# Microsoft SQL Server Import Integration

[Learn more about Microsoft SQL Server Tables Export Integration](/int/microsoft-sql-server-tables-export-integration).

This article describes how to use the data connector for Microsoft SQL Server, which allows you to directly import data from your Microsoft SQL Server to Treasure Data.

For sample workflows on how to import data from your Microsoft SQL Server, view [Treasure Boxes](https://github.com/treasure-data/treasure-boxes/tree/master/td_load/sql_server).

## Prerequisites

- Basic knowledge of Treasure Data
- Basic knowledge of Microsoft SQL Server
- A Microsoft SQL Server instance running **remotely**, for example on RDS.


## Supported SQL Server Versions

• Microsoft SQL Server 2017

• Microsoft SQL Server 2016

• Microsoft SQL Server 2014

• Microsoft SQL Server 2012

• Microsoft SQL Server 2008 R2

• Azure SQL Database

• Azure SQL Data Warehouse or Parallel Data Warehouse

• Azure SQL Managed Instance (Extended Private Preview)

## Use the TD Console to Create Your Connection

You can use the TD Console to configure your connection.

### Create a New Connection

When you configure a data connection, you provide authentication to access the integration. In Treasure Data, you configure the authentication and then specify the source information.

Go to Integrations Hub ->  Catalog and search and select Microsoft SQL Server.

![](/assets/mssqltile.8e9463b5dd47f74d1593fc06d953a8d76ffb9dc5713817e1c908f273f687c4d7.53f040ef.png)

The following dialog opens.

![](/assets/unadjustednonraw_thumb_34.0eccea67146d6dfae1bc7c365762112760f8232e83fac86765e8f1ec0270c31f.53f040ef.jpg)

Provide the required credentials, including the host name, port, user name and password required to access Microsoft SQL Server.

![](/assets/unadjustednonraw_thumb_35.375b2e45d46508591169dd358db6699df178945cf30765a66a5babb87c22bd93.53f040ef.jpg)

If you add the option encrypt=true and your certificate can't be validated, you need to add the option trustServerCertificate=true to bypass the certificate validation.

Select **Continue**.

Name your new Microsoft SQL Server Connection. Select **New Source.**

### Transfer Your Microsoft SQL Server Data to Treasure Data

You created your authenticated connection, now you create the source connection.
Complete the details and select **Next**.

![](/assets/unadjustednonraw_thumb_37.fd7e3e5464b8772de86bd3d9acc4c6571c3be1c7a08f8dd019c082789d40d354.53f040ef.jpg)

If you want to specify the schema, select **Use custom SELECT query** as follows:

![](/assets/unadjustednonraw_thumb_39.75ad4fc4dc90492dbefd8098cc4e8933fe6d4ceb74957f803524ee10f817bbb6.53f040ef.jpg)

### Preview

You’ll see a [preview](https://docs.treasuredata.com/smart/project-product-documentation/about-data-preview) of your data. To make changes, select **Advanced Settings** otherwise, select **Next**.

![](/assets/unadjustednonraw_thumb_3a.e90756b663ed5316dccd5c1b0455799edbfbd7705e739063bd5864021001d397.53f040ef.jpg)

Choose the Target Database and Table

Choose an existing or create a new database and table where you want to transfer data to:

![](/assets/unadjustednonraw_thumb_3c.2a6795708f6a8d2c9d4a38ee05abe2da9997c2c63d5838cb1b8e8801f1b2b4d1.53f040ef.jpg)

Create new database and give your database a name. Complete similar steps for **Create new table.**

Select whether to **append** records to an existing table or **replace** your existing table.

If you want to set a different **partition key seed** rather than use the default key, you can specify one.

### Scheduling

In the **Schedule** tab, you can specify a one-time transfer, or you can schedule an automated recurring transfer. If you select **Once now,** select **Start Transfer.** If you select **Repeat…** specify your schedule options, then select **Schedule Transfer.**

![](/assets/unadjustednonraw_thumb_3d.95885abce5169b2092d105ae287bdf32013f54ddee46dfe3ac7d3287045bd955.53f040ef.jpg)

Name your Source connector. You can just save the connector or select **Create & Run**.

![](/assets/unadjustednonraw_thumb_3e.a2ec3743addb1fbec57860c783f264d5605d09d7441112eb2d39f52854f54fa0.53f040ef.jpg)

## Use Command-Line to Create Your Microsoft SQL Server Connection

You can use the CLI to configure your connection.

### Install ‘td’ command

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

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

Prepare `seed.yml` with your Microsoft SQL Server access information:


```yaml
in:
  type: sqlserver
  host: sqlserver_host_name
  port: 1433
  user: test_user
  password: test_password
  database: test_database
  table: test_table
out:
  mode: replace
```

This example will dump all records inside the table. You can have more detailed control with [additional parameters](https://github.com/embulk/embulk-input-jdbc/tree/master/embulk-input-sqlserver).

For more details on available out modes, see the Appendix below.

### Guess Fields (Generate load.yml)

Next use connector:guess. This command automatically reads the target data, and intelligently guesses the data format.


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

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

### Optional: Preview data to be imported

You can preview data to be imported using the command td connector:preview.


```
$ td connector:preview load.yml
+---------+--------------+----------------------------------+------------+---------------------------+
| id:long | name:string  | description:string               | price:long | created_at:timestamp      |
+---------+--------------+----------------------------------+------------+---------------------------+
| 1       | "item name1" | "26e3c3625366591bc2ffc6e262976e" | 2419       | "2014-02-16 13:01:06 UTC" |
| 2       | "item name2" | "3e9dd9474dacb78afd607f9e0a3366" | 1298       | "2014-05-24 13:59:26 UTC" |
| 3       | "item name3" | "9b6c9e4a140284d3951681e9e047f6" | 9084       | "2014-06-21 00:18:21 UTC" |
| 4       | "item name4" | "a11faf5e63c1b02a3d4c2b5cbb7331" | 669        | "2014-05-02 03:44:08 UTC" |
| 6       | "item name6" | "6aa15471c373ddc8a6469e1c918f98" | 3556       | "2014-03-29 08:30:23 UTC" |
+---------+--------------+----------------------------------+------------+---------------------------+
```

### Execute Load Job

Finally, submit the load job. The job may take a couple of hours to run depending on the data size. Users need to specify the database and table where their data is stored.

It is recommended to specify --time-column option, since Treasure Data’s storage is [partitioned by time](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).


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

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
```

You can assign Time Format column to the "Partitioning Key" by "--time-column" option.

You can load records incrementally by specifying columns in your table by utilizing the incremental_columns and last_record options.


```yaml
in:
  type: sqlserver
  host: sqlserver_host_name
  port: 1433
  user: test_user
  password: test_password
  database: test_database
  table: test_table
  incremental: true
  incremental_columns: [id, sub_id]
  last_record: [10000, 300]
out:
  mode: append
  exec: {}
```

Then plugin will automatically recreate query and sort value at internal.


```sql
# when last_record wasn't given
SELECT * FROM(
    ...original query is here
)
ORDER BY id, sub_id

::: terminal
# when last_record was given
SELECT * FROM(
    ...original query is here
)
WHERE id > 10000 OR (id = 10000 AND sub_id > 300)
ORDER BY id, sub_id
```

If you’re using with [scheduled execution](https://docs.treasuredata.com/smart/project-product-documentation/scheduling-jobs-using-td-console), plugin will automatically generate last_record and hold it internally. Then you can use it at next scheduled execution.


```
in:
  type: sqlserver
  ...
out:
  ...

Config Diff
---
in:
  last_record:
  - 20000
  - 400
```

`query` option can't available when you set `incremental: true`.
Only strings and integers are supported as incremental_columns.

| --- |

### Scheduled execution

You can schedule periodic Data Connector execution for periodic Microsoft SQL Server import. We carefully configure our scheduler to ensure high availability. By using this feature, you no longer need a cron daemon on your local data center.

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


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

The `cron` parameter also accepts three 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.

### Appendix

#### Modes for out plugin

You can specify file import mode in out section of load.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
```

## Further Information

- [List of Options for Microsoft SQL Server Data Connector](https://github.com/embulk/embulk-input-jdbc/tree/master/embulk-input-sqlserver)