It is another effective technique for decomposing table data sets into more manageable parts. 0 votes. If this documentation includes code, including but not limited to, code examples, Cloudera makes this available to you under the terms of the Apache License, Version 2.0, including any required notices. SELECT statement creates Parquet files with a 256 MB block size. – Or, while partitions are of comparatively equal size. However, we can not directly load bucketed tables with LOAD DATA (LOCAL) INPATH command, similar to partitioned tables. Moreover,  to divide the table into buckets we use CLUSTERED BY clause. We … IMPALA-5891: fix PeriodicCounterUpdater initialization Avoid running static destructors and constructors to avoid the potential for startup and teardown races and … MapReduce Total cumulative CPU time: 54 seconds 130 msec ii. Here are performance guidelines and best practices that you can use during planning, experimentation, and performance tuning for an Impala-enabled CDH cluster. user@tri03ws-386:~$ hive -f bucketed_user_creation.hql, 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, Table default.temp_user stats: [numFiles=1, totalSize=283212], Query ID = user_20141222163030_3f024f2b-e682-4b08-b25c-7775d7af4134, Number of reduce tasks determined at compile time: 32. Total MapReduce CPU Time Spent: 54 seconds 130 msec At last, we will discuss Features of Bucketing in Hive, Advantages of Bucketing in Hive, Limitations of Bucketing in Hive, Example Use Case of Bucketing in Hive with some Hive Bucketing with examples. Showing posts with label Bucketing.Show all posts. Over-partitioning can also cause query planning to take longer than necessary, as Impala prunes the unnecessary partitions. Let’s see in Depth Tutorial for Hive Data Types with Example, Moreover, in hive lets execute this script.  set hive.exec.reducers.max= Basically, for decomposing table data sets into more manageable parts, Apache Hive offers another technique. © 2020 Cloudera, Inc. All rights reserved. Jan 2018. apache-sqoop hive hadoop. Before comparison, we will also discuss the introduction of both these technologies. Ideally, keep the number of partitions in the table under 30 Each Parquet file written by Impala is a single block, allowing the whole file to be processed as a unit by a single host. i. Time taken for load dynamic partitions : 2421 2014-12-22 16:32:28,037 Stage-1 map = 100%,  reduce = 13%, Cumulative CPU 3.19 sec 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. Impala Tutorial | Hadoop Impala Tutorial | Hadoop for Beginners | Hadoop Training ... Hive Bucketing in Apache Spark - Tejas Patil - Duration: 25:17. ii. 2014-12-22 16:36:14,301 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 54.13 sec Ended Job = job_1419243806076_0002 (Specify the file size as an absolute number of bytes, or in Impala 2.0 and later, in units ending with m for megabytes or g for gigabytes.) © 2020 Cloudera, Inc. All rights reserved. The total number of tablets is the product of the number of hash buckets and the number of split rows plus one. However, in partitioning the property hive.enforce.bucketing = true is similar to hive.exec.dynamic.partition=true property. In this article, we will explain Apache Hive Performance Tuning Best Practices and steps to be followed to achieve high performance. CREATE TABLE bucketed_user( Where the hash_function depends on the type of the bucketing column. – When there is the limited number of partitions. Basically, this concept is based on hashing function on the bucketed column. Follow DataFlair on Google News & Stay ahead of the game. functions such as, Filtering. 2014-12-22 16:30:36,164 Stage-1 map = 0%,  reduce = 0% MapReduce Total cumulative CPU time: 54 seconds 130 msec Query ID = user_20141222163030_3f024f2b-e682-4b08-b25c-7775d7af4134 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 v. Along with Partitioning on Hive tables bucketing can be done and even without partitioning. Use all applicable tests in the, Avoid overhead from pretty-printing the result set and displaying it on the screen. i. 2014-12-22 16:35:53,559 Stage-1 map = 100%,  reduce = 94%, Cumulative CPU 51.14 sec I have many tables in Hive and suspect size of these tables are causing space issues on HDFS FS. OK Hash bucketing can be combined with range partitioning. Each compression codec offers 2014-12-22 16:34:52,731 Stage-1 map = 100%,  reduce = 56%, Cumulative CPU 32.01 sec Further, it automatically selects the clustered by column from table definition. Moreover, let’s suppose we have created the temp_user temporary table. See Using the Query Profile for Performance Tuning for details. Generally, in the table directory, each bucket is just a file, and Bucket numbering is 1-based. When deciding which column(s) to use for partitioning, choose the right level of granularity. Although, it is not possible in all scenarios. 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. Also, it includes why even we need Hive Bucketing after Hive Partitioning Concept, Features of Bucketing in Hive, Advantages of Bucketing in Hive, Limitations of Bucketing in Hive, And Example Use Case of Bucketing in Hive. Don't become Obsolete & get a Pink Slip Partition default.bucketed_user{country=UK} stats: [numFiles=32, numRows=500, totalSize=85604, rawDataSize=75292] Examine the EXPLAIN plan for a query before actually running it. OK Partition default.bucketed_user{country=US} stats: [numFiles=32, numRows=500, totalSize=75468, rawDataSize=65383] Due to the deterministic nature of the scheduler, single nodes can become bottlenecks for highly concurrent queries Further, for populating the bucketed table with the temp_user table below is the HiveQL. Reply. volume. 2014-12-22 16:32:28,037 Stage-1 map = 100%,  reduce = 13%, Cumulative CPU 3.19 sec return on investment. Could you please let me know by default, how many buckets are created in hdfs location while inserting data if buckets are not defined in create statement? This concept offers the flexibility to keep the records in each bucket to be sorted by one or more columns. In order to limit the maximum number of reducers: If so - how? i. Partition default.bucketed_user{country=AU} stats: [numFiles=32, numRows=500, totalSize=78268, rawDataSize=67936] also available in more detail elsewhere in the Impala documentation; it is gathered together here to serve as a cookbook and emphasize which performance techniques typically provide the highest Outside the US: +1 650 362 0488. user@tri03ws-386:~$ Time taken: 396.486 seconds If you need to reduce the overall number of partitions and increase the amount of data in each partition, first look for partition key columns that are rarely referenced or are 386:8088/proxy/application_1419243806076_0002/ In this tutorial, we are going to cover the feature wise difference between Hive partitioning vs bucketing. Further, it automatically selects the clustered by column from table definition. Before discussing the options to tackle this issue some background is first required to understand how this problem can occur. Verify that the low-level aspects of I/O, memory usage, network bandwidth, CPU utilization, and so on are within expected ranges by examining the query profile for a query after running iv. Loading partition {country=AU} Your email address will not be published. It includes Impala’s benefits, working as well as its features. Queries for details. Bucketing; Indexing Data Extending Hive; SerDes; Datentransformationen mit Custom Scripts; Benutzerdefinierte Funktionen; Parameterübergabe bei Abfragen; Einheit 14 – Einführung in Impala. 2014-12-22 16:33:58,642 Stage-1 map = 100%,  reduce = 38%, Cumulative CPU 21.69 sec 7. Loading partition {country=AU} We can use the use database_name; command to use a particular database which is available in the Hive metastore database to create tables and to perform operations on that table, according to the requirement.  set mapreduce.job.reduces= Impala Date and Time Functions for details. 2014-12-22 16:30:36,164 Stage-1 map = 0%,  reduce = 0% hadoop ; big-data; hive; Feb 11, 2019 in Big Data Hadoop by Dinesh • 529 views. Databricks 15,674 views. that use the same tables. Apache Hive Performance Tuning Best Practices . iii. A copy of the Apache License Version 2.0 can be found here. The complexity of materializing a tuple depends on a few factors, namely: decoding and Run benchmarks with different file sizes to find the right balance point for your particular data Time taken for load dynamic partitions : 2421 vi. Linux kernel setting to a non-zero value improves overall performance. Typically, for large volumes of data (multiple gigabytes per table or partition), the Parquet file format performs best because of its combination of columnar storage layout, large I/O ii. Is there a way to check the size of Hive tables?         STORED AS SEQUENCEFILE; ii. Monday, July 20, 2020 In order to set a constant number of reducers: Moreover,  to divide the table into buckets we use CLUSTERED BY clause. Loading partition {country=country} 2014-12-22 16:35:22,493 Stage-1 map = 100%,  reduce = 75%, Cumulative CPU 41.45 sec 1. So, in this article, “Impala vs Hive” we will compare Impala vs Hive performance on the basis of different features and discuss why Impala is faster than Hive, when to use Impala vs hive. Starting Job = job_1419243806076_0002, Tracking URL = http://tri03ws- is duplicated by. Partition default.bucketed_user{country=CA} stats: [numFiles=32, numRows=500, totalSize=76564, rawDataSize=66278] 2014-12-22 16:35:21,369 Stage-1 map = 100%,  reduce = 63%, Cumulative CPU 35.08 sec Hive Incremental Update using Sqoop. host the scan. SELECT syntax. MapReduce Jobs Launched: Table default.temp_user stats: [numFiles=1, totalSize=283212] user@tri03ws-386:~$ For a complete list of trademarks, click here.         web       STRING Basically, to overcome the slowness of Hive Queries, Cloudera offers a separate tool and that tool is what we call Impala. See How Impala Works with Hadoop File Formats for comparisons of all file formats also it is a good practice to collect statistics for the table it will help in the performance side . in Impala 2.0. Kill Command = /home/user/bigdata/hadoop-2.6.0/bin/hadoop job  -kill job_1419243806076_0002 Total jobs = 1 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 IMPALA-1990 Add bucket join.         lastname  VARCHAR(64), Total jobs = 1 Loading data to table default.temp_user Tools. Hence, let’s create the table partitioned by country and bucketed by state and sorted in ascending order of cities. Map-side joins will be faster on bucketed tables than non-bucketed tables, as the data files are equal sized parts. 2014-12-22 16:31:09,770 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.66 sec OK OK 2)Bucketing Manual partition: In Manual partition we are partitioning the table using partition variables. DDL and DML support for bucketed tables: … (Specify the file size as an absolute number of bytes, or in Impala 2.0 and later, in units ending with. Along with mod (by the total number of buckets). You want to find a sweet spot between "many tiny files" and "single giant file" that balances See EXPLAIN Statement and Using the EXPLAIN Plan for Performance Tuning for details. 2014-12-22 16:32:10,368 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.66 sec In order to change the average load for a reducer (in bytes): In order to limit the maximum number of reducers: 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/. Time taken for adding to write entity : 17 neighbours”. Related Topic- Hive Operators different performance tradeoffs and should be considered before writing the data. Instead to populate the bucketed tables we need to use INSERT OVERWRITE TABLE … SELECT …FROM clause from another table. ii. Launching Job 1 out of 1 appropriate range of values, typically TINYINT for MONTH and DAY, and SMALLINT for YEAR. 2014-12-22 16:32:10,368 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.66 sec Basically, the concept of Hive Partitioning provides a way of segregating hive table data into multiple files/directories. decompression. In particular, you might find that changing the vm.swappiness 2014-12-22 16:35:53,559 Stage-1 map = 100%,  reduce = 94%, Cumulative CPU 51.14 sec v. Since the join of each bucket becomes an efficient merge-sort, this makes map-side joins even more efficient. Loading data to table default.bucketed_user partition (country=null) In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=. 2014-12-22 16:35:22,493 Stage-1 map = 100%,  reduce = 75%, Cumulative CPU 41.45 sec Loading partition {country=UK} perhaps you only need to partition by year, month, and day. In this post I’m going to write what are the features I reckon missing in Impala. 2014-12-22 16:32:36,480 Stage-1 map = 100%,  reduce = 14%, Cumulative CPU 7.06 sec If you need to know how many rows match a condition, the total values of matching values from some column, the lowest or highest matching value, and so on, call aggregate Show All; Show Open; Bulk operation; Open issue navigator; Sub-Tasks. Moreover, we can create a bucketed_user table with above-given requirement with the help of the below HiveQL. On comparing with non-bucketed tables, Bucketed tables offer the efficient sampling. As you copy Parquet files into HDFS or between HDFS 2014-12-22 16:36:14,301 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 54.13 sec First computer dell inspiron 14r Favorite editor Vim Company data powered by . See Partitioning for Impala Tables for full details and performance considerations for partitioning. Loading partition {country=CA} Partition default.bucketed_user{country=US} stats: [numFiles=32, numRows=500, totalSize=75468, rawDataSize=65383] Important: After adding or replacing data in a table used in performance-critical queries, issue a COMPUTE STATS statement to make sure all statistics are up-to-date. Schema Alterations. Then, to solve that problem of over partitioning, Hive offers Bucketing concept.         PARTITIONED BY (country VARCHAR(64)) When preparing data files to go in a partition directory, create several large files rather than many small ones. 2014-12-22 16:34:52,731 Stage-1 map = 100%,  reduce = 56%, Cumulative CPU 32.01 sec As shown in above code for state and city columns Bucketed columns are included in the table definition, Unlike partitioned columns. 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. iii. 2014-12-22 16:32:40,317 Stage-1 map = 100%,  reduce = 19%, Cumulative CPU 7.63 sec Total MapReduce CPU Time Spent: 54 seconds 130 msec However, with the help of CLUSTERED BY clause and optional SORTED BY clause in CREATE TABLE statement we can create bucketed tables. Bucketed tables are hash partitioned which means joins and aggregations bucketing columns can be done without exchange. Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 32         address   STRING, Moreover, we can create a bucketed_user table with above-given requirement with the help of the below HiveQL.CREATE TABLE bucketed_user( firstname VARCHAR(64), lastname VARCHAR(64), address STRING, city VARCHAR(64),state VARCHAR(64), post STRING, p… 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. Kevin Mitnick: Live Hack at CeBIT Global Conferences 2015 - … 28:49. CDAPHIH Training von Cloudera Detaillierte Kursinhalte & weitere Infos zur Schulung | Kompetente Beratung Mehrfach ausgezeichnet Weltweit präsent It is another effective technique for decomposing table data sets into more manageable parts. You can adapt number of steps to tune the performance in Hive … i.  set mapreduce.job.reduces= OK So, we can enable dynamic bucketing while loading data into hive table By setting this property. first_name,last_name, address, country, city, state, post,phone1,phone2, email, web Rebbecca, Didio, 171 E 24th St, AU, Leith, TA, 7315, 03-8174-9123, 0458-665-290, rebbecca.didio@didio.com.au,http://www.brandtjonathanfesq.com.au  set hive.exec.reducers.max= For example, your web site log data might be partitioned by year, month, day, and hour, but if most queries roll up the results by day, If there is only one or a few data block in your Parquet table, or in a partition that is the only one accessed by a query, then you might experience a slowdown for a different reason: However, the Records with the same bucketed column will always be stored in the same bucket. i. SELECT to copy all the data to a different table; the data will be reorganized into a smaller number of larger files by i. Parquet files as part of your data preparation process, do that and skip the conversion step inside Impala. flag; 1 answer to this question. See Performance Considerations for Join Some points are important to Note: OK Also, see the output of the above script execution below. 2014-12-22 16:33:54,846 Stage-1 map = 100%,  reduce = 31%, Cumulative CPU 17.45 sec Do you Know Feature Wise Difference between Hive vs HBase. Hence, let’s create the table partitioned by country and bucketed by state and sorted in ascending order of cities. OK 2014-12-22 16:33:40,691 Stage-1 map = 100%,  reduce = 19%, Cumulative CPU 12.28 sec Adding hash bucketing to a range partitioned table has the effect of parallelizing operations that would otherwise operate sequentially over the range. create table if not exists empl_part (empid int,ename string,salary double,deptno int) comment 'manual partition example' partitioned by (country string,city string) So, in this article, we will cover the whole concept of Bucketing in Hive. For example, should you partition by year, month, and day, or only by year and month? queries. SELECT to copy significant volumes of data from table to table within Impala. potentially process thousands of data files simultaneously. Number of reduce tasks determined at compile time: 32 In order to set a constant number of reducers: As a result, we have seen the whole concept of Hive Bucketing. However, with the help of CLUSTERED BY clause and optional SORTED BY clause in CREATE TABLE statement we can create bucketed tables. the size of each generated Parquet file. Along with script required for temporary hive table creation, Below is the combined HiveQL. Moreover, Bucketed tables will create almost equally distributed data file parts. thousand. Time taken: 12.144 seconds Do not compress the table data. However, we can not directly load bucketed tables with LOAD DATA (LOCAL) INPATH command, similar to partitioned tables.        COMMENT ‘A bucketed sorted user table’ 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. See Moreover, Bucketed tables will create almost equally distributed data file parts. Let’s read about Apache Hive View and Hive Index. Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 32 It includes one of the major questions, that why even we need Bucketing in Hive after Hive Partitioning Concept. Then, to solve that problem of over partitioning, Hive offers Bucketing concept. iv. Moreover, let’s suppose we have created the temp_user temporary table. v. Along with Partitioning on Hive tables bucketing can be done and even without partitioning. Hive and Impala are most widely used to build data warehouse on the Hadoop framework. Loading partition {country=UK} This means that for multiple queries needing to read the same block of data, the same node will be picked to However, in partitioning the property hive.enforce.bucketing = true is similar to hive.exec.dynamic.partition=true property. Map-side joins will be faster on bucketed tables than non-bucketed tables, as the data files are equal sized parts. Although it is tempting to use strings for partition key columns, since those values are turned into HDFS directory names anyway, you can minimize memory usage by using numeric values This will cause the Impala scheduler to randomly pick (from. Showing posts with label Bucketing.Show all posts. Cloudera Search and Other Cloudera Components, Displaying Cloudera Manager Documentation, Displaying the Cloudera Manager Server Version and Server Time, EMC DSSD D5 Storage Appliance Integration for Hadoop DataNodes, Using the Cloudera Manager API for Cluster Automation, Cloudera Manager 5 Frequently Asked Questions, Cloudera Navigator Data Management Overview, Cloudera Navigator 2 Frequently Asked Questions, Cloudera Navigator Key Trustee Server Overview, Frequently Asked Questions About Cloudera Software, QuickStart VM Software Versions and Documentation, Cloudera Manager and CDH QuickStart Guide, Before You Install CDH 5 on a Single Node, Installing CDH 5 on a Single Linux Node in Pseudo-distributed Mode, Installing CDH 5 with MRv1 on a Single Linux Host in Pseudo-distributed mode, Installing CDH 5 with YARN on a Single Linux Host in Pseudo-distributed mode, Components That Require Additional Configuration, Prerequisites for Cloudera Search QuickStart Scenarios, Configuration Requirements for Cloudera Manager, Cloudera Navigator, and CDH 5, Permission Requirements for Package-based Installations and Upgrades of CDH, Ports Used by Cloudera Manager and Cloudera Navigator, Ports Used by Cloudera Navigator Encryption, Ports Used by Apache Flume and Apache Solr, Managing Software Installation Using Cloudera Manager, Cloudera Manager and Managed Service Datastores, Configuring an External Database for Oozie, Configuring an External Database for Sqoop, Storage Space Planning for Cloudera Manager, Installation Path A - Automated Installation by Cloudera Manager (Non-Production Mode), Installation Path B - Installation Using Cloudera Manager Parcels or Packages, (Optional) Manually Install CDH and Managed Service Packages, Installation Path C - Manual Installation Using Cloudera Manager Tarballs, Understanding Custom Installation Solutions, Creating and Using a Remote Parcel Repository for Cloudera Manager, Creating and Using a Package Repository for Cloudera Manager, Installing Lower Versions of Cloudera Manager 5, Creating a CDH Cluster Using a Cloudera Manager Template, Uninstalling Cloudera Manager and Managed Software, Uninstalling a CDH Component From a Single Host, Installing the Cloudera Navigator Data Management Component, Installing Cloudera Navigator Key Trustee Server, Installing and Deploying CDH Using the Command Line, Migrating from MapReduce (MRv1) to MapReduce (MRv2), Configuring Dependencies Before Deploying CDH on a Cluster, Deploying MapReduce v2 (YARN) on a Cluster, Deploying MapReduce v1 (MRv1) on a Cluster, Configuring Hadoop Daemons to Run at Startup, Installing the Flume RPM or Debian Packages, Files Installed by the Flume RPM and Debian Packages, New Features and Changes for HBase in CDH 5, Configuring HBase in Pseudo-Distributed Mode, Installing and Upgrading the HCatalog RPM or Debian Packages, Configuration Change on Hosts Used with HCatalog, Starting and Stopping the WebHCat REST server, Accessing Table Information with the HCatalog Command-line API, Installing Impala without Cloudera Manager, Starting, Stopping, and Using HiveServer2, Starting HiveServer1 and the Hive Console, Installing the Hive JDBC Driver on Clients, Configuring the Metastore to Use HDFS High Availability, Starting, Stopping, and Accessing the Oozie Server, Installing Cloudera Search without Cloudera Manager, Installing MapReduce Tools for use with Cloudera Search, Installing the Lily HBase Indexer Service, Upgrading Sqoop 1 from an Earlier CDH 5 release, Installing the Sqoop 1 RPM or Debian Packages, Upgrading Sqoop 2 from an Earlier CDH 5 Release, Starting, Stopping, and Accessing the Sqoop 2 Server, Feature Differences - Sqoop 1 and Sqoop 2, Upgrading ZooKeeper from an Earlier CDH 5 Release, Setting Up an Environment for Building RPMs, Installation and Upgrade with the EMC DSSD D5, DSSD D5 Installation Path A - Automated Installation by Cloudera Manager Installer (Non-Production), DSSD D5 Installation Path B - Installation Using Cloudera Manager Parcels, DSSD D5 Installation Path C - Manual Installation Using Cloudera Manager Tarballs, Adding an Additional DSSD D5 to a Cluster, Troubleshooting Installation and Upgrade Problems, Managing CDH and Managed Services Using Cloudera Manager, Modifying Configuration Properties Using Cloudera Manager, Modifying Configuration Properties (Classic Layout), Viewing and Reverting Configuration Changes, Exporting and Importing Cloudera Manager Configuration, Starting, Stopping, Refreshing, and Restarting a Cluster, Comparing Configurations for a Service Between Clusters, Starting, Stopping, and Restarting Services, Decommissioning and Recommissioning Hosts, Cloudera Manager Configuration Properties, Starting CDH Services Using the Command Line, Configuring init to Start Hadoop System Services, Starting and Stopping HBase Using the Command Line, Stopping CDH Services Using the Command Line, Migrating Data between Clusters Using distcp, Copying Data between a Secure and an Insecure Cluster using DistCp and WebHDFS, Decommissioning DataNodes Using the Command Line, Configuring the Storage Policy for the Write-Ahead Log (WAL), Exposing HBase Metrics to a Ganglia Server, Backing Up and Restoring NameNode Metadata, Configuring Storage Directories for DataNodes, Configuring Storage Balancing for DataNodes, Configuring Centralized Cache Management in HDFS, Configuring Heterogeneous Storage in HDFS, Managing User-Defined Functions (UDFs) with HiveServer2, Enabling Hue Applications Using Cloudera Manager, Post-Installation Configuration for Impala, Adding the Oozie Service Using Cloudera Manager, Configuring Oozie Data Purge Settings Using Cloudera Manager, Dumping and Loading an Oozie Database Using Cloudera Manager, Adding Schema to Oozie Using Cloudera Manager, Scheduling in Oozie Using Cron-like Syntax, Configuring Oozie to Enable MapReduce Jobs To Read/Write from Amazon S3, Managing Spark Standalone Using the Command Line, Managing YARN (MRv2) and MapReduce (MRv1), Configuring Services to Use the GPL Extras Parcel, Choosing and Configuring Data Compression, YARN (MRv2) and MapReduce (MRv1) Schedulers, Enabling and Disabling Fair Scheduler Preemption, Creating a Custom Cluster Utilization Report, Configuring Other CDH Components to Use HDFS HA, Administering an HDFS High Availability Cluster, Changing a Nameservice Name for Highly Available HDFS Using Cloudera Manager, MapReduce (MRv1) and YARN (MRv2) High Availability, YARN (MRv2) ResourceManager High Availability, Work Preserving Recovery for YARN Components, MapReduce (MRv1) JobTracker High Availability, Cloudera Navigator Key Trustee Server High Availability, High Availability for Other CDH Components, Configuring Cloudera Manager for High Availability With a Load Balancer, Introduction to Cloudera Manager Deployment Architecture, Prerequisites for Setting up Cloudera Manager High Availability, High-Level Steps to Configure Cloudera Manager High Availability, Step 1: Setting Up Hosts and the Load Balancer, Step 2: Installing and Configuring Cloudera Manager Server for High Availability, Step 3: Installing and Configuring Cloudera Management Service for High Availability, Step 4: Automating Failover with Corosync and Pacemaker, TLS and Kerberos Configuration for Cloudera Manager High Availability, Port Requirements for Backup and Disaster Recovery, Monitoring the Performance of HDFS Replications, Enabling Replication Between Clusters in Different Kerberos Realms, How To Back Up and Restore Apache Hive Data Using Cloudera Enterprise BDR, How To Back Up and Restore HDFS Data Using Cloudera Enterprise BDR, Starting, Stopping, and Restarting the Cloudera Manager Server, Configuring Cloudera Manager Server Ports, Moving the Cloudera Manager Server to a New Host, Migrating from the Cloudera Manager Embedded PostgreSQL Database Server to an External PostgreSQL Database, Starting, Stopping, and Restarting Cloudera Manager Agents, Sending Usage and Diagnostic Data to Cloudera, Other Cloudera Manager Tasks and Settings, Cloudera Navigator Data Management Component Administration, Configuring Service Audit Collection and Log Properties, Managing Hive and Impala Lineage Properties, How To Create a Multitenant Enterprise Data Hub, Downloading HDFS Directory Access Permission Reports, Introduction to Cloudera Manager Monitoring, Viewing Charts for Cluster, Service, Role, and Host Instances, Monitoring Multiple CDH Deployments Using the Multi Cloudera Manager Dashboard, Installing and Managing the Multi Cloudera Manager Dashboard, Using the Multi Cloudera Manager Status Dashboard, Viewing and Filtering MapReduce Activities, Viewing the Jobs in a Pig, Oozie, or Hive Activity, Viewing Activity Details in a Report Format, Viewing the Distribution of Task Attempts, Troubleshooting Cluster Configuration and Operation, Impala Llama ApplicationMaster Health Tests, HBase RegionServer Replication Peer Metrics, Security Overview for an Enterprise Data Hub, How to Configure TLS Encryption for Cloudera Manager, Configuring Authentication in Cloudera Manager, Configuring External Authentication for Cloudera Manager, Kerberos Concepts - Principals, Keytabs and Delegation Tokens, Enabling Kerberos Authentication Using the Wizard, Step 2: If You are Using AES-256 Encryption, Install the JCE Policy File, Step 3: Get or Create a Kerberos Principal for the Cloudera Manager Server, Step 4: Enabling Kerberos Using the Wizard, Step 6: Get or Create a Kerberos Principal for Each User Account, Step 7: Prepare the Cluster for Each User, Step 8: Verify that Kerberos Security is Working, Step 9: (Optional) Enable Authentication for HTTP Web Consoles for Hadoop Roles, Enabling Kerberos Authentication for Single User Mode or Non-Default Users, Configuring a Cluster with Custom Kerberos Principals, Managing Kerberos Credentials Using Cloudera Manager, Using a Custom Kerberos Keytab Retrieval Script, Mapping Kerberos Principals to Short Names, Moving Kerberos Principals to Another OU Within Active Directory, Using Auth-to-Local Rules to Isolate Cluster Users, Enabling Kerberos Authentication Without the Wizard, Step 4: Import KDC Account Manager Credentials, Step 5: Configure the Kerberos Default Realm in the Cloudera Manager Admin Console, Step 8: Wait for the Generate Credentials Command to Finish, Step 9: Enable Hue to Work with Hadoop Security using Cloudera Manager, Step 10: (Flume Only) Use Substitution Variables for the Kerberos Principal and Keytab, Step 13: Create the HDFS Superuser Principal, Step 14: Get or Create a Kerberos Principal for Each User Account, Step 15: Prepare the Cluster for Each User, Step 16: Verify that Kerberos Security is Working, Step 17: (Optional) Enable Authentication for HTTP Web Consoles for Hadoop Roles, Configuring Authentication in the Cloudera Navigator Data Management Component, Configuring External Authentication for the Cloudera Navigator Data Management Component, Managing Users and Groups for the Cloudera Navigator Data Management Component, Configuring Authentication in CDH Using the Command Line, Enabling Kerberos Authentication for Hadoop Using the Command Line, Step 2: Verify User Accounts and Groups in CDH 5 Due to Security, Step 3: If you are Using AES-256 Encryption, Install the JCE Policy File, Step 4: Create and Deploy the Kerberos Principals and Keytab Files, Optional Step 8: Configuring Security for HDFS High Availability, Optional Step 9: Configure secure WebHDFS, Optional Step 10: Configuring a secure HDFS NFS Gateway, Step 11: Set Variables for Secure DataNodes, Step 14: Set the Sticky Bit on HDFS Directories, Step 15: Start up the Secondary NameNode (if used), Step 16: Configure Either MRv1 Security or YARN Security, Using kadmin to Create Kerberos Keytab Files, Configuring the Mapping from Kerberos Principals to Short Names, Enabling Debugging Output for the Sun Kerberos Classes, Configuring Kerberos for Flume Thrift Source and Sink Using Cloudera Manager, Configuring Kerberos for Flume Thrift Source and Sink Using the Command Line, Testing the Flume HDFS Sink Configuration, Configuring Kerberos Authentication for HBase, Configuring the HBase Client TGT Renewal Period, Hive Metastore Server Security Configuration, Using Hive to Run Queries on a Secure HBase Server, Configuring Kerberos Authentication for Hue, Enabling Kerberos Authentication for Impala, Using Multiple Authentication Methods with Impala, Configuring Impala Delegation for Hue and BI Tools, Configuring Kerberos Authentication for the Oozie Server, Configuring Spark on YARN for Long-Running Applications, Configuring a Cluster-dedicated MIT KDC with Cross-Realm Trust, Integrating Hadoop Security with Active Directory, Integrating Hadoop Security with Alternate Authentication, Authenticating Kerberos Principals in Java Code, Using a Web Browser to Access an URL Protected by Kerberos HTTP SPNEGO, Private Key and Certificate Reuse Across Java Keystores and OpenSSL, Configuring TLS Security for Cloudera Manager, Configuring TLS (Encryption Only) for Cloudera Manager, Level 1: Configuring TLS Encryption for Cloudera Manager Agents, Level 2: Configuring TLS Verification of Cloudera Manager Server by the Agents, Level 3: Configuring TLS Authentication of Agents to the Cloudera Manager Server, TLS/SSL Communication Between Cloudera Manager and Cloudera Management Services, Troubleshooting TLS/SSL Issues in Cloudera Manager, Using Self-Signed Certificates (Level 1 TLS), Configuring TLS/SSL for the Cloudera Navigator Data Management Component, Configuring TLS/SSL for Publishing Cloudera Navigator Audit Events to Kafka, Configuring TLS/SSL for Cloudera Management Service Roles, Configuring TLS/SSL Encryption for CDH Services, Configuring TLS/SSL for HDFS, YARN and MapReduce, Configuring TLS/SSL for Flume Thrift Source and Sink, Configuring Encrypted Communication Between HiveServer2 and Client Drivers, Deployment Planning for Data at Rest Encryption, Data at Rest Encryption Reference Architecture, Resource Planning for Data at Rest Encryption, Optimizing Performance for HDFS Transparent Encryption, Enabling HDFS Encryption Using the Wizard, Configuring the Key Management Server (KMS), Migrating Keys from a Java KeyStore to Cloudera Navigator Key Trustee Server, Configuring CDH Services for HDFS Encryption, Backing Up and Restoring Key Trustee Server and Clients, Initializing Standalone Key Trustee Server, Configuring a Mail Transfer Agent for Key Trustee Server, Verifying Cloudera Navigator Key Trustee Server Operations, Managing Key Trustee Server Organizations, HSM-Specific Setup for Cloudera Navigator Key HSM, Creating a Key Store with CA-Signed Certificate, Integrating Key HSM with Key Trustee Server, Registering Cloudera Navigator Encrypt with Key Trustee Server, Preparing for Encryption Using Cloudera Navigator Encrypt, Encrypting and Decrypting Data Using Cloudera Navigator Encrypt, Migrating eCryptfs-Encrypted Data to dm-crypt, Configuring Encrypted On-disk File Channels for Flume, Configuring Encrypted HDFS Data Transport, Configuring Encrypted HBase Data Transport, Cloudera Navigator Data Management Component User Roles, Installing and Upgrading the Sentry Service, Migrating from Sentry Policy Files to the Sentry Service, Synchronizing HDFS ACLs and Sentry Permissions, Installing and Upgrading Sentry for Policy File Authorization, Configuring Sentry Policy File Authorization Using Cloudera Manager, Configuring Sentry Policy File Authorization Using the Command Line, Configuring Sentry Authorization for Cloudera Search, Installation Considerations for Impala Security, Jsvc, Task Controller and Container Executor Programs, YARN ONLY: Container-executor Error Codes, Sqoop, Pig, and Whirr Security Support Status, Setting Up a Gateway Node to Restrict Cluster Access, How to Configure Resource Management for Impala, ARRAY Complex Type (CDH 5.5 or higher only), MAP Complex Type (CDH 5.5 or higher only), STRUCT Complex Type (CDH 5.5 or higher only), VARIANCE, VARIANCE_SAMP, VARIANCE_POP, VAR_SAMP, VAR_POP, Validating the Cloudera Search Deployment, Preparing to Index Sample Tweets with Cloudera Search, Using MapReduce Batch Indexing to Index Sample Tweets, Near Real Time (NRT) Indexing Tweets Using Flume, Flume Morphline Solr Sink Configuration Options, Flume Morphline Interceptor Configuration Options, Flume Solr UUIDInterceptor Configuration Options, Flume Solr BlobHandler Configuration Options, Flume Solr BlobDeserializer Configuration Options, Extracting, Transforming, and Loading Data With Cloudera Morphlines, Using the Lily HBase Batch Indexer for Indexing, Configuring the Lily HBase NRT Indexer Service for Use with Cloudera Search, Schemaless Mode Overview and Best Practices, Using Search through a Proxy for High Availability, Cloudera Search Frequently Asked Questions, Developing and Running a Spark WordCount Application, Accessing Data Stored in Amazon S3 through Spark, Accessing Avro Data Files From Spark SQL Applications, Accessing Parquet Files From Spark SQL Applications, Building and Running a Crunch Application with Spark, Choose the appropriate file format for the data, Avoid data ingestion processes that produce many small files, Choose partitioning granularity based on actual data volume, Use smallest appropriate integer types for partition key columns, Gather statistics for all tables used in performance-critical or high-volume join queries, Minimize the overhead of transmitting results back to the client, Verify that your queries are planned in an efficient logical manner, Verify performance characteristics of queries, Use appropriate operating system settings, How Impala Works with Hadoop File Formats, Using the Parquet File Format with Impala Tables, Performance Considerations for Join Create almost equally distributed data file parts to copy significant volumes of data from table definition are to! 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