dbt-starrocks 1.11.0
The Starrocks adapter plugin for dbt
Requires Python: <3.13,>=3.9

# dbt-starrocks

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This project is **under development**.


The `dbt-starrocks` package contains all the code to enable [dbt](https://getdbt.com) to work with [StarRocks](https://www.starrocks.io).

This is an experimental plugin:
- We have not tested it extensively
- Requires StarRocks version 2.5.0 or higher  
  - version 3.1.x is recommended
  - StarRocks versions 2.4 and below are no longer supported


## Installation

This plugin can be installed via pip:

```shell
$ pip install dbt-starrocks
```

## Supported features
| StarRocks <= 2.5 | StarRocks 2.5 ~ 3.1 | StarRocks >= 3.1 | StarRocks >= 3.4 |              Feature              |
|:----------------:|:-------------------:|:----------------:|:----------------:|:---------------------------------:|
|        ✅         |          ✅          |        ✅         |        ✅         |       Table materialization       |
|        ✅         |          ✅          |        ✅         |        ✅         |       View materialization        |
|        ❌         |          ❌          |        ✅         |        ✅         | Materialized View materialization |
|        ❌         |          ✅          |        ✅         |        ✅         |    Incremental materialization    |
|        ❌         |          ✅          |        ✅         |        ✅         |         Primary Key Model         |
|        ✅         |          ✅          |        ✅         |        ✅         |              Sources              |
|        ✅         |          ✅          |        ✅         |        ✅         |         Custom data tests         |
|        ✅         |          ✅          |        ✅         |        ✅         |           Docs generate           |
|        ❌         |          ❌          |        ✅         |        ✅         |       Expression Partition        |
|        ❌         |          ❌          |        ❌         |        ❌         |               Kafka               |
|        ❌         |          ❌          |        ❌         |        ✅         |         Dynamic Overwrite         |
|        ❌         |         *4          |        *4        |        ✅         |            Submit task            |
|        ❌         |          ✅          |        ✅         |        ✅         |  Microbatch (Insert Overwrite)   |
|        ❌         |          ❌          |        ❌         |        ✅         | Microbatch (Dynamic Overwrite)   |

### Notice
1. When StarRocks Version < 2.5, `Create table as` can only set engine='OLAP' and table_type='DUPLICATE'
2. When StarRocks Version >= 2.5, `Create table as` supports table_type='PRIMARY'
3. When StarRocks Version < 3.1 distributed_by is required
4. Verify the specific `submit task` support for your version, see [SUBMIT TASK](https://docs.starrocks.io/docs/sql-reference/sql-statements/loading_unloading/ETL/SUBMIT_TASK/). 

## Profile Configuration

**Example entry for profiles.yml:**

```
starrocks:
  target: dev
  outputs:
    dev:
      type: starrocks
      host: localhost
      port: 9030
      schema: analytics
      username: your_starrocks_username
      password: your_starrocks_password
```

| Option              | Description                                                        | Required? | Example                        |
|---------------------|--------------------------------------------------------------------|-----------|--------------------------------|
| type                | The specific adapter to use                                        | Required  | `starrocks`                    |
| host                | The hostname to connect to                                         | Required  | `192.168.100.28`               |
| port                | The port to use                                                    | Required  | `9030`                         |
| catalog             | Specify the catalog to build models into                           | Optional  | `default_catalog`
| schema              | Specify the schema (database in StarRocks) to build models into    | Required  | `analytics`                    |
| username            | The username to use to connect to the server                       | Required  | `dbt_admin`                    |
| password            | The password to use for authenticating to the server               | Required  | `correct-horse-battery-staple` |
| version             | Let Plugin try to go to a compatible starrocks version             | Optional  | `3.1.0`                        |
| use_pure            | set to "true" to use C extensions                                  | Optional  | `true`                         |
| is_async            | "true" to submit suitable tasks as etl tasks.                      | Optional  | `true`                         |
| async_query_timeout | Sets the `query_timeout` value when submitting a task to StarRocks | Optional  | `300`                            |

More details about setting `use_pure` and other connection arguments [here](https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html)


## Example

### dbt seed properties(yml):
#### Complete configuration:
```
models:
  materialized: table                   // table, view, materialized_view or incremental
  engine: 'OLAP'
  keys: ['id', 'name', 'some_date']
  table_type: 'PRIMARY'                 // PRIMARY or DUPLICATE or UNIQUE
  distributed_by: ['id']
  buckets: 3                            // leave empty for auto bucketing
  indexs=[{ 'columns': 'idx_column' }]  
  partition_by: ['some_date']
  partition_by_init: ["PARTITION p1 VALUES [('1971-01-01 00:00:00'), ('1991-01-01 00:00:00')),PARTITION p1972 VALUES [('1991-01-01 00:00:00'), ('1999-01-01 00:00:00'))"]
  // RANGE, LIST, or Expr partition types should be used in conjunction with partition_by configuration
  // Expr partition type requires an expression (e.g., date_trunc) specified in partition_by
  order_by: ['some_column']             // only for PRIMARY table_type
  partition_type: 'RANGE'               // RANGE or LIST or Expr Need to be used in combination with partition_by configuration
  properties: {"replication_num":"1", "in_memory": "true"}
  refresh_method: 'async'               // only for materialized view default manual
  
  // For 'materialized=incremental' in version >= 3.4
  incremental_strategy: 'dynamic_overwrite' // Supported values: ['default', 'insert_overwrite', 'dynamic_overwrite']

  // For 'materialized=incremental' and 'incremental_strategy=microbatch'
  event_time: 'some_timestamp_column'     // The column name of the event time
  begin: '2025-01-01'                     // The start time of the incremental data
  lookback: 1                             // The lookback time of the each incremental run
  batch_size: 'day'                       // The batch size. Supported values ['year', 'month', 'day', 'hour']
  microbatch_use_dynamic_overwrite: true  // Whether to use dynamic_overwrite in version >= 3.4
```
  
### dbt run config:
#### Example configuration:
```
{{ config(materialized='view') }}
{{ config(materialized='table', engine='OLAP', buckets=32, distributed_by=['id']) }}
{{ config(materialized='table', indexs=[{ 'columns': 'idx_column' }]) }}
{{ config(materialized='table', partition_by=['date_trunc("day", first_order)'], partition_type='Expr') }}
{{ config(materialized='table', table_type='PRIMARY', keys=['customer_id'], order_by=['first_name', 'last_name'] }}
{{ config(materialized='incremental', table_type='PRIMARY', engine='OLAP', buckets=32, distributed_by=['id']) }}
{{ config(materialized='incremental', partition_by=['my_partition_key'], partition_type='Expr', incremental_strategy='dynamic_overwrite') }}
{{ config(materialized='incremental', partition_by=['my_partition_key'], partition_type='Expr', incremental_strategy='microbatch', event_time='report_day', begin='2025-01-01', lookback=1, batch_size='day') }}
{{ config(materialized='incremental', partition_by=['my_partition_key'], partition_type='Expr', incremental_strategy='microbatch', event_time='report_day', begin='2025-01-01', lookback=1, batch_size='day', microbatch_use_dynamic_overwrite=true) }}
{{ config(materialized='materialized_view') }}
{{ config(materialized='materialized_view', properties={"storage_medium":"SSD"}) }}
{{ config(materialized='materialized_view', refresh_method="ASYNC START('2022-09-01 10:00:00') EVERY (interval 1 day)") }}
```
For materialized view only support partition_by、buckets、distributed_by、properties、refresh_method configuration.

## Read From Catalog
First you need to add this catalog to starrocks. The following is an example of hive.
```mysql
CREATE EXTERNAL CATALOG `hive_catalog`
PROPERTIES (
    "hive.metastore.uris"  =  "thrift://127.0.0.1:8087",
    "type"="hive"
);
```
How to add other types of catalogs can be found in the documentation.
https://docs.starrocks.io/en-us/latest/data_source/catalog/catalog_overview
Then write the sources.yaml file.
```yaml
sources:
  - name: external_example
    schema: hive_catalog.hive_db
    tables:
      - name: hive_table_name
```
Finally, you might use below marco quote 
```
{{ source('external_example', 'hive_table_name') }}
```

## Dynamic Overwrite (StarRocks >= 3.4)
Add a new `incremental_strategy` property that supports the following values:
- `default` (or omitted): Standard inserts without `overwrite`.
- `insert_overwrite`: Will apply `overwrite` with `dynamic_overwrite = false` to the inserts.
- `dynamic_overwrite`: Will apply `overwrite` with `dynamic_overwrite = true` to the inserts.

For more details on the different behaviors, see [StarRocks' documentation for INSERT](https://docs.starrocks.io/docs/sql-reference/sql-statements/loading_unloading/INSERT).

## Submittable ETL tasks

> The implementation of the submittable etl is located in the `impl.py` file.

Setting `is_async: true` in your `profiles.yml` will enable submitting suitable ETL tasks using the `submit task` feature of StarRocks.

This will be automatically wrapped around any statement that supports submission. Setting this manually is currently not supported by the adapter.

The following statements will be submitted automatically:

- `CREATE AS ... SELECT`
- `INSERT INTO|OVERWRITE`
- `CACHE SELECT ...`

> See [StarRocks' documentation on SUBMIT TASK](https://docs.starrocks.io/docs/sql-reference/sql-statements/loading_unloading/ETL/SUBMIT_TASK/)

### Task Polling

Once the task has been submitted, the adapter will periodically poll StarRocks' `information_schema.task_runs` to retrieve the task status. 

The polling is implemented using an exponential backoff, with a maximum delay of 10 minutes. The adapter's connection to the StarRocks' cluster will not be maintained during the waiting period. It will be re-opened right before the next status polling phase.

### Controlling the task timeout

Using the `async_query_timeout` property in the `profiles.yml` will control the value of the `query_timeout` when submitting task.

It's going to be injected in the SQL query submitted to StarRocks:

```sql
submit /*+set_var(query_timeout={async_query_timeout})*/ task ...
```

### Example `profiles.yml` configuration

```yml
my_profile:
  target: dev
  outputs:
    dev:
      type: starrocks
      host: host
      port: 9030
      schema: schema
      username: username
      password: password
      is_async: true
      async_query_timeout: 3600 # 1 hour
```

## Test Adapter
Run the following
```
python3 -m pytest tests/functional
```
consult [the project](https://github.com/dbt-labs/dbt-adapter-tests)

## Contributing
We welcome you to contribute to dbt-starrocks. Please see the [Contributing Guide](https://github.com/StarRocks/starrocks/blob/main/CONTRIBUTING.md) for more information.