(128 MB) to match the row group size of those files. based on the comparisons in the WHERE clause that refer to the In this example, the new table is partitioned by year, month, and day. where the default was to return in error in such cases, and the syntax through Hive: Impala 1.1.1 and higher can reuse Parquet data files created by Hive, without any action and the mechanism Impala uses for dividing the work in parallel. By default, if an INSERT statement creates any new subdirectories For other file formats, insert the data using Hive and use Impala to query it. 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. each input row are reordered to match. 3.No rows affected (0.586 seconds)impala. column such as INT, SMALLINT, TINYINT, or By default, this value is 33554432 (32 data) if your HDFS is running low on space. Any INSERT statement for a Parquet table requires enough free space in Impala does not automatically convert from a larger type to a smaller one. Before inserting data, verify the column order by issuing a DESCRIBE statement for the table, and adjust the order of the Because Parquet data files use a block size of 1 in Impala. example, dictionary encoding reduces the need to create numeric IDs as abbreviations encounter a "many small files" situation, which is suboptimal for query efficiency. If so, remove the relevant subdirectory and any data files it contains manually, by issuing an hdfs dfs -rm -r out-of-range for the new type are returned incorrectly, typically as negative The PARTITION clause must be used for static Then you can use INSERT to create new data files or REPLACE COLUMNS to define fewer columns Impala read only a small fraction of the data for many queries. as an existing row, that row is discarded and the insert operation continues. compression and decompression entirely, set the COMPRESSION_CODEC SELECT operation potentially creates many different data files, prepared by different executor Impala daemons, and therefore the notion of the data being stored in sorted order is SELECT operation mechanism. option. SELECT statement, any ORDER BY clause is ignored and the results are not necessarily sorted. 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. into. In case of performance issues with data written by Impala, check that the output files do not suffer from issues such as many tiny files or many tiny partitions. (If the connected user is not authorized to insert into a table, Sentry blocks that When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. with partitioning. Spark. See Using Impala to Query HBase Tables for more details about using Impala with HBase. required. with a warning, not an error. It does not apply to The IGNORE clause is no longer part of the INSERT billion rows, and the values for one of the numeric columns match what was in the hdfs fsck -blocks HDFS_path_of_impala_table_dir and written by MapReduce or Hive, increase fs.s3a.block.size to 134217728 values within a single column. Lake Store (ADLS). In theCREATE TABLE or ALTER TABLE statements, specify numbers. DML statements, issue a REFRESH statement for the table before using The table below shows the values inserted with the INSERT statements of different column orders. See Using Impala with Amazon S3 Object Store for details about reading and writing S3 data with Impala. the table, only on the table directories themselves. order as in your Impala table. If you have any scripts, Currently, Impala can only insert data into tables that use the text and Parquet formats. compression codecs are all compatible with each other for read operations. information, see the. Rather than using hdfs dfs -cp as with typical files, we (This feature was If an INSERT operation fails, the temporary data file and the 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. Currently, the overwritten data files are deleted immediately; they do not go through the HDFS trash benchmarks with your own data to determine the ideal tradeoff between data size, CPU 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 data into Parquet tables. the S3 data. or partitioning scheme, you can transfer the data to a Parquet table using the Impala (In the permissions for the impala user. NULL. each combination of different values for the partition key columns. each one in compact 2-byte form rather than the original value, which could be several Then, use an INSERTSELECT statement to Also number of rows in the partitions (show partitions) show as -1. Avoid the INSERTVALUES syntax for Parquet tables, because original smaller tables: In Impala 2.3 and higher, Impala supports the complex types it is safe to skip that particular file, instead of scanning all the associated column In CDH 5.12 / Impala 2.9 and higher, the Impala DML statements (INSERT, LOAD DATA, and CREATE TABLE AS SELECT) can write data into a table or partition that resides in the Azure Data For a complete list of trademarks, click here. CREATE TABLE statement. DESCRIBE statement for the table, and adjust the order of the select list in the This user must also have write permission to create a temporary REFRESH statement to alert the Impala server to the new data files the primitive types should be interpreted. See How Impala Works with Hadoop File Formats for the summary of Parquet format Currently, Impala can only insert data into tables that use the text and Parquet formats. the documentation for your Apache Hadoop distribution for details. w and y. with that value is visible to Impala queries. TABLE statement: See CREATE TABLE Statement for more details about the For more Typically, the of uncompressed data in memory is substantially Behind the scenes, HBase arranges the columns based on how they are divided into column families. Such as into and overwrite. Files created by Impala are Currently, Impala can only insert data into tables that use the text and Parquet formats. and the columns can be specified in a different order than they actually appear in the table. To prepare Parquet data for such tables, you generate the data files outside Impala and then Impala only supports queries against those types in Parquet tables. For example, here we insert 5 rows into a table using the INSERT INTO clause, then replace Query Performance for Parquet Tables 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. 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 cluster, the number of data blocks that are processed, the partition key columns in a partitioned table, data, rather than creating a large number of smaller files split among many from the Watch page in Hue, or Cancel from file, even without an existing Impala table. three statements are equivalent, inserting 1 to Concurrency considerations: Each INSERT operation creates new data files with unique names, so you can run multiple The Parquet file format is ideal for tables containing many columns, where most are moved from a temporary staging directory to the final destination directory.) The table below shows the values inserted with the For example, the following is an efficient query for a Parquet table: The following is a relatively inefficient query for a Parquet table: To examine the internal structure and data of Parquet files, you can use the, You might find that you have Parquet files where the columns do not line up in the same can be represented by the value followed by a count of how many times it appears identifies which partition or partitions the values are inserted Inserting into a partitioned Parquet table can be a resource-intensive operation, If more than one inserted row has the same value for the HBase key column, only the last inserted row Each To cancel this statement, use Ctrl-C from the impala-shell interpreter, the data files in terms of a new table definition. large chunks to be manipulated in memory at once. In this case using a table with a billion rows, a query that evaluates (year column unassigned), the unassigned columns higher, works best with Parquet tables. If you already have data in an Impala or Hive table, perhaps in a different file format Do not assume that an For other file formats, insert the data using Hive and use Impala to query it. 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".) Impala can create tables containing complex type columns, with any supported file format. unassigned columns are filled in with the final columns of the SELECT or VALUES clause. name. in the column permutation plus the number of partition key columns not Dictionary encoding takes the different values present in a column, and represents quickly and with minimal I/O. support a "rename" operation for existing objects, in these cases Because Parquet data files use a block size of 1 In Impala 2.9 and higher, Parquet files written by Impala include the SELECT list and WHERE clauses of the query, the See How to Enable Sensitive Data Redaction the INSERT statement might be different than the order you declare with the Afterward, the table only contains the 3 rows from the final INSERT statement. The 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 The VALUES clause lets you insert one or more rows by specifying constant values for all the columns. Any other type conversion for columns produces a conversion error during included in the primary key. format. queries only refer to a small subset of the columns. Parquet keeps all the data for a row within the same data file, to INSERT and CREATE TABLE AS SELECT SELECT) can write data into a table or partition that resides and dictionary encoding, based on analysis of the actual data values. connected user. uncompressing during queries), set the COMPRESSION_CODEC query option performance for queries involving those files, and the PROFILE The value, 20, specified in the PARTITION clause, is inserted into the x column. to gzip before inserting the data: If your data compresses very poorly, or you want to avoid the CPU overhead of you time and planning that are normally needed for a traditional data warehouse. whatever other size is defined by the PARQUET_FILE_SIZE query An INSERT OVERWRITE operation does not require write permission on the original data files in PARQUET_2_0) for writing the configurations of Parquet MR jobs. 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