government (MEST) (no. 2011‐0016282)