Python API

Connecting to or creating a database

Database objects are constructed by passing in either a path to a file on disk or an existing SQLite3 database connection:

from sqlite_utils import Database

db = Database("my_database.db")

This will create my_database.db if it does not already exist. You can also pass in an existing SQLite connection:

import sqlite3

db = Database(sqlite3.connect("my_database.db"))

If you want to create an in-memory database, you can do so like this:

db = Database(sqlite3.connect(":memory:"))

Tables are accessed using the indexing operator, like so:

table = db["my_table"]

If the table does not yet exist, it will be created the first time you attempt to insert or upsert data into it.

Listing tables

You can list the names of tables in a database using the .table_names() method:

>>> db.table_names()

To see just the FTS4 tables, use .table_names(fts4=True). For FTS5, use .table_names(fts5=True).

You can also iterate through the table objects themselves using the .tables property:

>>> db.tables
[<Table dogs>]

Listing rows

To iterate through dictionaries for each of the rows in a table, use .rows:

>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows:
...     print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
{'id': 2, 'age': 2, 'name': 'Pancakes'}

You can filter rows by a WHERE clause using .rows_where(where, where_args):

>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows_where("age > ?", [3]):
...     print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}

Retrieving a specific record

You can retrieve a record by its primary key using table.get():

>>> db = sqlite_utils.Database("dogs.db")
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 4, 'name': 'Cleo'}

If the table has a compound primary key you can pass in the primary key values as a tuple:

>>> db["compound_dogs"].get(("mixed", 3))

Creating tables

The easiest way to create a new table is to insert a record into it:

from sqlite_utils import Database
import sqlite3

db = Database(sqlite3.connect("/tmp/dogs.db"))
dogs = db["dogs"]
    "name": "Cleo",
    "twitter": "cleopaws",
    "age": 3,
    "is_good_dog": True,

This will automatically create a new table called “dogs” with the following schema:

    name TEXT,
    twitter TEXT,
    age INTEGER,
    is_good_dog INTEGER

You can also specify a primary key by passing the pk= parameter to the .insert() call. This will only be obeyed if the record being inserted causes the table to be created:

    "id": 1,
    "name": "Cleo",
    "twitter": "cleopaws",
    "age": 3,
    "is_good_dog": True,
}, pk="id")

The order of the columns in the table will be derived from the order of the keys in the dictionary, provided you are using Python 3.6 or later.

If you want to explicitly set the order of the columns you can do so using the column_order= parameter:

    "id": 1,
    "name": "Cleo",
    "twitter": "cleopaws",
    "age": 3,
    "is_good_dog": True,
}, pk="id", column_order=("id", "twitter", "name"))

You don’t need to pass all of the columns to the column_order parameter. If you only pass a subset of the columns the remaining columns will be ordered based on the key order of the dictionary.

After inserting a row like this, the dogs.last_rowid property will return the SQLite rowid assigned to the most recently inserted record.

The dogs.last_pk property will return the last inserted primary key value, if you specified one. This can be very useful when writing code that creates foreign keys or many-to-many relationships.

Explicitly creating a table

You can directly create a new table without inserting any data into it using the .create() method:

    "id": int,
    "name": str,
    "weight": float,
}, pk="id")

The first argument here is a dictionary specifying the columns you would like to create. Each column is paired with a Python type indicating the type of column. See Adding columns for full details on how these types work.

This method takes optional arguments pk=, column_order=, foreign_keys=, not_null=set() and defaults=dict() - explained below.

Compound primary keys

If you want to create a table with a compound primary key that spans multiple columns, you can do so by passing a tuple of column names to any of the methods that accept a pk= parameter. For example:

    "id": int,
    "breed": str,
    "name": str,
    "weight": float,
}, pk=("breed", "id"))

This also works for the .insert(), .insert_all(), .upsert() and .upsert_all() methods.

Specifying foreign keys

Any operation that can create a table (.create(), .insert(), .insert_all(), .upsert() and .upsert_all()) accepts an optional foreign_keys= argument which can be used to set up foreign key constraints for the table that is being created.

If you are using your database with Datasette, Datasette will detect these constraints and use them to generate hyperlinks to associated records.

The foreign_keys argument takes a list that indicates which foreign keys should be created. The list can take several forms. The simplest is a list of columns:


The library will guess which tables you wish to reference based on the column names using the rules described in Adding foreign key constraints.

You can also be more explicit, by passing in a list of tuples:

    ("author_id", "authors", "id")

This means that the author_id column should be a foreign key that references the id column in the authors table.

You can leave off the third item in the tuple to have the referenced column automatically set to the primary key of that table. A full example:

    {"id": 1, "name": "Sally"},
    {"id": 2, "name": "Asheesh"}
], pk="id")
    {"title": "Hedgehogs of the world", "author_id": 1},
    {"title": "How to train your wolf", "author_id": 2},
], foreign_keys=[
    ("author_id", "authors")

Setting defaults and not null constraints

Each of the methods that can cause a table to be created take optional arguments not_null=set() and defaults=dict(). The methods that take these optional arguments are:

  • db.create_table(...)
  • table.create(...)
  • table.insert(...)
  • table.insert_all(...)
  • table.upsert(...)
  • table.upsert_all(...)

You can use not_null= to pass a set of column names that should have a NOT NULL constraint set on them when they are created.

You can use defaults= to pass a dictionary mapping columns to the default value that should be specified in the CREATE TABLE statement.

Here’s an example that uses these features:

    [{"id": 1, "name": "Sally", "score": 2}],
    not_null={"name", "score"},
    defaults={"score": 1},
db["authors"].insert({"name": "Dharma"})

# Outputs:
# [{'id': 1, 'name': 'Sally', 'score': 2},
#  {'id': 3, 'name': 'Dharma', 'score': 1}]
print(db["authors"].schema)                                                                                                                    # Outputs:
# CREATE TABLE [authors] (
#     [name] TEXT NOT NULL,
# )

Bulk inserts

If you have more than one record to insert, the insert_all() method is a much more efficient way of inserting them. Just like insert() it will automatically detect the columns that should be created, but it will inspect the first batch of 100 items to help decide what those column types should be.

Use it like this:

    "id": 1,
    "name": "Cleo",
    "twitter": "cleopaws",
    "age": 3,
    "is_good_dog": True,
}, {
    "id": 2,
    "name": "Marnie",
    "twitter": "MarnieTheDog",
    "age": 16,
    "is_good_dog": True,
}], pk="id", column_order=("id", "twitter", "name"))

The column types used in the CREATE TABLE statement are automatically derived from the types of data in that first batch of rows. Any additional or missing columns in subsequent batches will be ignored.

The function can accept an iterator or generator of rows and will commit them according to the batch size. The default batch size is 100, but you can specify a different size using the batch_size parameter:

    "id": 1,
    "name": "Name {}".format(i),
} for i in range(10000)), batch_size=1000)

You can skip inserting any records that have a primary key that already exists using ignore=True. This works with both .insert({...}, ignore=True) and .insert_all([...], ignore=True).

Upserting data

Upserting allows you to insert records if they do not exist and update them if they DO exist, based on matching against their primary key.

For example, given the dogs database you could upsert the record for Cleo like so:

    "id": 1,
    "name": "Cleo",
    "twitter": "cleopaws",
    "age": 4,
    "is_good_dog": True,
}, pk="id", column_order=("id", "twitter", "name"))

If a record exists with id=1, it will be updated to match those fields. If it does not exist it will be created.

Note that the pk and column_order parameters here are optional if you are certain that the table has already been created. You should pass them if the table may not exist at the time the first upsert is performed.

An upsert_all() method is also available, which behaves like insert_all() but performs upserts instead.

Adding columns

You can add a new column to a table using the .add_column(col_name, col_type) method:

db["dogs"].add_column("instagram", str)
db["dogs"].add_column("weight", float)
db["dogs"].add_column("image", "BLOB")
db["dogs"].add_column("website") # str by default

You can specify the col_type argument either using a SQLite type as a string, or by directly passing a Python type e.g. str or float.

The col_type is optional - if you omit it the type of TEXT will be used.

SQLite types you can specify are "TEXT", "INTEGER", "FLOAT" or "BLOB".

If you pass a Python type, it will be mapped to SQLite types as shown here:

float: "FLOAT"
int: "INTEGER"
bool: "INTEGER"
str: "TEXT"
bytes: "BLOB"
datetime.datetime: "TEXT" "TEXT"
datetime.time: "TEXT"

# If numpy is installed
np.int8: "INTEGER"
np.int16: "INTEGER"
np.int32: "INTEGER"
np.int64: "INTEGER"
np.uint8: "INTEGER"
np.uint16: "INTEGER"
np.uint32: "INTEGER"
np.uint64: "INTEGER"
np.float16: "FLOAT"
np.float32: "FLOAT"
np.float64: "FLOAT"

You can also add a column that is a foreign key reference to another table using the fk parameter:

db["dogs"].add_column("species_id", fk="species")

This will automatically detect the name of the primary key on the species table and use that (and its type) for the new column.

You can explicitly specify the column you wish to reference using fk_col:

db["dogs"].add_column("species_id", fk="species", fk_col="ref")

You can set a NOT NULL DEFAULT 'x' constraint on the new column using not_null_default:

db["dogs"].add_column("friends_count", int, not_null_default=0)

Adding columns automatically on insert/update

You can insert or update data that includes new columns and have the table automatically altered to fit the new schema using the alter=True argument. This can be passed to all four of .insert(), .upsert(), .insert_all() and .upsert_all():

db["new_table"].insert({"name": "Gareth"})
# This will throw an exception:
db["new_table"].insert({"name": "Gareth", "age": 32})
# This will succeed and add a new "age" integer column:
db["new_table"].insert({"name": "Gareth", "age": 32}, alter=True)
# You can see confirm the new column like so:
# Outputs this:
# {'name': <class 'str'>, 'age': <class 'int'>}

Adding foreign key constraints

The SQLite ALTER TABLE statement doesn’t have the ability to add foreign key references to an existing column.

It’s possible to add these references through very careful manipulation of SQLite’s sqlite_master table, using PRAGMA writable_schema.

sqlite-utils can do this for you, though there is a significant risk of data corruption if something goes wrong so it is advisable to create a fresh copy of your database file before attempting this.

Here’s an example of this mechanism in action:

    {"id": 1, "name": "Sally"},
    {"id": 2, "name": "Asheesh"}
], pk="id")
    {"title": "Hedgehogs of the world", "author_id": 1},
    {"title": "How to train your wolf", "author_id": 2},
db["books"].add_foreign_key("author_id", "authors", "id")

The table.add_foreign_key(column, other_table, other_column) method takes the name of the column, the table that is being referenced and the key column within that other table. If you ommit the other_column argument the primary key from that table will be used automatically. If you omit the other_table argument the table will be guessed based on some simple rules:

  • If the column is of format author_id, look for tables called author or authors
  • If the column does not end in _id, try looking for a table with the exact name of the column or that name with an added s

Adding multiple foreign key constraints at once

The final step in adding a new foreign key to a SQLite database is to run VACUUM, to ensure the new foreign key is available in future introspection queries.

VACUUM against a large (multi-GB) database can take several minutes or longer. If you are adding multiple foreign keys using table.add_foreign_key(...) these can quickly add up.

Instead, you can use db.add_foreign_keys(...) to add multiple foreign keys within a single transaction. This method takes a list of four-tuples, each one specifying a table, column, other_table and other_column.

Here’s an example adding two foreign keys at once:

    ("dogs", "breed_id", "breeds", "id"),
    ("dogs", "home_town_id", "towns", "id")

Adding indexes for all foreign keys

If you want to ensure that every foreign key column in your database has a corresponding index, you can do so like this:


Setting an ID based on the hash of the row contents

Sometimes you will find yourself working with a dataset that includes rows that do not have a provided obvious ID, but where you would like to assign one so that you can later upsert into that table without creating duplicate records.

In these cases, a useful technique is to create an ID that is derived from the sha1 hash of the row contents.

sqlite-utils can do this for you using the hash_id= option. For example:

db = sqlite_utils.Database("dogs.db")
db["dogs"].upsert({"name": "Cleo", "twitter": "cleopaws"}, hash_id="id")


[{'id': 'f501265970505d9825d8d9f590bfab3519fb20b1', 'name': 'Cleo', 'twitter': 'cleopaws'}]

If you are going to use that ID straight away, you can access it using last_pk:

dog_id = db["dogs"].upsert({
    "name": "Cleo",
    "twitter": "cleopaws"
}, hash_id="id").last_pk
# dog_id is now "f501265970505d9825d8d9f590bfab3519fb20b1"

Creating views

The .create_view() method on the database class can be used to create a view:

db.create_view("good_dogs", """
    select * from dogs where is_good_dog = 1

Storing JSON

SQLite has excellent JSON support, and sqlite-utils can help you take advantage of this: if you attempt to insert a value that can be represented as a JSON list or dictionary, sqlite-utils will create TEXT column and store your data as serialized JSON. This means you can quickly store even complex data structures in SQLite and query them using JSON features.

For example:

    "name": "The Bigfoot Discovery Museum",
    "url": ""
    "hours": {
        "Monday": [11, 18],
        "Wednesday": [11, 18],
        "Thursday": [11, 18],
        "Friday": [11, 18],
        "Saturday": [11, 18],
        "Sunday": [11, 18]
    "address": {
        "streetAddress": "5497 Highway 9",
        "addressLocality": "Felton, CA",
        "postalCode": "95018"
    select json_extract(address, '$.addressLocality')
    from niche_museums
# Returns [('Felton, CA',)]


If you have loaded an existing table, you can use introspection to find out more about it:

>>> db["PlantType"]
<sqlite_utils.db.Table at 0x10f5960b8>

The .count property shows the current number of rows (select count(*) from table):

>>> db["PlantType"].count
>>> db["Street_Tree_List"].count

The .columns property shows the columns in the table:

>>> db["PlantType"].columns
[Column(cid=0, name='id', type='INTEGER', notnull=0, default_value=None, is_pk=1),
 Column(cid=1, name='value', type='TEXT', notnull=0, default_value=None, is_pk=0)]

The .columns_dict property returns a dictionary version of this with just the names and types:

>>> db["PlantType"].columns_dict
{'id': <class 'int'>, 'value': <class 'str'>}

The .foreign_keys property shows if the table has any foreign key relationships:

>>> db["Street_Tree_List"].foreign_keys
[ForeignKey(table='Street_Tree_List', column='qLegalStatus', other_table='qLegalStatus', other_column='id'),
 ForeignKey(table='Street_Tree_List', column='qCareAssistant', other_table='qCareAssistant', other_column='id'),
 ForeignKey(table='Street_Tree_List', column='qSiteInfo', other_table='qSiteInfo', other_column='id'),
 ForeignKey(table='Street_Tree_List', column='qSpecies', other_table='qSpecies', other_column='id'),
 ForeignKey(table='Street_Tree_List', column='qCaretaker', other_table='qCaretaker', other_column='id'),
 ForeignKey(table='Street_Tree_List', column='PlantType', other_table='PlantType', other_column='id')]

The .schema property outputs the table’s schema as a SQL string:

>>> print(db["Street_Tree_List"].schema)
CREATE TABLE "Street_Tree_List" (
  "qLegalStatus" INTEGER,
  "qSpecies" INTEGER,
  "qAddress" TEXT,
  "SiteOrder" INTEGER,
  "qSiteInfo" INTEGER,
  "PlantType" INTEGER,
  "qCaretaker" INTEGER,
  "qCareAssistant" INTEGER,
  "PlantDate" TEXT,
  "PlotSize" TEXT,
  "PermitNotes" TEXT,
  "XCoord" REAL,
  "YCoord" REAL,
  "Latitude" REAL,
  "Longitude" REAL,
  "Location" TEXT
FOREIGN KEY ("PlantType") REFERENCES [PlantType](id),
    FOREIGN KEY ("qCaretaker") REFERENCES [qCaretaker](id),
    FOREIGN KEY ("qSpecies") REFERENCES [qSpecies](id),
    FOREIGN KEY ("qSiteInfo") REFERENCES [qSiteInfo](id),
    FOREIGN KEY ("qCareAssistant") REFERENCES [qCareAssistant](id),
    FOREIGN KEY ("qLegalStatus") REFERENCES [qLegalStatus](id))

The .indexes property shows you all indexes created for a table:

>>> db["Street_Tree_List"].indexes
[Index(seq=0, name='"Street_Tree_List_qLegalStatus"', unique=0, origin='c', partial=0, columns=['qLegalStatus']),
 Index(seq=1, name='"Street_Tree_List_qCareAssistant"', unique=0, origin='c', partial=0, columns=['qCareAssistant']),
 Index(seq=2, name='"Street_Tree_List_qSiteInfo"', unique=0, origin='c', partial=0, columns=['qSiteInfo']),
 Index(seq=3, name='"Street_Tree_List_qSpecies"', unique=0, origin='c', partial=0, columns=['qSpecies']),
 Index(seq=4, name='"Street_Tree_List_qCaretaker"', unique=0, origin='c', partial=0, columns=['qCaretaker']),
 Index(seq=5, name='"Street_Tree_List_PlantType"', unique=0, origin='c', partial=0, columns=['PlantType'])]

Optimizing a full-text search table

Once you have populated a FTS table you can optimize it to dramatically reduce its size like so:


This runs the following SQL:

INSERT INTO dogs_fts (dogs_fts) VALUES ("optimize");

Creating indexes

You can create an index on a table using the .create_index(columns) method. The method takes a list of columns:


By default the index will be named idx_{table-name}_{columns} - if you want to customize the name of the created index you can pass the index_name parameter:

    ["is_good_dog", "age"],

You can create a unique index by passing unique=True:

dogs.create_index(["name"], unique=True)

Use if_not_exists=True to do nothing if an index with that name already exists.


You can optimize your database by running VACUUM against it like so: