Use the Beeline client with Apache Hive

Learn how to use Beeline to run Hive queries on HDInsight.

Beeline is a Hive client that is included on the head nodes of your HDInsight cluster. Beeline uses JDBC to connect to HiveServer2, a service hosted on your HDInsight cluster. You can also use Beeline to access Hive on HDInsight remotely over the internet. The following table provides connection strings for use with Beeline:

Where you run Beeline from Parameters
An SSH connection to a headnode or edge node -u 'jdbc:hive2://headnodehost:10001/;transportMode=http'
Outside the cluster -u 'jdbc:hive2://;ssl=true;transportMode=http;httpPath=/hive2' -n admin -p password

Replace admin with the cluster login account for your cluster.

Replace password with the password for the cluster login account.

Replace clustername with the name of your HDInsight cluster.


  • A Linux-based Hadoop on HDInsight cluster.


    Linux is the only operating system used on HDInsight version 3.4 or greater. For more information, see HDInsight retirement on Windows.

  • An SSH client or a local Beeline client. Most of the steps in this document assume that you are using Beeline from an SSH session to the cluster. For information on running Beeline from outside the cluster, see the use Beeline remotely section.

    For more information on using SSH, see Use SSH with HDInsight.

Use Beeline

  1. When starting Beeline, you must provide a connection string for HiveServer2 on your HDInsight cluster. To run the command from outside the cluster, you must also provide the cluster login account name (default admin) and password. Use the following table to find the connection string format and parameters to use:

    Where you run Beeline from Parameters
    An SSH connection to a headnode or edge node -u 'jdbc:hive2://headnodehost:10001/;transportMode=http'
    Outside the cluster -u 'jdbc:hive2://;ssl=true;transportMode=http;httpPath=/hive2' -n admin -p password

    For example, the following command can be used to start Beeline from an SSH session to the cluster:

    beeline -u 'jdbc:hive2://headnodehost:10001/;transportMode=http'

    This command starts the Beeline client, and connects to HiveServer2 on the cluster head node. Once the command completes, you arrive at a jdbc:hive2://headnodehost:10001/> prompt.

  2. Beeline commands begin with a ! character, for example !help displays help. However the ! can be omitted for some commands. For example, help also works.

    There is a !sql, which is used to execute HiveQL statements. However, HiveQL is so commonly used that you can omit the preceding !sql. The following two statements are equivalent:

    !sql show tables;
    show tables;

    On a new cluster, only one table is listed: hivesampletable.

  3. Use the following command to display the schema for the hivesampletable:

    describe hivesampletable;

    This command returns the following information:

     |       col_name        | data_type  | comment  |
     | clientid              | string     |          |
     | querytime             | string     |          |
     | market                | string     |          |
     | deviceplatform        | string     |          |
     | devicemake            | string     |          |
     | devicemodel           | string     |          |
     | state                 | string     |          |
     | country               | string     |          |
     | querydwelltime        | double     |          |
     | sessionid             | bigint     |          |
     | sessionpagevieworder  | bigint     |          |

    This information describes the columns in the table. While we could perform some queries against this data, let's instead create a brand new table to demonstrate how to load data into Hive and apply a schema.

  4. Enter the following statements to create a table named log4jLogs by using sample data provided with the HDInsight cluster:

    DROP TABLE log4jLogs;
    CREATE EXTERNAL TABLE log4jLogs (t1 string, t2 string, t3 string, t4 string, t5 string, t6 string, t7 string)
    STORED AS TEXTFILE LOCATION 'wasb:///example/data/';
    SELECT t4 AS sev, COUNT(*) AS count FROM log4jLogs WHERE t4 = '[ERROR]' AND INPUT__FILE__NAME LIKE '%.log' GROUP BY t4;

    These statements perform the following actions:

    • DROP TABLE - If the table exists, it is deleted.

    • CREATE EXTERNAL TABLE - Creates an external table in Hive. External tables only store the table definition in Hive. The data is left in the original location.

    • ROW FORMAT - How the data is formatted. In this case, the fields in each log are separated by a space.

    • STORED AS TEXTFILE LOCATION - Where the data is stored and in what file format.

    • SELECT - Selects a count of all rows where column t4 contains the value [ERROR]. This query returns a value of 3 as there are three rows that contain this value.

    • INPUT__FILE__NAME LIKE '%.log' - Hive attempts to apply the schema to all files in the directory. In this case, the directory contains files that do not match the schema. To prevent garbage data in the results, this statement tells Hive that we should only return data from files ending in .log.


    External tables should be used when you expect the underlying data to be updated by an external source. For example, an automated data upload process or a MapReduce operation.

    Dropping an external table does not delete the data, only the table definition.

    The output of this command is similar to the following text:

     INFO  : Tez session hasn't been created yet. Opening session
     INFO  :
     INFO  : Status: Running (Executing on YARN cluster with App id application_1443698635933_0001)
     INFO  : Map 1: -/-      Reducer 2: 0/1
     INFO  : Map 1: 0/1      Reducer 2: 0/1
     INFO  : Map 1: 0/1      Reducer 2: 0/1
     INFO  : Map 1: 0/1      Reducer 2: 0/1
     INFO  : Map 1: 0/1      Reducer 2: 0/1
     INFO  : Map 1: 0(+1)/1  Reducer 2: 0/1
     INFO  : Map 1: 0(+1)/1  Reducer 2: 0/1
     INFO  : Map 1: 1/1      Reducer 2: 0/1
     INFO  : Map 1: 1/1      Reducer 2: 0(+1)/1
     INFO  : Map 1: 1/1      Reducer 2: 1/1
     |   sev    | count  |
     | [ERROR]  | 3      |
     1 row selected (47.351 seconds)
  5. To exit Beeline, use !exit.

Use Beeline to run a HiveQL file

Use the following steps to create a file, then run it using Beeline.

  1. Use the following command to create a file named query.hql:

    nano query.hql
  2. Use the following text as the contents of the file. This query creates a new 'internal' table named errorLogs:

    CREATE TABLE IF NOT EXISTS errorLogs (t1 string, t2 string, t3 string, t4 string, t5 string, t6 string, t7 string) STORED AS ORC;
    INSERT OVERWRITE TABLE errorLogs SELECT t1, t2, t3, t4, t5, t6, t7 FROM log4jLogs WHERE t4 = '[ERROR]' AND INPUT__FILE__NAME LIKE '%.log';

    These statements perform the following actions:

    • CREATE TABLE IF NOT EXISTS - If the table does not already exist, it is created. Since the EXTERNAL keyword is not used, this statement creates an internal table. Internal tables are stored in the Hive data warehouse and are managed completely by Hive.
    • STORED AS ORC - Stores the data in Optimized Row Columnar (ORC) format. ORC format is a highly optimized and efficient format for storing Hive data.
    • INSERT OVERWRITE ... SELECT - Selects rows from the log4jLogs table that contain [ERROR], then inserts the data into the errorLogs table.


      Unlike external tables, dropping an internal table deletes the underlying data as well.

  3. To save the file, use Ctrl+_X, then enter Y, and finally Enter.

  4. Use the following to run the file using Beeline:

    beeline -u 'jdbc:hive2://headnodehost:10001/;transportMode=http' -i query.hql

    The -i parameter starts Beeline, runs the statements in the query.hql file. Once the query completes, you arrive at the jdbc:hive2://headnodehost:10001/> prompt. You can also run a file using the -f parameter, which exits Beeline after the query completes.

  5. To verify that the errorLogs table was created, use the following statement to return all the rows from errorLogs:

    SELECT * from errorLogs;

    Three rows of data should be returned, all containing [ERROR] in column t4:

     | errorlogs.t1  | errorlogs.t2  | errorlogs.t3  | errorlogs.t4  | errorlogs.t5  | errorlogs.t6  | errorlogs.t7  |
     | 2012-02-03    | 18:35:34      | SampleClass0  | [ERROR]       | incorrect     | id            |               |
     | 2012-02-03    | 18:55:54      | SampleClass1  | [ERROR]       | incorrect     | id            |               |
     | 2012-02-03    | 19:25:27      | SampleClass4  | [ERROR]       | incorrect     | id            |               |
     3 rows selected (1.538 seconds)

Use Beeline remotely

If you have Beeline installed locally, or are using it through a Docker image such as sutoiku/beeline, you must use the following parameters:

  • Connection string: -u 'jdbc:hive2://;ssl=true;transportMode=http;httpPath=/hive2'

  • Cluster login name: -n admin

  • Cluster login password -p 'password'

Replace the clustername in the connection string with the name of your HDInsight cluster.

Replace admin with the name of your cluster login, and replace password with the password for your cluster login.

Use Beeline with Spark

Spark provides its own implementation of HiveServer2, which is often refered to as the Spark Thrift server. This service uses Spark SQL to resolve queries instead of Hive, and may provide better performance depending on your query.

To connect to the Spark Thrift server of a Spark on HDInsight cluster, use port 10002 instead of 10001. For example, beeline -u 'jdbc:hive2://headnodehost:10002/;transportMode=http'.


The Spark Thrift server is not directly accessible over the internet. You can only connect to it from an SSH session or inside the same Azure Virtual Network as the HDInsight cluster.

Next steps

For more general information on Hive in HDInsight, see the following document:

For more information on other ways you can work with Hadoop on HDInsight, see the following documents:

If you are using Tez with Hive, see the following documents: