These procedures presume that you have already set up an Azure account, configured an ADLS store, and configured your Hadoop cluster with appropriate credentials to be able to access ADLS. See the following resources for information:

• Get started with Azure Data Lake Store using the Azure Portal

• Hadoop Azure Data Lake Support

Impala SQL statements work with data on ADLS as follows:

• The CREATE TABLE Statement or ALTER TABLE Statement statements can specify that a table resides on the ADLS filesystem by encoding an adl:// prefix for the LOCATION property. ALTER TABLE can also set the LOCATION property for an individual partition, so that some data in a table resides on ADLS and other data in the same table resides on HDFS.

  The full format of the location URI is typically:

  adl://your_account.azuredatalakestore.net/rest_of_directory_path
  

• Once a table or partition is designated as residing on ADLS, the SELECT Statement statement transparently accesses the data files from the appropriate storage layer.

• If the ADLS table is an internal table, the DROP TABLE Statement statement removes the corresponding data files from ADLS when the table is dropped.

• The TRUNCATE TABLE Statement (CDH 5.5 or higher only) statement always removes the corresponding data files from ADLS when the table is truncated.

• The LOAD DATA Statement can move data files residing in HDFS into an ADLS table.

• The INSERT Statement, or the CREATE TABLE AS SELECT form of the CREATE TABLE statement, can copy data from an HDFS table or another ADLS table into an ADLS table.

For usage information about Impala SQL statements with ADLS tables, see Creating Impala Databases, Tables, and Partitions for Data Stored on ADLS and Using Impala DML Statements for ADLS Data.

To allow Impala to access data in ADLS, specify values for the following configuration settings in your core-site.xml file:

<property>
   <name>dfs.adls.oauth2.access.token.provider.type</name>
   <value>ClientCredential</value>
</property>
<property>
   <name>dfs.adls.oauth2.client.id</name>
   <value><varname>your_client_id</varname></value>
</property>
<property>
   <name>dfs.adls.oauth2.credential</name>
   <value><varname>your_client_secret</varname></value>
</property>
<property>
   <name>dfs.adls.oauth2.refresh.url</name>
   <value><varname>refresh_URL</varname></value>
</property>

  Note:

Check if your Hadoop distribution or cluster management tool includes support for filling in and distributing credentials across the cluster in an automated way.

After specifying the credentials, restart both the Impala and Hive services. (Restarting Hive is required because Impala queries, CREATE TABLE statements, and so on go through the Hive metastore.)

If your ETL pipeline involves moving data into ADLS and then querying through Impala, you can either use Impala DML statements to create, move, or copy the data, or use the same data loading techniques as you would for non-Impala data.

Impala reads data for a table or partition from ADLS based on the LOCATION attribute for the table or partition. Specify the ADLS details in the LOCATION clause of a CREATE TABLE or ALTER TABLE statement. The notation for the LOCATION clause is adl://store/path/to/file.

For a partitioned table, either specify a separate LOCATION clause for each new partition, or specify a base LOCATION for the table and set up a directory structure in ADLS to mirror the way Impala partitioned tables are structured in HDFS. Although, strictly speaking, ADLS filenames do not have directory paths, Impala treats ADLS filenames with / characters the same as HDFS pathnames that include directories.

To point a nonpartitioned table or an individual partition at ADLS, specify a single directory path in ADLS, which could be any arbitrary directory. To replicate the structure of an entire Impala partitioned table or database in ADLS requires more care, with directories and subdirectories nested and named to match the equivalent directory tree in HDFS. Consider setting up an empty staging area if necessary in HDFS, and recording the complete directory structure so that you can replicate it in ADLS.

For example, the following session creates a partitioned table where only a single partition resides on ADLS. The partitions for years 2013 and 2014 are located on HDFS. The partition for year 2015 includes a LOCATION attribute with an adl:// URL, and so refers to data residing on ADLS, under a specific path underneath the store impalademo.

[localhost:21000] > create database db_on_hdfs;
[localhost:21000] > use db_on_hdfs;
[localhost:21000] > create table mostly_on_hdfs (x int) partitioned by (year int);
[localhost:21000] > alter table mostly_on_hdfs add partition (year=2013);
[localhost:21000] > alter table mostly_on_hdfs add partition (year=2014);
[localhost:21000] > alter table mostly_on_hdfs add partition (year=2015)
                  >   location 'adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3/t1';

For convenience when working with multiple tables with data files stored in ADLS, you can create a database with a LOCATION attribute pointing to an ADLS path. Specify a URL of the form adl://store/root/path/for/database for the LOCATION attribute of the database. Any tables created inside that database automatically create directories underneath the one specified by the database LOCATION attribute.

The following session creates a database and two partitioned tables residing entirely on ADLS, one partitioned by a single column and the other partitioned by multiple columns. Because a LOCATION attribute with an adl:// URL is specified for the database, the tables inside that database are automatically created on ADLS underneath the database directory. To see the names of the associated subdirectories, including the partition key values, we use an ADLS client tool to examine how the directory structure is organized on ADLS. For example, Impala partition directories such as month=1 do not include leading zeroes, which sometimes appear in partition directories created through Hive.

[localhost:21000] > create database db_on_adls location 'adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3';
[localhost:21000] > use db_on_adls;

[localhost:21000] > create table partitioned_on_adls (x int) partitioned by (year int);
[localhost:21000] > alter table partitioned_on_adls add partition (year=2013);
[localhost:21000] > alter table partitioned_on_adls add partition (year=2014);
[localhost:21000] > alter table partitioned_on_adls add partition (year=2015);

[localhost:21000] > ! hadoop fs -ls adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3 --recursive;
2015-03-17 13:56:34          0 dir1/dir2/dir3/
2015-03-17 16:43:28          0 dir1/dir2/dir3/partitioned_on_adls/
2015-03-17 16:43:49          0 dir1/dir2/dir3/partitioned_on_adls/year=2013/
2015-03-17 16:43:53          0 dir1/dir2/dir3/partitioned_on_adls/year=2014/
2015-03-17 16:43:58          0 dir1/dir2/dir3/partitioned_on_adls/year=2015/

[localhost:21000] > create table partitioned_multiple_keys (x int)
                  >   partitioned by (year smallint, month tinyint, day tinyint);
[localhost:21000] > alter table partitioned_multiple_keys
                  >   add partition (year=2015,month=1,day=1);
[localhost:21000] > alter table partitioned_multiple_keys
                  >   add partition (year=2015,month=1,day=31);
[localhost:21000] > alter table partitioned_multiple_keys
                  >   add partition (year=2015,month=2,day=28);

[localhost:21000] > ! hadoop fs -ls adl://impalademo.azuredatalakestore.net/dir1/dir2/dir3 --recursive;
2015-03-17 13:56:34          0 dir1/dir2/dir3/
2015-03-17 16:47:13          0 dir1/dir2/dir3/partitioned_multiple_keys/
2015-03-17 16:47:44          0 dir1/dir2/dir3/partitioned_multiple_keys/year=2015/month=1/day=1/
2015-03-17 16:47:50          0 dir1/dir2/dir3/partitioned_multiple_keys/year=2015/month=1/day=31/
2015-03-17 16:47:57          0 dir1/dir2/dir3/partitioned_multiple_keys/year=2015/month=2/day=28/
2015-03-17 16:43:28          0 dir1/dir2/dir3/partitioned_on_adls/
2015-03-17 16:43:49          0 dir1/dir2/dir3/partitioned_on_adls/year=2013/
2015-03-17 16:43:53          0 dir1/dir2/dir3/partitioned_on_adls/year=2014/
2015-03-17 16:43:58          0 dir1/dir2/dir3/partitioned_on_adls/year=2015/

The CREATE DATABASE and CREATE TABLE statements create the associated directory paths if they do not already exist. You can specify multiple levels of directories, and the CREATE statement creates all appropriate levels, similar to using mkdir -p.

Use the standard ADLS file upload methods to actually put the data files into the right locations. You can also put the directory paths and data files in place before creating the associated Impala databases or tables, and Impala automatically uses the data from the appropriate location after the associated databases and tables are created.

You can switch whether an existing table or partition points to data in HDFS or ADLS. For example, if you have an Impala table or partition pointing to data files in HDFS or ADLS, and you later transfer those data files to the other filesystem, use an ALTER TABLE statement to adjust the LOCATION attribute of the corresponding table or partition to reflect that change. Because Impala does not have an ALTER DATABASE statement, this location-switching technique is not practical for entire databases that have a custom LOCATION attribute.

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 CREATE TABLE or CREATE EXTERNAL TABLE 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 Overview of Impala Tables for details.

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 CREATE EXTERNAL TABLE syntax, so that the files are not deleted from ADLS when the table is dropped.

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 CREATE TABLE syntax, so that dropping the table also deletes the corresponding data files on ADLS.

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 adl:// URL in the LOCATION 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 REFRESH statement prompts Impala to update its cached information about the data files.

[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    |
...
+------------------------------+----------------------------------------------------+---------+

In this case, we have already uploaded a Parquet file with a million rows of data to the sample_data directory underneath the impalademo 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 REFRESH statement is required.

[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     |
+---------+-----------+

Once the appropriate LOCATION 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:

• Queries against ADLS data support all the same file formats as for HDFS data.
• 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 ALTER TABLE ... ADD PARTITION to set up the appropriate paths in ADLS.
• HDFS, Kudu, and HBase tables can be joined to ADLS tables, or ADLS tables can be joined with each other.
• Authorization using the Sentry framework to control access to databases, tables, or columns works the same whether the data is in HDFS or in ADLS.
• The catalogd daemon caches metadata for both HDFS and ADLS tables. Use REFRESH and INVALIDATE METADATA for ADLS tables in the same situations where you would issue those statements for HDFS tables.
• Queries against ADLS tables are subject to the same kinds of admission control and resource management as HDFS tables.
• Metadata about ADLS tables is stored in the same metastore database as for HDFS tables.
• You can set up views referring to ADLS tables, the same as for HDFS tables.
• The COMPUTE STATS, SHOW TABLE STATS, and SHOW COLUMN STATS statements work for ADLS tables also.

Impala requires that the default filesystem for the cluster be HDFS. You cannot use ADLS as the only filesystem in the cluster.

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 How Impala Works with Hadoop 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.

You cannot use the ALTER TABLE ... SET CACHED statement for tables or partitions that are located in ADLS.

The following guidelines represent best practices derived from testing and real-world experience with Impala on ADLS:

• Any reference to an ADLS location must be fully qualified. (This rule applies when ADLS is not designated as the default filesystem.)

• Set any appropriate configuration settings for impalad.