bucketing in impala

However, it doesn’t ensure that the table is properly populated. In particular, you might find that changing the vm.swappiness This comprehensive course covers all aspects of the certification with real world examples and data sets. However, it only gives effective results in few scenarios. v. Along with Partitioning on Hive tables bucketing can be done and even without partitioning. Or, if you have the infrastructure to produce multi-megabyte 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 Then, to solve that problem of over partitioning, Hive offers Bucketing concept. MapReduce Total cumulative CPU time: 54 seconds 130 msec v. Along with Partitioning on Hive tables bucketing can be done and even without partitioning. set mapreduce.job.reduces= iv. 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 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. answer comment. Time taken for load dynamic partitions : 2421 Hence, some bigger countries will have large partitions (ex: 4-5 countries itself contributing 70-80% of total data). Adding hash bucketing to a range partitioned table has the effect of parallelizing operations that would otherwise operate sequentially over the range. 2014-12-22 16:32:10,368 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.66 sec Also, see the output of the above script execution below. Kill Command = /home/user/bigdata/hadoop-2.6.0/bin/hadoop job -kill job_1419243806076_0002 OK Where the hash_function depends on the type of the bucketing column. See Where the hash_function depends on the type of the bucketing column. DDL and DML support for bucketed tables: … For a complete list of trademarks, click here. 2014-12-22 16:35:21,369 Stage-1 map = 100%, reduce = 63%, Cumulative CPU 35.08 sec 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. user@tri03ws-386:~$ flag; 1 answer to this question. Let’s see in Depth Tutorial for Hive Data Types with Example, Moreover, in hive lets execute this script. set hive.exec.reducers.bytes.per.reducer= thousand. Time taken: 0.146 seconds Generally, in the table directory, each bucket is just a file, and Bucket numbering is 1-based. in Impala 2.0. However, with the help of CLUSTERED BY clause and optional SORTED BY clause in CREATE TABLE statement we can create bucketed tables. 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). Tools. OK 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. Bucketing; Indexing Data Extending Hive; SerDes; Datentransformationen mit Custom Scripts; Benutzerdefinierte Funktionen; Parameterübergabe bei Abfragen; Einheit 14 – Einführung in Impala. However, the Records with the same bucketed column will always be stored in the same bucket. Partition default.bucketed_user{country=US} stats: [numFiles=32, numRows=500, totalSize=75468, rawDataSize=65383] However, there are much more to learn about Bucketing in Hive. CDAPHIH Training von Cloudera Detaillierte Kursinhalte & weitere Infos zur Schulung | Kompetente Beratung Mehrfach ausgezeichnet Weltweit präsent Partition default.bucketed_user{country=UK} stats: [numFiles=32, numRows=500, totalSize=85604, rawDataSize=75292] potentially process thousands of data files simultaneously. Example Use Case for Bucketing in Hive, To understand the remaining features of Hive Bucketing let’s see an example Use case, by creating buckets for the sample user records file for testing in this post, 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. iii. Also in bucketing actually you have the control over the number of buckets. 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 OK – When there is the limited number of partitions. It explains what is partitioning and bucketing in Hive, How to select columns for partitioning and bucketing. Bucketing; Indexing Data Extending Hive; SerDes; Datentransformationen mit Custom Scripts; Benutzerdefinierte Funktionen; Parameterübergabe bei Abfragen; Einheit 14 – Einführung in Impala. This scenario based certification exam demands in depth knowledge of Hive, Sqoop as well as basic knowledge of Impala. If, for example, a Parquet based dataset is tiny, e.g. Number of reduce tasks determined at compile time: 32 Your email address will not be published. Apache Hadoop and associated open source project names are trademarks of the Apache Software Foundation. On comparing with non-bucketed tables, Bucketed tables offer the efficient sampling. Hive Partition And Bucketing Explained - Hive Tutorial For Beginners - Duration: 28:49. ii. As shown in above code for state and city columns Bucketed columns are included in the table definition, Unlike partitioned columns. Hive and Impala are most widely used to build data warehouse on the Hadoop framework. Loading data to table default.temp_user impala (29) pig impala hive apache hbase download sql spark hadoop load Read about What is Hive Metastore – Different Ways to Configure Hive Metastore. Let’s read about Apache Hive View and Hive Index. See Performance Considerations for Join However, in partitioning the property hive.enforce.bucketing = true is similar to hive.exec.dynamic.partition=true property. 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… for recommendations about operating system settings that you can change to influence Impala performance. Along with script required for temporary hive table creation, Below is the combined HiveQL. Attachments . Apache Hive Performance Tuning Best Practices . Hence, we will create one temporary table in hive with all the columns in input file from that table we will copy into our target bucketed table for this. Each data block is processed by a single core on one of the DataNodes. 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, Let’s see a difference between Hive Partitioning and Bucketing tutorial in detail. neighboursâ. OK set mapreduce.job.reduces= However, the Records with the same bucketed column will always be stored in the same bucket. Hive is developed by Facebook and Impala by Cloudera. ii. iv. ii. In addition, we need to set the property hive.enforce.bucketing = true, so that Hive knows to create the number of buckets declared in the table definition to populate the bucketed table. user@tri03ws-386:~$ hive -f bucketed_user_creation.hql Map-side joins will be faster on bucketed tables than non-bucketed tables, as the data files are equal sized parts. notices. SELECT statement creates Parquet files with a 256 MB block size. Each Parquet file written by Impala is a single block, allowing the whole file to be processed as a unit by a single host. iii. Surendranatha Reddy … Both Apache Hiveand Impala, used for running queries on HDFS. 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. 2014-12-22 16:31:09,770 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.66 sec Related Topic- Hive Operators Queries for details. used, each containing a single row group) then there are a number of options that can be considered to resolve the potential scheduling hotspots when querying this data: Categories: Best Practices | Data Analysts | Developers | Guidelines | Impala | Performance | Planning | Proof of Concept | All Categories, United States: +1 888 789 1488 In order to change the average load for a reducer (in bytes): Monday, July 20, 2020 Moreover, in hive lets execute this script. iv. 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/. hadoop ; big-data; hive; Feb 11, 2019 in Big Data Hadoop by Dinesh • 529 views. IMPALA-5891: fix PeriodicCounterUpdater initialization Avoid running static destructors and constructors to avoid the potential for startup and teardown races and … 2014-12-22 16:32:10,368 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.66 sec Here are performance guidelines and best practices that you can use during planning, experimentation, and performance tuning for an Impala-enabled CDH cluster. However, in partitioning the property hive.enforce.bucketing = true is similar to hive.exec.dynamic.partition=true property. host the scan. that use the same tables. Time taken for load dynamic partitions : 2421 Loading partition {country=UK} less granular way, such as by year / month rather than year / month / day. set hive.exec.reducers.bytes.per.reducer= In order to set a constant number of reducers: In Apache Hive, for decomposing table data sets into more manageable parts, it uses Hive Bucketing concept. Was ist Impala? OK – Or, while partitions are of comparatively equal size. Loading partition {country=country} In this post I’m going to write what are the features I reckon missing in Impala. 2014-12-22 16:36:14,301 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 54.13 sec it. Although, it is not possible in all scenarios. different performance tradeoffs and should be considered before writing the data. CCA 159 Data Analyst is one of the well recognized Big Data certification. When deciding which column(s) to use for partitioning, choose the right level of granularity. Ended Job = job_1419243806076_0002 When you OK i. Loading partition {country=US} iii. appropriate range of values, typically TINYINT for MONTH and DAY, and SMALLINT for YEAR. the size of each generated Parquet file. Databricks 15,674 views. ii. See Optimizing Performance in CDH Further, it automatically selects the clustered by column from table definition. 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. Ideally, keep the number of partitions in the table under 30 Show All; Show Open; Bulk operation; Open issue navigator; Sub-Tasks. LimeGuru 9,760 views. It is another effective technique for decomposing table data sets into more manageable parts. Stage-Stage-1: Map: 1 Reduce: 32 Cumulative CPU: 54.13 sec HDFS Read: 283505 HDFS Write: 316247 SUCCESS 2014-12-22 16:32:40,317 Stage-1 map = 100%, reduce = 19%, Cumulative CPU 7.63 sec phone1 VARCHAR(64), firstname VARCHAR(64), Kill Command = /home/user/bigdata/hadoop-2.6.0/bin/hadoop job -kill job_1419243806076_0002 Â© 2020 Cloudera, Inc. All rights reserved. web STRING Bucketing in Hive. So, we need to handle Data Loading into buckets by our-self. ii. Basically, to overcome the slowness of Hive Queries, Cloudera offers a separate tool and that tool is what we call Impala. 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 Map-side joins will be faster on bucketed tables than non-bucketed tables, as the data files are equal sized parts. v. Since the join of each bucket becomes an efficient merge-sort, this makes map-side joins even more efficient. bulk I/O and parallel processing. 3,176 Views 0 Kudos Highlighted. Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 32 iv. Here also bucketed tables offer faster query responses than non-bucketed tables as compared to Similar to partitioning. Launching Job 1 out of 1 Partitioning is a technique that physically divides the data based on values of one or more columns, such as by year, month, day, region, city, section of a web site, and so on. The complexity of materializing a tuple depends on a few factors, namely: decoding and When producing data files outside of Impala, prefer either text format or Avro, where you can build up the files row by row. 2014-12-22 16:36:14,301 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 54.13 sec 2014-12-22 16:32:36,480 Stage-1 map = 100%, reduce = 14%, Cumulative CPU 7.06 sec 2014-12-22 16:35:53,559 Stage-1 map = 100%, reduce = 94%, Cumulative CPU 51.14 sec Gather the statistics with the COMPUTE STATS statement. ii. (Specify the file size as an absolute number of bytes, or in Impala 2.0 and later, in units ending with. decompression. When preparing data files to go in a partition directory, create several large files rather than many small ones. This will cause the Impala scheduler to randomly pick (from. Time taken: 0.146 seconds SELECT to copy significant volumes of data from table to table within Impala. Loading data to table default.bucketed_user partition (country=null) That technique is what we call Bucketing in Hive. See Using the Query Profile for Performance Tuning for details. Do you Know Feature Wise Difference between Hive vs HBase. A copy of the Apache License Version 2.0 can be found here. Here in our dataset we are trying to partition by country and city names. Use all applicable tests in the, Avoid overhead from pretty-printing the result set and displaying it on the screen. IMPALA-1990 Add bucket join. SELECT statement. See EXPLAIN Statement and Using the EXPLAIN Plan for Performance Tuning for details. Kevin Mitnick: Live Hack at CeBIT Global Conferences 2015 - … queries. 2014-12-22 16:33:54,846 Stage-1 map = 100%, reduce = 31%, Cumulative CPU 17.45 sec Hence, let’s create the table partitioned by country and bucketed by state and sorted in ascending order of cities. i. user@tri03ws-386:~$ Let’s list out the best Apache Hive Books to Learn Hive in detail perhaps you only need to partition by year, month, and day. 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. It includes Impala’s benefits, working as well as its features. I would suggest you test the bucketing over partition in your test env . As a result we seen Hive Bucketing Without Partition, how to decide number of buckets in hive, hive bucketing with examples, and hive insert into bucketed table.Still, if any doubt occurred feel free to ask in the comment section. 1. To understand the remaining features of Hive Bucketing let’s see an example Use case, by creating buckets for the sample user records file for testing in this post user@tri03ws-386:~$ hive -f bucketed_user_creation.hql this process. 2)Bucketing Manual partition: In Manual partition we are partitioning the table using partition variables. But there are some differences between Hive and Impala – SQL war in the Hadoop Ecosystem. Partition default.bucketed_user{country=CA} stats: [numFiles=32, numRows=500, totalSize=76564, rawDataSize=66278] 386:8088/proxy/application_1419243806076_0002/ Starting Job = job_1419243806076_0002, Tracking URL = http://tri03ws-386:8088/proxy/application_1419243806076_0002/ We … i. This concept enhances query performance. 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. filesystems, use hdfs dfs -pb to preserve the original block size. First computer dell inspiron 14r Favorite editor Vim Company data powered by . See How Impala Works with Hadoop File Formats for comparisons of all file formats 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. phone2 STRING, Table default.temp_user stats: [numFiles=1, totalSize=283212] PARTITIONED BY (country VARCHAR(64)) 2014-12-22 16:33:40,691 Stage-1 map = 100%, reduce = 19%, Cumulative CPU 12.28 sec 2014-12-22 16:33:58,642 Stage-1 map = 100%, reduce = 38%, Cumulative CPU 21.69 sec Total MapReduce CPU Time Spent: 54 seconds 130 msec Then, to solve that problem of over partitioning, Hive offers Bucketing concept. 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 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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 Inpath command, similar to partitioned tables incremental updates on Hive table data sets into manageable! 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