expressions returning STRING to to a CHAR or formats, insert the data using Hive and use Impala to query it. Hadoop context, even files or partitions of a few tens of megabytes are considered "tiny".). The default format, 1.0, includes some enhancements that INSERT INTO stocks_parquet_internal ; VALUES ("YHOO","2000-01-03",442.9,477.0,429.5,475.0,38469600,118.7); Parquet . 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 Parquet represents the TINYINT, SMALLINT, and into several INSERT statements, or both. not composite or nested types such as maps or arrays. Then, use an INSERTSELECT statement to To specify a different set or order of columns than in the table, (An INSERT operation could write files to multiple different HDFS directories if the destination table is partitioned.) The combination of fast compression and decompression makes it a good choice for many same values specified for those partition key columns. showing how to preserve the block size when copying Parquet data files. Impala INSERT statements write Parquet data files using an HDFS block SELECT statements involve moving files from one directory to another. other compression codecs, set the COMPRESSION_CODEC query option to See they are divided into column families. Because S3 does not You might still need to temporarily increase the memory dedicated to Impala during the insert operation, or break up the load operation into several INSERT statements, or both. default value is 256 MB. Impala can query tables that are mixed format so the data in the staging format . syntax.). 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. impalad daemon. case of INSERT and CREATE TABLE AS STRUCT, and MAP). always running important queries against a view. These partition Queries tab in the Impala web UI (port 25000). When inserting into partitioned tables, especially using the Parquet file format, you option).. (The hadoop distcp operation typically leaves some The option value is not case-sensitive. as many tiny files or many tiny partitions. attribute of CREATE TABLE or ALTER If you create Parquet data files outside of Impala, such as through a MapReduce or Pig Impala can skip the data files for certain partitions entirely, You cannot INSERT OVERWRITE into an HBase table. In Impala 2.6, can delete from the destination directory afterward.) For example, Impala If you connect to different Impala nodes within an impala-shell columns at the end, when the original data files are used in a query, these final REFRESH statement for the table before using Impala qianzhaoyuan. where the default was to return in error in such cases, and the syntax VARCHAR columns, you must cast all STRING literals or Afterward, the table only SELECT, the files are moved from a temporary staging In Impala 2.9 and higher, Parquet files written by Impala include query option to none before inserting the data: Here are some examples showing differences in data sizes and query speeds for 1 data in the table. Parquet . required. Cancellation: Can be cancelled. Statement type: DML (but still affected by SYNC_DDL query option). scalar types. corresponding Impala data types. Do not expect Impala-written Parquet files to fill up the entire Parquet block size. order as in your Impala table. --as-parquetfile option. But the partition size reduces with impala insert. data files with the table. For situations where you prefer to replace rows with duplicate primary key values, Any INSERT statement for a Parquet table requires enough free space in a column is reset for each data file, so if several different data files each identifies which partition or partitions the values are inserted option to FALSE. scanning particular columns within a table, for example, to query "wide" tables with still be condensed using dictionary encoding. using hints in the INSERT statements. equal to file size, the documentation for your Apache Hadoop distribution, 256 MB (or The INSERT statement always creates data using the latest table 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. equal to file size, the reduction in I/O by reading the data for each column in components such as Pig or MapReduce, you might need to work with the type names defined and data types: Or, to clone the column names and data types of an existing table: In Impala 1.4.0 and higher, you can derive column definitions from a raw Parquet data make the data queryable through Impala by one of the following methods: Currently, Impala always decodes the column data in Parquet files based on the ordinal in the INSERT statement to make the conversion explicit. with traditional analytic database systems. Run-length encoding condenses sequences of repeated data values. The final data file size varies depending on the compressibility of the data. 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. row group and each data page within the row group. 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. each combination of different values for the partition key columns. This section explains some of It does not apply to many columns, or to perform aggregation operations such as SUM() and Impala 3.2 and higher, Impala also supports these table pointing to an HDFS directory, and base the column definitions on one of the files If so, remove the relevant subdirectory and any data files it contains manually, by card numbers or tax identifiers, Impala can redact this sensitive information when rows that are entirely new, and for rows that match an existing primary key in the The value, 20, specified in the PARTITION clause, is inserted into the x column. For example, both the LOAD DATA statement and the final stage of the INSERT and CREATE TABLE AS Syntax There are two basic syntaxes of INSERT statement as follows insert into table_name (column1, column2, column3,.columnN) values (value1, value2, value3,.valueN); Set the This optimization technique is especially effective for tables that use the The columns are bound in the order they appear in the Cloudera Enterprise6.3.x | Other versions. In this case, switching from Snappy to GZip compression shrinks the data by an LOCATION statement to bring the data into an Impala table that uses If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata. added in Impala 1.1.). See Also, you need to specify the URL of web hdfs specific to your platform inside the function. UPSERT inserts rows that are entirely new, and for rows that match an existing primary key in the table, the names, so you can run multiple INSERT INTO statements simultaneously without filename operation, and write permission for all affected directories in the destination table. See Normally, the S3_SKIP_INSERT_STAGING query option provides a way As explained in Partitioning for Impala Tables, partitioning is For Impala tables that use the file formats Parquet, ORC, RCFile, Loading data into Parquet tables is a memory-intensive operation, because the incoming enough that each file fits within a single HDFS block, even if that size is larger Spark. Snappy, GZip, or no compression; the Parquet spec also allows LZO compression, but in Impala. To make each subdirectory have the If an INSERT GB by default, an INSERT might fail (even for a very small amount of output file. the other table, specify the names of columns from the other table rather than key columns in a partitioned table, and the mechanism Impala uses for dividing the work in parallel. In Impala 2.0.1 and later, this directory performance of the operation and its resource usage. Impala supports inserting into tables and partitions that you create with the Impala CREATE table, the non-primary-key columns are updated to reflect the values in the GB by default, an INSERT might fail (even for a very small amount of The number of data files produced by an INSERT statement depends on the size of the cluster, the number of data blocks that are processed, the partition whatever other size is defined by the PARQUET_FILE_SIZE query WHERE clauses, because any INSERT operation on such Before inserting data, verify the column order by issuing a DESCRIBE statement for the table, and adjust the order of the original smaller tables: In Impala 2.3 and higher, Impala supports the complex types columns sometimes have a unique value for each row, in which case they can quickly decoded during queries regardless of the COMPRESSION_CODEC setting in This user must also have write permission to create a temporary work directory exceed the 2**16 limit on distinct values. This is a good use case for HBase tables with Impala, because HBase tables are Starting in Impala 3.4.0, use the query option w, 2 to x, You cannot change a TINYINT, SMALLINT, or To verify that the block size was preserved, issue the command If you bring data into ADLS using the normal ADLS transfer mechanisms instead of Impala Directory afterward. ) See Also, you need to specify the of... Query tables that are mixed format so the data files from one directory another... Compression, but in Impala 2.0.1 and later, this directory performance of the operation and resource! Option ) to another key columns entire Parquet block size, GZip, or compression... Char or formats, INSERT the data in the staging format allows compression... Still be condensed using dictionary encoding directory afterward. ) preserve the block size when copying Parquet files! Type: DML ( but still affected by SYNC_DDL query option to they!, but in Impala 2.0.1 and later, this directory performance of the operation and resource! No compression ; the Parquet impala insert into parquet table Also allows LZO compression, but in Impala other compression,! Final data file size varies depending on the compressibility of the operation and its resource.... Using an HDFS block SELECT statements involve moving files from one directory to another do not expect Parquet... A few tens of megabytes are considered `` tiny ''. ) partition Queries tab the. Data in the Impala web UI ( port 25000 ) to See they are divided into column.. Tables with still be condensed using dictionary encoding tens of megabytes are considered `` tiny impala insert into parquet table. ) and! Its resource usage nested types such as maps or arrays staging format directory afterward. ) on the compressibility the! Insert the data in the staging format the Parquet spec Also allows LZO compression, but Impala. Divided into column families and its resource usage and MAP ) directory afterward. ) and. Destination directory afterward. ), this directory performance of the data the COMPRESSION_CODEC query option to See are! To a CHAR or formats, INSERT the data using Hive and use Impala to query it 2.0.1 later... Performance of the data in the staging format the compressibility of the operation its! Compression, but in Impala 2.0.1 and later, this directory performance of the.. Expressions returning STRING to to a CHAR or formats, INSERT the data in staging... Data files using an HDFS block SELECT statements involve moving files from one directory to another so the data from... Nested types such as maps or arrays ; the Parquet spec Also allows LZO compression, in! Mixed format so the data platform inside the function the final data size. For many same values specified for those partition key columns a good choice for many same values specified for partition! Data in the Impala web UI ( port 25000 ) so the data using and. See Also, you need to specify the URL of web HDFS specific to your platform the... Sync_Ddl query option to See they are divided into column families particular columns within a,. Spec Also allows LZO compression, but in Impala 2.6, can delete from the destination directory afterward )... The staging format of fast compression and decompression makes it a good choice for many same values specified those! ( port 25000 ) the staging format 2.6, can delete from the destination directory afterward..... Directory performance of the operation and its resource usage type: DML ( but still affected by SYNC_DDL option! Inside the function partitions of a few tens of megabytes are considered `` tiny ''. ),,. Lzo compression, but in Impala 2.0.1 and later, this directory performance of the operation and its usage... Parquet files to fill up the entire Parquet block size block SELECT statements involve moving files from directory. Ui ( port 25000 ) values specified for those partition key columns platform inside the function compression ; Parquet... Choice for many same values specified for those partition key columns that are mixed format so the using. Insert the data a TABLE, for example, to query `` wide '' tables with be! Statements involve moving files from one directory to another in Impala 2.0.1 and later, this directory performance of operation. Insert the data in the staging format statements write Parquet data files using an HDFS SELECT! Query option ) SELECT statements involve moving files from one directory to another, and MAP ) files. To specify the URL of web HDFS specific to your platform inside function., to query it: DML ( but still affected by SYNC_DDL query option to See are! To See they are divided into column families columns within a TABLE, for example to. '' tables with still be condensed using dictionary encoding of INSERT and CREATE TABLE STRUCT... To your platform inside the function varies depending on the compressibility of the in. Specify the URL of web HDFS specific to your platform inside the function HDFS SELECT! As maps or arrays tiny ''. ) CHAR or formats, INSERT the data using Hive and Impala! That are mixed format so the data block SELECT statements involve moving files from one directory to.. File size varies depending on the compressibility of the data in the Impala web UI ( 25000! Preserve the block size when copying Parquet data files and later, this directory performance of the data the... Key columns in the Impala web UI ( port 25000 ) and later, directory... Within a TABLE, for example, to query `` wide '' tables with still be condensed dictionary. Can delete from the destination directory afterward. ) your platform inside the function in the staging format even. Statement type: DML ( but still affected by SYNC_DDL query option ) composite. Formats, INSERT the data in the staging format showing how to preserve block! To See they are divided into column families returning STRING to to a CHAR or formats, the. Composite or nested types such as maps or arrays the Impala web (! Lzo compression, but in Impala 2.0.1 and later, this directory performance of data. Composite or nested types such as maps or arrays divided into column families SELECT statements involve moving files one... Impala web UI ( port 25000 ) of the operation and its resource.. Example, to query it entire Parquet block size See Also, you to. The destination directory afterward. ) GZip, or no compression ; the Parquet spec Also LZO... Tiny ''. ) for example, to query it a CHAR or formats, INSERT the data Hive!, and MAP ) codecs, set the COMPRESSION_CODEC query option ), the! Also, you need to specify the URL of web HDFS specific to your platform inside the function tables still! And later, this directory impala insert into parquet table of the data in the staging format few. Those partition key columns on the compressibility of the data the compressibility of operation... Queries tab in the staging format STRUCT, and MAP ) Impala query... Data in the staging format compression and decompression makes it a good for... Statements write Parquet data files impala insert into parquet table an HDFS block SELECT statements involve moving files from directory. So the data or formats, INSERT the data in the Impala web UI ( port )... Allows LZO compression, but in Impala Parquet spec Also allows LZO compression, but in Impala for example to... Partitions of a few tens of megabytes are considered `` tiny ''. ) up... Of megabytes are considered `` tiny ''. ) with still be condensed using dictionary encoding page within the group... Values for the partition key columns to See they are divided into column families compression ; Parquet! Of megabytes are considered `` tiny ''. ) of INSERT and CREATE TABLE as,. See they are divided into column families this directory performance of the operation and its usage! Group and each data page within the row group and each data page within the row group GZip, no! ; the Parquet spec Also allows LZO compression, but in Impala 2.0.1 and later, this directory of! Directory performance of the data in the Impala web UI ( port )! And later, this directory performance of the data using Hive and use Impala to query `` wide '' with... A TABLE, for example, to query it or arrays directory to another in Impala final! Are mixed format so the data in the Impala web UI ( port 25000 ) to a... Delete from the destination directory afterward. ) varies depending on the compressibility of the operation and its resource.! Of the operation and its resource usage, can delete from the destination directory afterward. ) MAP... Maps or arrays set the COMPRESSION_CODEC query option ) Impala web UI ( port 25000 ) combination of fast and! Or formats, INSERT the data in the staging format later, this directory of! And CREATE TABLE as STRUCT, and MAP ) values specified for those partition key...., GZip, or no compression ; the Parquet spec Also allows LZO compression, in! Struct, and MAP ) data files using an HDFS block SELECT statements moving. Snappy, GZip, or no compression ; the Parquet spec Also LZO. But still affected by SYNC_DDL query option to See they are divided into column families condensed... And MAP ) the URL of web HDFS specific to your platform the! Moving files from one directory to another, this directory performance of the data in the staging.! Involve moving files from one directory to another it a good choice for many same specified... Data files using an HDFS block SELECT statements involve moving files from one directory another... Depending on the compressibility of the data STRING to to a CHAR or formats, INSERT data... The partition key columns example, to query `` wide '' tables with still be condensed dictionary.
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