Regarding the possible benefits that could be obtained with bucketing when joining two or more tables, and with several bucketing attributes, the results show a clear disadvantage for this type of organization strategy, since in 92% of the cases this bucketing strategy did not show any performance benefits. OK If, for example, a Parquet based dataset is tiny, e.g. 2014-12-22 16:36:14,301 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 54.13 sec Moreover, let’s suppose we have created the temp_user temporary table. Ended Job = job_1419243806076_0002 See EXPLAIN Statement and Using the EXPLAIN Plan for Performance Tuning for details. Jan 2018. apache-sqoop hive hadoop. Logging initialized using configuration in jar:file:/home/user/bigdata/apache-hive-0.14.0-bin/lib/hive-common-0.14.0.jar!/hive-log4j.properties Instead to populate the bucketed tables we need to use INSERT OVERWRITE TABLE … SELECT …FROM clause from another table. vi. Impala is an MPP (Massive Parallel Processing) SQL query engine for processing huge volumes of data that is stored in a Hadoop cluster. Let’s see in Depth Tutorial for Hive Data Types with Example, Moreover, in hive lets execute this script. You can adapt number of steps to tune the performance in Hive … host the scan. CREATE TABLE bucketed_user( Consider updating statistics for a table after any INSERT, LOAD DATA, or CREATE TABLE AS SELECT statement in Impala, or after loading data through Hive and doing a REFRESH table_name in Impala. Where the hash_function depends on the type of the bucketing column. In order to limit the maximum number of reducers: In order to set a constant number of reducers: Starting Job = job_1419243806076_0002, Tracking URL = http://tri03ws-, 386:8088/proxy/application_1419243806076_0002/, Kill Command = /home/user/bigdata/hadoop-2.6.0/bin/hadoop job  -kill job_1419243806076_0002, Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 32, 2014-12-22 16:30:36,164 Stage-1 map = 0%,  reduce = 0%, 2014-12-22 16:31:09,770 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.66 sec, 2014-12-22 16:32:10,368 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.66 sec, 2014-12-22 16:32:28,037 Stage-1 map = 100%,  reduce = 13%, Cumulative CPU 3.19 sec, 2014-12-22 16:32:36,480 Stage-1 map = 100%,  reduce = 14%, Cumulative CPU 7.06 sec, 2014-12-22 16:32:40,317 Stage-1 map = 100%,  reduce = 19%, Cumulative CPU 7.63 sec, 2014-12-22 16:33:40,691 Stage-1 map = 100%,  reduce = 19%, Cumulative CPU 12.28 sec, 2014-12-22 16:33:54,846 Stage-1 map = 100%,  reduce = 31%, Cumulative CPU 17.45 sec, 2014-12-22 16:33:58,642 Stage-1 map = 100%,  reduce = 38%, Cumulative CPU 21.69 sec, 2014-12-22 16:34:52,731 Stage-1 map = 100%,  reduce = 56%, Cumulative CPU 32.01 sec, 2014-12-22 16:35:21,369 Stage-1 map = 100%,  reduce = 63%, Cumulative CPU 35.08 sec, 2014-12-22 16:35:22,493 Stage-1 map = 100%,  reduce = 75%, Cumulative CPU 41.45 sec, 2014-12-22 16:35:53,559 Stage-1 map = 100%,  reduce = 94%, Cumulative CPU 51.14 sec, 2014-12-22 16:36:14,301 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 54.13 sec, MapReduce Total cumulative CPU time: 54 seconds 130 msec, Loading data to table default.bucketed_user partition (country=null), Time taken for load dynamic partitions : 2421, Time taken for adding to write entity : 17, Partition default.bucketed_user{country=AU} stats: [numFiles=32, numRows=500, totalSize=78268, rawDataSize=67936], Partition default.bucketed_user{country=CA} stats: [numFiles=32, numRows=500, totalSize=76564, rawDataSize=66278], Partition default.bucketed_user{country=UK} stats: [numFiles=32, numRows=500, totalSize=85604, rawDataSize=75292], Partition default.bucketed_user{country=US} stats: [numFiles=32, numRows=500, totalSize=75468, rawDataSize=65383], Partition default.bucketed_user{country=country} stats: [numFiles=32, numRows=1, totalSize=2865, rawDataSize=68], Stage-Stage-1: Map: 1  Reduce: 32 Cumulative CPU: 54.13 sec   HDFS Read: 283505 HDFS Write: 316247 SUCCESS, Total MapReduce CPU Time Spent: 54 seconds 130 msec, Starting Job = job_1419243806076_0002, Tracking URL = http://tri03ws-386:8088/proxy/application_1419243806076_0002/. ii. OK © 2020 Cloudera, Inc. All rights reserved. So, in this article, we will cover the whole concept of Bucketing in Hive. While small countries data will create small partitions (remaining all countries in the world may contribute to just 20-30 % of total data). Partition default.bucketed_user{country=country} stats: [numFiles=32, numRows=1, totalSize=2865, rawDataSize=68] Also, we have to manually convey the same information to Hive that, number of reduce tasks to be run (for example in our case, by using set mapred.reduce.tasks=32) and CLUSTER BY (state) and SORT BY (city) clause in the above INSERT …Statement at the end since we do not set this property in Hive Session. Moreover, it will automatically set the number of reduce tasks to be equal to the number of buckets mentioned in the table definition (for example 32 in our case).         city  VARCHAR(64), The total number of tablets is the product of the number of hash buckets and the number of split rows plus one. Enable reading from bucketed tables: Closed: Norbert Luksa: 2. This blog also covers Hive Partitioning example, Hive Bucketing example, Advantages and Disadvantages of Hive Partitioning and Bucketing.So, let’s start Hive Partitioning vs Bucketing. CLUSTERED BY (state) SORTED BY (city) INTO 32 BUCKETS. Partition default.bucketed_user{country=AU} stats: [numFiles=32, numRows=500, totalSize=78268, rawDataSize=67936] 2014-12-22 16:36:14,301 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 54.13 sec The default scheduling logic does not take into account node workload from prior queries. Partition default.bucketed_user{country=US} stats: [numFiles=32, numRows=500, totalSize=75468, rawDataSize=65383] Loading partition {country=US} Hence, we have seen that MapReduce job initiated 32 reduce tasks for 32 buckets and four partitions are created by country in the above box. Follow DataFlair on Google News & Stay ahead of the game. Map-side joins will be faster on bucketed tables than non-bucketed tables, as the data files are equal sized parts. Loading partition {country=UK} However,  let’s save this HiveQL into bucketed_user_creation.hql.  set hive.exec.reducers.bytes.per.reducer= On comparing with non-bucketed tables, Bucketed tables offer the efficient sampling. See Optimizing Performance in CDH Loading partition {country=UK} So, in this Impala Tutorial for beginners, we will learn the whole concept of Cloudera Impala. Table default.temp_user stats: [numFiles=1, totalSize=283212] Each compression codec offers For reference, Tags: Advantages of Bucketing in HiveCreation of Bucketed TablesFeatures of Hive Bucketinghive bucket external tablehive bucketing with exampleshive bucketing without partitionLimitations of Hive Bucketingwhat is Hive BucketingWhy Bucketing, How can I select particular bucket in bucketing as well as how can I select particular partition in partitioning……, how to decide the number of buckets in the hive, Your email address will not be published.