Fast and scalable inequality joins
Inequality joins, which is to join relations with inequality conditions, are used in various applications. Optimizing joins has been the subject of intensive research ranging from efficient join algorithms such as sort-merge join, to the use of efficient indices such as B + -tree, R ∗ -tree and Bitm...
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| Published in | The VLDB journal Vol. 26; no. 1; pp. 125 - 150 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1066-8888 0949-877X |
| DOI | 10.1007/s00778-016-0441-6 |
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| Abstract | Inequality joins, which is to join relations with inequality conditions, are used in various applications. Optimizing joins has been the subject of intensive research ranging from efficient join algorithms such as sort-merge join, to the use of efficient indices such as
B
+
-tree,
R
∗
-tree and Bitmap. However, inequality joins have received little attention and queries containing such joins are notably very slow. In this paper, we introduce fast inequality join algorithms based on sorted arrays and space-efficient bit-arrays. We further introduce a simple method to estimate the selectivity of inequality joins which is then used to optimize multiple predicate queries and multi-way joins. Moreover, we study an incremental inequality join algorithm to handle scenarios where data keeps changing. We have implemented a centralized version of these algorithms on top of PostgreSQL, a distributed version on top of Spark SQL, and an existing data cleaning system,
Nadeef
. By comparing our algorithms against well-known optimization techniques for inequality joins, we show our solution is more scalable and several orders of magnitude faster. |
|---|---|
| AbstractList | Inequality joins, which is to join relations with inequality conditions, are used in various applications. Optimizing joins has been the subject of intensive research ranging from efficient join algorithms such as sort-merge join, to the use of efficient indices such as
B
+
-tree,
R
∗
-tree and Bitmap. However, inequality joins have received little attention and queries containing such joins are notably very slow. In this paper, we introduce fast inequality join algorithms based on sorted arrays and space-efficient bit-arrays. We further introduce a simple method to estimate the selectivity of inequality joins which is then used to optimize multiple predicate queries and multi-way joins. Moreover, we study an incremental inequality join algorithm to handle scenarios where data keeps changing. We have implemented a centralized version of these algorithms on top of PostgreSQL, a distributed version on top of Spark SQL, and an existing data cleaning system,
Nadeef
. By comparing our algorithms against well-known optimization techniques for inequality joins, we show our solution is more scalable and several orders of magnitude faster. Inequality joins, which is to join relations with inequality conditions, are used in various applications. Optimizing joins has been the subject of intensive research ranging from efficient join algorithms such as sort-merge join, to the use of efficient indices such as B + -tree, R ∗ -tree and Bitmap. However, inequality joins have received little attention and queries containing such joins are notably very slow. In this paper, we introduce fast inequality join algorithms based on sorted arrays and space-efficient bit-arrays. We further introduce a simple method to estimate the selectivity of inequality joins which is then used to optimize multiple predicate queries and multi-way joins. Moreover, we study an incremental inequality join algorithm to handle scenarios where data keeps changing. We have implemented a centralized version of these algorithms on top of PostgreSQL, a distributed version on top of Spark SQL, and an existing data cleaning system, Nadeef . By comparing our algorithms against well-known optimization techniques for inequality joins, we show our solution is more scalable and several orders of magnitude faster. |
| Author | Tang, Nan Ouzzani, Mourad Khayyat, Zuhair Papotti, Paolo Lucia, William Quiané-Ruiz, Jorge-Arnulfo Kalnis, Panos Singh, Meghna |
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| Cites_doi | 10.1145/1327452.1327492 10.1145/2723372.2747646 10.1007/3-540-48482-5_7 10.4018/987-1-59904-364-7.ch007 10.1145/1739041.1739056 10.1109/ICDE.2007.367920 10.1007/s00778-003-0111-3 10.1145/67544.66937 10.1145/304182.304201 10.1145/276304.276336 10.1145/2463676.2465327 10.1145/2588555.2594511 10.1145/1292609.1292616 10.1145/1529282.1529582 10.1145/1007568.1007645 10.1145/602259.602266 10.1145/503099.503101 10.1145/2723372.2742797 10.1145/1142473.1142511 10.1016/j.pmcj.2013.10.001 10.1145/1989323.1989423 10.1145/582095.582099 10.1007/978-3-642-82375-6_2 |
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In: Pervasive Computing (2012) Govindaraju, N.K., Gray, J., Kumar, R., Manocha, D.: GPUTeraSort: high performance graphics co-processor sorting for large database management. In: SIGMOD, pp. 325–336 (2006) Agrawal, D., Chawla, S., Elmagarmid, A.K., Ouzzani, Z.K.M., Papotti, P., Quiané-Ruiz, J., Tang, N., Zaki, M.J.: Road to freedom in big data analytics. In: EDBT, pp. 479–484 (2016) MamoulisNPapadiasDMultiway spatial joinsTODS200126442447510.1145/503099.5031011136.68388 Dallachiesa, M., Ebaid, A., Eldawy, A., Elmagarmid, A., Ilyas, I.F. Ouzzani, M., Tang, N.: NADEEF: a commodity data cleaning system. In: SIGMOD (2013) DeWitt, D.J., Naughton, J.F., Schneider, D.A.: An evaluation of non-equijoin algorithms. In: VLDB, pp. 443–452 (1991) Schneider, D.A., DeWitt, D.J.: A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment. In: SIGMOD (1989) AbiteboulSHullRVianuVFoundations of Databases1995ReadingAddison-Wesley0848.68031 Chan, C.-Y., Ioannidis, Y. E.: Bitmap index design and evaluation. In: SIGMOD, pp. 355–366 (1998) Bender, M.A., Hu, H.: An adaptive packed-memory array. TODS 32(4) 26:1–26:43 (2007) Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: SIGMOD, pp. 23–34 (1979) DeanJGhemawatSMapReduce: Simplified data processing on large clustersCommun. ACM200851110711310.1145/1327452.1327492 Morris, J., Ramesh, B.: Dynamic Partition Enhanced Inequality Joining Using a Value-count Index, 1 2011. US Patent 7,873,629 B1 EbaidAElmagarmidAKIlyasIFOuzzaniMQuiané-RuizJTangNYinSNADEEF: a generalized data cleaning systemPVLDB201361212181221 Knuth, D. E.: The Art of Computer Programming, Volume III: Sorting and Searching. Addison-Wesley, Reading (1973) Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: HotCloud, pp. 10–10 (2010) GaoDJensenCSSnodgrassRTSooMDJoin operations in temporal databasesVLDB J.200514122910.1007/s00778-003-0111-3 Elmagarmid, A.K., Ilyas, I.F., Ouzzani, M., Quiané-Ruiz, J., Tang, N., Yin, S.: NADEEF/ER: generic and interactive entity resolution. In: SIGMOD, pp. 1071–1074 (2014) Armbrust, M., Xin, R.S., Lian, C., Huai, Y., Liu, D., Bradley, J.K., Meng, X., Kaftan, T., Franklin, M.J., Ghodsi, A., Zaharia, M.: Spark SQL: relational data processing in spark. In: SIGMOD, pp. 1383–1394 (2015) Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., Laurila, J.: Towards rich mobile phone datasets: lausanne data collection campaign. In: ICPS (2010) Kemper, A., Kossmann, D., Wiesner, C.: Generalised hash teams for join and group-by. In: VLDB, pp. 30–41 (1999) Khayyat, Z., Ilyas, I.F., Jindal, A., Madden, S., Ouzzani, M., Papotti, P., Quiané-Ruiz, J.-A., Tang, N., Yin, S.: BigDansing: a system for big data cleansing. In: SIGMOD, pp. 1215–1230 (2015) Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984) Afrati, F.N., Ullman, J.D.: Optimizing joins in a map-reduce environment. In: EDBT, pp. 99–110 (2010) ZhangXChenLWangMEfficient multi-way theta-join processing using MapReducePVLDB201251111841195 Lohman, G., Mohan, C., Haas, L., Daniels, D., Lindsay, B., Selinger, P., Wilms, P.: Query processing in R*. In: Query Processing in Database Systems, pp. 31–47 (1985) Lopes Siqueira, T.L., Ciferri, R.R., Times, V.C., de Aguiar Ciferri, C.D.: A spatial bitmap-based index for geographical data warehouses. In: SAC, pp. 1336–1342 (2009) Okcan, A., Riedewald, M.: Processing theta-joins using MapReduce. In: SIGMOD, pp. 949–960 (2011) Enderle, J., Hampel, M., Seidl, T.: Joining interval data in relational databases. In: SIGMOD, pp. 683–694 (2004) Stockinger, K., Wu, K.: Bitmap indices for data warehouses. Data Wareh OLAP Concepts Archit Solut 5, 157–178 (2007) Chan, C.-Y., Ioannidis, Y.E.: An efficient bitmap encoding scheme for selection queries. In: SIGMOD, pp. 215–226 (1999) DittrichJQuiané-RuizJJindalAKarginYSettyVSchadJHadoop++: making a yellow elephant run like a cheetah (without it even noticing)PVLDB201031515529 441_CR9 441_CR20 441_CR7 441_CR22 441_CR8 441_CR21 441_CR5 441_CR24 441_CR6 441_CR23 441_CR3 A Ebaid (441_CR15) 2013; 6 441_CR4 441_CR2 D Gao (441_CR18) 2005; 14 Z Khayyat (441_CR25) 2015; 8 S Abiteboul (441_CR1) 1995 J Dean (441_CR12) 2008; 51 441_CR37 441_CR36 441_CR17 441_CR16 441_CR19 441_CR30 441_CR11 441_CR33 441_CR10 441_CR32 441_CR13 441_CR35 N Mamoulis (441_CR31) 2001; 26 441_CR34 441_CR26 441_CR28 J Dittrich (441_CR14) 2010; 3 441_CR27 441_CR29 X Zhang (441_CR38) 2012; 5 |
| References_xml | – reference: Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems. Pearson Education (2009) – reference: GaoDJensenCSSnodgrassRTSooMDJoin operations in temporal databasesVLDB J.200514122910.1007/s00778-003-0111-3 – reference: Laurila, J.K., Gatica-Perez, D., Aad, I., Bornet, O., Do, T.-M.-T., Dousse, O., Eberle, J., Miettinen, M.: The mobile data challenge: big data for mobile computing research. In: Pervasive Computing (2012) – reference: Enderle, J., Hampel, M., Seidl, T.: Joining interval data in relational databases. In: SIGMOD, pp. 683–694 (2004) – reference: Okcan, A., Riedewald, M.: Processing theta-joins using MapReduce. In: SIGMOD, pp. 949–960 (2011) – reference: Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: HotCloud, pp. 10–10 (2010) – reference: Hellerstein, J.M., Naughton, J.F., Pfeffer, A.: Generalized search trees for database systems. In: VLDB, pp. 562–573 (1995) – reference: Dallachiesa, M., Ebaid, A., Eldawy, A., Elmagarmid, A., Ilyas, I.F. Ouzzani, M., Tang, N.: NADEEF: a commodity data cleaning system. In: SIGMOD (2013) – reference: EbaidAElmagarmidAKIlyasIFOuzzaniMQuiané-RuizJTangNYinSNADEEF: a generalized data cleaning systemPVLDB201361212181221 – reference: DeanJGhemawatSMapReduce: Simplified data processing on large clustersCommun. ACM200851110711310.1145/1327452.1327492 – reference: Lopes Siqueira, T.L., Ciferri, R.R., Times, V.C., de Aguiar Ciferri, C.D.: A spatial bitmap-based index for geographical data warehouses. In: SAC, pp. 1336–1342 (2009) – reference: Afrati, F.N., Ullman, J.D.: Optimizing joins in a map-reduce environment. In: EDBT, pp. 99–110 (2010) – reference: Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: SIGMOD, pp. 23–34 (1979) – reference: Morris, J., Ramesh, B.: Dynamic Partition Enhanced Inequality Joining Using a Value-count Index, 1 2011. US Patent 7,873,629 B1 – reference: Agrawal, D., Chawla, S., Elmagarmid, A.K., Ouzzani, Z.K.M., Papotti, P., Quiané-Ruiz, J., Tang, N., Zaki, M.J.: Road to freedom in big data analytics. In: EDBT, pp. 479–484 (2016) – reference: Elmagarmid, A.K., Ilyas, I.F., Ouzzani, M., Quiané-Ruiz, J., Tang, N., Yin, S.: NADEEF/ER: generic and interactive entity resolution. In: SIGMOD, pp. 1071–1074 (2014) – reference: Stockinger, K., Wu, K.: Bitmap indices for data warehouses. Data Wareh OLAP Concepts Archit Solut 5, 157–178 (2007) – reference: Bender, M.A., Hu, H.: An adaptive packed-memory array. TODS 32(4) 26:1–26:43 (2007) – reference: Knuth, D. E.: The Art of Computer Programming, Volume III: Sorting and Searching. Addison-Wesley, Reading (1973) – reference: Govindaraju, N.K., Gray, J., Kumar, R., Manocha, D.: GPUTeraSort: high performance graphics co-processor sorting for large database management. In: SIGMOD, pp. 325–336 (2006) – reference: KhayyatZLuciaWSinghMOuzzaniMPapottiPQuiané-RuizJ-ATangNKalnisPLightning fast and space efficient inequality joinsPVLDB201581320742085 – reference: DittrichJQuiané-RuizJJindalAKarginYSettyVSchadJHadoop++: making a yellow elephant run like a cheetah (without it even noticing)PVLDB201031515529 – reference: AbiteboulSHullRVianuVFoundations of Databases1995ReadingAddison-Wesley0848.68031 – reference: Khayyat, Z., Ilyas, I.F., Jindal, A., Madden, S., Ouzzani, M., Papotti, P., Quiané-Ruiz, J.-A., Tang, N., Yin, S.: BigDansing: a system for big data cleansing. In: SIGMOD, pp. 1215–1230 (2015) – reference: Schneider, D.A., DeWitt, D.J.: A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment. In: SIGMOD (1989) – reference: Böhm, C., Klump, G., Kriegel, H.-P.: XZ-Ordering: A space-filling curve for objects with spatial extension. In: SSD, pp. 75–90 (1999) – reference: DeWitt, D.J., Naughton, J.F., Schneider, D.A.: An evaluation of non-equijoin algorithms. In: VLDB, pp. 443–452 (1991) – reference: Chan, C.-Y., Ioannidis, Y. E.: Bitmap index design and evaluation. In: SIGMOD, pp. 355–366 (1998) – reference: Chan, C.-Y., Ioannidis, Y.E.: An efficient bitmap encoding scheme for selection queries. In: SIGMOD, pp. 215–226 (1999) – reference: Bohannon, P., Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for data cleaning. In: ICDE, pp. 746–755 (2007) – reference: Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., Laurila, J.: Towards rich mobile phone datasets: lausanne data collection campaign. In: ICPS (2010) – reference: Lohman, G., Mohan, C., Haas, L., Daniels, D., Lindsay, B., Selinger, P., Wilms, P.: Query processing in R*. 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| Snippet | Inequality joins, which is to join relations with inequality conditions, are used in various applications. Optimizing joins has been the subject of intensive... |
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| SubjectTerms | Algorithms Arrays Computer Science Database Management Optimization Optimization techniques Queries Selectivity Special Issue Paper |
| Title | Fast and scalable inequality joins |
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