# Reducer Load Balancing and Lazy Initialization in Map Reduce Environment S.Mohanapriya, P.Natesan

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3 Consider the example set of keys, { star, cash, fish, anger, aim, fill, fire, quick, man, steal, fight, foot, quiz, stick, steel, angel }. The above strings are stored in the trie as in Figure 1. Fig 1.Strings stored in Trie The formula for calculating the TrieCode is shown in (1) below which is used as an index to the array. Here W n represents the ASCII value of n th letter in the word and n represents the number of letters in the word. n-1 n-2 0 Triecode=W n 256 +W n W TotalWord n n-1 (1) n=1 = W 256 Obtaining the counter value for each prefix, the cutpoints are obtained using (2), Total number of stringsin each prefix cutpoints= Number of Reducers (2) Based on this the partitions are made among reducers so that the load are equally balanced among the reducers. By using two level trie the above set of keys shown in Figure 1 is reduced to { ai, an, ca, fi, fo, ma, qu, st } Consider there are 4 reducers and the above set is divided into 4 partitions. Each partition is send to one reducer. Thus, Reducer-1 process keys starting with { ai, an }.Total:3 keys Reducer-2 process keys starting with { ca, fi }.Total:5 keys Reducer-3 process keys starting with { fo, ma }.Total:2 keys Reducer-4 process keys starting with { qu, st }.Total:6 keys This results in workload imbalance among the reducers. 574

4 To overcome the above problem, in Xtrie it uses counter for each key value. Using counter, partitioner can distribute the total number of keys among the reducers evenly. Cut-points based on counter value is, Cut-points=16/4=4 Using these cut-points, split strings are obtained. The obtained spilt-strings are, { fi, fo, st } So strings with prefix less than fi goes to partition 1, strings with prefix equal to fi and less than fo goes to partition 2, strings with prefix equal to fo and less than st so goes to partition 3 and finally the string with prefix st goes to partition 4. The working of Xtrie is shown in Table 1. Table 1. Xtrie Partitioner using Two-level Trie Prefix Keys Trie Count Partition Code ai Aim 0X an Angel 0X616e 2 1 Anger ca Cash 0X Fight fi Fill 0X Fire Fish fo Foot 0X666f 1 ma Man 0X6d qu Quick 0X Quiz Star st Steal 0X Steel Stick C. Reducing Memory Requirements of Trie The trie is represented by using array. Each node in the trie will contain a maximum of 256 children. It is not possible to have all the 256 children for a single node. This problem will make the trie to occupy lots of memory space. To reduce the memory requirements of trie, an algorithm called ReMap algorithm is used which reduces the 256characters on ASCII to 64 elements as required by Etrie method. Using less memory allows deeper tries to be built. Deeper tries also provides the chance of distributing keys evenly among reducers. Consider the following set of keys, { grab, gram, gray, grew, grid, grip, grow, grub, gulf }. Attempting to partition keys evenly using a two-level trie is impossible in this case. Using a two-level trie would create set of prefixes { gr, gu }. Although if identified that the prefix gr represents 8 keys and prefix gu represents 1 key, the prefix themselves are indivisible, which creates a load imbalance. This problem can be overcome using deeper tries. By using deeper tries, the length of the prefix can be increased. This increases the number of prefix and reduces the number of keys per prefix. Using this even distribution of keys among the reducers can be created. The above set of keys using three-level is represented using prefix { gra, gre, gri, gro, gru, gul } thus dividing the 8 keys represented by prefix gr into five smaller categories. 575

6 V. EXPERIMENTAL PROCEDURE The experiment is conducted by using Hadoop Hadoop is installed in fully-distributed mode among the multiple VMs created in Virtual Box. The system requirements are 4GB RAM, 100GB hard disk, java version 1.6 or any other versions above version 6, Ubuntu Linux OS. A. Hadoop Hadoop is the open source implementation of mapreduce. Many versions of hadoop are available. The Hadoop version is one of the latest releases when the work is carried out and is considered as the stable one among various Hadoop versions available. B. Results Using the above experimental setup, the work is carried out with a small data set. As a result the loads will be equally balanced among the reducers using custom partitioner and thus improve the completion time of MapReduce. Table 5 shows the Execution Time of MapReduce job at three different reducers without using the Xtrie partitioner and Table 6 shows how the Execution time get decreased when using Xtrie partitioner. Table 5. Execution Time at Different Reducers without Xtrie Partitioner Number Number Execution Time(sec) of Of Reducer1 Reducer2 Reducer3 Records Partitions 7,00, ,00, Table 6. Execution Time at Different Reducers using Xtrie Partitioner Number Number Execution Time(sec) of Of Reducer1 Reducer2 Reducer3 Records Partitions 7,00, ,00, s Figure 2 shows Execution time (in seconds) of MapReduce job with and without using Xtrie partitioner. The graph shows that the Execution time is reduced when using Xtrie partitioner thus improving job performance. Fig 2. Execution Time with and without Xtrie Partitioner 577

7 VI. CONCLUSION Hadoop is the distributed processing environment which is used for processing huge volume of data. It is the partitioning of the data which determines the workload of the reducers. In order to overcome the problem of skew, the data should be partitioned efficiently among the reducers. If the data are partitioned equally among the nodes then the execution time for overall MapReduce job is decreased. The proposed techniques can be used to efficiently eliminate the problem of data skew in reducers and thereby decreasing the processing time of MapReduce job. Moreover, by lazy initialization of reducers less memory will be utilized by reducers which will cause the more number of map tasks to be executed in a node. As a result load balancing and lazy utilization of reducers improve the performance of MapReduce job in Hadoop. REFERENCES [1] D.Borthakur, The Hadoop Distributed File System: Architecture and Design, the Apache Software Foundation, (2007). [2] J. Dean and S. Ghemawat, MapReduce: simplified data processing on large clusters, Commun. ACM, pages: , (2008). [3] S. Ghemawat, H. Gobioff, S-T Leung, The Google file system, Proceedings of the 19th ACM symposium on operating systems principles (SOSP), (2003). [4] B. Gufler, N. Augsten, A. Reiser and A. Kemper, Handling Data skew in Mapreduce, Proceedings of the international conference on Cloud Computing and Services Science(CLOSER), pp , (2011). [5] S. Heinz, J. Zobel, H.William, Burst tries: a fast, efficient data structure for string keys, ACM Trans Inf Syst 20(12): , (2002). [6] O. O Malley, Tera Byte sort on Apache Hadoop, (2008). [7] A. Matsunaga, M. Tsugawa, J. Fortes, Programming abstractions for data intensive computing on clouds and grids, In: IEEE fourth international conference on escience, pp , (2008). [8] J. Shafer, S. Rixner, AL. Cox, The hadoop distributed file system: balancing portability and performance, Proceedings of the IEEE international symposium on performance analysis of system and software (ISPASS), p 123, (2010). [9] Hadoop, [10] 578

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