Insert a row (Python)
Inserts a single row into a table.
This method can insert a row in an existing CQL table, but the Data API does not support all CQL data types or modifiers. For more information, see Data type compatibility in tables (Python).
For general information about working with tables and rows, see About tables with the Data API (Python).
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Ready to write code? See the examples for this method to get started. If you are new to the Data API, check out the quickstart. |
Result
Inserts the specified row and returns a TableInsertOneResult object that includes the primary key of the inserted row as a dictionary and as a tuple.
If a row with the specified primary key already exists in the table, the row will be overwritten with the specified column values. Unspecified columns will remain unchanged.
If any of the inserted columns use the wrong datatype or improper encoding, then the entire insert fails.
Parameters
Use the insert_one method, which belongs to the astrapy.Table class.
Method signature
insert_one(
row: Dict[str, Any],
*,
general_method_timeout_ms: int,
request_timeout_ms: int,
timeout_ms: int,
) -> TableInsertOneResult:| Name | Type | Summary |
|---|---|---|
|
|
A dictionary that defines the row to insert. All primary key values are required. To reduce tombstones, you should not explicitly set a column to The table definition determines the columns in the row, the type for each column, and the primary key. To get this information, see List table metadata (Python). |
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Optional. The maximum time, in milliseconds, that the client should wait for the underlying HTTP request. This parameter is aliased as Default: The default value for the table. This default is 30 seconds unless you specified a different default when you initialized the |
Examples
The following examples demonstrate how to insert a row into a table.
Insert a row
When you insert a row, you must specify a non-null value for each primary key column.
Non-primary key columns are optional.
To reduce tombstones, you should not explicitly set a column to null.
The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.
from astrapy import DataAPIClient
from astrapy.data_types import (
DataAPIDate,
DataAPISet,
)
# Get an existing table
client = DataAPIClient()
database = client.get_database(
"API_ENDPOINT", token="APPLICATION_TOKEN"
)
table = database.get_table("TABLE_NAME")
# Insert a row into the table
result = table.insert_one(
{
"title": "Computed Wilderness",
"author": "Ryan Eau",
"number_of_pages": 432,
"due_date": DataAPIDate.from_string("2024-12-18"),
"genres": DataAPISet(["History", "Biography"]),
}
)Insert a row with vector embeddings
You can only insert vector embeddings into vector columns.
To create a table with a vector column, see Create a table (Python). To add a vector column to an existing table, see Alter a table (Python).
All embeddings in the column should use the same provider, model, and dimensions. Mismatched embeddings can cause inaccurate vector searches.
You can use the astrapy.data_types.DataAPIVector class to binary-encode your vector embeddings.
DataStax recommends that you always use a DataAPIVector object instead of a list of floats to improve performance.
from astrapy import DataAPIClient
from astrapy.data_types import (
DataAPIVector,
)
# Get an existing table
client = DataAPIClient()
database = client.get_database(
"API_ENDPOINT", token="APPLICATION_TOKEN"
)
table = database.get_table("TABLE_NAME")
# Insert a row into the table
result = table.insert_one(
{
"title": "Computed Wilderness",
"author": "Ryan Eau",
"summary_genres_vector": DataAPIVector([0.08, -0.62, 0.39]),
}
)Insert a row and generate vector embeddings
To automatically generate vector embeddings, your table must have a vector column with an embedding provider integration. You can configure embedding provider integrations when you create a table, add a vector column to an existing table, or alter an existing vector column.
When you insert a row, you can pass a string to the vector column. Astra DB uses the embedding provider integration to generate vector embeddings from that string.
The strings used to generate the vector embeddings are not stored. If you want to store the original strings, you must store them in a separate column.
In the following examples, summary_genres_vector is a vector column that has an embedding provider integration configured, and summary_genres_original_text is a text column to store the original text.
The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.
from astrapy import DataAPIClient
# Get an existing table
client = DataAPIClient()
database = client.get_database(
"API_ENDPOINT", token="APPLICATION_TOKEN"
)
table = database.get_table("TABLE_NAME")
# Insert a row into the table
result = table.insert_one(
{
"title": "Computed Wilderness",
"author": "Ryan Eau",
"summary_genres_vector": "Text to vectorize",
"summary_genres_original_text": "Text to vectorize",
}
)Insert a row with a map column that uses non-string keys
To insert a row with a map column that includes non-string keys, you must use an array of key-value pairs to represent the map column.
With the Python client, you can also use DataAPIMap to encode maps that use non-string keys.
from astrapy import DataAPIClient
from astrapy.data_types import DataAPIMap
# Get an existing table
client = DataAPIClient()
database = client.get_database(
"API_ENDPOINT", token="APPLICATION_TOKEN"
)
table = database.get_table("TABLE_NAME")
# Insert a row into the table
result = table.insert_one(
{
# This map has non-string keys,
# so the insertion is an array of key-value pairs
"map_column_int_str": [[1, "value1"], [2, "value2"]],
# Alternatively, use DataAPIMap to encode maps with non-string keys
"map_column_int_str_2": DataAPIMap({1: "value1", 2: "value2"}),
# This map does not have non-string keys,
# so the insertion does not need to be an array of key-value pairs
"map_column_str_str": {"key1": "value1", "key2": "value2"},
"title": "Once in a Living Memory",
"author": "Kayla McMaster",
}
)Client reference
For more information, see the client reference.
