Create Table | Glide Docs

Create a new Big Table, define its structure, and (optionally) populate it with data.

Row IDs for any added rows are returned in the response in the same order as the input row data is passed in the request. Row data may be passed in JSON, CSV, or TSV format.

When using a CSV or TSV request body, the name of the table must be passed as a query parameter and the schema of the table is always inferred from the content. Alternatively, the CSV/TSV content may be stashed, and then the schema and name may be passed in the regular JSON payload.

If a schema is passed in the payload, any passed row data must match that schema. If a column is not included in the passed row data, it will be empty in the added row. If a column is passed that does not exist in the schema, or with a value that does not match the column’s type, the default behavior is for the table to not be created and the API call to return an error. However, you can control this behavior with the onSchemaError query parameter.

Examples

Create Table w/ Row Data

If you want to create a table and populate it with an initial dataset, you can do so by providing the data inline in the rows field (being sure that row object structure matches the table schema):

1 {
2     "name": "Employees",
3     "schema": { ... },
4     "rows": [
5         {
6             "fullName": "Alex Bard",
7             "ageInYears": 30,
8             "hiredOn": "2021-07-03"
9         }
10     ]
11 }

However, this is only appropriate for relatively small initial datasets (around a few hundred rows or less, depending on schema complexity). If you need to work with a larger dataset you should utilize stashing.

Create Table from Stash

Stashing is our process for handling the upload of large datasets. Break down your dataset into smaller, more manageable, pieces and upload them to a single stash ID.

Then, to create a table from a stash, you can use the $stashID reference in the rows field instead of providing the data inline:

1 {
2     "name": "Employees",
3     "schema": { ... },
4     "rows": {
5         "$stashID": "20240215-job32"
6     }
7 }

Create a new Big Table, define its structure, and (optionally) populate it with data. Row IDs for any added rows are returned in the response in the same order as the input row data is passed in the request. Row data may be passed in JSON, CSV, or TSV format. When using a CSV or TSV request body, the name of the table must be passed as a query parameter and the schema of the table is always inferred from the content. Alternatively, the CSV/TSV content may be [stashed](/docs/classic-api/stashing/introduction), and then the schema and name may be passed in the regular JSON payload. If a schema is passed in the payload, any passed row data must match that schema. If a column is not included in the passed row data, it will be empty in the added row. If a column is passed that does not exist in the schema, or with a value that does not match the column's type, the default behavior is for the table to not be created and the API call to [return an error](/docs/classic-api/general/errors#invalid-row-data). However, you can control this behavior with the `onSchemaError` query parameter. ## Examples <AccordionGroup> <Accordion title="Create Table w/ Row Data"> If you want to create a table and populate it with an initial dataset, you can do so by providing the data inline in the `rows` field (being sure that row object structure matches the table schema): ```json { "name": "Employees", "schema": { ... }, "rows": [ { "fullName": "Alex Bard", "ageInYears": 30, "hiredOn": "2021-07-03" } ] } ``` However, this is only appropriate for relatively small initial datasets (around a few hundred rows or less, depending on schema complexity). If you need to work with a larger dataset you should utilize stashing. </Accordion> <Accordion title="Create Table from Stash"> [Stashing](/docs/classic-api/stashing/introduction) is our process for handling the upload of large datasets. Break down your dataset into smaller, more manageable, pieces and [upload them to a single stash ID](/docs/classic-api/stashing/stash-data). Then, to create a table from a stash, you can use the `$stashID` reference in the `rows` field instead of providing the data inline: ```json { "name": "Employees", "schema": { ... }, "rows": { "$stashID": "20240215-job32" } } ``` </Accordion> </AccordionGroup>

Authentication

AuthorizationBearer

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Query parameters

namestringOptional

appsToLinklist of stringsOptional

onSchemaErrorenumOptional

The action to take when the passed data does not match the table schema:

abort: Abort the entire operation and return an error.
dropColumns: Ignore the data that caused the error, and do not import those columns in the affected rows.
updateSchema: Update the schema as needed to add any missing columns or widen the data types of existing columns, and then import the data from them.

The action to take when the passed data does not match the table schema: - `abort`: Abort the entire operation and return an error. - `dropColumns`: Ignore the data that caused the error, and do not import those columns in the affected rows. - `updateSchema`: Update the schema as needed to add any missing columns or widen the data types of existing columns, and then import the data from them.

Allowed values:

Request

This endpoint expects an object or a list of maps from strings to any.

objectRequired

A_collection_of_row_objectslist of maps from strings to anyRequired

A collection of row objects conforming to the schema of the table where keys are the column IDs and values are the column values:

1 [
2 	{
3 		"fullName": "Alex Bard",
4 		"invoiceDate": "2024-07-29T14:04:15.561Z",
5 		"totalAmount": 34.50,
6 		"amountPaid": 0
7 	},
8 	{
9 		"fullName": "Alicia Hines",
10 		"invoiceDate": "2023-06-15T10:30:00.000Z",
11 		"totalAmount": 50.75,
12 		"amountPaid": 20
13 	}
14 ]

Response

dataobject

Errors

400

Bad Request Error

402

Payment Required Error

422

Unprocessable Entity Error

Create a new Big Table, define its structure, and (optionally) populate it with data.

Row IDs for any added rows are returned in the response in the same order as the input row data is passed in the request. Row data may be passed in JSON, CSV, or TSV format.

Examples

Create Table w/ Row Data

If you want to create a table and populate it with an initial dataset, you can do so by providing the data inline in the rows field (being sure that row object structure matches the table schema):

1 {
2     "name": "Employees",
3     "schema": { ... },
4     "rows": [
5         {
6             "fullName": "Alex Bard",
7             "ageInYears": 30,
8             "hiredOn": "2021-07-03"
9         }
10     ]
11 }

Create Table from Stash

Stashing is our process for handling the upload of large datasets. Break down your dataset into smaller, more manageable, pieces and upload them to a single stash ID.

Then, to create a table from a stash, you can use the $stashID reference in the rows field instead of providing the data inline:

1 {
2     "name": "Employees",
3     "schema": { ... },
4     "rows": {
5         "$stashID": "20240215-job32"
6     }
7 }

1	{
2	"data": {
3	"tableID": "2a1bad8b-cf7c-44437-b8c1-e3782df6",
4	"rowIDs": [
5	"zcJWnyI8Tbam21V34K8MNA",
6	"93a19-cf7c-44437-b8c1-e9acbbb"
7	],
8	"linkedAppIDs": [
9	"string"
10	]
11	}
12	}

1	{
2	"name": "Employees",
3	"schema": { ... },
4	"rows": [
5	{
6	"fullName": "Alex Bard",
7	"ageInYears": 30,
8	"hiredOn": "2021-07-03"
9	}
10	]
11	}

1	{
2	"name": "Employees",
3	"schema": { ... },
4	"rows": {
5	"$stashID": "20240215-job32"
6	}
7	}

Examples

Create Table w/ Row Data

Create Table from Stash

Authentication

Headers

Query parameters

Request

Response

Errors

Examples

Create Table w/ Row Data

Create Table from Stash