Use the Apache Beeline client with Apache Hive

Άρθρο
05/10/2024

This article describes how to use the command-line Apache Beeline client to create and execute Apache Hive queries over an SSH connection.

Background

Beeline is a Hive client that is included on the head nodes of your HDInsight cluster. This article describes how to use this tool through examples using a Hive query and a HiveQL file.

To connect to the Beeline client installed on your HDInsight cluster, or install Beeline locally, follow our guide to connect with, or install Apache Beeline.

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 examples provide the most common connection strings used to connect to HDInsight from Beeline.

Prerequisites for examples

A Hadoop cluster on Azure HDInsight. If you need a cluster, follow our guide to create an HDInsight cluster.
Notice the URI scheme for your cluster's primary storage. For example, wasb:// for Azure Storage, abfs:// for Azure Data Lake Storage Gen2, or adl:// for Azure Data Lake Storage Gen1. If secure transfer is enabled for Azure Storage, the URI is wasbs://. For more information, see secure transfer.
An SSH client. For more information, see Connect to HDInsight (Apache Hadoop) using SSH. Most of the steps in this document assume that you're using Beeline from an SSH session to the cluster. You can also use a local Beeline client, but those steps are not covered in this article.

Run a Hive query

This example is based on using the Beeline client from an SSH connection.

Open an SSH connection to the cluster with the code below. Replace sshuser with the SSH user for your cluster, and replace CLUSTERNAME with the name of your cluster. When prompted, enter the password for the SSH user account.
```
ssh sshuser@CLUSTERNAME-ssh.azurehdinsight.net
```
Connect to HiveServer2 with your Beeline client from your open SSH session by entering the following command:
```
beeline -u 'jdbc:hive2://headnodehost:10001/;transportMode=http'
```
Note

Refer to "To HDInsight Enterprise Security Package (ESP) cluster using Kerberos" part in Connect to HiveServer2 using Beeline or install Beeline locally to connect from your local if you are using an Enterprise Security Package (ESP) enabled cluster
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's !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.

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.

Enter the following statements to create a table named log4jLogs by using sample data provided with the HDInsight cluster: (Revise as needed based on your URI scheme.)

DROP TABLE log4jLogs;
CREATE EXTERNAL TABLE log4jLogs (
    t1 string,
    t2 string,
    t3 string,
    t4 string,
    t5 string,
    t6 string,
    t7 string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ' '
STORED AS TEXTFILE LOCATION 'wasbs:///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 do the following actions:

Statement	Description
DROP TABLE	If the table exists, it's 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 don't match the schema. To prevent garbage data in the results, this statement tells Hive that it should only return data from files ending in .log.

Note

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)

Exit Beeline:
```
!exit
```

Run a HiveQL file

This example is a continuation from the prior example. Use the following steps to create a file, then run it using Beeline.

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

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 do the following actions:

Statement	Description
CREATE TABLE IF NOT EXISTS	If the table doesn't already exist, it's created. Since the EXTERNAL keyword isn't 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.

Note

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

To save the file, use Ctrl+X, then enter Y, and finally Enter.
Use the following to run the file using Beeline:
```
beeline -u 'jdbc:hive2://headnodehost:10001/;transportMode=http' -i query.hql
```
Note

The -i parameter starts Beeline and 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.

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 (0.813 seconds)

Next steps

For more general information on Hive in HDInsight, see Use Apache Hive with Apache Hadoop on HDInsight
You can find the HiveQL language reference in the language manual
For more information on other ways you can work with Hadoop on HDInsight, see Use MapReduce with Apache Hadoop on HDInsight

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