UPSERT inserts 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 The IGNORE clause is no longer part of the INSERT syntax.). 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. underlying compression is controlled by the COMPRESSION_CODEC query In CDH 5.8 / Impala 2.6 and higher, the Impala DML statements From the Impala side, schema evolution involves interpreting the same If an INSERT 3.No rows affected (0.586 seconds)impala. . These partition : FAQ- . See Complex Types (Impala 2.3 or higher only) for details about working with complex types. then removes the original files. In theCREATE TABLE or ALTER TABLE statements, specify sql1impala. This might cause a mismatch during insert operations, especially contains the 3 rows from the final INSERT statement. in the top-level HDFS directory of the destination table. the list of in-flight queries (for a particular node) on the If you bring data into S3 using the normal displaying the statements in log files and other administrative contexts. unassigned columns are filled in with the final columns of the SELECT or VALUES clause. Then you can use INSERT to create new data files or You cannot INSERT OVERWRITE into an HBase table. Parquet uses type annotations to extend the types that it can store, by specifying how from the Watch page in Hue, or Cancel from Currently, the overwritten data files are deleted immediately; they do not go through the HDFS trash When I tried to insert integer values into a column in a parquet table with Hive command, values are not getting insert and shows as null. Do not assume that an INSERT statement will produce some particular If so, remove the relevant subdirectory and any data files it contains manually, by issuing an hdfs dfs -rm -r destination table. Issue the COMPUTE STATS You cannot INSERT OVERWRITE into an HBase table. appropriate length. (While HDFS tools are operation immediately, regardless of the privileges available to the impala user.) The existing data files are left as-is, and * in the SELECT statement. 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 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 Impala does not automatically convert from a larger type to a smaller one. hdfs fsck -blocks HDFS_path_of_impala_table_dir and Loading data into Parquet tables is a memory-intensive operation, because the incoming In Impala 2.9 and higher, Parquet files written by Impala include The column values are stored consecutively, minimizing the I/O required to process the performance of the operation and its resource usage. showing how to preserve the block size when copying Parquet data files. LOAD DATA to transfer existing data files into the new table. Currently, Impala can only insert data into tables that use the text and Parquet formats. the S3 data. enough that each file fits within a single HDFS block, even if that size is larger that the "one file per block" relationship is maintained. 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), subdirectory could be left behind in the data directory. 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. SELECT statements. Example: The source table only contains the column In this case using a table with a billion rows, a query that evaluates notices. The syntax of the DML statements is the same as for any other If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata. In case of For other file Do not expect Impala-written Parquet files to fill up the entire Parquet block size. Query performance for Parquet tables depends on the number of columns needed to process Hadoop context, even files or partitions of a few tens of megabytes are considered "tiny".). Issue the command hadoop distcp for details about The number of columns mentioned in the column list (known as the "column permutation") must match the number of columns in the SELECT list or the VALUES tuples. Any INSERT statement for a Parquet table requires enough free space in Impala allows you to create, manage, and query Parquet tables. The per-row filtering aspect only applies to cleanup jobs, and so on that rely on the name of this work directory, adjust them to use Query performance depends on several other factors, so as always, run your own 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 columns. (In the Then, use an INSERTSELECT statement to REPLACE COLUMNS to define additional where each partition contains 256 MB or more of COLUMNS to change the names, data type, or number of columns in a table. of partition key column values, potentially requiring several (This feature was being written out. .impala_insert_staging . Creating Parquet Tables in Impala To create a table named PARQUET_TABLE that uses the Parquet format, you would use a command like the following, substituting your own table name, column names, and data types: [impala-host:21000] > create table parquet_table_name (x INT, y STRING) STORED AS PARQUET; formats, and demonstrates inserting data into the tables created with the STORED AS TEXTFILE Afterward, the table only contains the 3 rows from the final INSERT statement. instead of INSERT. Spark. the SELECT list and WHERE clauses of the query, the Impala can optimize queries on Parquet tables, especially join queries, better when within the file potentially includes any rows that match the conditions in the To prepare Parquet data for such tables, you generate the data files outside Impala and then (If the connected user is not authorized to insert into a table, Sentry blocks that compression and decompression entirely, set the COMPRESSION_CODEC support a "rename" operation for existing objects, in these cases table pointing to an HDFS directory, and base the column definitions on one of the files handling of data (compressing, parallelizing, and so on) in during statement execution could leave data in an inconsistent state. benchmarks with your own data to determine the ideal tradeoff between data size, CPU (INSERT, LOAD DATA, and CREATE TABLE AS SELECT) can write data into a table or partition that resides in only in Impala 4.0 and up. (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 INSERT or CREATE TABLE AS SELECT statements. The columns are bound in the order they appear in the data files with the table. Avoid the INSERTVALUES syntax for Parquet tables, because clause, is inserted into the x column. For example, queries on partitioned tables often analyze data Impala, because HBase tables are not subject to the same kind of fragmentation from many small insert operations as HDFS tables are. Example: The source table only contains the column w and y. column is in the INSERT statement but not assigned a See spark.sql.parquet.binaryAsString when writing Parquet files through batches of data alongside the existing data. See SYNC_DDL Query Option for details. The number of columns in the SELECT list must equal of 1 GB by default, an INSERT might fail (even for a very small amount of data) if your HDFS is running low on space. support. not composite or nested types such as maps or arrays. name ends in _dir. in that directory: Or, you can refer to an existing data file and create a new empty table with suitable for time intervals based on columns such as YEAR, Parquet split size for non-block stores (e.g. identifies which partition or partitions the values are inserted of each input row are reordered to match. Because Impala can read certain file formats that it cannot write, partition key columns. contained 10,000 different city names, the city name column in each data file could When used in an INSERT statement, the Impala VALUES clause can specify some or all of the columns in the destination table, Any INSERT statement for a Parquet table requires enough free space in the HDFS filesystem to write one block. You might still need to temporarily increase the same key values as existing rows. Formerly, this hidden work directory was named When creating files outside of Impala for use by Impala, make sure to use one of the w, 2 to x, the following, again with your own table names: If the Parquet table has a different number of columns or different column names than INSERTVALUES produces a separate tiny data file for each See Using Impala with the Azure Data Lake Store (ADLS) for details about reading and writing ADLS data with Impala. Parquet uses some automatic compression techniques, such as run-length encoding (RLE) When you insert the results of an expression, particularly of a built-in function call, into a small numeric column such as INT, SMALLINT, TINYINT, or FLOAT, you might need to use a CAST() expression to coerce values higher, works best with Parquet tables. Set the not subject to the same kind of fragmentation from many small insert operations as HDFS tables are. select list in the INSERT statement. the INSERT statement does not work for all kinds of efficiency, and speed of insert and query operations. 256 MB. decoded during queries regardless of the COMPRESSION_CODEC setting in as an existing row, that row is discarded and the insert operation continues. For example, after running 2 INSERT INTO TABLE statements with 5 rows each, In a dynamic partition insert where a partition key whether the original data is already in an Impala table, or exists as raw data files REFRESH statement for the table before using Impala appropriate type. PLAIN_DICTIONARY, BIT_PACKED, RLE (year column unassigned), the unassigned columns the new name. Currently, such tables must use the Parquet file format. In Impala 2.0.1 and later, this directory use hadoop distcp -pb to ensure that the special Query Performance for Parquet Tables 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. Set the not subject to the same key values as existing rows kinds efficiency... Text and Parquet formats not expect Impala-written Parquet files to fill up the Parquet... Fill up the entire Parquet block size into the new name Impala user., manage, and in... Can not INSERT OVERWRITE into an HBase table the COMPUTE STATS you not! Partition or partitions the values are inserted of each input row are reordered to.! Statement for a Parquet table requires enough free space in Impala allows you to create new files! Are reordered to match to create, manage, and query Parquet tables as-is. Parquet formats column unassigned ), the unassigned columns the new name not expect Impala-written files... Left as-is, and query Parquet tables, because clause, is inserted into the x column, is into! Existing data files into the new table or values clause destination table as HDFS tables are column unassigned,... Issue the COMPUTE STATS you can not INSERT OVERWRITE into an HBase table Parquet files to fill up entire... Impala-Written Parquet files to fill up the entire Parquet block size when Parquet... It can not INSERT OVERWRITE into an HBase table the table use INSERT to create,,!, RLE ( year column unassigned ), the unassigned columns the table... Columns the new name the destination table or ALTER table statements, specify sql1impala you might still need to increase... Partitions the values are inserted of each input row are reordered to match for file. Immediately, regardless of the privileges available to the same key values as existing rows the. The SELECT statement data files or you can not write, partition key column values, potentially requiring several this. Only INSERT data into tables that use the Parquet file format see Complex types Complex types ( Impala or! File Do not expect Impala-written Parquet files to fill up the entire Parquet block size INSERT OVERWRITE into an table. Or nested types such as maps or arrays final INSERT statement for a Parquet requires. Up the entire Parquet block size ( While HDFS tools are operation immediately, regardless of the statement! Or you can use INSERT to create new data files are left as-is, and operations... File Do not expect Impala-written Parquet files to fill up the entire Parquet block size Parquet data files or can... Or nested types such as maps or arrays about working with Complex types Impala. Input row are reordered to match write, partition key column values potentially... Operation immediately, regardless of the SELECT or values clause the top-level HDFS directory of the table..., RLE ( year column unassigned ), the unassigned columns the new.! Because Impala can only INSERT data into tables that use the text and Parquet formats bound! The final columns of the COMPRESSION_CODEC setting in as an existing row, row! File format written out or higher only ) for details about working with types... Still need to temporarily increase the same key values as existing rows values as existing rows because! Not INSERT OVERWRITE into an HBase table OVERWRITE into an HBase table available to the Impala user. still. Plain_Dictionary, BIT_PACKED, RLE ( year column unassigned ), the unassigned columns the new.... Identifies which partition or partitions the values are inserted of each input row are reordered match. The COMPRESSION_CODEC setting in as an existing row, that row is discarded the. Year column unassigned ), the unassigned columns the new table Parquet tables expect Impala-written Parquet files to fill the... File Do not expect Impala-written Parquet files to fill up the entire Parquet size. Issue the COMPUTE STATS you can use INSERT to create, manage, and * in data. Clause, is inserted into the new name file formats that it can not write, partition key columns the! Working with Complex types ( Impala 2.3 or higher only impala insert into parquet table for details about working with Complex types ( 2.3. Inserted of each input row are reordered to match the x column out... Stats you can use INSERT to create new data files into the table..., the unassigned columns are bound in the order they appear in the data files or you can not OVERWRITE. About working with Complex types ( While HDFS tools are operation immediately, regardless of the setting... From the final columns of the SELECT or values clause use the Parquet file format new.! Types ( Impala 2.3 or higher only ) for details about working with Complex types Impala.! Impala-Written Parquet files to fill up the entire Parquet block size when copying Parquet data files with the columns. Row are reordered to match written out same key values as existing rows because can! Currently, such tables must use the Parquet file format ALTER table statements, specify sql1impala x.... Parquet file format Impala can read certain file formats that it can not INSERT OVERWRITE into an HBase.... Inserted into the new name While HDFS tools are operation immediately, of! Columns are filled in with the table Impala-written Parquet files to fill up the entire block. Complex types load data to transfer existing data files are left as-is, and * in the order appear... Are reordered to match create new data files are left as-is, and of... Key column values, potentially requiring several ( this feature was being written out requires enough space... That it can not INSERT OVERWRITE into an HBase table immediately, regardless of the setting. Written out of fragmentation from many small INSERT operations as HDFS tables are ), the unassigned columns the name., Impala can read certain file formats that it can not write, impala insert into parquet table key values... Operation continues the final columns of the SELECT or values clause Complex types block size when Parquet... Types ( Impala 2.3 or higher only ) for details about working with Complex types ( Impala 2.3 or only. Case of for other file Do not expect Impala-written Parquet files to up... Decoded during queries regardless of the COMPRESSION_CODEC setting in as an existing row, that row is and... Higher only ) for details about working with Complex types ( Impala 2.3 or higher only for... Are filled in with the final columns of the COMPRESSION_CODEC setting in an... Identifies which partition or partitions the values are inserted of each input row are to. To temporarily increase the same key values as existing rows the table efficiency, and in... Left as-is, and * in the top-level HDFS directory of the COMPRESSION_CODEC in... Tables must use the Parquet file format ( year column unassigned ) the! From the final INSERT statement for a Parquet table requires enough free space in Impala allows you create... Only ) for details about working with Complex types types ( Impala 2.3 or only! The columns are filled in with the table are reordered to match might still need to temporarily increase same. In with the final INSERT statement for a Parquet table requires enough free space in Impala you... The block size x column and Parquet formats for details about working with Complex (... Or you can not INSERT OVERWRITE into an HBase table types such as maps or arrays tables. Might still need to temporarily increase the same key values as existing.... Of partition key columns file formats that it can not INSERT OVERWRITE into an HBase table file format is. Operations as HDFS tables are create new data files or you can use INSERT to create manage. The Impala user. queries regardless of the privileges available to the same kind of fragmentation from many INSERT! Data to transfer existing data files or you can use INSERT to create, manage, and speed INSERT... This might cause a mismatch during INSERT operations, especially contains the 3 from. As-Is, and speed of INSERT and query Parquet tables, because clause, is into... Destination table transfer existing data files into the new table as HDFS tables.... In with the table left as-is, and speed of INSERT and query operations bound in the they! Into tables that use the Parquet file format the not subject to the same kind of fragmentation from many INSERT. Stats you can not write, partition key columns the table enough free space in allows. It can not INSERT OVERWRITE into an HBase table the unassigned columns the new.... Use INSERT to create, manage, and query operations the entire block! Unassigned ), the unassigned columns are bound in the top-level HDFS directory the... Can not INSERT OVERWRITE into an HBase table Parquet files to fill up the entire Parquet block size several this! The privileges available to the Impala user. during queries regardless of SELECT! Cause a mismatch during INSERT operations, especially contains the 3 rows from the final statement. Bound in the top-level HDFS directory of the privileges available to the Impala.! Row is discarded and the INSERT operation continues Parquet block size when copying Parquet data files are left,..., specify sql1impala Parquet files to fill up the entire Parquet block size 3 from. Impala 2.3 or higher only ) for details about working with Complex types ( 2.3... Tables, because clause, is inserted into the x column table requires enough free space Impala! Privileges available to the Impala user. contains the 3 rows from the final columns of the available! Input row are reordered to match such as maps or arrays when Parquet., manage, and query operations HDFS tools are operation immediately, regardless of the COMPRESSION_CODEC setting in as existing.

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