# Riak CS Import Connector

The Data Connector for Riak CS enables the import of the contents of *.tsv and* .csv files stored in your Riak CS bucket.

## Prerequisites

- Basic knowledge of Treasure Data


## Using TD Console

### Configure the Connection

To configure a connection for Riak CS:

1. In the Treasure Data console, navigate to Integration Hub > Catalog**.
2. Click the search icon on the far-right of the Catalog screen and enter **Riak**.
3. Hover over the RiakCS connector and select **Create Authentication**.
![](/assets/riak.4dee70c291f2f35752204f7e3f348b1410f4ffd77a8f584c9b31363a9fc665db.ec09b8e3.png)
4. Set the following parameters:


- **Endpoint**
- **Authentication Method**
- **Access key ID**
- **Secret access key**
![](/assets/riaknewauthentication.333a440dd8fb0540e5713d1225a9df7035090e0376b14bb69a043342a5752531.ec09b8e3.png)


1. Select **Continue** after entering the required connection details.
2. Name the connection so you can find it later should you need to modify any of the connection details.
3. If you would like to share this connection with other users in your organization, check the **Share with others** checkbox. If this box is unchecked this connection is visible only to you.
4. Select **Create Connection** to complete the connection.


The connection you just created appears in your list of connections with the name you provided.

### Data Preview

You can see a [preview](/products/customer-data-platform/integration-hub/batch/import/previewing-your-source-data) of your data before running the import by selecting Generate Preview. Data preview is optional and you can safely skip to the next page of the dialog if you choose to.

1. Select **Next**. The Data Preview page opens.
2. If you want to preview your data, select **Generate Preview**.
3. Verify the data.


### Data Placement

For data placement, select the target database and table where you want your data placed and indicate how often the import should run.

1. Select **Next.** Under Storage, you will create a new or select an existing database and create a new or select an existing table for where you want to place the imported data.
2. Select a **Database** > **Select an existing** or **Create New Database**.
3. Optionally, type a database name.
4. Select a **Table**> **Select an existing** or **Create New Table**.
5. Optionally, type a table name.
6. Choose the method for importing the data.
  - **Append** (default)-Data import results are appended to the table.
If the table does not exist, it will be created.
  - **Always Replace**-Replaces the entire content of an existing table with the result output of the query. If the table does not exist, a new table is created.
  - **Replace on New Data**-Only replace the entire content of an existing table with the result output when there is new data.
7. Select the **Timestamp-based Partition Key** column.
If you want to set a different partition key seed than the default key, you can specify the long or timestamp column as the partitioning time. As a default time column, it uses upload_time with the add_time filter.
8. Select the **Timezone** for your data storage.
9. Under **Schedule**, you can choose when and how often you want to run this query.


#### Run once

1. Select **Off**.
2. Select **Scheduling Timezone**.
3. Select **Create & Run Now**.


#### Repeat Regularly

1. Select **On**.
2. Select the **Schedule**. The UI provides these four options: *@hourly*, *@daily* and *@monthly* or custom *cron*.
3. You can also select **Delay Transfer** and add a delay of execution time.
4. Select **Scheduling Timezone**.
5. Select **Create & Run Now**.


After your transfer has run, you can see the results of your transfer in **Data Workbench** > **Databases.**

## Using the TD Toolbelt

### Install ‘td’ Command v0.11.9 or Later

Install the newest [Treasure Data Toolbelt](https://toolbelt.treasuredata.com/).


```
$ td --version
0.11.10
```

### Create Seed Config File (seed.yml)

Prepare seed.yml as below, with your AWS access key and secret access key. You must also specify bucket name, and target file name (or prefix for multiple files).


```yaml
in:
  type: riak_cs
  access_key_id: XXXXXXXXXX
  secret_access_key: YYYYYYYYYY
  bucket: sample_bucket
  path_prefix: path/to/sample_file # path the the *.csv or *.tsv file on your Riak CS bucket
  endpoint: host
out:
  mode: append
```

The Data Connector for Riak CS imports all files that match a specified prefix. (e.g. path_prefix: path/to/sample_ –> path/to/sample_201501.csv.gz, path/to/sample_201502.csv.gz, …, path/to/sample_201505.csv.gz)

For more details on available out modes, see [Appendix](/int/riak-cs-import-connector#h1__1835053169).

### Guess Fields (Generate load.yml)

Use connector:guess. This command automatically reads the target file, and intelligently guesses the file format.


```
$ td connector:guess seed.yml -o load.yml
```

If you open up load.yml, you’ll see the guessed file format definitions including file formats, encodings, column names, and types.


```yaml
in:
  type: riak_cs
  access_key_id: XXXXXXXXXX
  secret_access_key: YYYYYYYYYY
  bucket: sample_bucket
  path_prefix: path/to/sample_file
  endpoint: host
  parser:
    charset: UTF-8
    newline: CRLF
    type: csv
    delimiter: ','
    quote: '"'
    escape: ''
    skip_header_lines: 1
    columns:
    - name: id
      type: long
    - name: company
      type: string
    - name: customer
      type: string
    - name: created_at
      type: timestamp
      format: '%Y-%m-%d %H:%M:%S'
out:
  mode: append
```

Then, you can preview how the system parses the file by using the preview command.


```
$ td connector:preview load.yml
+-------+---------+----------+---------------------+
| id | company | customer | created_at |
+-------+---------+----------+---------------------+
| 11200 | AA Inc. | David | 2015-03-31 06:12:37 |
| 20313 | BB Imc. |Tom | 2015-04-01 01:00:07 |
| 32132 | CC Inc. | Fernando | 2015-04-01 10:33:41 |
| 40133 | DD Inc. | Cesar | 2015-04-02 05:12:32 |
| 93133 | EE Inc. |  Jake | 2015-04-02 14:11:13 |
+-------+---------+----------+---------------------+
```

The guess command needs over 3 rows and 2 columns in source data file, because it guesses column definition using sample rows from source data. | If the system detects your column name or column type unexpectedly, modify `load.yml` directly and preview again.

The Data Connector supports parsing of "boolean", "long", "double", "string", and "timestamp" types.

You also must have created the local database and table, prior to executing the load job.
To do this, run the following command.


```
$ td database:create td_sample_db
$ td table:create td_sample_db td_sample_table
```

### Execute Load Job

Submit the load job. It may take a couple of hours depending on the size of the data. Users need to specify the database and table where their data is stored.

It’s also recommended to specify --time-column option, because Treasure Data’s storage is partitioned by time (see also [data partitioning](https://docs.treasuredata.com/smart/project-product-documentation/data-partitioning-in-treasure-data)) If the option is not given, the Data Connector will choose the first long or timestamp column as the partitioning time. The type of the column specified by --time-column must be either of long and timestamp type.

If your data doesn’t have a time column you may add it using add_time filter option. More details at [add_time filter plugin](https://docs.treasuredata.com/smart/project-product-documentation/add_time-filter-function).


```bash
$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column created_at
```

The above command assumes you have already created *database(td_sample_db)* and *table(td_sample_table)*. If the database or the table do not exist in TD this command will not succeed, so create the database and table [manually](https://docs.treasuredata.com/smart/project-product-documentation/data-management) or use --auto-create-table option with td connector:issue command to auto create the database and table:


```
$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column created_at --auto-create-table
```

At present, the Data Connector does not sort records server-side. To use time-based partitioning effectively, sort records in files beforehand.

If you have a field called time, you don’t have to specify the --time-column option.


```
$ td connector:issue load.yml --database td_sample_db --table td_sample_table
```

## Mode (append/replace)

You can specify file import mode in out section of seed.yml.

### append (default)


```
in:
  ...
out:
  mode: append
```

This is the default mode. The imported records are appended to the target table.

### replace (In td 0.11.10 and later)


```
in:
  ...
out:
  mode: replace
```

If the target table already exists, the rows of the existing table are replaced with imported records.

### Scheduled Execution

You can schedule periodic Data Connector execution for incremental Riak CS file import. We manage our scheduler carefully to ensure high availability. By using this feature, you no longer need a cron daemon on your local data center.

For the scheduled import, the Data Connector for Riak CS imports all files that match with the specified prefix (e.g. path_prefix: path/to/sample_ –> path/to/sample_201501.csv.gz, path/to/sample_201502.csv.gz, …, path/to/sample_201505.csv.gz) at first and remembers the last path (path/to/sample_201505.csv.gz) for the next execution.

On the second and subsequent runs, it only imports files that comes after the last path in alphabetical (lexicographic) order. (path/to/sample_201506.csv.gz, …)

### Create the Schedule

A new schedule can be created using the td connector:create command. The following are required: the name of the schedule, the cron-style schedule, the database and table where their data will be stored, and the data connector configuration file.


```
$ td connector:create \
 daily_import \
 "10 0 * * *" \
 td_sample_db \
 td_sample_table \
 load.yml
```

It’s also recommended to specify the --time-column option, since TD Storage is partitioned by time (see also [data partitioning](https://docs.treasuredata.com/smart/project-product-documentation/data-partitioning-in-treasure-data)).


```
$ td connector:create \
 daily_import \
 "10 0 * * *" \
 td_sample_db \
 td_sample_table \
 load.yml \
 --time-column created_at
```

The `cron` parameter also accepts three special options: `@hourly`, `@daily` and `@monthly`. | By default, schedule is setup in UTC timezone. You can set the schedule in a timezone using -t or --timezone option. The `--timezone` option only supports extended timezone formats like 'Asia/Tokyo', 'America/Los_Angeles' etc. Timezone abbreviations like PST, CST are *not* supported and may lead to unexpected schedules.

### List the Schedules

You can see the list of currently scheduled entries by running the command td connector:list.


```
$ td connector:list
```

## Show the Setting and Schedule History

td connector:show shows the execution setting of a schedule entry.


```
td connector:show daily_import
```

td connector:history shows the execution history of a schedule entry. To investigate the results of each individual run, use td job jobid.


```
td connector:history daily_import
```

## Delete the Schedule

td connector:delete will remove the schedule.


```
$ td connector:delete daily_import
```

# Appendix

## Modes for Out Plugin

You can specify file import mode in out section of seed.yml.

### append (default)

This is the default mode and records are appended to the target table.


```
in:
  ...
out:
  mode: append
```

### replace (In td 0.11.10 and later)

This mode replaces data in the target table. Any manual schema changes made to the target table will remain intact with this mode.


```
in:
  ...
out:
  mode: replace
```