# DDL script file
### :map: Overview
A SQL workflow is a sequence of tasks that feed tables in parallel or sequentially. The DDL file gives instructions to create a destination table.
### ⎨⎬ Creation task
Once the SQL queries are ready, you need to use one or several DDL files to create the destination BigQuery tables that will contain the output data.
### :video\_camera: DDL script video
```
{
"bq_table_description": "Iowa Liquor aggregation by store for 2019's sales",
"bq_table_clustering_fields": ["store_number"],
"bq_table_timepartitioning_field": "isoweek_monday",
"bq_table_schema": [{
"name": "isoweek_monday",
"type": "DATE",
"description": "Unique number assigned to the store who ordered the liquor."
},
{
"name": "isoweek_number",
"type": "INTEGER",
"description": "Name of store who ordered the liquor."
},
{
"name": "store_number",
"type": "STRING",
"description": "Unique number assigned to the store who ordered the liquor."
},
{
"name": "store_name",
"type": "STRING",
"description": "Name of store who ordered the liquor."
},
{
"name": "sale_dollars",
"type": "FLOAT",
"description": "sum of the 2019's sales for the store"
},
{
"name": "volume_sold_liters",
"type": "FLOAT",
"description": "sum of the volume sold in 2019 for the store in liters"
},
{
"name": "p_vodka",
"type": "FLOAT",
"description": "ratio of the sale for the vodka category"
},
{
"name": "p_whisky",
"type": "FLOAT",
"description": "ratio of the sale for the whisky category"
},
{
"name": "p_rum",
"type": "FLOAT",
"description": "ratio of the sale for the rum category"
},
{
"name": "p_liqueur",
"type": "FLOAT",
"description": "ratio of the sale for the liqueur category"
},
{
"name": "p_tequila",
"type": "FLOAT",
"description": "ratio of the sale for the tequila category"
},
{
"name": "p_schnapps",
"type": "FLOAT",
"description": "ratio of the sale for the schnapps category"
},
{
"name": "p_gin",
"type": "FLOAT",
"description": "ratio of the sale for the gin category"
},
{
"name": "p_cocktail",
"type": "FLOAT",
"description": "ratio of the sale for the cocktail category"
},
{
"name": "p_brandy",
"type": "FLOAT",
"description": "ratio of the sale for the brandy category"
},
{
"name": "p_spirit",
"type": "FLOAT",
"description": "ratio of the sale for the spirit category"
},
{
"name": "p_special",
"type": "FLOAT",
"description": "ratio of the sale for the special category"
}
]
}
```
### **Parameters**
| Parameter | Description |
| ------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| bq\_table\_description
type: string
mandatory
| Description of the BigQuery table. |
| bq\_table\_schema
type: array
mandatory
| BigQuery table schema. It contains a list of fields corresponding to the number of columns it will contain.
Each field described has three attributes:
|
| bq\_table\_clustering\_fields
type: array
optional
| List of fields used when clustering is enabled.
The table data will be automatically organized based on the contents of the fields you specify. Their order determines the sort order of the data.
If this parameter is set, time partitioning will be automatically enabled on the table. If you don't set partitioning parameters, default values will be used.
|
| bq\_table\_timepartitioning\_field
type: string
optional
| If this parameter is set, the table will be partitioned by this field.
If not, the table will be partitioned by pseudo column \_PARTITIONTIME.
The field must be a top-level TIMESTAMP or DATE field. Its mode must be NULLABLE or REQUIRED.
(Refer to BigQuery documentation for more information.)
Note: You can set this parameter to a field that equals to DATE(''). Then, if you relaunch an execution with a partition, and if default\_write\_disposition is set to "WRITE\_APPEND" in the JSON configuration file, Tailer will check if the corresponding partition already exists in the table:
- If it does, it will delete it, and replace it with the current execution data.
- If not, it will add them.
|
| bq\_table\_timepartitioning\_expiration\_ms
type: integer
optional
| Number of milliseconds for which to keep the storage for a partition.
(Refer to BigQuery documentation for more information.)
|
| bq\_table\_timepartitioning\_require\_partition\_filter
type: boolean
optional
| If set to true, queries over the partitioned table require a partition filter that can be used for partition elimination to be specified.
(Refer to BigQuery documentation for more information.)
|
### Data types
Tailer Platform supports the following data types.
#### Numeric types
| Name | Description |
| --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `int64` | Integers are numeric values that do not have fractional components.
They range from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807.
|
| `float64` | Floating point values are approximate numeric values with fractional components. |
| `numeric` | This data type represents decimal values with 38 decimal digits of precision and 9 decimal digits of scale. (Precision is the number of digits that the number contains. Scale is how many of these digits appear after the decimal point.)
It is particularly useful for financial calculations.
|
#### Boolean type
| Name | Description |
| --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `boolean` | This data type supports the `true`, `false`, and `null` values. It can perform some basic conversions, such as `'true'`, `'True'`, `True`, or 1 becoming `true`. |
#### String type
| Name | Description |
| -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `string` | Variable-length character data.
When converting data from string to a different data type, makes sure to use safe\_cast when you're unsure about the data quality.
|
#### Bytes type
| Name | Description |
| ------- | ------------------------------------------------------------------------------------------------------------------ |
| `bytes` | Variable-length binary data. This data type is rarely used but can be useful for characters with unusual encoding. |
#### Time types
{% hint style="info" %}
Only the `date`, `datetime` and `timestamp` data types (not `time`) allow table partitioning.
{% endhint %}
{% hint style="info" %}
Time zone management being difficult with BigQuery, prefer the UTC format.
{% endhint %}
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `date` | This data type represents a calendar date. It includes the year, month, and day. |
| `time` | This data type represents a time, as might be displayed on a watch, independent of a specific date. It includes the hour, minute, second, and subsecond. |
| `datetime` | This data type represents a date and time, as they might be displayed on a calendar or clock. It includes the year, month, day, hour, minute, second, and subsecond. |
| `timestamp` | This data type represents an absolute point in time, with microsecond precision. |