# US3 Support

Note:

- Only supports for newly created uhadoop-2.2 version instances;

## 1. Use Cases

- Cold data or low-frequency calculation analysis scenarios
- Small-scale computing analysis scenarios
- Data backup

## 2. Access Steps

1. UHadoop Resource Creation: Create UHadoop instance of Hadoop-2.2 version via console;

2. US3 Resource Creation:
    - Create the required Bucket on the US3 console. If it has been created, skip this step. For details, refer to: [US3-Create Storage Space](/docs/ufile/guide/space#Creating a Storage Space)
    - Create tokens on the US3 console and associate them with the Bucket. If it has been created, skip this step. For details, refer to: [US3-Token Management](/docs/ufile/guide/token)

3. UHadoop Configuration Modification:

    - Select the created UHadoop instance, enter Cluster Management-> Service Management page, click on Hadoop-> Parameter Configuration button

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    - Modify US3 token information

      - fs.us3.access.key：US3 token public key
      - fs.us3.secret.key：US3 token private key

      Perform the above steps, after these two parameters are modified, click the OK button, then check the re-option reminder option to restart the Hadoop service.

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4. Hive Adaptation: After the Hadoop service restarts, restart the Hive service. Warning: Not restarting the service will report an error when accessing us3.

## 3. Usage Examples

### 3.1 HDFS

The format is `hadoop fs -ls us3://<bucket name>/<path>`. Example of use is as follows:

* Upload a file

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* View file list

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### 3.2 Hive

You can use one of the following two methods to store data on US3.

1. The entire database is established on US3

   ```
   hive (default)> create database hive_us3 location "us3://us3-uhadoop/hive_us3"
   ```

2. Specify the table to be established on US3

   ```
   hive (hive_db)> create table us3_test ( name string) row format delimited fields terminated by '\t' location 'us3://sniper-s3-adapter/hive/us3-test';
   ```

### 3.3 Spark

Whether it is through spark-submit or pyspark, spark-shell, spark-sql and other read and write US3, you need to specify the file path format as: `us3://<bucket name>/path`, as follows:

* Upload the test script：

  ```
  hadoop fs -put $SPARK_HOME/examples/src/main/python/pi.py us3://us3-uhadoop/example
  ```

* Perform the test:

  ```
  spark-submit --master yarn --deploy-mode client --num-executors 2 --executor-cores 1 --executor-memory 1G us3://us3-uhadoop/example/pi.py 100
  ```