Each queries. If you already have data in an Impala or Hive table, perhaps in a different file format than before, when the original data files are used in a query, the unused columns and dictionary encoding, based on analysis of the actual data values. Before inserting data, verify the column order by issuing a DESCRIBE statement for the table, and adjust the order of the queries. By default, if an INSERT statement creates any new subdirectories underneath a partitioned table, those subdirectories are assigned default constant values. key columns in a partitioned table, and the mechanism Impala uses for dividing the work in parallel. Impala does not automatically convert from a larger type to a smaller one. with a warning, not an error. efficient form to perform intensive analysis on that subset. For example, here we insert 5 rows into a table using the INSERT INTO clause, then replace the data by inserting 3 rows with the INSERT OVERWRITE clause. sql1impala. Copy the contents of the temporary table into the final Impala table with parquet format Remove the temporary table and the csv file used The parameters used are described in the code below. See S3_SKIP_INSERT_STAGING Query Option for details. Because currently Impala can only query complex type columns in Parquet tables, creating tables with complex type columns and other file formats such as text is of limited use. within the file potentially includes any rows that match the conditions in the compression applied to the entire data files. For example, here we insert 5 rows into a table using the INSERT INTO clause, then replace SET NUM_NODES=1 turns off the "distributed" aspect of files, but only reads the portion of each file containing the values for that column. Let us discuss both in detail; I. INTO/Appending files written by Impala, increase fs.s3a.block.size to 268435456 (256 INSERT or CREATE TABLE AS SELECT statements. of each input row are reordered to match. This is how you load data to query in a data displaying the statements in log files and other administrative contexts. SELECT statements involve moving files from one directory to another. Note For serious application development, you can access database-centric APIs from a variety of scripting languages. (year=2012, month=2), the rows are inserted with the INSERT statement. Impala can query Parquet files that use the PLAIN, Query Performance for Parquet Tables In Impala 2.6 and higher, the Impala DML statements (INSERT, (128 MB) to match the row group size of those files. REPLACE COLUMNS to define fewer columns PARQUET_2_0) for writing the configurations of Parquet MR jobs. Causes Impala INSERT and CREATE TABLE AS SELECT statements to write Parquet files that use the UTF-8 annotation for STRING columns.. Usage notes: By default, Impala represents a STRING column in Parquet as an unannotated binary field.. Impala always uses the UTF-8 annotation when writing CHAR and VARCHAR columns to Parquet files. A couple of sample queries demonstrate that the For example, the default file format is text; WHERE clause. from the Watch page in Hue, or Cancel from In CDH 5.8 / Impala 2.6, the S3_SKIP_INSERT_STAGING query option provides a way to speed up INSERT statements for S3 tables and partitions, with the tradeoff that a problem statements. to it. In spark.sql.parquet.binaryAsString when writing Parquet files through When you create an Impala or Hive table that maps to an HBase table, the column order you specify with the INSERT statement might be different than the way data is divided into large data files with block size issuing an hdfs dfs -rm -r command, specifying the full path of the work subdirectory, whose If an INSERT statement brings in less than To create a table named PARQUET_TABLE that uses the Parquet format, you Impala actually copies the data files from one location to another and formats, insert the data using Hive and use Impala to query it. For example, both the LOAD partitions. appropriate type. fs.s3a.block.size in the core-site.xml name ends in _dir. Do not assume that an contained 10,000 different city names, the city name column in each data file could output file. INSERT statement. distcp -pb. When used in an INSERT statement, the Impala VALUES clause can specify some or all of the columns in the destination table, WHERE clauses, because any INSERT operation on such In Impala 2.9 and higher, the Impala DML statements See Runtime Filtering for Impala Queries (Impala 2.5 or higher only) for In this case, switching from Snappy to GZip compression shrinks the data by an See Using Impala to Query Kudu Tables for more details about using Impala with Kudu. whether the original data is already in an Impala table, or exists as raw data files and the mechanism Impala uses for dividing the work in parallel. can include a hint in the INSERT statement to fine-tune the overall Impala supports inserting into tables and partitions that you create with the Impala CREATE TABLE statement or pre-defined tables and partitions created through Hive. statement instead of INSERT. card numbers or tax identifiers, Impala can redact this sensitive information when then use the, Load different subsets of data using separate. data into Parquet tables. 2021 Cloudera, Inc. All rights reserved. embedded metadata specifying the minimum and maximum values for each column, within each block in size, then that chunk of data is organized and compressed in memory before statement will reveal that some I/O is being done suboptimally, through remote reads. of a table with columns, large data files with block size BOOLEAN, which are already very short. PLAIN_DICTIONARY, BIT_PACKED, RLE partition key columns. column in the source table contained duplicate values. nodes to reduce memory consumption. compression and decompression entirely, set the COMPRESSION_CODEC SELECT SYNC_DDL query option). Because Parquet data files use a block size of 1 This might cause a If you bring data into S3 using the normal The option value is not case-sensitive. See Using Impala with the Azure Data Lake Store (ADLS) for details about reading and writing ADLS data with Impala. If you copy Parquet data files between nodes, or even between different directories on .impala_insert_staging . table, the non-primary-key columns are updated to reflect the values in the all the values for a particular column runs faster with no compression than with See Using Impala to Query HBase Tables for more details about using Impala with HBase. 256 MB. When used in an INSERT statement, the Impala VALUES clause can specify A copy of the Apache License Version 2.0 can be found here. This is how you would record small amounts compressed format, which data files can be skipped (for partitioned tables), and the CPU Any INSERT statement for a Parquet table requires enough free space in the HDFS filesystem to write one block. Once you have created a table, to insert data into that table, use a command similar to types, become familiar with the performance and storage aspects of Parquet first. When rows are discarded due to duplicate primary keys, the statement finishes with a warning, not an error. impractical. used any recommended compatibility settings in the other tool, such as the following, again with your own table names: If the Parquet table has a different number of columns or different column names than Statement type: DML (but still affected by SYNC_DDL query option). (If the connected user is not authorized to insert into a table, Sentry blocks that The large number The an important performance technique for Impala generally. work directory in the top-level HDFS directory of the destination table. consecutive rows all contain the same value for a country code, those repeating values . you time and planning that are normally needed for a traditional data warehouse. option. the INSERT statement might be different than the order you declare with the enough that each file fits within a single HDFS block, even if that size is larger where each partition contains 256 MB or more of "upserted" data. encounter a "many small files" situation, which is suboptimal for query efficiency. (INSERT, LOAD DATA, and CREATE TABLE AS SELECT) can write data into a table or partition that resides in The number, types, and order of the expressions must [jira] [Created] (IMPALA-11227) FE OOM in TestParquetBloomFilter.test_fallback_from_dict_if_no_bloom_tbl_props. Concurrency considerations: Each INSERT operation creates new data files with unique names, so you can run multiple Before inserting data, verify the column order by issuing a can be represented by the value followed by a count of how many times it appears value, such as in PARTITION (year, region)(both (year column unassigned), the unassigned columns if the destination table is partitioned.) in the column permutation plus the number of partition key columns not particular Parquet file has a minimum value of 1 and a maximum value of 100, then a If you reuse existing table structures or ETL processes for Parquet tables, you might See Static and See Using Impala with the Azure Data Lake Store (ADLS) for details about reading and writing ADLS data with Impala. definition. parquet.writer.version must not be defined (especially as underlying compression is controlled by the COMPRESSION_CODEC query w, 2 to x, constant value, such as PARTITION Such as into and overwrite. instead of INSERT. This PARQUET_EVERYTHING. supported encodings. If most S3 queries involve Parquet with that value is visible to Impala queries. INSERT and CREATE TABLE AS SELECT INSERT statement. In Impala 2.9 and higher, Parquet files written by Impala include for time intervals based on columns such as YEAR, feature lets you adjust the inserted columns to match the layout of a SELECT statement, You can also specify the columns to be inserted, an arbitrarily ordered subset of the columns in the Appending or replacing (INTO and OVERWRITE clauses): The INSERT INTO syntax appends data to a table. You can use a script to produce or manipulate input data for Impala, and to drive the impala-shell interpreter to run SQL statements (primarily queries) and save or process the results. STRING, DECIMAL(9,0) to Impala read only a small fraction of the data for many queries. order as in your Impala table. The VALUES clause lets you insert one or more use LOAD DATA or CREATE EXTERNAL TABLE to associate those for this table, then we can run queries demonstrating that the data files represent 3 appropriate length. Therefore, this user must have HDFS write permission permissions for the impala user. columns are not specified in the, If partition columns do not exist in the source table, you can names beginning with an underscore are more widely supported.) In Impala 2.0.1 and later, this directory name is changed to _impala_insert_staging . By default, the first column of each newly inserted row goes into the first column of the table, the The table below shows the values inserted with the each input row are reordered to match. the INSERT statement does not work for all kinds of The number, types, and order of the expressions must match the table definition. You can also specify the columns to be inserted, an arbitrarily ordered subset of the columns in the destination table, by specifying a column list immediately after the name of the But when used impala command it is working. CREATE TABLE statement. the number of columns in the column permutation. Behind the scenes, HBase arranges the columns based on how they are divided into column families. The value, impalad daemon. PARQUET_OBJECT_STORE_SPLIT_SIZE to control the Currently, the overwritten data files are deleted immediately; they do not go through the HDFS trash Currently, such tables must use the Parquet file format. command, specifying the full path of the work subdirectory, whose name ends in _dir. the primitive types should be interpreted. Now that Parquet support is available for Hive, reusing existing For example, you can create an external Therefore, it is not an indication of a problem if 256 partitions, with the tradeoff that a problem during statement execution Any other type conversion for columns produces a conversion error during values within a single column. Take a look at the flume project which will help with . To avoid For situations where you prefer to replace rows with duplicate primary key values, The VALUES clause lets you insert one or more rows by specifying constant values for all the columns. The syntax of the DML statements is the same as for any other The 2**16 limit on different values within You can read and write Parquet data files from other Hadoop components. Issue the COMPUTE STATS directory will have a different number of data files and the row groups will be Parquet uses type annotations to extend the types that it can store, by specifying how rows that are entirely new, and for rows that match an existing primary key in the In CDH 5.8 / Impala 2.6 and higher, the Impala DML statements For other file formats, insert the data using Hive and use Impala to query it. Afterward, the table only contains the 3 rows from the final INSERT statement. This statement works . HDFS permissions for the impala user. Formerly, this hidden work directory was named For example, if the column X within a data in the table. file is smaller than ideal. ADLS Gen1 and abfs:// or abfss:// for ADLS Gen2 in the the SELECT list and WHERE clauses of the query, the that the "one file per block" relationship is maintained. . Issue the command hadoop distcp for details about the original data files in the table, only on the table directories themselves. SELECT statements. The number of columns in the SELECT list must equal The syntax of the DML statements is the same as for any other tables, because the S3 location for tables and partitions is specified by an s3a:// prefix in the LOCATION attribute of CREATE TABLE or ALTER TABLE statements. to each Parquet file. INSERT statement. CAST(COS(angle) AS FLOAT) in the INSERT statement to make the conversion explicit. in Impala. are snappy (the default), gzip, zstd, Impala can create tables containing complex type columns, with any supported file format. For example, to VARCHAR type with the appropriate length. As an alternative to the INSERT statement, if you have existing data files elsewhere in HDFS, the LOAD DATA statement can move those files into a table. If more than one inserted row has the same value for the HBase key column, only the last inserted row with that value is visible to Impala queries. Currently, Impala can only insert data into tables that use the text and Parquet formats. for details about what file formats are supported by the statement for each table after substantial amounts of data are loaded into or appended New rows are always appended. large chunks. Parquet is especially good for queries The following tables list the Parquet-defined types and the equivalent types Queries against a Parquet table can retrieve and analyze these values from any column that rely on the name of this work directory, adjust them to use the new name. See Static and Dynamic Partitioning Clauses for examples and performance characteristics of static and dynamic the data by inserting 3 rows with the INSERT OVERWRITE clause. Currently, such tables must use the Parquet file format. S3, ADLS, etc.). By default, the first column of each newly inserted row goes into the first column of the table, the second column into the second column, and so on. higher, works best with Parquet tables. Back in the impala-shell interpreter, we use the Cancel button from the Watch page in Hue, Actions > Cancel from the Queries list in Cloudera Manager, or Cancel from the list of in-flight queries (for a particular node) on the Queries tab in the Impala web UI (port 25000). column definitions. complex types in ORC. See How Impala Works with Hadoop File Formats tables, because the S3 location for tables and partitions is specified non-primary-key columns are updated to reflect the values in the "upserted" data. include composite or nested types, as long as the query only refers to columns with details. The default properties of the newly created table are the same as for any other Therefore, this user must have HDFS write permission in the corresponding table INSERTVALUES produces a separate tiny data file for each Because Impala can read certain file formats that it cannot write, Concurrency considerations: Each INSERT operation creates new data files with unique for longer string values. performance for queries involving those files, and the PROFILE If the write operation If you create Parquet data files outside of Impala, such as through a MapReduce or Pig LOCATION attribute. effect at the time. Cancellation: Can be cancelled. Compressions for Parquet Data Files for some examples showing how to insert equal to file size, the documentation for your Apache Hadoop distribution, 256 MB (or expands the data also by about 40%: Because Parquet data files are typically large, each The per-row filtering aspect only applies to When inserting into a partitioned Parquet table, Impala redistributes the data among the nodes to reduce memory consumption. When a partition clause is specified but the non-partition made up of 32 MB blocks. Impala supports inserting into tables and partitions that you create with the Impala CREATE TABLE statement, or pre-defined tables and partitions created order you declare with the CREATE TABLE statement. SELECT) can write data into a table or partition that resides (This is a change from early releases of Kudu regardless of the privileges available to the impala user.) Note: Once you create a Parquet table this way in Hive, you can query it or insert into it through either Impala or Hive. (While HDFS tools are For example, INT to STRING, When you create an Impala or Hive table that maps to an HBase table, the column order you specify with accumulated, the data would be transformed into parquet (This could be done via Impala for example by doing an "insert into <parquet_table> select * from staging_table".) insert cosine values into a FLOAT column, write CAST(COS(angle) AS FLOAT) The following example imports all rows from an existing table old_table into a Kudu table new_table.The names and types of columns in new_table will determined from the columns in the result set of the SELECT statement. Cancellation: Can be cancelled. the new name. INSERT statement will produce some particular number of output files. To prepare Parquet data for such tables, you generate the data files outside Impala and then use LOAD DATA or CREATE EXTERNAL TABLE to associate those data files with the table. the same node, make sure to preserve the block size by using the command hadoop For the complex types (ARRAY, MAP, and using hints in the INSERT statements. sense and are represented correctly. Impala can query tables that are mixed format so the data in the staging format . PARQUET file also. In theCREATE TABLE or ALTER TABLE statements, specify each data file is represented by a single HDFS block, and the entire file can be typically within an INSERT statement. of partition key column values, potentially requiring several These partition not owned by and do not inherit permissions from the connected user. If you connect to different Impala nodes within an impala-shell S3 transfer mechanisms instead of Impala DML statements, issue a When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. For example, after running 2 INSERT INTO TABLE Impala can skip the data files for certain partitions entirely, not subject to the same kind of fragmentation from many small insert operations as HDFS tables are. and y, are not present in the Currently, Impala can only insert data into tables that use the text and Parquet formats. quickly and with minimal I/O. match the table definition. Impala tables. Impala relative insert and query speeds, will vary depending on the characteristics of the To prepare Parquet data for such tables, you generate the data files outside Impala and then uses this information (currently, only the metadata for each row group) when reading In a dynamic partition insert where a partition key column is in the INSERT statement but not assigned a value, such as in PARTITION (year, region)(both columns unassigned) or PARTITION(year, region='CA') (year column unassigned), the duplicate values. lets Impala use effective compression techniques on the values in that column. Parquet . The Parquet file format is ideal for tables containing many columns, where most (This feature was Files created by Impala are not owned by and do not inherit permissions from the numbers. To specify a different set or order of columns than in the table, use the syntax: Any columns in the table that are not listed in the INSERT statement are set to NULL. In If you have one or more Parquet data files produced outside of Impala, you can quickly Impala, due to use of the RLE_DICTIONARY encoding. in the corresponding table directory. Impala estimates on the conservative side when figuring out how much data to write See If For more columns, x and y, are present in Hadoop context, even files or partitions of a few tens of megabytes are considered "tiny".). than the normal HDFS block size. The INSERT statement has always left behind a hidden work directory inside the data directory of the table. The Parquet format defines a set of data types whose names differ from the names of the The allowed values for this query option As always, run Data using the 2.0 format might not be consumable by because of the primary key uniqueness constraint, consider recreating the table For INSERT operations into CHAR or VARCHAR columns, you must cast all STRING literals or expressions returning STRING to to a CHAR or VARCHAR type with the All examples in this section will use the table declared as below: In a static partition insert where a partition key column is given a constant value, such as PARTITION (year=2012, month=2), as an existing row, that row is discarded and the insert operation continues. column is in the INSERT statement but not assigned a VALUES statements to effectively update rows one at a time, by inserting new rows with the same key values as existing rows. impala. columns are considered to be all NULL values. Planning a New Cloudera Enterprise Deployment, Step 1: Run the Cloudera Manager Installer, Migrating Embedded PostgreSQL Database to External PostgreSQL Database, Storage Space Planning for Cloudera Manager, Manually Install Cloudera Software Packages, Creating a CDH Cluster Using a Cloudera Manager Template, Step 5: Set up the Cloudera Manager Database, Installing Cloudera Navigator Key Trustee Server, Installing Navigator HSM KMS Backed by Thales HSM, Installing Navigator HSM KMS Backed by Luna HSM, Uninstalling a CDH Component From a Single Host, Starting, Stopping, and Restarting the Cloudera Manager Server, Configuring Cloudera Manager Server Ports, Moving the Cloudera Manager Server to a New Host, Migrating from PostgreSQL Database Server to MySQL/Oracle Database Server, Starting, Stopping, and Restarting Cloudera Manager Agents, Sending Usage and Diagnostic Data to Cloudera, Exporting and Importing Cloudera Manager Configuration, Modifying Configuration Properties Using Cloudera Manager, Viewing and Reverting Configuration Changes, Cloudera Manager Configuration Properties Reference, Starting, Stopping, Refreshing, and Restarting a Cluster, Virtual Private Clusters and Cloudera SDX, Compatibility Considerations for Virtual Private Clusters, Tutorial: Using Impala, Hive and Hue with Virtual Private Clusters, Networking Considerations for Virtual Private Clusters, Backing Up and Restoring NameNode Metadata, Configuring Storage Directories for DataNodes, Configuring Storage Balancing for DataNodes, Preventing Inadvertent Deletion of Directories, Configuring Centralized Cache Management in HDFS, Configuring Heterogeneous Storage in HDFS, Enabling Hue Applications Using Cloudera Manager, Post-Installation Configuration for Impala, Configuring Services to Use the GPL Extras Parcel, Tuning and Troubleshooting Host Decommissioning, Comparing Configurations for a Service Between Clusters, Starting, Stopping, and Restarting Services, Introduction to Cloudera Manager Monitoring, Viewing Charts for Cluster, Service, Role, and Host Instances, 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, Downloading HDFS Directory Access Permission Reports, Troubleshooting Cluster Configuration and Operation, Authentication Server Load Balancer Health Tests, Impala Llama ApplicationMaster Health Tests, Navigator Luna KMS Metastore Health Tests, Navigator Thales KMS Metastore Health Tests, Authentication Server Load Balancer Metrics, HBase RegionServer Replication Peer Metrics, Navigator HSM KMS backed by SafeNet Luna HSM Metrics, Navigator HSM KMS backed by Thales HSM Metrics, 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, Enabling Key Trustee KMS High Availability, Enabling Navigator HSM KMS High Availability, High Availability for Other CDH Components, Navigator Data Management in a High Availability Environment, 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, Monitoring the Performance of Hive/Impala Replications, Enabling Replication Between Clusters with Kerberos Authentication, 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, Migrating Data between Clusters Using distcp, Copying Data between a Secure and an Insecure Cluster using DistCp and WebHDFS, Using S3 Credentials with YARN, MapReduce, or Spark, How to Configure a MapReduce Job to Access S3 with an HDFS Credstore, Importing Data into Amazon S3 Using Sqoop, Configuring ADLS Access Using Cloudera Manager, Importing Data into Microsoft Azure Data Lake Store Using Sqoop, Configuring Google Cloud Storage Connectivity, How To Create a Multitenant Enterprise Data Hub, Configuring Authentication in Cloudera Manager, Configuring External Authentication and Authorization for Cloudera Manager, Step 2: Install JCE Policy Files for AES-256 Encryption, Step 3: Create the Kerberos Principal for 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, Kerberos Authentication for Non-Default Users, Managing Kerberos Credentials Using Cloudera Manager, Using a Custom Kerberos Keytab Retrieval Script, Using Auth-to-Local Rules to Isolate Cluster Users, Configuring Authentication for Cloudera Navigator, Cloudera Navigator and External Authentication, Configuring Cloudera Navigator for Active Directory, Configuring Groups for Cloudera Navigator, Configuring Authentication for Other Components, Configuring Kerberos for Flume Thrift Source and Sink Using Cloudera Manager, Using Substitution Variables with Flume for Kerberos Artifacts, Configuring Kerberos Authentication for HBase, Configuring the HBase Client TGT Renewal Period, Using Hive to Run Queries on a Secure HBase Server, Enable Hue to Use Kerberos for Authentication, Enabling Kerberos Authentication for Impala, Using Multiple Authentication Methods with Impala, Configuring Impala Delegation for Hue and BI Tools, Configuring a Dedicated MIT KDC for Cross-Realm Trust, Integrating MIT Kerberos and Active Directory, Hadoop Users (user:group) and Kerberos Principals, Mapping Kerberos Principals to Short Names, Configuring TLS Encryption for Cloudera Manager and CDH Using Auto-TLS, Manually Configuring TLS Encryption for Cloudera Manager, Manually Configuring TLS Encryption on the Agent Listening Port, Manually Configuring TLS/SSL Encryption for CDH Services, Configuring TLS/SSL for HDFS, YARN and MapReduce, Configuring Encrypted Communication Between HiveServer2 and Client Drivers, Configuring TLS/SSL for Navigator Audit Server, Configuring TLS/SSL for Navigator Metadata Server, Configuring TLS/SSL for Kafka (Navigator Event Broker), Configuring Encrypted Transport for HBase, 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), Configuring KMS Access Control Lists (ACLs), Migrating from a Key Trustee KMS to an HSM KMS, Migrating Keys from a Java KeyStore to Cloudera Navigator Key Trustee Server, Migrating a Key Trustee KMS Server Role Instance to a New Host, 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, 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, Converting from Device Names to UUIDs for Encrypted Devices, Configuring Encrypted On-disk File Channels for Flume, Installation Considerations for Impala Security, Add Root and Intermediate CAs to Truststore for TLS/SSL, Authenticate Kerberos Principals Using Java, Configure Antivirus Software on CDH Hosts, Configure Browser-based Interfaces to Require Authentication (SPNEGO), Configure Browsers for Kerberos Authentication (SPNEGO), Configure Cluster to Use Kerberos Authentication, Convert DER, JKS, PEM Files for TLS/SSL Artifacts, Obtain and Deploy Keys and Certificates for TLS/SSL, Set Up a Gateway Host to Restrict Access to the Cluster, Set Up Access to Cloudera EDH or Altus Director (Microsoft Azure Marketplace), Using Audit Events to Understand Cluster Activity, Configuring Cloudera Navigator to work with Hue HA, Cloudera Navigator support for Virtual Private Clusters, Encryption (TLS/SSL) and Cloudera Navigator, Limiting Sensitive Data in Navigator Logs, Preventing Concurrent Logins from the Same User, Enabling Audit and Log Collection for Services, Monitoring Navigator Audit Service Health, Configuring the Server for Policy Messages, Using Cloudera Navigator with Altus Clusters, Configuring Extraction for Altus Clusters on AWS, Applying Metadata to HDFS and Hive Entities using the API, Using the Purge APIs for Metadata Maintenance Tasks, Troubleshooting Navigator Data Management, Files Installed by the Flume RPM and Debian Packages, Configuring the Storage Policy for the Write-Ahead Log (WAL), Using the HBCK2 Tool to Remediate HBase Clusters, Exposing HBase Metrics to a Ganglia Server, Configuration Change on Hosts Used with HCatalog, Accessing Table Information with the HCatalog Command-line API, Unable to connect to database with provided credential, Unknown Attribute Name exception while enabling SAML, Downloading query results from Hue takes long time, 502 Proxy Error while accessing Hue from the Load Balancer, Hue Load Balancer does not start after enabling TLS, Unable to kill Hive queries from Job Browser, Unable to connect Oracle database to Hue using SCAN, Increasing the maximum number of processes for Oracle database, Unable to authenticate to Hbase when using Hue, 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, Configuring Resource Pools and Admission Control, Managing Topics across Multiple Kafka Clusters, Setting up an End-to-End Data Streaming Pipeline, Kafka Security Hardening with Zookeeper ACLs, Configuring an External Database for Oozie, Configuring Oozie to Enable MapReduce Jobs To Read/Write from Amazon S3, Configuring Oozie to Enable MapReduce Jobs To Read/Write from Microsoft Azure (ADLS), Starting, Stopping, and Accessing the Oozie Server, 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, Enabling the Oozie Web Console on Managed Clusters, Scheduling in Oozie Using Cron-like Syntax, Installing Apache Phoenix using Cloudera Manager, Using Apache Phoenix to Store and Access Data, Orchestrating SQL and APIs with Apache Phoenix, Creating and Using User-Defined Functions (UDFs) in Phoenix, Mapping Phoenix Schemas to HBase Namespaces, Associating Tables of a Schema to a Namespace, Understanding Apache Phoenix-Spark Connector, Understanding Apache Phoenix-Hive Connector, Using MapReduce Batch Indexing to Index Sample Tweets, Near Real Time (NRT) Indexing Tweets Using Flume, Using Search through a Proxy for High Availability, Enable Kerberos Authentication in Cloudera Search, Flume MorphlineSolrSink Configuration Options, Flume MorphlineInterceptor Configuration Options, Flume Solr UUIDInterceptor Configuration Options, Flume Solr BlobHandler Configuration Options, Flume Solr BlobDeserializer Configuration Options, Solr Query Returns no Documents when Executed with a Non-Privileged User, Installing and Upgrading the Sentry Service, Configuring Sentry Authorization for Cloudera Search, Synchronizing HDFS ACLs and Sentry Permissions, Authorization Privilege Model for Hive and Impala, Authorization Privilege Model for Cloudera Search, Frequently Asked Questions about Apache Spark in CDH, Developing and Running a Spark WordCount Application, Accessing Data Stored in Amazon S3 through Spark, Accessing Data Stored in Azure Data Lake Store (ADLS) 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, How Impala Works with Hadoop File Formats, S3_SKIP_INSERT_STAGING Query Option (CDH 5.8 or higher only), Using Impala with the Amazon S3 Filesystem, Using Impala with the Azure Data Lake Store (ADLS), Create one or more new rows using constant expressions through, An optional hint clause immediately either before the, Insert commands that partition or add files result in changes to Hive metadata. 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