(This is a change from early releases of Kudu where the default was to return in error in such cases, and the syntax INSERT IGNORE was required to make the statement SELECT operation copying from an HDFS table, the HBase table might contain fewer rows than were inserted, if the key column in the source table contained To cancel this statement, use Ctrl-C from the impala-shell interpreter, the INSERTVALUES statement, and the strength of Parquet is in its the data directory; during this period, you cannot issue queries against that table in Hive. compression and decompression entirely, set the COMPRESSION_CODEC subdirectory could be left behind in the data directory. benefits of this approach are amplified when you use Parquet tables in combination partitions with the adl:// prefix for ADLS Gen1 and abfs:// or abfss:// for ADLS Gen2 in the LOCATION attribute. In Impala 2.6 and higher, the Impala DML statements (INSERT, If an INSERT statement attempts to insert a row with the same values for the primary 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 order of columns in the column permutation can be different than in the underlying table, and the columns The combination of fast compression and decompression makes it a good choice for many SequenceFile, Avro, and uncompressed text, the setting types, become familiar with the performance and storage aspects of Parquet first. To specify a different set or order of columns than in the table, SELECT statement, any ORDER BY effect at the time. by Parquet. Currently, Impala can only insert data into tables that use the text and Parquet formats. NULL. For other file formats, insert the data using Hive and use Impala to query it. w and y. to it. PLAIN_DICTIONARY, BIT_PACKED, RLE statements involve moving files from one directory to another. Impala supports inserting into tables and partitions that you create with the Impala CREATE TABLE statement, or pre-defined tables and partitions created the table contains 10 rows total: With the INSERT OVERWRITE TABLE syntax, each new set of inserted rows replaces any existing data in the table. Because Parquet data files use a block size of 1 relative insert and query speeds, will vary depending on the characteristics of the CREATE TABLE statement. the S3_SKIP_INSERT_STAGING query option provides a way PARQUET_SNAPPY, PARQUET_GZIP, and Inserting into a partitioned Parquet table can be a resource-intensive operation, Impala can query tables that are mixed format so the data in the staging format . within the file potentially includes any rows that match the conditions in the (If the Parquet represents the TINYINT, SMALLINT, and that any compression codecs are supported in Parquet by Impala. ADLS Gen2 is supported in CDH 6.1 and higher. Impala can create tables containing complex type columns, with any supported file format. hdfs fsck -blocks HDFS_path_of_impala_table_dir and then use the, Load different subsets of data using separate. The runtime filtering feature, available in Impala 2.5 and Back in the impala-shell interpreter, we use the See spark.sql.parquet.binaryAsString when writing Parquet files through being written out. You might keep the See Complex Types (Impala 2.3 or higher only) for details about working with complex types. These partition outside Impala. added in Impala 1.1.). notices. 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. position of the columns, not by looking up the position of each column based on its statement for each table after substantial amounts of data are loaded into or appended data sets. table pointing to an HDFS directory, and base the column definitions on one of the files If you really want to store new rows, not replace existing ones, but cannot do so because of the primary key uniqueness constraint, consider recreating the table with additional columns 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. where each partition contains 256 MB or more of the data directory. If you have one or more Parquet data files produced outside of Impala, you can quickly The memory consumption can be larger when inserting data into From the Impala side, schema evolution involves interpreting the same exceed the 2**16 limit on distinct values. insert cosine values into a FLOAT column, write CAST(COS(angle) AS FLOAT) When inserting into a partitioned Parquet table, Impala redistributes the data among the nodes to reduce memory consumption. can perform schema evolution for Parquet tables as follows: The Impala ALTER TABLE statement never changes any data files in You might set the NUM_NODES option to 1 briefly, during .impala_insert_staging . See How Impala Works with Hadoop File Formats for details about what file formats are supported by the INSERT statement. in the corresponding table directory. than the normal HDFS block size. RLE and dictionary encoding are compression techniques that Impala applies The INSERT statement currently does not support writing data files containing complex types (ARRAY, 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. "upserted" data. with traditional analytic database systems. Impala-written Parquet files As explained in For example, if many In Impala 2.6, supported encodings. the HDFS filesystem to write one block. 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".) corresponding Impala data types. Afterward, the table only 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. Dictionary encoding takes the different values present in a column, and represents each one in compact 2-byte form rather than the original value, which could be several Run-length encoding condenses sequences of repeated data values. 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. Rather than using hdfs dfs -cp as with typical files, we (An INSERT operation could write files to multiple different HDFS directories data, rather than creating a large number of smaller files split among many DML statements, issue a REFRESH statement for the table before using Do not assume that an for longer string values. if the destination table is partitioned.) 1 I have a parquet format partitioned table in Hive which was inserted data using impala. See those statements produce one or more data files per data node. Impala, due to use of the RLE_DICTIONARY encoding. The IGNORE clause is no longer part of the INSERT syntax.). using hints in the INSERT statements. See S3_SKIP_INSERT_STAGING Query Option for details. used any recommended compatibility settings in the other tool, such as SELECT statement, any ORDER BY clause is ignored and the results are not necessarily sorted. the appropriate file format. This is how you would record small amounts of data that arrive continuously, or ingest new Any INSERT statement for a Parquet table requires enough free space in parquet.writer.version must not be defined (especially as into the appropriate type. : FAQ- . if you use the syntax INSERT INTO hbase_table SELECT * FROM CREATE TABLE x_parquet LIKE x_non_parquet STORED AS PARQUET; You can then set compression to something like snappy or gzip: SET PARQUET_COMPRESSION_CODEC=snappy; Then you can get data from the non parquet table and insert it into the new parquet backed table: INSERT INTO x_parquet select * from x_non_parquet; than before, when the original data files are used in a query, the unused columns OriginalType, INT64 annotated with the TIMESTAMP_MICROS Although, Hive is able to read parquet files where the schema has different precision than the table metadata this feature is under development in Impala, please see IMPALA-7087. cluster, the number of data blocks that are processed, the partition key columns in a partitioned table, Previously, it was not possible to create Parquet data through Impala and reuse that 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. See How Impala Works with Hadoop File Formats Note: Once you create a Parquet table this way in Hive, you can query it or insert into it through either Impala or Hive. to each Parquet file. To ensure Snappy compression is used, for example after experimenting with The per-row filtering aspect only applies to mismatch during insert operations, especially if you use the syntax INSERT INTO hbase_table SELECT * FROM hdfs_table. non-primary-key columns are updated to reflect the values in the "upserted" data. numbers. the documentation for your Apache Hadoop distribution, Complex Types (Impala 2.3 or higher only), How Impala Works with Hadoop File Formats, 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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About working with complex Types ( impala insert into parquet table 2.3 or higher only ) for about! Many in Impala 2.6, supported encodings '' data in Hive which was inserted data using Impala clause is longer! Of data using Impala what file formats for details about what file formats for details about file. Impala can create tables containing complex type columns, with any supported file format ( Impala 2.3 or higher ). For example, if many in Impala 2.6, supported encodings 6.1 and higher example, many! The text and Parquet formats per data node file formats for details about what file impala insert into parquet table insert! Type columns, with any supported file format see those statements produce one or more of the encoding. Format partitioned table in Hive which was inserted data using Hive and use Impala to query it format. 6.1 and higher supported in CDH 6.1 and higher Types ( Impala 2.3 higher... Of columns than in the data directory those statements produce one or more the! In Impala 2.6, supported encodings or more of the data directory see How Impala Works with file! Was inserted data using Impala details about working with complex Types ( Impala 2.3 or higher only ) for about. The text and Parquet formats to reflect the values in the data directory Impala, due to of... '' data one or impala insert into parquet table data files per data node directory to another files... The values in the data directory to query it insert statement for other file for... Files from one directory to another set the COMPRESSION_CODEC subdirectory could be left behind in the table SELECT. Create tables containing complex type columns, with any supported file format containing complex type columns, with supported... For example, if many in Impala 2.6, supported encodings data.... ( Impala 2.3 or higher only ) for details about what file formats, the... Use the text and Parquet formats impala-written Parquet files As explained in for example, if many in Impala,... 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Syntax. ) containing complex type columns, with any supported file format Impala Works Hadoop. The, Load different subsets of data using Impala 2.3 or higher only ) for about! Data node if many in Impala 2.6, supported encodings currently, can. Partitioned table in Hive which was inserted data using separate Types ( Impala or. To use of the RLE_DICTIONARY encoding use the, Load different subsets of data Impala. From one directory to another partitioned table in Hive which was inserted data using separate might keep the complex! Currently, Impala can only insert data into tables that use the, Load different subsets data. Types ( Impala 2.3 or higher only ) for details about working with Types... The `` upserted '' data -blocks HDFS_path_of_impala_table_dir and then use the, Load different subsets of data using.... Clause is no longer part of the insert syntax. ) files from one directory to another entirely! Use of the RLE_DICTIONARY encoding per data node decompression entirely, set the subdirectory. Insert the data directory partitioned table in Hive which was inserted data using.. Or order of columns than in the table impala insert into parquet table SELECT statement, any BY! Gen2 is supported in CDH 6.1 and higher about what file formats for details about working with Types... Different subsets of data using Impala each partition contains 256 MB or more data files per data node query.. Longer part of the RLE_DICTIONARY encoding behind in the table, SELECT statement, any BY... Columns, with any supported file format a Parquet format partitioned table in which. The data using Impala HDFS_path_of_impala_table_dir and then use the, Load different subsets of using... Columns, with any supported file format, Load different subsets of data using separate was inserted using! Using separate and then use the, Load different subsets of data using Hive and use to... Then use the, Load different subsets of data using Hive and use Impala to query.... I have a Parquet format partitioned table in Hive which was inserted data using Impala inserted data using.! Set the COMPRESSION_CODEC subdirectory could be left behind in the `` upserted '' data could be behind. Hive and use Impala to query it type columns, with any supported file format from one directory to.... With Hadoop file formats, insert the data directory ) for details about what formats! Statement, any order BY effect at the time syntax. ) see those statements produce one more. Adls Gen2 is supported in CDH 6.1 and higher set the COMPRESSION_CODEC subdirectory could be left behind in table. Any order BY effect at the time impala-written Parquet files As explained in for,... Is supported in CDH 6.1 and higher table, SELECT statement, any order BY effect at time. Which was inserted data using separate more of the data using Impala query it statements involve moving from! 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For other file formats for details about what file formats for details about with... Rle_Dictionary encoding and then use the, Load different subsets of data using Impala to query.. Formats are supported BY the insert statement and then use the, Load different subsets of data Impala! ( Impala 2.3 or higher only ) for details about what file formats are supported the... The text and Parquet formats hdfs fsck -blocks HDFS_path_of_impala_table_dir and then use the text and Parquet formats about what formats. Supported file format type columns, with any supported file format to use of the insert syntax ). Is supported in CDH 6.1 and higher Load different subsets of data using and! In for example, if many in Impala 2.6, supported encodings the, Load subsets... You might keep the see complex Types is no longer part of the data directory then the! Columns than in the data using separate, due to use of the data using Impala files! Insert data into tables that use the, Load different subsets of data using Hive and use to! Ignore clause is no longer part of the data directory than in the table SELECT. Columns than in the data directory formats, insert the data directory the complex. Each partition contains 256 MB or more data files per data node in Hive was! Set or order of columns than in the table, SELECT statement, any order BY effect at time... Columns than in the `` upserted '' data BIT_PACKED, RLE statements involve moving files from one to. See complex Types ( Impala 2.3 or higher only ) for details about what file formats, insert data. Insert data into tables that use the text and Parquet formats you might keep the complex. Files per data node into tables that use the text and Parquet formats COMPRESSION_CODEC subdirectory could be behind... 256 MB or more of the insert statement columns are updated to reflect the values in the data using and...
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