.. _cli:

================================
 sqlite-utils command-line tool
================================

The ``sqlite-utils`` command-line tool can be used to manipulate SQLite databases in a number of different ways.

Once :ref:`installed <installation>` the tool should be available as ``sqlite-utils``. It can also be run using ``python -m sqlite_utils``.

.. contents:: :local:
   :class: this-will-duplicate-information-and-it-is-still-useful-here

.. _cli_query:

Running SQL queries
===================

The ``sqlite-utils query`` command lets you run queries directly against a SQLite database file. This is the default subcommand, so the following two examples work the same way:

.. code-block:: bash

    sqlite-utils query dogs.db "select * from dogs"

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs"

.. note::
    In Python: :ref:`db.query() <python_api_query>`  CLI reference: :ref:`sqlite-utils query <cli_ref_query>`

Pass ``-`` as the SQL query to read the query from standard input. This is useful for longer queries that would otherwise require careful shell escaping, or for piping in SQL generated by another tool:

.. code-block:: bash

    echo "select * from dogs" | sqlite-utils query dogs.db -

.. code-block:: bash

    sqlite-utils query dogs.db - < query.sql

.. _cli_query_json:

Returning JSON
--------------

The default format returned for queries is JSON:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs"

.. code-block:: output

    [{"id": 1, "age": 4, "name": "Cleo"},
     {"id": 2, "age": 2, "name": "Pancakes"}]

If the query returns more than one column with the same name, later occurrences are renamed with a numeric suffix - ``select 1 as id, 2 as id`` returns ``[{"id": 1, "id_2": 2}]``. This only applies to JSON output: :ref:`CSV and TSV <cli_query_csv>` and :ref:`table <cli_query_table>` output keep the duplicate column headers unchanged.

.. _cli_query_nl:

Newline-delimited JSON
~~~~~~~~~~~~~~~~~~~~~~

Use ``--nl`` to get back newline-delimited JSON objects:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --nl

.. code-block:: output

    {"id": 1, "age": 4, "name": "Cleo"}
    {"id": 2, "age": 2, "name": "Pancakes"}

.. _cli_query_arrays:

JSON arrays
~~~~~~~~~~~

You can use ``--arrays`` to request arrays instead of objects:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --arrays

.. code-block:: output

    [[1, 4, "Cleo"],
     [2, 2, "Pancakes"]]

You can also combine ``--arrays`` and ``--nl``:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --arrays --nl

.. code-block:: output

    [1, 4, "Cleo"]
    [2, 2, "Pancakes"]

If you want to pretty-print the output further, you can pipe it through ``python -mjson.tool``:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" | python -mjson.tool

.. code-block:: output

    [
        {
            "id": 1,
            "age": 4,
            "name": "Cleo"
        },
        {
            "id": 2,
            "age": 2,
            "name": "Pancakes"
        }
    ]

.. _cli_query_json_ascii:

Unicode characters in JSON
~~~~~~~~~~~~~~~~~~~~~~~~~~

JSON output includes unicode characters directly, without escaping them:

.. code-block:: bash

    sqlite-utils dogs.db "select '日本語' as text"

.. code-block:: output

    [{"text": "日本語"}]

Use ``--ascii`` to escape non-ASCII characters as ``\uXXXX`` sequences instead:

.. code-block:: bash

    sqlite-utils dogs.db "select '日本語' as text" --ascii

.. code-block:: output

    [{"text": "\u65e5\u672c\u8a9e"}]

The ``--ascii`` option can help on systems that cannot display or process UTF-8, such as Windows consoles using a legacy code page. On Windows, setting the ``PYTHONUTF8=1`` environment variable is an alternative fix for ``UnicodeEncodeError`` crashes when redirecting output to a file.

.. _cli_query_binary_json:

Binary data in JSON
~~~~~~~~~~~~~~~~~~~

Binary strings are not valid JSON, so BLOB columns containing binary data will be returned as a JSON object containing base64 encoded data, that looks like this:

.. code-block:: bash

    sqlite-utils dogs.db "select name, content from images" | python -mjson.tool

.. code-block:: output

    [
        {
            "name": "transparent.gif",
            "content": {
                "$base64": true,
                "encoded": "R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
            }
        }
    ]

.. _cli_json_values:

Nested JSON values
~~~~~~~~~~~~~~~~~~

If one of your columns contains JSON, by default it will be returned as an escaped string:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" | python -mjson.tool

.. code-block:: output

    [
        {
            "id": 1,
            "name": "Cleo",
            "friends": "[{\"name\": \"Pancakes\"}, {\"name\": \"Bailey\"}]"
        }
    ]

You can use the ``--json-cols`` option to automatically detect these JSON columns and output them as nested JSON data:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --json-cols | python -mjson.tool

.. code-block:: output

    [
        {
            "id": 1,
            "name": "Cleo",
            "friends": [
                {
                    "name": "Pancakes"
                },
                {
                    "name": "Bailey"
                }
            ]
        }
    ]

.. _cli_query_csv:

Returning CSV or TSV
--------------------

You can use the ``--csv`` option to return results as CSV:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --csv

.. code-block:: output

    id,age,name
    1,4,Cleo
    2,2,Pancakes

This will default to including the column names as a header row. To exclude the headers, use ``--no-headers``:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --csv --no-headers

.. code-block:: output

    1,4,Cleo
    2,2,Pancakes

Use ``--tsv`` instead of ``--csv`` to get back tab-separated values:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --tsv

.. code-block:: output

    id	age	name
    1	4	Cleo
    2	2	Pancakes

.. _cli_query_table:

Table-formatted output
----------------------

You can use the ``--table`` option (or ``-t`` shortcut) to output query results as a table:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --table

.. code-block:: output

      id    age  name
    ----  -----  --------
       1      4  Cleo
       2      2  Pancakes

You can use the ``--fmt`` option to specify different table formats, for example ``rst`` for reStructuredText:

.. code-block:: bash

    sqlite-utils dogs.db "select * from dogs" --fmt rst

.. code-block:: output

    ====  =====  ========
      id    age  name
    ====  =====  ========
       1      4  Cleo
       2      2  Pancakes
    ====  =====  ========

Available ``--fmt`` options are:

.. [[[cog
    import tabulate
    cog.out("\n" + "\n".join('- ``{}``'.format(t) for t in tabulate.tabulate_formats) + "\n\n")
.. ]]]

- ``asciidoc``
- ``colon_grid``
- ``double_grid``
- ``double_outline``
- ``fancy_grid``
- ``fancy_outline``
- ``github``
- ``grid``
- ``heavy_grid``
- ``heavy_outline``
- ``html``
- ``jira``
- ``latex``
- ``latex_booktabs``
- ``latex_longtable``
- ``latex_raw``
- ``mediawiki``
- ``mixed_grid``
- ``mixed_outline``
- ``moinmoin``
- ``orgtbl``
- ``outline``
- ``pipe``
- ``plain``
- ``presto``
- ``pretty``
- ``psql``
- ``rounded_grid``
- ``rounded_outline``
- ``rst``
- ``simple``
- ``simple_grid``
- ``simple_outline``
- ``textile``
- ``tsv``
- ``unsafehtml``
- ``youtrack``

.. [[[end]]]

This list can also be found by running ``sqlite-utils query --help``.

.. _cli_query_raw:

Returning raw data, such as binary content
------------------------------------------

If your table contains binary data in a ``BLOB`` you can use the ``--raw`` option to output specific columns directly to standard out.

For example, to retrieve a binary image from a ``BLOB`` column and store it in a file you can use the following:

.. code-block:: bash

    sqlite-utils photos.db "select contents from photos where id=1" --raw > myphoto.jpg

To return the first column of each result as raw data, separated by newlines, use ``--raw-lines``:

.. code-block:: bash

    sqlite-utils photos.db "select caption from photos" --raw-lines > captions.txt

.. _cli_query_parameters:

Using named parameters
----------------------

You can pass named parameters to the query using ``-p name value``:

.. code-block:: bash

    sqlite-utils query dogs.db "select :num * :num2" -p num 5 -p num2 6

.. code-block:: output

    [{":num * :num2": 30}]

These will be correctly quoted and escaped in the SQL query, providing a safe way to combine other values with SQL.

.. _cli_query_update_insert_delete:

UPDATE, INSERT and DELETE
-------------------------

If you execute an ``UPDATE``, ``INSERT`` or ``DELETE`` query the command will return the number of affected rows:

.. code-block:: bash

    sqlite-utils dogs.db "update dogs set age = 5 where name = 'Cleo'"

.. code-block:: output

    [{"rows_affected": 1}]

.. _cli_query_functions:

Defining custom SQL functions
-----------------------------

You can use the ``--functions`` option to pass a block of Python code that defines additional functions which can then be called by your SQL query.

This example defines a function which extracts the domain from a URL:

.. code-block:: bash

    sqlite-utils query sites.db "select url, domain(url) from urls" --functions '
    from urllib.parse import urlparse

    def domain(url):
        return urlparse(url).netloc
    '

Every callable object defined in the block will be registered as a SQL function with the same name, with the exception of functions with names that begin with an underscore.

You can also pass the path to a Python file containing function definitions:

.. code-block:: bash

    sqlite-utils query sites.db "select url, domain(url) from urls" --functions functions.py

The ``--functions`` option can be used multiple times to load functions from multiple sources:

.. code-block:: bash

    sqlite-utils query sites.db "select url, domain(url), extract_path(url) from urls" \
      --functions domain_funcs.py \
      --functions 'def extract_path(url):
        from urllib.parse import urlparse
        return urlparse(url).path'

.. note::
    In Python: :ref:`db.register_function() <python_api_register_function>`

.. _cli_query_extensions:

SQLite extensions
-----------------

You can load SQLite extension modules using the ``--load-extension`` option, see :ref:`cli_load_extension`.

.. code-block:: bash

    sqlite-utils dogs.db "select spatialite_version()" --load-extension=spatialite

.. code-block:: output

    [{"spatialite_version()": "4.3.0a"}]

.. _cli_query_attach:

Attaching additional databases
------------------------------

SQLite supports cross-database SQL queries, which can join data from tables in more than one database file.

You can attach one or more additional databases using the ``--attach`` option, providing an alias to use for that database and the path to the SQLite file on disk.

This example attaches the ``books.db`` database under the alias ``books`` and then runs a query that combines data from that database with the default ``dogs.db`` database:

.. code-block:: bash

    sqlite-utils dogs.db --attach books books.db \
       'select * from sqlite_master union all select * from books.sqlite_master'

.. note::
    In Python: :ref:`db.attach() <python_api_attach>`

.. _cli_memory:

Querying data directly using an in-memory database
==================================================

The ``sqlite-utils memory`` command works similar to ``sqlite-utils query``, but allows you to execute queries against an in-memory database.

You can also pass this command CSV or JSON files which will be loaded into a temporary in-memory table, allowing you to execute SQL against that data without a separate step to first convert it to SQLite.

Without any extra arguments, this command executes SQL against the in-memory database directly:

.. code-block:: bash

    sqlite-utils memory 'select sqlite_version()'

.. code-block:: output

    [{"sqlite_version()": "3.35.5"}]

It takes all of the same output formatting options as :ref:`sqlite-utils query <cli_query>`: ``--csv`` and ``--csv`` and ``--table`` and ``--nl``:

.. code-block:: bash

    sqlite-utils memory 'select sqlite_version()' --csv

.. code-block:: output

    sqlite_version()
    3.35.5

.. code-block:: bash

    sqlite-utils memory 'select sqlite_version()' --fmt grid

.. code-block:: output

    +--------------------+
    | sqlite_version()   |
    +====================+
    | 3.35.5             |
    +--------------------+

.. _cli_memory_csv_json:

Running queries directly against CSV or JSON
--------------------------------------------

If you have data in CSV or JSON format you can load it into an in-memory SQLite database and run queries against it directly in a single command using ``sqlite-utils memory`` like this:

.. code-block:: bash

    sqlite-utils memory data.csv "select * from data"

You can pass multiple files to the command if you want to run joins between data from different files:

.. code-block:: bash

    sqlite-utils memory one.csv two.json \
      "select * from one join two on one.id = two.other_id"

If your data is JSON it should be the same format supported by the :ref:`sqlite-utils insert command <cli_inserting_data>` - so either a single JSON object (treated as a single row) or a list of JSON objects.

CSV data can be comma- or tab- delimited.

The in-memory tables will be named after the files without their extensions. The tool also sets up aliases for those tables (using SQL views) as ``t1``, ``t2`` and so on, or you can use the alias ``t`` to refer to the first table:

.. code-block:: bash

    sqlite-utils memory example.csv "select * from t"

If two files have the same name they will be assigned a numeric suffix:

.. code-block:: bash

    sqlite-utils memory foo/data.csv bar/data.csv "select * from data_2"

To read from standard input, use either ``-`` or ``stdin`` as the filename - then use ``stdin`` or ``t`` or ``t1`` as the table name:

.. code-block:: bash

    cat example.csv | sqlite-utils memory - "select * from stdin"

Incoming CSV data will be assumed to use ``utf-8``. If your data uses a different character encoding you can specify that with ``--encoding``:

.. code-block:: bash

    cat example.csv | sqlite-utils memory - "select * from stdin" --encoding=latin-1

If you are joining across multiple CSV files they must all use the same encoding.

Column types will be automatically detected in CSV or TSV data, as described in :ref:`cli_insert_csv_tsv`. You can pass the ``--no-detect-types`` option to disable this automatic type detection and treat all CSV and TSV columns as ``TEXT``.

.. _cli_memory_explicit:

Explicitly specifying the format
--------------------------------

By default, ``sqlite-utils memory`` will attempt to detect the incoming data format (JSON, TSV or CSV) automatically.

You can instead specify an explicit format by adding a ``:csv``, ``:tsv``, ``:json`` or ``:nl`` (for newline-delimited JSON) suffix to the filename. For example:

.. code-block:: bash
    
    sqlite-utils memory one.dat:csv two.dat:nl \
      "select * from one union select * from two"

Here the contents of ``one.dat`` will be treated as CSV and the contents of ``two.dat`` will be treated as newline-delimited JSON.

To explicitly specify the format for data piped into the tool on standard input, use ``stdin:format`` - for example:

.. code-block:: bash

    cat one.dat | sqlite-utils memory stdin:csv "select * from stdin"

.. _cli_memory_attach:

Joining in-memory data against existing databases using \-\-attach
------------------------------------------------------------------

The :ref:`attach option <cli_query_attach>` can be used to attach database files to the in-memory connection, enabling joins between in-memory data loaded from a file and tables in existing SQLite database files. An example:

.. code-block:: bash

    echo "id\n1\n3\n5" | sqlite-utils memory - --attach trees trees.db \
      "select * from trees.trees where rowid in (select id from stdin)"

Here the ``--attach trees trees.db`` option makes the ``trees.db`` database available with an alias of ``trees``.

``select * from trees.trees where ...`` can then query the ``trees`` table in that database.

The CSV data that was piped into the script is available in the ``stdin`` table, so  ``... where rowid in (select id from stdin)`` can be used to return rows from the ``trees`` table that match IDs that were piped in as CSV content.

.. _cli_memory_schema_dump_save:

\-\-schema, \-\-analyze, \-\-dump and \-\-save
----------------------------------------------

To see the in-memory database schema that would be used for a file or for multiple files, use ``--schema``:

.. code-block:: bash

    sqlite-utils memory dogs.csv --schema

.. code-block:: output

    CREATE TABLE "dogs" (
        "id" INTEGER,
        "age" INTEGER,
        "name" TEXT
    );
    CREATE VIEW "t1" AS select * from "dogs";
    CREATE VIEW "t" AS select * from "dogs";

You can run the equivalent of the :ref:`analyze-tables <cli_analyze_tables>` command using ``--analyze``:

.. code-block:: bash

    sqlite-utils memory dogs.csv --analyze

.. code-block:: output

    dogs.id: (1/3)

      Total rows: 2
      Null rows: 0
      Blank rows: 0

      Distinct values: 2

    dogs.name: (2/3)

      Total rows: 2
      Null rows: 0
      Blank rows: 0

      Distinct values: 2

    dogs.age: (3/3)

      Total rows: 2
      Null rows: 0
      Blank rows: 0

      Distinct values: 2

You can output SQL that will both create the tables and insert the full data used to populate the in-memory database using ``--dump``:

.. code-block:: bash

    sqlite-utils memory dogs.csv --dump

.. code-block:: output

    BEGIN TRANSACTION;
    CREATE TABLE "dogs" (
        "id" INTEGER,
        "age" INTEGER,
        "name" TEXT
    );
    INSERT INTO "dogs" VALUES('1','4','Cleo');
    INSERT INTO "dogs" VALUES('2','2','Pancakes');
    CREATE VIEW "t1" AS select * from "dogs";
    CREATE VIEW "t" AS select * from "dogs";
    COMMIT;

Passing ``--save other.db`` will instead use that SQL to populate a new database file:

.. code-block:: bash

    sqlite-utils memory dogs.csv --save dogs.db

These features are mainly intended as debugging tools - for much more finely grained control over how data is inserted into a SQLite database file see :ref:`cli_inserting_data` and :ref:`cli_insert_csv_tsv`.

.. _cli_rows:

Returning all rows in a table
=============================

You can return every row in a specified table using the ``rows`` command:

.. code-block:: bash

    sqlite-utils rows dogs.db dogs

.. code-block:: output

    [{"id": 1, "age": 4, "name": "Cleo"},
     {"id": 2, "age": 2, "name": "Pancakes"}]

This command accepts the same output options as ``query`` - so you can pass ``--nl``, ``--csv``, ``--tsv``, ``--no-headers``, ``--table`` and ``--fmt``.

You can use the ``-c`` option to specify a subset of columns to return:

.. code-block:: bash

    sqlite-utils rows dogs.db dogs -c age -c name

.. code-block:: output

    [{"age": 4, "name": "Cleo"},
     {"age": 2, "name": "Pancakes"}]

You can filter rows using a where clause with the ``--where`` option:

.. code-block:: bash

    sqlite-utils rows dogs.db dogs -c name --where 'name = "Cleo"'

.. code-block:: output

    [{"name": "Cleo"}]

Or pass named parameters using ``--where`` in combination with ``-p``:

.. code-block:: bash

    sqlite-utils rows dogs.db dogs -c name --where 'name = :name' -p name Cleo

.. code-block:: output

    [{"name": "Cleo"}]

You can define a sort order using ``--order column`` or ``--order 'column desc'``.

Use ``--limit N`` to only return the first ``N`` rows. Use ``--offset N`` to return rows starting from the specified offset.

.. note::
    In Python: :ref:`table.rows <python_api_rows>`  CLI reference: :ref:`sqlite-utils rows <cli_ref_rows>`

.. _cli_tables:

Listing tables
==============

You can list the names of tables in a database using the ``tables`` command:

.. code-block:: bash

    sqlite-utils tables mydb.db

.. code-block:: output

    [{"table": "dogs"},
     {"table": "cats"},
     {"table": "chickens"}]

You can output this list in CSV using the ``--csv`` or ``--tsv`` options:

.. code-block:: bash

    sqlite-utils tables mydb.db --csv --no-headers

.. code-block:: output

    dogs
    cats
    chickens

If you just want to see the FTS4 tables, you can use ``--fts4`` (or ``--fts5`` for FTS5 tables):

.. code-block:: bash

    sqlite-utils tables docs.db --fts4

.. code-block:: output

    [{"table": "docs_fts"}]

Use ``--counts`` to include a count of the number of rows in each table:

.. code-block:: bash

    sqlite-utils tables mydb.db --counts

.. code-block:: output

    [{"table": "dogs", "count": 12},
     {"table": "cats", "count": 332},
     {"table": "chickens", "count": 9}]

Use ``--columns`` to include a list of columns in each table:

.. code-block:: bash

    sqlite-utils tables dogs.db --counts --columns

.. code-block:: output

    [{"table": "Gosh", "count": 0, "columns": ["c1", "c2", "c3"]},
     {"table": "Gosh2", "count": 0, "columns": ["c1", "c2", "c3"]},
     {"table": "dogs", "count": 2, "columns": ["id", "age", "name"]}]

Use ``--schema`` to include the schema of each table:

.. code-block:: bash

    sqlite-utils tables dogs.db --schema --table

.. code-block:: output

    table    schema
    -------  -----------------------------------------------
    Gosh     CREATE TABLE Gosh (c1 text, c2 text, c3 text)
    Gosh2    CREATE TABLE Gosh2 (c1 text, c2 text, c3 text)
    dogs     CREATE TABLE "dogs" (
               "id" INTEGER,
               "age" INTEGER,
               "name" TEXT)

The ``--nl``, ``--csv``, ``--tsv``, ``--table`` and ``--fmt`` options are also available.

.. note::
    In Python: :ref:`db.tables or db.table_names() <python_api_tables>`  CLI reference: :ref:`sqlite-utils tables <cli_ref_tables>`

.. _cli_views:

Listing views
=============

The ``views`` command shows any views defined in the database:

.. code-block:: bash

    sqlite-utils views sf-trees.db --table --counts --columns --schema

.. code-block:: output

    view         count  columns               schema
    ---------  -------  --------------------  --------------------------------------------------------------
    demo_view   189144  ['qSpecies']          CREATE VIEW demo_view AS select qSpecies from Street_Tree_List
    hello            1  ['sqlite_version()']  CREATE VIEW hello as select sqlite_version()

It takes the same options as the ``tables`` command:

* ``--columns``
* ``--schema``
* ``--counts``
* ``--nl``
* ``--csv``
* ``--tsv``
* ``--table``

.. note::
    In Python: :ref:`db.views or db.view_names() <python_api_views>`  CLI reference: :ref:`sqlite-utils views <cli_ref_views>`

.. _cli_indexes:

Listing indexes
===============

The ``indexes`` command lists any indexes configured for the database:

.. code-block:: bash

    sqlite-utils indexes covid.db --table

.. code-block:: output

    table                             index_name                                                seqno    cid  name                 desc  coll      key
    --------------------------------  ------------------------------------------------------  -------  -----  -----------------  ------  ------  -----
    johns_hopkins_csse_daily_reports  idx_johns_hopkins_csse_daily_reports_combined_key             0     12  combined_key            0  BINARY      1
    johns_hopkins_csse_daily_reports  idx_johns_hopkins_csse_daily_reports_country_or_region        0      1  country_or_region       0  BINARY      1
    johns_hopkins_csse_daily_reports  idx_johns_hopkins_csse_daily_reports_province_or_state        0      2  province_or_state       0  BINARY      1
    johns_hopkins_csse_daily_reports  idx_johns_hopkins_csse_daily_reports_day                      0      0  day                     0  BINARY      1
    ny_times_us_counties              idx_ny_times_us_counties_date                                 0      0  date                    1  BINARY      1
    ny_times_us_counties              idx_ny_times_us_counties_fips                                 0      3  fips                    0  BINARY      1
    ny_times_us_counties              idx_ny_times_us_counties_county                               0      1  county                  0  BINARY      1
    ny_times_us_counties              idx_ny_times_us_counties_state                                0      2  state                   0  BINARY      1

It shows indexes across all tables. To see indexes for specific tables, list those after the database:

.. code-block:: bash

    sqlite-utils indexes covid.db johns_hopkins_csse_daily_reports --table

The command defaults to only showing the columns that are explicitly part of the index. To also include auxiliary columns use the ``--aux`` option - these columns will be listed with a ``key`` of ``0``.

The command takes the same format options as the ``tables`` and ``views`` commands.

.. note::
    In Python: :ref:`table.indexes <python_api_introspection_indexes>`  CLI reference: :ref:`sqlite-utils indexes <cli_ref_indexes>`

.. _cli_triggers:

Listing triggers
================

The ``triggers`` command shows any triggers configured for the database:

.. code-block:: bash

    sqlite-utils triggers global-power-plants.db --table

.. code-block:: output

    name             table      sql
    ---------------  ---------  -----------------------------------------------------------------
    plants_insert    plants     CREATE TRIGGER "plants_insert" AFTER INSERT ON "plants"
                                BEGIN
                                    INSERT OR REPLACE INTO "_counts"
                                    VALUES (
                                      'plants',
                                      COALESCE(
                                        (SELECT count FROM "_counts" WHERE "table" = 'plants'),
                                      0
                                      ) + 1
                                    );
                                END

It defaults to showing triggers for all tables. To see triggers for one or more specific tables pass their names as arguments:

.. code-block:: bash

    sqlite-utils triggers global-power-plants.db plants

The command takes the same format options as the ``tables`` and ``views`` commands.

.. note::
    In Python: :ref:`table.triggers or db.triggers <python_api_introspection_triggers>`  CLI reference: :ref:`sqlite-utils triggers <cli_ref_triggers>`

.. _cli_schema:

Showing the schema
==================

The ``sqlite-utils schema`` command shows the full SQL schema for the database:

.. code-block:: bash

    sqlite-utils schema dogs.db

.. code-block:: output

    CREATE TABLE "dogs" (
        "id" INTEGER PRIMARY KEY,
        "name" TEXT
    );

This will show the schema for every table and index in the database. To view the schema just for a specified subset of tables pass those as additional arguments:

.. code-block:: bash

    sqlite-utils schema dogs.db dogs chickens

.. note::
    In Python: :ref:`table.schema <python_api_introspection_schema>` or :ref:`db.schema <python_api_schema>`  CLI reference: :ref:`sqlite-utils schema <cli_ref_schema>`

.. _cli_analyze_tables:

Analyzing tables
================

When working with a new database it can be useful to get an idea of the shape of the data. The ``sqlite-utils analyze-tables`` command inspects specified tables (or all tables) and calculates some useful details about each of the columns in those tables.

To inspect the ``tags`` table in the ``github.db`` database, run the following:

.. code-block:: bash

    sqlite-utils analyze-tables github.db tags

.. code-block:: output

    tags.repo: (1/3)

      Total rows: 261
      Null rows: 0
      Blank rows: 0

      Distinct values: 14

      Most common:
        88: 107914493
        75: 140912432
        27: 206156866

      Least common:
        1: 209590345
        2: 206649770
        2: 303218369

    tags.name: (2/3)

      Total rows: 261
      Null rows: 0
      Blank rows: 0

      Distinct values: 175

      Most common:
        10: 0.2
        9: 0.1
        7: 0.3

      Least common:
        1: 0.1.1
        1: 0.11.1
        1: 0.1a2

    tags.sha: (3/3)

      Total rows: 261
      Null rows: 0
      Blank rows: 0

      Distinct values: 261

For each column this tool displays the number of null rows, the number of blank rows (rows that contain an empty string), the number of distinct values and, for columns that are not entirely distinct, the most common and least common values.

If you do not specify any tables every table in the database will be analyzed:

.. code-block:: bash

    sqlite-utils analyze-tables github.db

If you wish to analyze one or more specific columns, use the ``-c`` option:

.. code-block:: bash

    sqlite-utils analyze-tables github.db tags -c sha

To show more than 10 common values, use ``--common-limit 20``.  To skip the most common or least common value analysis, use ``--no-most`` or ``--no-least``:

.. code-block:: bash

    sqlite-utils analyze-tables github.db tags --common-limit 20 --no-least

.. note::
    In Python: :ref:`table.analyze_column() <python_api_analyze_column>`  CLI reference: :ref:`sqlite-utils analyze-tables <cli_ref_analyze_tables>`

.. _cli_analyze_tables_save:

Saving the analyzed table details
---------------------------------

``analyze-tables`` can take quite a while to run for large database files. You can save the results of the analysis to a database table called ``_analyze_tables_`` using the ``--save`` option:

.. code-block:: bash

    sqlite-utils analyze-tables github.db --save

The ``_analyze_tables_`` table has the following schema:

.. code-block:: sql

    CREATE TABLE "_analyze_tables_" (
        "table" TEXT,
        "column" TEXT,
        "total_rows" INTEGER,
        "num_null" INTEGER,
        "num_blank" INTEGER,
        "num_distinct" INTEGER,
        "most_common" TEXT,
        "least_common" TEXT,
        PRIMARY KEY ("table", "column")
    );

The ``most_common`` and ``least_common`` columns will contain nested JSON arrays of the most common and least common values that look like this:

.. code-block:: json

    [
        ["Del Libertador, Av", 5068],
        ["Alberdi Juan Bautista Av.", 4612],
        ["Directorio Av.", 4552],
        ["Rivadavia, Av", 4532],
        ["Yerbal", 4512],
        ["Cosquín", 4472],
        ["Estado Plurinacional de Bolivia", 4440],
        ["Gordillo Timoteo", 4424],
        ["Montiel", 4360],
        ["Condarco", 4288]
    ]

.. _cli_create_database:

Creating an empty database
==========================

You can create a new empty database file using the ``create-database`` command:

.. code-block:: bash

    sqlite-utils create-database empty.db

To enable :ref:`cli_wal` on the newly created database add the ``--enable-wal`` option:

.. code-block:: bash

    sqlite-utils create-database empty.db --enable-wal

To enable SpatiaLite metadata on a newly created database, add the ``--init-spatialite`` flag:

.. code-block:: bash

    sqlite-utils create-database empty.db --init-spatialite

That will look for SpatiaLite in a set of predictable locations. To load it from somewhere else, use the ``--load-extension`` option:

.. code-block:: bash

    sqlite-utils create-database empty.db --init-spatialite --load-extension /path/to/spatialite.so

.. _cli_migrate:

Running migrations
==================

The ``migrate`` command applies pending Python migrations to a database. For the full migration file format and Python API, see :ref:`migrations`.

.. code-block:: bash

    sqlite-utils migrate creatures.db path/to/migrations.py

If you omit the migration path it will search the current directory and subdirectories for files called ``migrations.py``:

.. code-block:: bash

    sqlite-utils migrate creatures.db

Use ``--list`` to list applied and pending migrations without running them:

.. code-block:: bash

    sqlite-utils migrate creatures.db --list

Use ``--stop-before`` to stop before a named migration. The option can be passed more than once, and can target a specific migration set using ``migration_set:migration_name``:

.. code-block:: bash

    sqlite-utils migrate creatures.db path/to/migrations.py \
      --stop-before creatures:add_weight \
      --stop-before sales:drop_index

.. _cli_inserting_data:

Inserting JSON data
===================

If you have data as JSON, you can use ``sqlite-utils insert tablename`` to insert it into a database. The table will be created with the correct (automatically detected) columns if it does not already exist.

You can pass in a single JSON object or a list of JSON objects, either as a filename or piped directly to standard-in (by using ``-`` as the filename).

Here's the simplest possible example:

.. code-block:: bash

    echo '{"name": "Cleo", "age": 4}' | sqlite-utils insert dogs.db dogs -

To specify a column as the primary key, use ``--pk=column_name``.

To create a compound primary key across more than one column, use ``--pk`` multiple times.

If you feed it a JSON list it will insert multiple records. For example, if ``dogs.json`` looks like this:

.. code-block:: json

    [
        {
            "id": 1,
            "name": "Cleo",
            "age": 4
        },
        {
            "id": 2,
            "name": "Pancakes",
            "age": 2
        },
        {
            "id": 3,
            "name": "Toby",
            "age": 6
        }
    ]

You can import all three records into an automatically created ``dogs`` table and set the ``id`` column as the primary key like so:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.json --pk=id

Pass ``--pk`` multiple times to define a compound primary key.

You can skip inserting any records that have a primary key that already exists using ``--ignore``:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.json --pk=id --ignore

You can delete all the existing rows in the table before inserting the new records using ``--truncate``:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.json --truncate

You can add the ``--analyze`` option to run ``ANALYZE`` against the table after the rows have been inserted.

.. note::
    In Python: :ref:`table.insert_all() <python_api_bulk_inserts>`  CLI reference: :ref:`sqlite-utils insert <cli_ref_insert>`

.. _cli_inserting_data_binary:

Inserting binary data
---------------------

You can insert binary data into a BLOB column by first encoding it using base64 and then structuring it like this:

.. code-block:: json

    [
        {
            "name": "transparent.gif",
            "content": {
                "$base64": true,
                "encoded": "R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
            }
        }
    ]

.. _cli_inserting_data_nl_json:

Inserting newline-delimited JSON
--------------------------------

You can also import newline-delimited JSON (see `JSON Lines <https://jsonlines.org/>`__) using the ``--nl`` option:

.. code-block:: bash

    echo '{"id": 1, "name": "Cleo"}
    {"id": 2, "name": "Suna"}' | sqlite-utils insert creatures.db creatures - --nl

Newline-delimited JSON consists of full JSON objects separated by newlines.

If you are processing data using ``jq`` you can use the ``jq -c`` option to output valid newline-delimited JSON.

Since `Datasette <https://datasette.io/>`__ can export newline-delimited JSON, you can combine the Datasette and ``sqlite-utils`` like so:

.. code-block:: bash

    curl -L "https://latest.datasette.io/fixtures/facetable.json?_shape=array&_nl=on" \
        | sqlite-utils insert nl-demo.db facetable - --pk=id --nl

You can also pipe ``sqlite-utils`` together to create a new SQLite database file containing the results of a SQL query against another database:

.. code-block:: bash

    sqlite-utils sf-trees.db \
        "select TreeID, qAddress, Latitude, Longitude from Street_Tree_List" --nl \
      | sqlite-utils insert saved.db trees - --nl
    
.. code-block:: bash

    sqlite-utils saved.db "select * from trees limit 5" --csv

.. code-block:: output

    TreeID,qAddress,Latitude,Longitude
    141565,501X Baker St,37.7759676911831,-122.441396661871
    232565,940 Elizabeth St,37.7517102172731,-122.441498017841
    119263,495X Lakeshore Dr,,
    207368,920 Kirkham St,37.760210314285,-122.47073935813
    188702,1501 Evans Ave,37.7422086702947,-122.387293152263

.. _cli_inserting_data_flatten:

Flattening nested JSON objects
------------------------------

``sqlite-utils insert`` and ``sqlite-utils memory`` both expect incoming JSON data to consist of an array of JSON objects, where the top-level keys of each object will become columns in the created database table.

If your data is nested you can use the ``--flatten`` option to create columns that are derived from the nested data.

Consider this example document, in a file called ``log.json``:

.. code-block:: json

    {
        "httpRequest": {
            "latency": "0.112114537s",
            "requestMethod": "GET",
            "requestSize": "534",
            "status": 200
        },
        "insertId": "6111722f000b5b4c4d4071e2",
        "labels": {
            "service": "datasette-io"
        }
    }

Inserting this into a table using ``sqlite-utils insert logs.db logs log.json`` will create a table with the following schema:

.. code-block:: sql

    CREATE TABLE "logs" (
       "httpRequest" TEXT,
       "insertId" TEXT,
       "labels" TEXT
    );

With the ``--flatten`` option columns will be created using ``topkey_nextkey`` column names - so running ``sqlite-utils insert logs.db logs log.json --flatten`` will create the following schema instead:

.. code-block:: sql

    CREATE TABLE "logs" (
       "httpRequest_latency" TEXT,
       "httpRequest_requestMethod" TEXT,
       "httpRequest_requestSize" TEXT,
       "httpRequest_status" INTEGER,
       "insertId" TEXT,
       "labels_service" TEXT
    );

.. _cli_insert_csv_tsv:

Inserting CSV or TSV data
=========================

If your data is in CSV format, you can insert it using the ``--csv`` option:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.csv --csv

For tab-delimited data, use ``--tsv``:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.tsv --tsv

Data is expected to be encoded as Unicode UTF-8. If your data is an another character encoding you can specify it using the ``--encoding`` option:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.tsv --tsv --encoding=latin-1

To stop inserting after a specified number of records - useful for getting a faster preview of a large file - use the ``--stop-after`` option:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.csv --csv --stop-after=10

A progress bar is displayed when inserting data from a file. You can hide the progress bar using the ``--silent`` option.

By default, column types are automatically detected for CSV or TSV files - resulting in a mix of ``TEXT``, ``INTEGER`` and ``REAL`` columns. To disable type detection and treat all columns as ``TEXT``, use the ``--no-detect-types`` option.

Detected types are only applied when the table is created by the command. Inserting CSV or TSV data into a table that already exists leaves the existing column types unchanged - values are inserted using the table's existing schema.

For example, given a ``creatures.csv`` file containing this:

.. code-block::

    name,age,weight
    Cleo,6,45.5
    Dori,1,3.5

The following command:

.. code-block:: bash

    sqlite-utils insert creatures.db creatures creatures.csv --csv

Will produce this schema with automatically detected types:

.. code-block:: bash

    sqlite-utils schema creatures.db

.. code-block:: output

    CREATE TABLE "creatures" (
       "name" TEXT,
       "age" INTEGER,
       "weight" REAL
    );

.. _cli_insert_csv_tsv_column_types:

Overriding column types
-----------------------

Use ``--type column-name type`` to override the type automatically chosen when the table is created. This option can be used more than once, and works with both ``insert`` and ``upsert``:

.. code-block:: bash

    sqlite-utils insert places.db places places.csv --csv \
        --type zipcode text \
        --type score real

This is useful for values such as ZIP codes, which may look like integers but should be stored as ``TEXT`` to preserve leading zeros.

The column type should be one of ``TEXT``, ``INTEGER``, ``FLOAT``, ``REAL``, ``BLOB`` or ``ANY``. Column types are matched case-insensitively.

``ANY`` is especially useful with ``--strict``. An ``ANY`` column in a strict table preserves values without coercion, so text such as ``000123`` remains text instead of being converted to an integer:

.. code-block:: bash

    sqlite-utils insert events.db events events.csv --csv --strict \
        --type payload any

As with detected column types, ``--type`` only affects tables created by the command. If the table already exists, its existing column types are left unchanged.

To disable type detection and treat all columns as TEXT, use ``--no-detect-types``:

.. code-block:: bash

    sqlite-utils insert creatures.db creatures creatures.csv --csv --no-detect-types

If a CSV or TSV file includes empty cells, like this one:

::

    name,age,weight
    Cleo,6,
    Dori,,3.5

They will be imported into SQLite as empty string values, ``""``.

To import them as ``NULL`` values instead, use the ``--empty-null`` option:

.. code-block:: bash

    sqlite-utils insert creatures.db creatures creatures.csv --csv --empty-null

.. _cli_insert_csv_tsv_delimiter:

Alternative delimiters and quote characters
-------------------------------------------

If your file uses a delimiter other than ``,`` or a quote character other than ``"`` you can attempt to detect delimiters or you can specify them explicitly.

The ``--sniff`` option can be used to attempt to detect the delimiters:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.csv --sniff

Alternatively, you can specify them using the ``--delimiter`` and ``--quotechar`` options.

Here's a CSV file that uses ``;`` for delimiters and the ``|`` symbol for quote characters::

    name;description
    Cleo;|Very fine; a friendly dog|
    Pancakes;A local corgi

You can import that using:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.csv --delimiter=";" --quotechar="|"

Passing ``--delimiter``, ``--quotechar`` or ``--sniff`` implies ``--csv``, so you can omit the ``--csv`` option.

.. _cli_insert_csv_tsv_no_header:

CSV files without a header row
------------------------------

The first row of any CSV or TSV file is expected to contain the names of the columns in that file.

If your file does not include this row, you can use the ``--no-headers`` option to specify that the tool should not use that fist row as headers.

If you do this, the table will be created with column names called ``untitled_1`` and ``untitled_2`` and so on. You can then rename them using the ``sqlite-utils transform ... --rename`` command, see :ref:`cli_transform_table`.

.. _cli_insert_unstructured:

Inserting unstructured data with \-\-lines and \-\-text
=======================================================

If you have an unstructured file you can insert its contents into a table with a single ``line`` column containing each line from the file using ``--lines``. This can be useful if you intend to further analyze those lines using SQL string functions or :ref:`sqlite-utils convert <cli_convert>`:

.. code-block:: bash

    sqlite-utils insert logs.db loglines logfile.log --lines

This will produce the following schema:

.. code-block:: sql

    CREATE TABLE "loglines" (
       "line" TEXT
    );

You can also insert the entire contents of the file into a single column called ``text`` using ``--text``:

.. code-block:: bash

    sqlite-utils insert content.db content file.txt --text

The schema here will be:

.. code-block:: sql

    CREATE TABLE "content" (
       "text" TEXT
    );

.. _cli_insert_convert:

Applying conversions while inserting data
=========================================

The ``--convert`` option can be used to apply a Python conversion function to imported data before it is inserted into the database. It works in a similar way to :ref:`sqlite-utils convert <cli_convert>`.

Your Python function will be passed a dictionary called ``row`` for each item that is being imported. You can modify that dictionary and return it - or return a fresh dictionary - to change the data that will be inserted.

Given a JSON file called ``dogs.json`` containing this:

.. code-block:: json

    [
        {"id": 1, "name": "Cleo"},
        {"id": 2, "name": "Pancakes"}
    ]

The following command will insert that data and add an ``is_good`` column set to ``1`` for each dog:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs dogs.json --convert 'row["is_good"] = 1'

The ``--convert`` option also works with the ``--csv``, ``--tsv`` and ``--nl`` insert options.

As with ``sqlite-utils convert`` you can use ``--import`` to import additional Python modules, see :ref:`cli_convert_import` for details.

You can also pass code that runs some initialization steps and defines a ``convert(value)`` function, see :ref:`cli_convert_complex`.

.. _cli_insert_convert_lines:

\-\-convert with \-\-lines
--------------------------

Things work slightly differently when combined with the ``--lines`` or ``--text`` options.

With ``--lines``, instead of being passed a ``row`` dictionary your function will be passed a ``line`` string representing each line of the input. Given a file called ``access.log`` containing the following::

    INFO:     127.0.0.1:60581 - GET / HTTP/1.1 200 OK
    INFO:     127.0.0.1:60581 - GET /foo/-/static/app.css?cead5a HTTP/1.1 200 OK

You could convert it into structured data like so:

.. code-block:: bash

    sqlite-utils insert logs.db loglines access.log --convert '
    type, source, _, verb, path, _, status, _ = line.split()
    return {
        "type": type,
        "source": source,
        "verb": verb,
        "path": path,
        "status": status,
    }' --lines

The resulting table would look like this:

======  ===============  ======  ============================  ========
type    source           verb    path                            status
======  ===============  ======  ============================  ========
INFO:   127.0.0.1:60581  GET     /                                  200
INFO:   127.0.0.1:60581  GET     /foo/-/static/app.css?cead5a       200
======  ===============  ======  ============================  ========

.. _cli_insert_convert_text:

\-\-convert with \-\-text
-------------------------

With ``--text`` the entire input to the command will be made available to the function as a variable called ``text``.

The function can return a single dictionary which will be inserted as a single row, or it can return a list or iterator of dictionaries, each of which will be inserted.

Here's how to use ``--convert`` and ``--text`` to insert one record per word in the input:

.. code-block:: bash

    echo 'A bunch of words' | sqlite-utils insert words.db words - \
        --text --convert '({"word": w} for w in text.split())'

The result looks like this:

.. code-block:: bash

    sqlite-utils dump words.db

.. code-block:: output

    BEGIN TRANSACTION;
    CREATE TABLE "words" (
       "word" TEXT
    );
    INSERT INTO "words" VALUES('A');
    INSERT INTO "words" VALUES('bunch');
    INSERT INTO "words" VALUES('of');
    INSERT INTO "words" VALUES('words');
    COMMIT;


.. _cli_insert_code:

Inserting rows generated by Python code
=======================================

Instead of providing a ``FILE`` to import, you can use the ``--code`` option to pass a block of Python code that generates the rows to insert. This is the command-line equivalent of calling ``db["creatures"].insert_all(rows())`` from the :ref:`Python API <python_api>`.

Your code should define either a ``rows()`` function that returns or yields dictionaries, or a ``rows`` iterable such as a list of dictionaries:

.. code-block:: bash

    sqlite-utils insert data.db creatures --code '
    def rows():
        yield {"id": 1, "name": "Cleo"}
        yield {"id": 2, "name": "Suna"}
    ' --pk id

``--code`` can also be given a path to a Python ``.py`` file.

The ``--code`` option works with both ``sqlite-utils insert`` and ``sqlite-utils upsert``, and composes with table options such as ``--pk``, ``--replace``, ``--alter``, ``--not-null`` and ``--default``. It cannot be combined with a ``FILE`` argument or with input format options such as ``--csv`` or ``--convert``.

.. _cli_insert_replace:

Insert-replacing data
=====================

The ``--replace`` option to ``insert`` causes any existing records with the same primary key to be replaced entirely by the new records.

To replace a dog with in ID of 2 with a new record, run the following:

.. code-block:: bash

    echo '{"id": 2, "name": "Pancakes", "age": 3}' | \
        sqlite-utils insert dogs.db dogs - --pk=id --replace

.. note::
    In Python: :ref:`table.insert(..., replace=True) <python_api_insert_replace>`  CLI reference: :ref:`sqlite-utils insert <cli_ref_insert>`

.. _cli_upsert:

Upserting data
==============

Upserting is update-or-insert. If a row exists with the specified primary key the provided columns will be updated. If no row exists that row will be created.

Unlike ``insert --replace``, an upsert will ignore any column values that exist but are not present in the upsert document.

For example:

.. code-block:: bash

    echo '{"id": 2, "age": 4}' | \
        sqlite-utils upsert dogs.db dogs - --pk=id

This will update the dog with an ID of 2 to have an age of 4, creating a new record (with a null name) if one does not exist. If a row DOES exist the name will be left as-is.

If the table already exists and has a primary key, you can omit the ``--pk`` option and ``sqlite-utils`` will use that existing primary key.

The command will fail if you reference columns that do not exist on the table. To automatically create missing columns, use the ``--alter`` option.

.. note::
    ``upsert`` in sqlite-utils 1.x worked like ``insert ... --replace`` does in 2.x. See `issue #66 <https://github.com/simonw/sqlite-utils/issues/66>`__ for details of this change.


.. note::
    In Python: :ref:`table.upsert() <python_api_upsert>`  CLI reference: :ref:`sqlite-utils upsert <cli_ref_upsert>`

.. _cli_bulk:

Executing SQL in bulk
=====================

If you have a JSON, newline-delimited JSON, CSV or TSV file you can execute a bulk SQL query using each of the records in that file using the ``sqlite-utils bulk`` command.

The command takes the database file, the SQL to be executed and the file containing records to be used when evaluating the SQL query.

The SQL query should include ``:named`` parameters that match the keys in the records.

For example, given a ``chickens.csv`` CSV file containing the following:

.. code-block::

    id,name
    1,Blue
    2,Snowy
    3,Azi
    4,Lila
    5,Suna
    6,Cardi

You could insert those rows into a pre-created ``chickens`` table like so:

.. code-block:: bash

    sqlite-utils bulk chickens.db \
      'insert into chickens (id, name) values (:id, :name)' \
      chickens.csv --csv

This command takes the same options as the ``sqlite-utils insert`` command - so it defaults to expecting JSON but can accept other formats using ``--csv`` or ``--tsv`` or ``--nl`` or other options described above.

By default all of the SQL queries will be executed in a single transaction. To commit every 20 records, use ``--batch-size 20``.

.. _cli_insert_files:

Inserting data from files
=========================

The ``insert-files`` command can be used to insert the content of files, along with their metadata, into a SQLite table.

Here's an example that inserts all of the GIF files in the current directory into a ``gifs.db`` database, placing the file contents in an ``images`` table:

.. code-block:: bash

    sqlite-utils insert-files gifs.db images *.gif

You can also pass one or more directories, in which case every file in those directories will be added recursively:

.. code-block:: bash

    sqlite-utils insert-files gifs.db images path/to/my-gifs

By default this command will create a table with the following schema:

.. code-block:: sql

    CREATE TABLE "images" (
        "path" TEXT PRIMARY KEY,
        "content" BLOB,
        "size" INTEGER
    );

Content will be treated as binary by default and stored in a ``BLOB`` column. You can use the ``--text`` option to store that content in a ``TEXT`` column instead.

You can customize the schema using one or more ``-c`` options. For a table schema that includes just the path, MD5 hash and last modification time of the file, you would use this:

.. code-block:: bash

    sqlite-utils insert-files gifs.db images *.gif -c path -c md5 -c mtime --pk=path

This will result in the following schema:

.. code-block:: sql

    CREATE TABLE "images" (
        "path" TEXT PRIMARY KEY,
        "md5" TEXT,
        "mtime" REAL
    );

Note that there's no ``content`` column here at all - if you specify custom columns using ``-c`` you need to include ``-c content`` to create that column.

You can change the name of one of these columns using a ``-c colname:coldef`` parameter. To rename the ``mtime`` column to ``last_modified`` you would use this:

.. code-block:: bash

    sqlite-utils insert-files gifs.db images *.gif \
        -c path -c md5 -c last_modified:mtime --pk=path

You can pass ``--replace`` or ``--upsert`` to indicate what should happen if you try to insert a file with an existing primary key. Pass ``--alter`` to cause any missing columns to be added to the table.

The full list of column definitions you can use is as follows:

``name``
    The name of the file, e.g. ``cleo.jpg``
``path``
    The path to the file relative to the root folder, e.g. ``pictures/cleo.jpg``
``fullpath``
    The fully resolved path to the image, e.g. ``/home/simonw/pictures/cleo.jpg``
``sha256``
    The SHA256 hash of the file contents
``md5``
    The MD5 hash of the file contents
``mode``
    The permission bits of the file, as an integer - you may want to convert this to octal
``content``
    The binary file contents, which will be stored as a BLOB
``content_text``
    The text file contents, which will be stored as TEXT
``mtime``
    The modification time of the file, as floating point seconds since the Unix epoch
``ctime``
    The creation time of the file, as floating point seconds since the Unix epoch
``mtime_int``
    The modification time as an integer rather than a float
``ctime_int``
    The creation time as an integer rather than a float
``mtime_iso``
    The modification time as an ISO timestamp, e.g. ``2020-07-27T04:24:06.654246``
``ctime_iso``
    The creation time is an ISO timestamp
``size``
    The integer size of the file in bytes
``stem``
    The filename without the extension - for ``file.txt.gz`` this would be ``file.txt``
``suffix``
    The file extension - for ``file.txt.gz`` this would be ``.gz``

You can insert data piped from standard input like this:

.. code-block:: bash

    cat dog.jpg | sqlite-utils insert-files dogs.db pics - --name=dog.jpg

The ``-`` argument indicates data should be read from standard input. The string passed using the ``--name`` option will be used for the file name and path values.

When inserting data from standard input only the following column definitions are supported: ``name``, ``path``, ``content``, ``content_text``, ``sha256``, ``md5`` and ``size``.

.. _cli_convert:

Converting data in columns
==========================

The ``convert`` command can be used to transform the data in a specified column - for example to parse a date string into an ISO timestamp, or to split a string of tags into a JSON array.

The command accepts a database, table, one or more columns and a string of Python code to be executed against the values from those columns. The following example would replace the values in the ``headline`` column in the ``articles`` table with an upper-case version:

.. code-block:: bash

    sqlite-utils convert content.db articles headline 'value.upper()'

The Python code is passed as a string. Within that Python code the ``value`` variable will be the value of the current column.

The code you provide will be compiled into a function that takes ``value`` as a single argument. If you break your function body into multiple lines the last line should be a ``return`` statement:

.. code-block:: bash

    sqlite-utils convert content.db articles headline '
    value = str(value)
    return value.upper()'

Your code will be automatically wrapped in a function, but you can also define a function called ``convert(value)`` which will be called, if available:

.. code-block:: bash

    sqlite-utils convert content.db articles headline '
    def convert(value):
        return value.upper()'

Use a ``CODE`` value of ``-`` to read from standard input:

.. code-block:: bash

    cat mycode.py | sqlite-utils convert content.db articles headline -

Where ``mycode.py`` contains a fragment of Python code that looks like this:

.. code-block:: python

    def convert(value):
        return value.upper()

The conversion will be applied to every row in the specified table. You can limit that to just rows that match a ``WHERE`` clause using ``--where``:

.. code-block:: bash

    sqlite-utils convert content.db articles headline 'value.upper()' \
        --where "headline like '%cat%'"

You can include named parameters in your where clause and populate them using one or more ``--param`` options:

.. code-block:: bash

    sqlite-utils convert content.db articles headline 'value.upper()' \
        --where "headline like :query" \
        --param query '%cat%'

The ``--dry-run`` option will output a preview of the conversion against the first ten rows, without modifying the database.

.. note::
    In Python: :ref:`table.convert() <python_api_convert>`  CLI reference: :ref:`sqlite-utils convert <cli_ref_convert>`

.. _cli_convert_import:

Importing additional modules
----------------------------

You can specify Python modules that should be imported and made available to your code using one or more ``--import`` options. This example uses the ``textwrap`` module to wrap the ``content`` column at 100 characters:

.. code-block:: bash

    sqlite-utils convert content.db articles content \
        '"\n".join(textwrap.wrap(value, 100))' \
        --import=textwrap

This supports nested imports as well, for example to use `ElementTree <https://docs.python.org/3/library/xml.etree.elementtree.html>`__:

.. code-block:: bash

    sqlite-utils convert content.db articles content \
        'xml.etree.ElementTree.fromstring(value).attrib["title"]' \
        --import=xml.etree.ElementTree

.. _cli_convert_debugger:

Using the debugger
------------------

If an error occurs while running your conversion operation you may see a message like this::

    user-defined function raised exception

Add the ``--pdb`` option to catch the error and open the Python debugger at that point. The conversion operation will exit after you type ``q`` in the debugger.

Here's an example debugging session. First, create a ``articles`` table with invalid XML in the ``content`` column:

.. code-block:: bash

    echo '{"content": "This is not XML"}' | sqlite-utils insert content.db articles -

Now run the conversion with the ``--pdb`` option:

.. code-block:: bash

    sqlite-utils convert content.db articles content \
        'xml.etree.ElementTree.fromstring(value).attrib["title"]' \
        --import=xml.etree.ElementTree \
        --pdb

When the error occurs the debugger will open::

    Exception raised, dropping into pdb...: syntax error: line 1, column 0
    > .../python3.11/xml/etree/ElementTree.py(1338)XML()
    -> parser.feed(text)
    (Pdb) args
    text = 'This is not XML'
    parser = <xml.etree.ElementTree.XMLParser object at 0x102c405e0>
    (Pdb) q

``args`` here shows the arguments to the current function in the stack. The Python `pdb documentation <https://docs.python.org/3/library/pdb.html#debugger-commands>`__ has full details on the other available commands.

.. _cli_convert_complex:

Defining a convert() function
-----------------------------

Instead of providing a single line of code to be executed against each value, you can define a function called ``convert(value)``.

This mechanism can be used to execute one-off initialization code that runs once at the start of the conversion run.

The following example adds a new ``score`` column, then updates it to list a random number - after first seeding the random number generator to ensure that multiple runs produce the same results:

.. code-block:: bash

    sqlite-utils add-column content.db articles score float --not-null-default 1.0
    sqlite-utils convert content.db articles score '
    import random
    random.seed(10)

    def convert(value):
        return random.random()
    '

.. _cli_convert_recipes:

sqlite-utils convert recipes
----------------------------

Various built-in recipe functions are available for common operations. These are:

``r.jsonsplit(value, delimiter=',', type=<class 'str'>)``
  Convert a string like ``a,b,c`` into a JSON array ``["a", "b", "c"]``

  The ``delimiter`` parameter can be used to specify a different delimiter.

  The ``type`` parameter can be set to ``float`` or ``int`` to produce a JSON array of different types, for example if the column's string value was ``1.2,3,4.5`` the following::

      r.jsonsplit(value, type=float)

  Would produce an array like this: ``[1.2, 3.0, 4.5]``

``r.parsedate(value, dayfirst=False, yearfirst=False, errors=None)``
  Parse a date and convert it to ISO date format: ``yyyy-mm-dd``

  In the case of dates such as ``03/04/05`` U.S. ``MM/DD/YY`` format is assumed - you can use ``dayfirst=True`` or ``yearfirst=True`` to change how these ambiguous dates are interpreted.

  Use the ``errors=`` parameter to specify what should happen if a value cannot be parsed.

  By default, if any value cannot be parsed an error will be occurred and all values will be left as they were.

  Set ``errors=r.IGNORE`` to ignore any values that cannot be parsed, leaving them unchanged.

  Set ``errors=r.SET_NULL`` to set any values that cannot be parsed to ``null``.

``r.parsedatetime(value, dayfirst=False, yearfirst=False, errors=None)``
  Parse a datetime and convert it to ISO datetime format: ``yyyy-mm-ddTHH:MM:SS``

These recipes can be used in the code passed to ``sqlite-utils convert`` like this:

.. code-block:: bash

    sqlite-utils convert my.db mytable mycolumn \
      'r.jsonsplit(value)'

You can also pass the recipe function directly without the ``(value)`` part - sqlite-utils will detect that it is a callable and use it automatically:

.. code-block:: bash

    sqlite-utils convert my.db mytable mycolumn r.parsedate

This shorter syntax works for any callable, including functions from imported modules:

.. code-block:: bash

    sqlite-utils convert my.db mytable mycolumn json.loads --import json

To use any of the documented parameters, use the full function call syntax:

.. code-block:: bash

    sqlite-utils convert my.db mytable mycolumn \
      'r.jsonsplit(value, delimiter=":")'

.. _cli_convert_output:

Saving the result to a different column
---------------------------------------

The ``--output`` and ``--output-type`` options can be used to save the result of the conversion to a separate column, which will be created if that column does not already exist:

.. code-block:: bash

    sqlite-utils convert content.db articles headline 'value.upper()' \
      --output headline_upper

The type of the created column defaults to ``text``, but a different column type can be specified using ``--output-type``. This example will create a new floating point column called ``id_as_a_float`` with a copy of each item's ID increased by 0.5:

.. code-block:: bash

    sqlite-utils convert content.db articles id 'float(value) + 0.5' \
      --output id_as_a_float \
      --output-type float

You can drop the original column at the end of the operation by adding ``--drop``.

.. _cli_convert_multi:

Converting a column into multiple columns
-----------------------------------------

Sometimes you may wish to convert a single column into multiple derived columns. For example, you may have a ``location`` column containing ``latitude,longitude`` values which you wish to split out into separate ``latitude`` and ``longitude`` columns.

You can achieve this using the ``--multi`` option to ``sqlite-utils convert``. This option expects your Python code to return a Python dictionary: new columns well be created and populated for each of the keys in that dictionary.

For the ``latitude,longitude`` example you would use the following:

.. code-block:: bash

    sqlite-utils convert demo.db places location \
    'bits = value.split(",")
    return {
      "latitude": float(bits[0]),
      "longitude": float(bits[1]),
    }' --multi

The type of the returned values will be taken into account when creating the new columns. In this example, the resulting database schema will look like this:

.. code-block:: sql

    CREATE TABLE "places" (
        "location" TEXT,
        "latitude" REAL,
        "longitude" REAL
    );

The code function can also return ``None``, in which case its output will be ignored. You can drop the original column at the end of the operation by adding ``--drop``.

.. _cli_create_table:

Creating tables
===============

Most of the time creating tables by inserting example data is the quickest approach. If you need to create an empty table in advance of inserting data you can do so using the ``create-table`` command:

.. code-block:: bash

    sqlite-utils create-table mydb.db mytable id integer name text --pk=id

This will create a table called ``mytable`` with two columns - an integer ``id`` column and a text ``name`` column. It will set the ``id`` column to be the primary key.

You can pass as many column-name column-type pairs as you like. Valid types are ``integer``, ``text``, ``float`` and ``blob``.

Pass ``--pk`` more than once for a compound primary key that covers multiple columns.

You can specify columns that should be NOT NULL using ``--not-null colname``. You can specify default values for columns using ``--default colname defaultvalue``.

.. code-block:: bash

    sqlite-utils create-table mydb.db mytable \
        id integer \
        name text \
        age integer \
        is_good integer \
        --not-null name \
        --not-null age \
        --default is_good 1 \
        --pk=id

.. code-block:: bash

    sqlite-utils tables mydb.db --schema -t

.. code-block:: output

    table    schema
    -------  --------------------------------
    mytable  CREATE TABLE "mytable" (
                "id" INTEGER PRIMARY KEY,
                "name" TEXT NOT NULL,
                "age" INTEGER NOT NULL,
                "is_good" INTEGER DEFAULT '1'
            )

You can specify foreign key relationships between the tables you are creating using ``--fk colname othertable othercolumn``:

.. code-block:: bash

    sqlite-utils create-table books.db authors \
        id integer \
        name text \
        --pk=id

    sqlite-utils create-table books.db books \
        id integer \
        title text \
        author_id integer \
        --pk=id \
        --fk author_id authors id

.. code-block:: bash

    sqlite-utils tables books.db --schema -t

.. code-block:: output

    table    schema
    -------  -------------------------------------------------
    authors  CREATE TABLE "authors" (
                "id" INTEGER PRIMARY KEY,
                "name" TEXT
             )
    books    CREATE TABLE "books" (
                "id" INTEGER PRIMARY KEY,
                "title" TEXT,
                "author_id" INTEGER REFERENCES "authors"("id")
             )

You can create a table in `SQLite STRICT mode <https://www.sqlite.org/stricttables.html>`__ using ``--strict``:

.. code-block:: bash

   sqlite-utils create-table mydb.db mytable id integer name text --strict

Use the ``any`` type for a strict column that should accept integers, floating point values, text, binary data or null without coercion:

.. code-block:: bash

   sqlite-utils create-table events.db events id integer payload any --strict

.. code-block:: bash

   sqlite-utils tables mydb.db --schema -t

.. code-block:: output

   table    schema
   -------  ------------------------
   mytable  CREATE TABLE "mytable" (
               "id" INTEGER,
               "name" TEXT
            ) STRICT

If a table with the same name already exists, you will get an error. You can choose to silently ignore this error with ``--ignore``, or you can replace the existing table with a new, empty table using ``--replace``.

You can also pass ``--transform`` to transform the existing table to match the new schema. See :ref:`python_api_explicit_create` in the Python library documentation for details of how this option works.

.. note::
    In Python: :ref:`table.create() <python_api_explicit_create>`  CLI reference: :ref:`sqlite-utils create-table <cli_ref_create_table>`

.. _cli_renaming_tables:

Renaming a table
================

Yo ucan rename a table using the ``rename-table`` command:

.. code-block:: bash

    sqlite-utils rename-table mydb.db oldname newname

Pass ``--ignore`` to ignore any errors caused by the table not existing, or the new name already being in use.

.. note::
    In Python: :ref:`db.rename_table() <python_api_rename_table>`  CLI reference: :ref:`sqlite-utils rename-table <cli_ref_rename_table>`

.. _cli_duplicate_table:

Duplicating tables
==================

The ``duplicate`` command duplicates a table - creating a new table with the same schema and a copy of all of the rows:

.. code-block:: bash

    sqlite-utils duplicate books.db authors authors_copy

.. note::
    In Python: :ref:`table.duplicate() <python_api_duplicate>`  CLI reference: :ref:`sqlite-utils duplicate <cli_ref_duplicate>`

.. _cli_drop_table:

Dropping tables
===============

You can drop a table using the ``drop-table`` command:

.. code-block:: bash

    sqlite-utils drop-table mydb.db mytable

Use ``--ignore`` to ignore the error if the table does not exist.

.. note::
    In Python: :ref:`table.drop() <python_api_drop>`  CLI reference: :ref:`sqlite-utils drop-table <cli_ref_drop_table>`

.. _cli_transform_table:

Transforming tables
===================

The ``transform`` command allows you to apply complex transformations to a table that cannot be implemented using a regular SQLite ``ALTER TABLE`` command. See :ref:`python_api_transform` for details of how this works. By default, the ``transform`` command preserves a table's ``STRICT`` mode.

.. code-block:: bash

    sqlite-utils transform mydb.db mytable \
        --drop column1 \
        --rename column2 column_renamed

Every option for this table (with the exception of ``--pk-none``) can be specified multiple times. The options are as follows:

``--type column-name new-type``
    Change the type of the specified column. Valid types are ``integer``, ``text``, ``float``, ``real``, ``blob`` and ``any``. Changing a ``TEXT`` column to ``INTEGER``, ``FLOAT`` or ``REAL`` converts exact empty-string values to ``NULL``.

``--drop column-name``
    Drop the specified column.

``--rename column-name new-name``
    Rename this column to a new name.

``--column-order column``
    Use this multiple times to specify a new order for your columns. ``-o`` shortcut is also available.

``--not-null column-name``
    Set this column as ``NOT NULL``.

``--not-null-false column-name``
    For a column that is currently set as ``NOT NULL``, remove the ``NOT NULL``.

``--pk column-name``
    Change the primary key column for this table. Pass ``--pk`` multiple times if you want to create a compound primary key.

``--pk-none``
    Remove the primary key from this table, turning it into a ``rowid`` table.

``--default column-name value``
    Set the default value of this column.

``--default-none column``
    Remove the default value for this column.

``--drop-foreign-key column``
    Drop the specified foreign key.

``--add-foreign-key column other_table other_column``
    Add a foreign key constraint to ``column`` pointing to ``other_table.other_column``.

``--strict``
    Convert the table to a `SQLite STRICT table <https://www.sqlite.org/stricttables.html>`__. The command fails if the available SQLite version does not support strict tables. If existing rows contain values that are incompatible with their declared column types the transformation fails and the original table is left unchanged.

``--no-strict``
    Convert a strict table back to a regular non-strict table.

If you want to see the SQL that will be executed to make the change without actually executing it, add the ``--sql`` flag. For example:

.. code-block:: bash

    sqlite-utils transform fixtures.db roadside_attractions \
        --rename pk id \
        --default name Untitled \
        --column-order id \
        --column-order longitude \
        --column-order latitude \
        --drop address \
        --sql

.. code-block:: output

    CREATE TABLE "roadside_attractions_new_4033a60276b9" (
       "id" INTEGER PRIMARY KEY,
       "longitude" FLOAT,
       "latitude" FLOAT,
       "name" TEXT DEFAULT 'Untitled'
    );
    INSERT INTO "roadside_attractions_new_4033a60276b9" ("longitude", "latitude", "id", "name")
       SELECT "longitude", "latitude", "pk", "name" FROM "roadside_attractions";
    DROP TABLE "roadside_attractions";
    PRAGMA legacy_alter_table=ON;
    ALTER TABLE "roadside_attractions_new_4033a60276b9" RENAME TO "roadside_attractions";
    PRAGMA legacy_alter_table=OFF;

Tables that are referenced by views can be transformed - the view definitions are left unchanged, see :ref:`python_api_transform_views` for details.

.. note::
    In Python: :ref:`table.transform() <python_api_transform>`  CLI reference: :ref:`sqlite-utils transform <cli_ref_transform>`

.. _cli_transform_table_add_primary_key_to_rowid:

Adding a primary key to a rowid table
-------------------------------------

SQLite tables that are created without an explicit primary key are created as `rowid tables <https://www.sqlite.org/rowidtable.html>`__. They still have a numeric primary key which is available in the ``rowid`` column, but that column is not included in the output of ``select *``. Here's an example:

.. code-block:: bash

    echo '[{"name": "Azi"}, {"name": "Suna"}]' | \
        sqlite-utils insert chickens.db chickens -
    sqlite-utils schema chickens.db

.. code-block:: output

    CREATE TABLE "chickens" (
       "name" TEXT
    );

.. code-block:: bash

    sqlite-utils chickens.db 'select * from chickens'

.. code-block:: output

    [{"name": "Azi"},
     {"name": "Suna"}]

.. code-block:: bash

    sqlite-utils chickens.db 'select rowid, * from chickens'

.. code-block:: output

    [{"rowid": 1, "name": "Azi"},
     {"rowid": 2, "name": "Suna"}]

You can use ``sqlite-utils transform ... --pk id`` to add a primary key column called ``id`` to the table. The primary key will be created as an ``INTEGER PRIMARY KEY`` and the existing ``rowid`` values will be copied across to it. It will automatically increment as new rows are added to the table:

.. code-block:: bash

    sqlite-utils transform chickens.db chickens --pk id

.. code-block:: bash

    sqlite-utils schema chickens.db

.. code-block:: output

    CREATE TABLE "chickens" (
       "id" INTEGER PRIMARY KEY,
       "name" TEXT
    );

.. code-block:: bash

    sqlite-utils chickens.db 'select * from chickens'

.. code-block:: output

    [{"id": 1, "name": "Azi"},
     {"id": 2, "name": "Suna"}]

.. code-block:: bash

    echo '{"name": "Cardi"}' | sqlite-utils insert chickens.db chickens -

.. code-block:: bash

    sqlite-utils chickens.db 'select * from chickens'

.. code-block:: output

    [{"id": 1, "name": "Azi"},
     {"id": 2, "name": "Suna"},
     {"id": 3, "name": "Cardi"}]

.. _cli_extract:

Extracting columns into a separate table
========================================

The ``sqlite-utils extract`` command can be used to extract specified columns into a separate table.

Take a look at the Python API documentation for :ref:`python_api_extract` for a detailed description of how this works, including examples of table schemas before and after running an extraction operation.

Rows where every extracted column is ``null`` are not extracted - those rows get a ``null`` value in their new foreign key column and no record is created for them in the lookup table.

The command takes a database, table and one or more columns that should be extracted. To extract the ``species`` column from the ``trees`` table you would run:

.. code-block:: bash

    sqlite-utils extract my.db trees species

This would produce the following schema:

.. code-block:: sql

    CREATE TABLE "trees" (
        "id" INTEGER PRIMARY KEY,
        "TreeAddress" TEXT,
        "species_id" INTEGER,
        FOREIGN KEY(species_id) REFERENCES species(id)
    );
    CREATE TABLE "species" (
        "id" INTEGER PRIMARY KEY,
        "species" TEXT
    );
    CREATE UNIQUE INDEX "idx_species_species"
        ON "species" ("species");

The command takes the following options:

``--table TEXT``
    The name of the lookup to extract columns to. This defaults to using the name of the columns that are being extracted.

``--fk-column TEXT``
    The name of the foreign key column to add to the table. Defaults to ``columnname_id``.

``--rename <TEXT TEXT>``
    Use this option to rename the columns created in the new lookup table.

``--silent``
    Don't display the progress bar.

Here's a more complex example that makes use of these options. It converts `this CSV file <https://github.com/wri/global-power-plant-database/blob/232a666653e14d803ab02717efc01cdd437e7601/output_database/global_power_plant_database.csv>`__ full of global power plants into SQLite, then extracts the ``country`` and ``country_long`` columns into a separate ``countries`` table:

.. code-block:: bash

    wget 'https://github.com/wri/global-power-plant-database/blob/232a6666/output_database/global_power_plant_database.csv?raw=true'
    sqlite-utils insert global.db power_plants \
        'global_power_plant_database.csv?raw=true' --csv
    # Extract those columns:
    sqlite-utils extract global.db power_plants country country_long \
        --table countries \
        --fk-column country_id \
        --rename country_long name

After running the above, the command ``sqlite-utils schema global.db`` reveals the following schema:

.. code-block:: sql

    CREATE TABLE "countries" (
       "id" INTEGER PRIMARY KEY,
       "country" TEXT,
       "name" TEXT
    );
    CREATE TABLE "power_plants" (
       "country_id" INTEGER,
       "name" TEXT,
       "gppd_idnr" TEXT,
       "capacity_mw" TEXT,
       "latitude" TEXT,
       "longitude" TEXT,
       "primary_fuel" TEXT,
       "other_fuel1" TEXT,
       "other_fuel2" TEXT,
       "other_fuel3" TEXT,
       "commissioning_year" TEXT,
       "owner" TEXT,
       "source" TEXT,
       "url" TEXT,
       "geolocation_source" TEXT,
       "wepp_id" TEXT,
       "year_of_capacity_data" TEXT,
       "generation_gwh_2013" TEXT,
       "generation_gwh_2014" TEXT,
       "generation_gwh_2015" TEXT,
       "generation_gwh_2016" TEXT,
       "generation_gwh_2017" TEXT,
       "generation_data_source" TEXT,
       "estimated_generation_gwh" TEXT,
       FOREIGN KEY("country_id") REFERENCES "countries"("id")
    );
    CREATE UNIQUE INDEX "idx_countries_country_name"
        ON "countries" ("country", "name");

.. note::
    In Python: :ref:`table.extract() <python_api_extract>`  CLI reference: :ref:`sqlite-utils extract <cli_ref_extract>`

.. _cli_create_view:

Creating views
==============

You can create a view using the ``create-view`` command:

.. code-block:: bash

    sqlite-utils create-view mydb.db version "select sqlite_version()"

.. code-block:: bash

    sqlite-utils mydb.db "select * from version"

.. code-block:: output

    [{"sqlite_version()": "3.31.1"}]

Use ``--replace`` to replace an existing view of the same name, and ``--ignore`` to do nothing if a view already exists.

.. note::
    In Python: :ref:`db.create_view() <python_api_create_view>`  CLI reference: :ref:`sqlite-utils create-view <cli_ref_create_view>`

.. _cli_drop_view:

Dropping views
==============

You can drop a view using the ``drop-view`` command:

.. code-block:: bash

    sqlite-utils drop-view myview

Use ``--ignore`` to ignore the error if the view does not exist.

.. note::
    In Python: :ref:`view.drop() <python_api_drop>`  CLI reference: :ref:`sqlite-utils drop-view <cli_ref_drop_view>`

.. _cli_add_column:

Adding columns
==============

You can add a column using the ``add-column`` command:

.. code-block:: bash

    sqlite-utils add-column mydb.db mytable nameofcolumn text

The last argument here is the type of the column to be created. This can be one of:

- ``text`` or ``str``
- ``integer`` or ``int``
- ``float``
- ``blob`` or ``bytes``

This argument is optional and defaults to ``text``.

You can add a column that is a foreign key reference to another table using the ``--fk`` option:

.. code-block:: bash

    sqlite-utils add-column mydb.db dogs 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``:

.. code-block:: bash

    sqlite-utils add-column mydb.db dogs species_id --fk species --fk-col ref

You can set a ``NOT NULL DEFAULT 'x'`` constraint on the new column using ``--not-null-default``:

.. code-block:: bash

    sqlite-utils add-column mydb.db dogs friends_count integer --not-null-default 0

.. note::
    In Python: :ref:`table.add_column() <python_api_add_column>`  CLI reference: :ref:`sqlite-utils add-column <cli_ref_add_column>`

.. _cli_add_column_alter:

Adding columns automatically on insert/update
=============================================

You can use the ``--alter`` option to automatically add new columns if the data you are inserting or upserting is of a different shape:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs new-dogs.json --pk=id --alter

.. note::
    In Python: :ref:`table.insert(..., alter=True) <python_api_add_column_alter>`

.. _cli_add_foreign_key:

Adding foreign key constraints
==============================

The ``add-foreign-key`` command can be used to add new foreign key references to an existing table - something which SQLite's ``ALTER TABLE`` command does not support.

To add a foreign key constraint pointing the ``books.author_id`` column to ``authors.id`` in another table, do this:

.. code-block:: bash

    sqlite-utils add-foreign-key books.db books author_id authors id

If you omit the other table and other column references ``sqlite-utils`` will attempt to guess them - so the above example could instead look like this:

.. code-block:: bash

    sqlite-utils add-foreign-key books.db books author_id

Add ``--ignore`` to ignore an existing foreign key (as opposed to returning an error):

.. code-block:: bash

    sqlite-utils add-foreign-key books.db books author_id --ignore

See :ref:`python_api_add_foreign_key` in the Python API documentation for further details, including how the automatic table guessing mechanism works.

.. note::
    In Python: :ref:`table.add_foreign_key() <python_api_add_foreign_key>`  CLI reference: :ref:`sqlite-utils add-foreign-key <cli_ref_add_foreign_key>`

.. _cli_add_foreign_keys:

Adding multiple foreign keys at once
------------------------------------

Adding a foreign key requires a ``VACUUM``. On large databases this can be an expensive operation, so if you are adding multiple foreign keys you can combine them into one operation (and hence one ``VACUUM``) using ``add-foreign-keys``:

.. code-block:: bash

    sqlite-utils add-foreign-keys books.db \
        books author_id authors id \
        authors country_id countries id

When you are using this command each foreign key needs to be defined in full, as four arguments - the table, column, other table and other column.

.. note::
    In Python: :ref:`db.add_foreign_keys() <python_api_add_foreign_keys>`  CLI reference: :ref:`sqlite-utils add-foreign-keys <cli_ref_add_foreign_keys>`

.. _cli_index_foreign_keys:

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:

.. code-block:: bash

    sqlite-utils index-foreign-keys books.db

.. note::
    In Python: :ref:`db.index_foreign_keys() <python_api_index_foreign_keys>`  CLI reference: :ref:`sqlite-utils index-foreign-keys <cli_ref_index_foreign_keys>`

.. _cli_defaults_not_null:

Setting defaults and not null constraints
=========================================

You can use the ``--not-null`` and ``--default`` options (to both ``insert`` and ``upsert``) to specify columns that should be ``NOT NULL`` or to set database defaults for one or more specific columns:

.. code-block:: bash

    sqlite-utils insert dogs.db dogs_with_scores dogs-with-scores.json \
        --not-null=age \
        --not-null=name \
        --default age 2 \
        --default score 5

.. note::
    In Python: :ref:`not_null= and defaults= arguments <python_api_defaults_not_null>`

.. _cli_create_index:

Creating indexes
================

You can add an index to an existing table using the ``create-index`` command:

.. code-block:: bash

    sqlite-utils create-index mydb.db mytable col1 [col2...]

This can be used to create indexes against a single column or multiple columns.

The name of the index will be automatically derived from the table and columns. To specify a different name, use ``--name=name_of_index``.

Use the ``--unique`` option to create a unique index.

Use ``--if-not-exists`` to avoid attempting to create the index if one with that name already exists.

To add an index on a column in descending order, prefix the column with a hyphen. Since this can be confused for a command-line option you need to construct that like this:

.. code-block:: bash

    sqlite-utils create-index mydb.db mytable -- col1 -col2 col3

This will create an index on that table on ``(col1, col2 desc, col3)``.

If your column names are already prefixed with a hyphen you'll need to manually execute a ``CREATE INDEX`` SQL statement to add indexes to them rather than using this tool.

Add the ``--analyze`` option to run ``ANALYZE`` against the index after it has been created.

.. note::
    In Python: :ref:`table.create_index() <python_api_create_index>`  CLI reference: :ref:`sqlite-utils create-index <cli_ref_create_index>`

.. _cli_drop_index:

Dropping indexes
================

You can drop an index from an existing table using the ``drop-index`` command:

.. code-block:: bash

    sqlite-utils drop-index mydb.db mytable idx_mytable_col1

Use ``--ignore`` to ignore the error if the index does not exist on that table.

.. note::
    In Python: :ref:`table.drop_index() <python_api_create_index>`  CLI reference: :ref:`sqlite-utils drop-index <cli_ref_drop_index>`

.. _cli_fts:

Configuring full-text search
============================

You can enable SQLite full-text search on a table and a set of columns like this:

.. code-block:: bash

    sqlite-utils enable-fts mydb.db documents title summary

This will use SQLite's FTS5 module by default. Use ``--fts4`` if you want to use FTS4:

.. code-block:: bash

    sqlite-utils enable-fts mydb.db documents title summary --fts4

The ``enable-fts`` command will populate the new index with all existing documents. If you later add more documents you will need to use ``populate-fts`` to cause them to be indexed as well:

.. code-block:: bash

    sqlite-utils populate-fts mydb.db documents title summary

A better solution here is to use database triggers. You can set up database triggers to automatically update the full-text index using the ``--create-triggers`` option when you first run ``enable-fts``:

.. code-block:: bash

    sqlite-utils enable-fts mydb.db documents title summary --create-triggers

To set a custom FTS tokenizer, e.g. to enable Porter stemming, use ``--tokenize=``:

.. code-block:: bash

    sqlite-utils populate-fts mydb.db documents title summary --tokenize=porter

To remove the FTS tables and triggers you created, use ``disable-fts``:

.. code-block:: bash

    sqlite-utils disable-fts mydb.db documents

To rebuild one or more FTS tables (see :ref:`python_api_fts_rebuild`), use ``rebuild-fts``:

.. code-block:: bash

    sqlite-utils rebuild-fts mydb.db documents

You can rebuild every FTS table by running ``rebuild-fts`` without passing any table names:

.. code-block:: bash

    sqlite-utils rebuild-fts mydb.db

.. note::
    In Python: :ref:`table.enable_fts() <python_api_fts_enable>`  CLI reference: :ref:`sqlite-utils enable-fts <cli_ref_enable_fts>`

.. _cli_search:

Executing searches
==================

Once you have configured full-text search for a table, you can search it using ``sqlite-utils search``:

.. code-block:: bash

    sqlite-utils search mydb.db documents searchterm

This command accepts the same output options as ``sqlite-utils query``: ``--table``, ``--csv``, ``--tsv``, ``--nl`` etc.

By default it shows the most relevant matches first. You can specify a different sort order using the ``-o`` option, which can take a column or a column followed by ``desc``:

.. code-block:: bash

    # Sort by rowid
    sqlite-utils search mydb.db documents searchterm -o rowid
    # Sort by created in descending order
    sqlite-utils search mydb.db documents searchterm -o 'created desc'

SQLite `advanced search syntax <https://www.sqlite.org/fts5.html#full_text_query_syntax>`__ is enabled by default. To run a search with automatic quoting applied to the terms to avoid them being potentially interpreted as advanced search syntax use the ``--quote`` option.

You can specify a subset of columns to be returned using the ``-c`` option one or more times:

.. code-block:: bash

    sqlite-utils search mydb.db documents searchterm -c title -c created

By default all search results will be returned. You can use ``--limit 20`` to return just the first 20 results.

Use the ``--sql`` option to output the SQL that would be executed, rather than running the query:

.. code-block:: bash

    sqlite-utils search mydb.db documents searchterm --sql

.. code-block:: output

    with original as (
        select
            rowid,
            *
        from "documents"
    )
    select
        "original".*
    from
        "original"
        join "documents_fts" on "original".rowid = "documents_fts".rowid
    where
        "documents_fts" match :query
    order by
        "documents_fts".rank

.. note::
    In Python: :ref:`table.search() <python_api_fts_search>`  CLI reference: :ref:`sqlite-utils search <cli_ref_search>`

.. _cli_enable_counts:

Enabling cached counts
======================

``select count(*)`` queries can take a long time against large tables. ``sqlite-utils`` can speed these up by adding triggers to maintain a ``_counts`` table, see :ref:`python_api_cached_table_counts` for details.

The ``sqlite-utils enable-counts`` command can be used to configure these triggers, either for every table in the database or for specific tables.

.. code-block:: bash

    # Configure triggers for every table in the database
    sqlite-utils enable-counts mydb.db

    # Configure triggers just for specific tables
    sqlite-utils enable-counts mydb.db table1 table2

If the ``_counts`` table ever becomes out-of-sync with the actual table counts you can repair it using the ``reset-counts`` command:

.. code-block:: bash

    sqlite-utils reset-counts mydb.db

.. note::
    In Python: :ref:`table.enable_counts() <python_api_cached_table_counts>`  CLI reference: :ref:`sqlite-utils enable-counts <cli_ref_enable_counts>`

.. _cli_analyze:

Optimizing index usage with ANALYZE
===================================

The `SQLite ANALYZE command <https://www.sqlite.org/lang_analyze.html>`__ builds a table of statistics which the query planner can use to make better decisions about which indexes to use for a given query.

You should run ``ANALYZE`` if your database is large and you do not think your indexes are being efficiently used.

To run ``ANALYZE`` against every index in a database, use this:

.. code-block:: bash

    sqlite-utils analyze mydb.db

You can run it against specific tables, or against specific named indexes, by passing them as optional arguments:

.. code-block:: bash

    sqlite-utils analyze mydb.db mytable idx_mytable_name

You can also run ``ANALYZE`` as part of another command using the ``--analyze`` option. This is supported by the ``create-index``, ``insert`` and ``upsert`` commands.

.. note::
    In Python: :ref:`db.analyze() <python_api_analyze>`  CLI reference: :ref:`sqlite-utils analyze <cli_ref_analyze>`

.. _cli_vacuum:

Vacuum
======

You can run VACUUM to optimize your database like so:

.. code-block:: bash

    sqlite-utils vacuum mydb.db

.. note::
    In Python: :ref:`db.vacuum() <python_api_vacuum>`  CLI reference: :ref:`sqlite-utils vacuum <cli_ref_vacuum>`

.. _cli_optimize:

Optimize
========

The optimize command can dramatically reduce the size of your database if you are using SQLite full-text search. It runs OPTIMIZE against all of your FTS4 and FTS5 tables, then runs VACUUM.

If you just want to run OPTIMIZE without the VACUUM, use the ``--no-vacuum`` flag.

.. code-block:: bash

    # Optimize all FTS tables and then VACUUM
    sqlite-utils optimize mydb.db

    # Optimize but skip the VACUUM
    sqlite-utils optimize --no-vacuum mydb.db

To optimize specific tables rather than every FTS table, pass those tables as extra arguments:

.. code-block:: bash

    sqlite-utils optimize mydb.db table_1 table_2

.. note::
    In Python: :ref:`table.optimize() <python_api_fts_optimize>`  CLI reference: :ref:`sqlite-utils optimize <cli_ref_optimize>`

.. _cli_wal:

WAL mode
========

You can enable `Write-Ahead Logging <https://www.sqlite.org/wal.html>`__ for a database file using the ``enable-wal`` command:

.. code-block:: bash

    sqlite-utils enable-wal mydb.db

You can disable WAL mode using ``disable-wal``:

.. code-block:: bash

    sqlite-utils disable-wal mydb.db

Both of these commands accept one or more database files as arguments.

.. note::
    In Python: :ref:`db.enable_wal() and db.disable_wal() <python_api_wal>`  CLI reference: :ref:`sqlite-utils enable-wal <cli_ref_enable_wal>`

.. _cli_dump:

Dumping the database to SQL
===========================

The ``dump`` command outputs a SQL dump of the schema and full contents of the specified database file:

.. code-block:: bash

    sqlite-utils dump mydb.db
    BEGIN TRANSACTION;
    CREATE TABLE ...
    ...
    COMMIT;

.. note::
    In Python: :ref:`db.iterdump() <python_api_itedump>`  CLI reference: :ref:`sqlite-utils dump <cli_ref_dump>`

.. _cli_load_extension:

Loading SQLite extensions
=========================

Many of these commands have the ability to load additional SQLite extensions using the ``--load-extension=/path/to/extension`` option - use ``--help`` to check for support, e.g. ``sqlite-utils rows --help``.

This option can be applied multiple times to load multiple extensions.

Since `SpatiaLite <https://www.gaia-gis.it/fossil/libspatialite/index>`__ is commonly used with SQLite, the value ``spatialite`` is special: it will search for SpatiaLite in the most common installation locations, saving you from needing to remember exactly where that module is located:

.. code-block:: bash

    sqlite-utils memory "select spatialite_version()" --load-extension=spatialite

.. code-block:: output

    [{"spatialite_version()": "4.3.0a"}]

.. _cli_spatialite:

SpatiaLite helpers
==================

`SpatiaLite <https://www.gaia-gis.it/fossil/libspatialite/home>`_ adds geographic capability to SQLite (similar to how PostGIS builds on PostgreSQL). The `SpatiaLite cookbook <http://www.gaia-gis.it/gaia-sins/spatialite-cookbook-5/index.html>`__ is a good resource for learning what's possible with it.

You can convert an existing table to a geographic table by adding a geometry column, use the ``sqlite-utils add-geometry-column`` command:

.. code-block:: bash

    sqlite-utils add-geometry-column spatial.db locations geometry --type POLYGON --srid 4326

The table (``locations`` in the example above) must already exist before adding a geometry column. Use ``sqlite-utils create-table`` first, then ``add-geometry-column``.

Use the ``--type`` option to specify a geometry type. By default, ``add-geometry-column`` uses a generic ``GEOMETRY``, which will work with any type, though it may not be supported by some desktop GIS applications. 

Eight (case-insensitive) types are allowed:

* POINT
* LINESTRING
* POLYGON
* MULTIPOINT
* MULTILINESTRING
* MULTIPOLYGON
* GEOMETRYCOLLECTION
* GEOMETRY

.. note::
    In Python: :ref:`table.add_geometry_column() <python_api_gis_add_geometry_column>`  CLI reference: :ref:`sqlite-utils add-geometry-column <cli_ref_add_geometry_column>`

.. _cli_spatialite_indexes:

Adding spatial indexes
----------------------

Once you have a geometry column, you can speed up bounding box queries by adding a spatial index:

.. code-block:: bash

    sqlite-utils create-spatial-index spatial.db locations geometry

See this `SpatiaLite Cookbook recipe <http://www.gaia-gis.it/gaia-sins/spatialite-cookbook-5/cookbook_topics.03.html#topic_Wonderful_RTree_Spatial_Index>`__ for examples of how to use a spatial index.

.. note::
    In Python: :ref:`table.create_spatial_index() <python_api_gis_create_spatial_index>`  CLI reference: :ref:`sqlite-utils create-spatial-index <cli_ref_create_spatial_index>`

.. _cli_install:

Installing packages
===================

The :ref:`convert command <cli_convert>` and the :ref:`insert -\\-convert <cli_insert_convert>` and :ref:`query -\\-functions <cli_query_functions>` options can be provided with a Python script that imports additional modules from the ``sqlite-utils`` environment.

You can install packages from PyPI directly into the correct environment using ``sqlite-utils install <package>``. This is a wrapper around ``pip install``.

.. code-block:: bash

    sqlite-utils install beautifulsoup4

Use ``-U`` to upgrade an existing package.

.. _cli_uninstall:

Uninstalling packages
=====================

You can uninstall packages that were installed using ``sqlite-utils install`` with ``sqlite-utils uninstall <package>``:

.. code-block:: bash

    sqlite-utils uninstall beautifulsoup4

Use ``-y`` to skip the request for confirmation.
