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1a84a1420c
Updated all the associated topics. Change-Id: Id4c5e9aa4d060ceaa426908a444d280a5564749d Reviewed-on: http://gerrit.cloudera.org:8080/17469 Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Joe McDonnell <joemcdonnell@cloudera.com>
717 lines
32 KiB
XML
717 lines
32 KiB
XML
<?xml version="1.0" encoding="UTF-8"?>
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<!--
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Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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-->
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<!DOCTYPE concept PUBLIC "-//OASIS//DTD DITA Concept//EN" "concept.dtd">
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<concept id="adls" rev="2.9.0">
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<title>Using Impala with the Azure Data Lake Store (ADLS)</title>
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<titlealts audience="PDF"><navtitle>ADLS Tables</navtitle></titlealts>
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<prolog>
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<metadata>
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<data name="Category" value="Impala"/>
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<data name="Category" value="ADLS"/>
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<data name="Category" value="Data Analysts"/>
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<data name="Category" value="Developers"/>
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<data name="Category" value="Querying"/>
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<data name="Category" value="Preview Features"/>
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</metadata>
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</prolog>
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<conbody>
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<p>
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You can use Impala to query data residing on the Azure Data Lake Store
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(ADLS) filesystem. This capability allows convenient access to a storage
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system that is remotely managed, accessible from anywhere, and integrated
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with various cloud-based services. Impala can query files in any supported
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file format from ADLS. The ADLS storage location can be for an entire
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table or individual partitions in a partitioned table.
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</p>
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<p>
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The default Impala tables use data files stored on HDFS, which are ideal for bulk loads and queries using
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full-table scans. In contrast, queries against ADLS data are less performant, making ADLS suitable for holding
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<q>cold</q> data that is only queried occasionally, while more frequently accessed <q>hot</q> data resides in
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HDFS. In a partitioned table, you can set the <codeph>LOCATION</codeph> attribute for individual partitions
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to put some partitions on HDFS and others on ADLS, typically depending on the age of the data.
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</p>
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<p>Starting in <keyword keyref="impala31"/>, Impala supports ADLS Gen2
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filesystem, Azure Blob File System (ABFS).</p>
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<p outputclass="toc inpage"/>
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</conbody>
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<concept id="prereqs">
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<title>Prerequisites</title>
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<conbody>
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<p>
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These procedures presume that you have already set up an Azure account,
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configured an ADLS store, and configured your Hadoop cluster with appropriate
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credentials to be able to access ADLS. See the following resources for information:
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</p>
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<ul>
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<li>
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<p>
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<xref href="https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-get-started-portal" scope="external" format="html">Get started with Azure Data Lake Store using the Azure Portal</xref>
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</p>
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</li>
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<li><xref
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href="https://docs.microsoft.com/en-us/azure/storage/data-lake-storage/quickstart-create-account"
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format="html" scope="external">Azure Data Lake Storage Gen2</xref></li>
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<li>
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<p>
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<xref href="https://hadoop.apache.org/docs/current/hadoop-azure-datalake/index.html" scope="external" format="html">Hadoop Azure Data Lake Support</xref>
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</p>
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</li>
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</ul>
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</conbody>
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</concept>
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<concept id="sql">
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<title>How Impala SQL Statements Work with ADLS</title>
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<conbody>
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<p> Impala SQL statements work with data on ADLS as follows. </p>
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<ul>
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<li><p> The <xref href="impala_create_table.xml#create_table"/> or <xref
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href="impala_alter_table.xml#alter_table"/> statements can specify
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that a table resides on the ADLS filesystem by using one of the
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following ADLS prefixes in the <codeph>LOCATION</codeph> property.<ul>
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<li>For ADLS Gen1: <codeph>adl://</codeph></li>
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<li>For ADLS Gen2: <codeph>abfs://</codeph> or
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<codeph>abfss://</codeph></li>
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</ul></p><p><codeph>ALTER TABLE</codeph> can also set the
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<codeph>LOCATION</codeph> property for an individual partition, so
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that some data in a table resides on ADLS and other data in the same
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table resides on HDFS. </p> See <xref href="impala_adls.xml#ddl"/>
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for usage information.</li>
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<li>
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<p>
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Once a table or partition is designated as residing on ADLS, the <xref href="impala_select.xml#select"/>
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statement transparently accesses the data files from the appropriate storage layer.
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</p>
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</li>
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<li>
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<p>
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If the ADLS table is an internal table, the <xref href="impala_drop_table.xml#drop_table"/> statement
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removes the corresponding data files from ADLS when the table is dropped.
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</p>
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</li>
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<li>
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<p>
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The <xref href="impala_truncate_table.xml#truncate_table"/> statement always removes the corresponding
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data files from ADLS when the table is truncated.
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</p>
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</li>
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<li>
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<p>
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The <xref href="impala_load_data.xml#load_data"/> can move data files residing in HDFS into
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an ADLS table.
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</p>
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</li>
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<li>
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<p>
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The <xref href="impala_insert.xml#insert"/>, or the <codeph>CREATE TABLE AS SELECT</codeph>
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form of the <codeph>CREATE TABLE</codeph> statement, can copy data from an HDFS table or another ADLS
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table into an ADLS table.
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</p>
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</li>
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</ul>
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<p> For usage information about Impala SQL statements with ADLS tables,
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see <xref href="impala_adls.xml#dml"/>. </p>
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</conbody>
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</concept>
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<concept id="creds">
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<title>Specifying Impala Credentials to Access Data in ADLS</title>
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<conbody>
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<p> To allow Impala to access data in ADLS, specify values for the
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following configuration settings in your
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<filepath>core-site.xml</filepath> file.</p>
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<p>For ADLS Gen1:</p>
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<codeblock><property>
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<name>dfs.adls.oauth2.access.token.provider.type</name>
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<value>ClientCredential</value>
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</property>
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<property>
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<name>dfs.adls.oauth2.client.id</name>
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<value><varname>your_client_id</varname></value>
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</property>
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<property>
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<name>dfs.adls.oauth2.credential</name>
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<value><varname>your_client_secret</varname></value>
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</property>
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<property>
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<name>dfs.adls.oauth2.refresh.url</name>
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<value>https://login.windows.net/<varname>your_azure_tenant_id</varname>/oauth2/token</value>
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</property>
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</codeblock>
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<p>For ADLS Gen2:</p>
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<codeblock> <property>
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<name>fs.azure.account.auth.type</name>
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<value>OAuth</value>
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</property>
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<property>
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<name>fs.azure.account.oauth.provider.type</name>
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<value>org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider</value>
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</property>
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<property>
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<name>fs.azure.account.oauth2.client.id</name>
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<value><varname>your_client_id</varname></value>
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</property>
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<property>
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<name>fs.azure.account.oauth2.client.secret</name>
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<value><varname>your_client_secret</varname></value>
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</property>
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<property>
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<name>fs.azure.account.oauth2.client.endpoint</name>
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<value>https://login.microsoftonline.com/<varname>your_azure_tenant_id</varname>/oauth2/token</value>
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</property></codeblock>
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<note>
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<p>
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Check if your Hadoop distribution or cluster management tool includes support for
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filling in and distributing credentials across the cluster in an automated way.
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</p>
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</note>
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<p> After specifying the credentials, restart both the Impala and Hive
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services. Restarting Hive is required because certain Impala queries,
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such as <codeph>CREATE TABLE</codeph> statements, go through the Hive
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metastore.</p>
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</conbody>
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</concept>
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<concept id="etl">
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<title>Loading Data into ADLS for Impala Queries</title>
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<prolog>
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<metadata>
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<data name="Category" value="ETL"/>
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<data name="Category" value="Ingest"/>
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</metadata>
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</prolog>
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<conbody>
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<p>
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If your ETL pipeline involves moving data into ADLS and then querying through Impala,
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you can either use Impala DML statements to create, move, or copy the data, or
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use the same data loading techniques as you would for non-Impala data.
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</p>
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</conbody>
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<concept id="dml">
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<title>Using Impala DML Statements for ADLS Data</title>
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<conbody>
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<p conref="../shared/impala_common.xml#common/adls_dml"
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conrefend="../shared/impala_common.xml#common/adls_dml_end"/>
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</conbody>
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</concept>
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<concept id="manual_etl">
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<title>Manually Loading Data into Impala Tables on ADLS</title>
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<conbody>
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<p>
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As an alternative, you can use the Microsoft-provided methods to bring data files
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into ADLS for querying through Impala. See
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<xref href="https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-copy-data-azure-storage-blob" scope="external" format="html">the Microsoft ADLS documentation</xref>
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for details.
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</p>
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<p>
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After you upload data files to a location already mapped to an Impala table or partition, or if you delete
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files in ADLS from such a location, issue the <codeph>REFRESH <varname>table_name</varname></codeph>
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statement to make Impala aware of the new set of data files.
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</p>
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</conbody>
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</concept>
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</concept>
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<concept id="ddl">
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<title>Creating Impala Databases, Tables, and Partitions for Data Stored on ADLS</title>
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<prolog>
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<metadata>
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<data name="Category" value="Databases"/>
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</metadata>
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</prolog>
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<conbody>
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<p>
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Impala reads data for a table or partition from ADLS based on the
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<codeph>LOCATION</codeph> attribute for the table or partition.
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Specify the ADLS details in the <codeph>LOCATION</codeph> clause of a
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<codeph>CREATE TABLE</codeph> or <codeph>ALTER TABLE</codeph>
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statement. The syntax for the <codeph>LOCATION</codeph> clause is:
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<ul>
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<li>
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For ADLS Gen1:
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<codeblock>adl://<varname>account</varname>.azuredatalakestore.net/<varname>path/file</varname></codeblock></li>
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<li>
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For ADLS Gen2:
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<codeblock>abfs://<varname>container</varname>@<varname>account</varname>.dfs.core.windows.net/<varname>path</varname>/<varname>file</varname></codeblock>
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<p>
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or
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</p>
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<codeblock>abfss://<varname>container</varname>@<varname>account</varname>.dfs.core.windows.net/<varname>path</varname>/<varname>file</varname></codeblock>
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</li>
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</ul>
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</p>
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<p>
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<codeph><varname>container</varname></codeph> denotes the parent
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location that holds the files and folders, which is the Containers in
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the Azure Storage Blobs service.
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</p>
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<p>
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<codeph><varname>account</varname></codeph> is the name given for your
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storage account.
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</p>
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<note>
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<p> By default, TLS is enabled both with <codeph>abfs://</codeph> and
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<codeph>abfss://</codeph>. </p>
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<p>
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When you set the <codeph>fs.azure.always.use.https=false</codeph>
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property, TLS is disabled with <codeph>abfs://</codeph>, and TLS is
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enabled with <codeph>abfss://</codeph>
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</p>
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</note>
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<p>
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For a partitioned table, either specify a separate <codeph>LOCATION</codeph> clause for each new partition,
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or specify a base <codeph>LOCATION</codeph> for the table and set up a directory structure in ADLS to mirror
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the way Impala partitioned tables are structured in HDFS. Although, strictly speaking, ADLS filenames do not
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have directory paths, Impala treats ADLS filenames with <codeph>/</codeph> characters the same as HDFS
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pathnames that include directories.
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</p>
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<p>
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To point a nonpartitioned table or an individual partition at ADLS, specify a single directory
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path in ADLS, which could be any arbitrary directory. To replicate the structure of an entire Impala
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partitioned table or database in ADLS requires more care, with directories and subdirectories nested and
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named to match the equivalent directory tree in HDFS. Consider setting up an empty staging area if
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necessary in HDFS, and recording the complete directory structure so that you can replicate it in ADLS.
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</p>
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<p>
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For example, the following session creates a partitioned table where only a single partition resides on ADLS.
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The partitions for years 2013 and 2014 are located on HDFS. The partition for year 2015 includes a
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<codeph>LOCATION</codeph> attribute with an <codeph>adl://</codeph> URL, and so refers to data residing on
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ADLS, under a specific path underneath the store <codeph>impalademo</codeph>.
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</p>
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<codeblock>[localhost:21000] > create database db_on_hdfs;
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[localhost:21000] > use db_on_hdfs;
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[localhost:21000] > create table mostly_on_hdfs (x int) partitioned by (year int);
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[localhost:21000] > alter table mostly_on_hdfs add partition (year=2013);
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[localhost:21000] > alter table mostly_on_hdfs add partition (year=2014);
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[localhost:21000] > alter table mostly_on_hdfs add partition (year=2015)
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> location 'adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3/t1';
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</codeblock>
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<p> For convenience when working with multiple tables with data files
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stored in ADLS, you can create a database with a
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<codeph>LOCATION</codeph> attribute pointing to an ADLS path. Specify
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a URL of the form as shown above. Any tables created inside that
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database automatically create directories underneath the one specified
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by the database <codeph>LOCATION</codeph> attribute. </p>
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<p>
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The following session creates a database and two partitioned tables residing entirely on ADLS, one
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partitioned by a single column and the other partitioned by multiple columns. Because a
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<codeph>LOCATION</codeph> attribute with an <codeph>adl://</codeph> URL is specified for the database, the
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tables inside that database are automatically created on ADLS underneath the database directory. To see the
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names of the associated subdirectories, including the partition key values, we use an ADLS client tool to
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examine how the directory structure is organized on ADLS. For example, Impala partition directories such as
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<codeph>month=1</codeph> do not include leading zeroes, which sometimes appear in partition directories created
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through Hive.
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</p>
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<codeblock>[localhost:21000] > create database db_on_adls location 'adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3';
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[localhost:21000] > use db_on_adls;
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[localhost:21000] > create table partitioned_on_adls (x int) partitioned by (year int);
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[localhost:21000] > alter table partitioned_on_adls add partition (year=2013);
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[localhost:21000] > alter table partitioned_on_adls add partition (year=2014);
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[localhost:21000] > alter table partitioned_on_adls add partition (year=2015);
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[localhost:21000] > ! hadoop fs -ls adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3 --recursive;
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2015-03-17 13:56:34 0 dir1/dir2/dir3/
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2015-03-17 16:43:28 0 dir1/dir2/dir3/partitioned_on_adls/
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2015-03-17 16:43:49 0 dir1/dir2/dir3/partitioned_on_adls/year=2013/
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2015-03-17 16:43:53 0 dir1/dir2/dir3/partitioned_on_adls/year=2014/
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2015-03-17 16:43:58 0 dir1/dir2/dir3/partitioned_on_adls/year=2015/
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[localhost:21000] > create table partitioned_multiple_keys (x int)
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> partitioned by (year smallint, month tinyint, day tinyint);
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[localhost:21000] > alter table partitioned_multiple_keys
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> add partition (year=2015,month=1,day=1);
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[localhost:21000] > alter table partitioned_multiple_keys
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> add partition (year=2015,month=1,day=31);
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[localhost:21000] > alter table partitioned_multiple_keys
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> add partition (year=2015,month=2,day=28);
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[localhost:21000] > ! hadoop fs -ls adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3 --recursive;
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2015-03-17 13:56:34 0 dir1/dir2/dir3/
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2015-03-17 16:47:13 0 dir1/dir2/dir3/partitioned_multiple_keys/
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2015-03-17 16:47:44 0 dir1/dir2/dir3/partitioned_multiple_keys/year=2015/month=1/day=1/
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2015-03-17 16:47:50 0 dir1/dir2/dir3/partitioned_multiple_keys/year=2015/month=1/day=31/
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2015-03-17 16:47:57 0 dir1/dir2/dir3/partitioned_multiple_keys/year=2015/month=2/day=28/
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2015-03-17 16:43:28 0 dir1/dir2/dir3/partitioned_on_adls/
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2015-03-17 16:43:49 0 dir1/dir2/dir3/partitioned_on_adls/year=2013/
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2015-03-17 16:43:53 0 dir1/dir2/dir3/partitioned_on_adls/year=2014/
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2015-03-17 16:43:58 0 dir1/dir2/dir3/partitioned_on_adls/year=2015/
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</codeblock>
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<p>
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The <codeph>CREATE DATABASE</codeph> and <codeph>CREATE TABLE</codeph> statements create the associated
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directory paths if they do not already exist. You can specify multiple levels of directories, and the
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<codeph>CREATE</codeph> statement creates all appropriate levels, similar to using <codeph>mkdir
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-p</codeph>.
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</p>
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<p>
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Use the standard ADLS file upload methods to actually put the data files into the right locations. You can
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also put the directory paths and data files in place before creating the associated Impala databases or
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tables, and Impala automatically uses the data from the appropriate location after the associated databases
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and tables are created.
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</p>
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<p> You can switch whether an existing table or partition points to data
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in HDFS or ADLS. For example, if you have an Impala table or partition
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pointing to data files in HDFS or ADLS, and you later transfer those
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data files to the other filesystem, use an <codeph>ALTER TABLE</codeph>
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|
statement to adjust the <codeph>LOCATION</codeph> attribute of the
|
|
corresponding table or partition to reflect that change. This
|
|
location-switching technique is not practical for entire databases that
|
|
have a custom <codeph>LOCATION</codeph> attribute. </p>
|
|
|
|
</conbody>
|
|
|
|
</concept>
|
|
|
|
<concept id="internal_external">
|
|
|
|
<title>Internal and External Tables Located on ADLS</title>
|
|
|
|
<conbody>
|
|
|
|
<p>
|
|
Just as with tables located on HDFS storage, you can designate ADLS-based tables as either internal (managed
|
|
by Impala) or external, by using the syntax <codeph>CREATE TABLE</codeph> or <codeph>CREATE EXTERNAL
|
|
TABLE</codeph> respectively. When you drop an internal table, the files associated with the table are
|
|
removed, even if they are on ADLS storage. When you drop an external table, the files associated with the
|
|
table are left alone, and are still available for access by other tools or components. See
|
|
<xref href="impala_tables.xml#tables"/> for details.
|
|
</p>
|
|
|
|
<p>
|
|
If the data on ADLS is intended to be long-lived and accessed by other tools in addition to Impala, create
|
|
any associated ADLS tables with the <codeph>CREATE EXTERNAL TABLE</codeph> syntax, so that the files are not
|
|
deleted from ADLS when the table is dropped.
|
|
</p>
|
|
|
|
<p>
|
|
If the data on ADLS is only needed for querying by Impala and can be safely discarded once the Impala
|
|
workflow is complete, create the associated ADLS tables using the <codeph>CREATE TABLE</codeph> syntax, so
|
|
that dropping the table also deletes the corresponding data files on ADLS.
|
|
</p>
|
|
|
|
<p>
|
|
For example, this session creates a table in ADLS with the same column layout as a table in HDFS, then
|
|
examines the ADLS table and queries some data from it. The table in ADLS works the same as a table in HDFS as
|
|
far as the expected file format of the data, table and column statistics, and other table properties. The
|
|
only indication that it is not an HDFS table is the <codeph>adl://</codeph> URL in the
|
|
<codeph>LOCATION</codeph> property. Many data files can reside in the ADLS directory, and their combined
|
|
contents form the table data. Because the data in this example is uploaded after the table is created, a
|
|
<codeph>REFRESH</codeph> statement prompts Impala to update its cached information about the data files.
|
|
</p>
|
|
|
|
<codeblock>[localhost:21000] > create table usa_cities_adls like usa_cities location 'adl://impalademo.azuredatalakestore.net/usa_cities';
|
|
[localhost:21000] > desc usa_cities_adls;
|
|
+-------+----------+---------+
|
|
| name | type | comment |
|
|
+-------+----------+---------+
|
|
| id | smallint | |
|
|
| city | string | |
|
|
| state | string | |
|
|
+-------+----------+---------+
|
|
|
|
-- Now from a web browser, upload the same data file(s) to ADLS as in the HDFS table,
|
|
-- under the relevant store and path. If you already have the data in ADLS, you would
|
|
-- point the table LOCATION at an existing path.
|
|
|
|
[localhost:21000] > refresh usa_cities_adls;
|
|
[localhost:21000] > select count(*) from usa_cities_adls;
|
|
+----------+
|
|
| count(*) |
|
|
+----------+
|
|
| 289 |
|
|
+----------+
|
|
[localhost:21000] > select distinct state from sample_data_adls limit 5;
|
|
+----------------------+
|
|
| state |
|
|
+----------------------+
|
|
| Louisiana |
|
|
| Minnesota |
|
|
| Georgia |
|
|
| Alaska |
|
|
| Ohio |
|
|
+----------------------+
|
|
[localhost:21000] > desc formatted usa_cities_adls;
|
|
+------------------------------+----------------------------------------------------+---------+
|
|
| name | type | comment |
|
|
+------------------------------+----------------------------------------------------+---------+
|
|
| # col_name | data_type | comment |
|
|
| | NULL | NULL |
|
|
| id | smallint | NULL |
|
|
| city | string | NULL |
|
|
| state | string | NULL |
|
|
| | NULL | NULL |
|
|
| # Detailed Table Information | NULL | NULL |
|
|
| Database: | adls_testing | NULL |
|
|
| Owner: | jrussell | NULL |
|
|
| CreateTime: | Mon Mar 16 11:36:25 PDT 2017 | NULL |
|
|
| LastAccessTime: | UNKNOWN | NULL |
|
|
| Protect Mode: | None | NULL |
|
|
| Retention: | 0 | NULL |
|
|
| Location: | adl://impalademo.azuredatalakestore.net/usa_cities | NULL |
|
|
| Table Type: | MANAGED_TABLE | NULL |
|
|
...
|
|
+------------------------------+----------------------------------------------------+---------+
|
|
</codeblock>
|
|
|
|
<p>
|
|
In this case, we have already uploaded a Parquet file with a million rows of data to the
|
|
<codeph>sample_data</codeph> directory underneath the <codeph>impalademo</codeph> store on ADLS. This
|
|
session creates a table with matching column settings pointing to the corresponding location in ADLS, then
|
|
queries the table. Because the data is already in place on ADLS when the table is created, no
|
|
<codeph>REFRESH</codeph> statement is required.
|
|
</p>
|
|
|
|
<codeblock>[localhost:21000] > create table sample_data_adls
|
|
> (id int, id bigint, val int, zerofill string,
|
|
> name string, assertion boolean, city string, state string)
|
|
> stored as parquet location 'adl://impalademo.azuredatalakestore.net/sample_data';
|
|
[localhost:21000] > select count(*) from sample_data_adls;
|
|
+----------+
|
|
| count(*) |
|
|
+----------+
|
|
| 1000000 |
|
|
+----------+
|
|
[localhost:21000] > select count(*) howmany, assertion from sample_data_adls group by assertion;
|
|
+---------+-----------+
|
|
| howmany | assertion |
|
|
+---------+-----------+
|
|
| 667149 | true |
|
|
| 332851 | false |
|
|
+---------+-----------+
|
|
</codeblock>
|
|
|
|
</conbody>
|
|
|
|
</concept>
|
|
|
|
<concept id="queries">
|
|
|
|
<title>Running and Tuning Impala Queries for Data Stored on ADLS</title>
|
|
|
|
<conbody>
|
|
|
|
<p>
|
|
Once the appropriate <codeph>LOCATION</codeph> attributes are set up at the table or partition level, you
|
|
query data stored in ADLS exactly the same as data stored on HDFS or in HBase:
|
|
</p>
|
|
|
|
<ul>
|
|
<li>
|
|
Queries against ADLS data support all the same file formats as for HDFS data.
|
|
</li>
|
|
|
|
<li>
|
|
Tables can be unpartitioned or partitioned. For partitioned tables, either manually construct paths in ADLS
|
|
corresponding to the HDFS directories representing partition key values, or use <codeph>ALTER TABLE ...
|
|
ADD PARTITION</codeph> to set up the appropriate paths in ADLS.
|
|
</li>
|
|
|
|
<li>
|
|
HDFS, Kudu, and HBase tables can be joined to ADLS tables, or ADLS tables can be joined with each other.
|
|
</li>
|
|
|
|
<li>
|
|
Authorization using the Ranger framework to control access to databases, tables, or columns works the
|
|
same whether the data is in HDFS or in ADLS.
|
|
</li>
|
|
|
|
<li>
|
|
The <cmdname>catalogd</cmdname> daemon caches metadata for both HDFS and ADLS tables. Use
|
|
<codeph>REFRESH</codeph> and <codeph>INVALIDATE METADATA</codeph> for ADLS tables in the same situations
|
|
where you would issue those statements for HDFS tables.
|
|
</li>
|
|
|
|
<li>
|
|
Queries against ADLS tables are subject to the same kinds of admission control and resource management as
|
|
HDFS tables.
|
|
</li>
|
|
|
|
<li>
|
|
Metadata about ADLS tables is stored in the same metastore database as for HDFS tables.
|
|
</li>
|
|
|
|
<li>
|
|
You can set up views referring to ADLS tables, the same as for HDFS tables.
|
|
</li>
|
|
|
|
<li>
|
|
The <codeph>COMPUTE STATS</codeph>, <codeph>SHOW TABLE STATS</codeph>, and <codeph>SHOW COLUMN
|
|
STATS</codeph> statements work for ADLS tables also.
|
|
</li>
|
|
</ul>
|
|
|
|
</conbody>
|
|
|
|
<concept id="performance">
|
|
|
|
<title>Understanding and Tuning Impala Query Performance for ADLS Data</title>
|
|
<prolog>
|
|
<metadata>
|
|
<data name="Category" value="Performance"/>
|
|
</metadata>
|
|
</prolog>
|
|
|
|
<conbody>
|
|
|
|
<p>
|
|
Although Impala queries for data stored in ADLS might be less performant than queries against the
|
|
equivalent data stored in HDFS, you can still do some tuning. Here are techniques you can use to
|
|
interpret explain plans and profiles for queries against ADLS data, and tips to achieve the best
|
|
performance possible for such queries.
|
|
</p>
|
|
|
|
<p>
|
|
All else being equal, performance is expected to be lower for queries running against data on ADLS rather
|
|
than HDFS. The actual mechanics of the <codeph>SELECT</codeph> statement are somewhat different when the
|
|
data is in ADLS. Although the work is still distributed across the datanodes of the cluster, Impala might
|
|
parallelize the work for a distributed query differently for data on HDFS and ADLS. ADLS does not have the
|
|
same block notion as HDFS, so Impala uses heuristics to determine how to split up large ADLS files for
|
|
processing in parallel. Because all hosts can access any ADLS data file with equal efficiency, the
|
|
distribution of work might be different than for HDFS data, where the data blocks are physically read
|
|
using short-circuit local reads by hosts that contain the appropriate block replicas. Although the I/O to
|
|
read the ADLS data might be spread evenly across the hosts of the cluster, the fact that all data is
|
|
initially retrieved across the network means that the overall query performance is likely to be lower for
|
|
ADLS data than for HDFS data.
|
|
</p>
|
|
|
|
<p conref="../shared/impala_common.xml#common/adls_block_splitting"/>
|
|
|
|
<p>
|
|
When optimizing aspects of for complex queries such as the join order, Impala treats tables on HDFS and
|
|
ADLS the same way. Therefore, follow all the same tuning recommendations for ADLS tables as for HDFS ones,
|
|
such as using the <codeph>COMPUTE STATS</codeph> statement to help Impala construct accurate estimates of
|
|
row counts and cardinality. See <xref href="impala_performance.xml#performance"/> for details.
|
|
</p>
|
|
|
|
<p>
|
|
In query profile reports, the numbers for <codeph>BytesReadLocal</codeph>,
|
|
<codeph>BytesReadShortCircuit</codeph>, <codeph>BytesReadDataNodeCached</codeph>, and
|
|
<codeph>BytesReadRemoteUnexpected</codeph> are blank because those metrics come from HDFS.
|
|
If you do see any indications that a query against an ADLS table performed <q>remote read</q>
|
|
operations, do not be alarmed. That is expected because, by definition, all the I/O for ADLS tables involves
|
|
remote reads.
|
|
</p>
|
|
|
|
</conbody>
|
|
|
|
</concept>
|
|
|
|
</concept>
|
|
|
|
<concept id="restrictions">
|
|
|
|
<title>Restrictions on Impala Support for ADLS</title>
|
|
|
|
<conbody>
|
|
|
|
<p>
|
|
Impala requires that the default filesystem for the cluster be HDFS. You cannot use ADLS as the only
|
|
filesystem in the cluster.
|
|
</p>
|
|
|
|
<p>
|
|
Although ADLS is often used to store JSON-formatted data, the current Impala support for ADLS does not include
|
|
directly querying JSON data. For Impala queries, use data files in one of the file formats listed in
|
|
<xref href="impala_file_formats.xml#file_formats"/>. If you have data in JSON format, you can prepare a
|
|
flattened version of that data for querying by Impala as part of your ETL cycle.
|
|
</p>
|
|
|
|
<p>
|
|
You cannot use the <codeph>ALTER TABLE ... SET CACHED</codeph> statement for tables or partitions that are
|
|
located in ADLS.
|
|
</p>
|
|
|
|
</conbody>
|
|
|
|
</concept>
|
|
|
|
<concept id="best_practices">
|
|
<title>Best Practices for Using Impala with ADLS</title>
|
|
<prolog>
|
|
<metadata>
|
|
<data name="Category" value="Guidelines"/>
|
|
<data name="Category" value="Best Practices"/>
|
|
</metadata>
|
|
</prolog>
|
|
<conbody>
|
|
<p>
|
|
The following guidelines represent best practices derived from testing and real-world experience with Impala on ADLS:
|
|
</p>
|
|
<ul>
|
|
<li>
|
|
<p>
|
|
Any reference to an ADLS location must be fully qualified. (This rule applies when
|
|
ADLS is not designated as the default filesystem.)
|
|
</p>
|
|
</li>
|
|
<li>
|
|
<p>
|
|
Set any appropriate configuration settings for <cmdname>impalad</cmdname>.
|
|
</p>
|
|
</li>
|
|
</ul>
|
|
|
|
</conbody>
|
|
</concept>
|
|
|
|
</concept>
|