Impala with Azure Data Lake Store (ADLS)

You can use Impala to query data residing on the Azure Data Lake Store (ADLS) filesystem and Azure Blob File System (ABFS). This capability allows convenient access to a storage system that is remotely managed, accessible from anywhere, and integrated with various cloud-based services.

Impala can query files in any supported file format from ADLS. The ADLS storage location can be for an entire table or individual partitions in a partitioned table.

The default Impala tables use data files stored on HDFS, which are ideal for bulk loads and queries using full-table scans. In contrast, queries against ADLS data are less performant, making ADLS suitable for holding cold data that is only queried occasionally, while more frequently accessed hot data resides in HDFS. In a partitioned table, you can set the LOCATION attribute for individual partitions to put some partitions on HDFS and others on ADLS, typically depending on the age of the data.

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

To be able to access ADLS, first set up an Azure account, configure an ADLS store, and configure your cluster with appropriate credentials.

Creating Impala Databases, Tables, and Partitions for Data Stored on ADLS

To create a table that resides on ADLS, specify the ADLS details in the LOCATION clause of the CREATE TABLE or ALTER TABLE statement. The syntax for the LOCATION clause is:

  • For ADLS Gen1:
    LOCATION 'adl://'
  • For ADLS Gen2:
    LOCATION 'abfs://'


    LOCATION 'abfss://'

container denotes the parent location that holds the files and folders, which is the Containers in the Azure Storage Blobs service.

account is the name given for your storage account.

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

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

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.

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.

CREATE TABLE mostly_on_hdfs (x INT) PARTITIONED BY (year INT);
ALTER TABLE mostly_on_hdfs ADD PARTITION (year=2013);
ALTER TABLE mostly_on_hdfs ADD PARTITION (year=2014);
ALTER TABLE mostly_on_hdfs ADD PARTITION (year=2015)
   LOCATION 'adl://';

When working with multiple tables with data files stored in ADLS, you can create a database with the LOCATION attribute pointing to an ADLS path. Specify a URL of the form as shown above. Any tables created inside that database automatically create directories underneath the one specified by the database LOCATION attribute.

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. This location-switching technique is not practical for entire databases that have a custom LOCATION attribute.

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

Internal and External Tables Located on ADLS

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.

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.

Loading Data into ADLS for Impala Queries

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.

Using Impala DML Statements for ADLS Data:

The Impala DML statements (INSERT, LOAD DATA, and CREATE TABLE AS SELECT) can write data into a table or partition that resides in the Azure Data Lake Store (ADLS) or ADLS Gen2.

Manually Loading Data into Impala Tables on ADLS:

You can use the Microsoft-provided methods to bring data files into ADLS for querying through Impala. See the Microsoft ADLS documentation for details.

After you upload data files to a location already mapped to an Impala table or partition, or if you delete files in ADLS from such a location, issue the REFRESH statement to make Impala aware of the new set of data files.

Running and Queries for Data Stored on ADLS

Once the appropriate LOCATION attributes are set up at the table or partition level, you query data stored in ADLS 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 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 support ADLS tables.

Query Performance for ADLS Data

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.

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 SELECT 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.

Because data files written to ADLS do not have a default block size the way HDFS data files do, any Impala INSERT or CREATE TABLE AS SELECT statements use the PARQUET_FILE_SIZE query option setting to define the size of Parquet data files. (Using a large block size is more important for Parquet tables than for tables that use other file formats.)

When optimizing aspects of for complex queries such as the join order, Impala treats tables on HDFS and ADLS the same way.

In query profile reports, the numbers for BytesReadLocal, BytesReadShortCircuit, BytesReadDataNodeCached, and BytesReadRemoteUnexpected are blank because those metrics come from HDFS.

All the I/O for ADLS tables involves remote reads, and they will appear as remote read operations in the query profile.