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SPyQL: SQL with Python in the middle




SQL with Python in the middle

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.. image:: :target: :alt: codecov

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.. image:: :target: :alt: code style: black

.. image:: :target: :alt: license: MIT


.. intro_start

SPyQL is a query language that combines:

  • the simplicity and structure of SQL;
  • with the power and readability of Python.

.. code-block:: sql

SELECT date.fromtimestamp(.purchase_ts) AS purchase_date, .price * .quantity AS total FROM json WHERE .department.upper() == 'IT' ORDER BY 2 DESC TO csv

SQL provides the structure of the query, while Python is used to define expressions, bringing along a vast ecosystem of packages.

SPyQL is fast and memory efficient. Take a look at the benchmarks with GB-size JSON data <>_.

SPyQL CLI ^^^^^^^^^

SPyQL offers a command-line interface that allows running SPyQL queries on top of text data (e.g. CSV, JSON). Data can come from files but also from data streams, such as as Kafka, or from databases such as PostgreSQL. Basically, data can come from any command that outputs text :-). More, data can be generated by a Python expression! And since SPyQL also writes to different formats, it allows to easily convert between data formats.

Take a look at the Command line examples to see how to query parquet, process API calls, transverse directories of zipped JSONs, convert CSV to JSON, and import JSON/CSV data into SQL databases, among many other things.

See also:

  • Tutorial (v0.8) <>_

  • Demo video (v0.4) <>_

SPyQL Module ^^^^^^^^^^^^

SPyQL is also available as a Python module. In addition to the CLI features, you can also:

  • query variables (e.g. lists of dicts);
  • get results into in-memory data structures.

Principles ^^^^^^^^^^

We aim for SPyQL to be:

  • Simple\ : simple to use with a straightforward implementation;
  • Familiar\ : you should feel at home if you are acquainted with SQL and Python;
  • Light\ : small memory footprint that allows you to process large data that fit into your machine;
  • Useful\ : it should make your life easier, filling a gap in the eco-system.

.. intro_end

Distinctive features of SPyQL ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  • Row order guarantee
  • Natural window for aggregations
  • No distinction between aggregate and window functions
  • IMPORT clause
  • Natural support for lists, sets, dictionaries, objects, etc
  • 1-liner by design
  • Multiple data formats supported



"I'm very impressed - this is some very neat pragmatic software design."

Simon Willison, Creator of Datasette, co-creator of Django


"I love this tool! I use it every day"...

Alin Panaitiu, Creator of Lunar


"Brilliant tool, thanks a lot for creating it and for the example here!"

Greg Sadetsky, Co-founder and CTO at Decibel Ads



The official documentation of SPyQL can be found at: <>_.


The easiest way to install SPyQL is from pip:

.. code-block:: sh

pip install spyql

Hello world

.. hello_start

To test your installation run in the terminal:

.. code-block:: sh

spyql "SELECT 'Hello world' as Message TO pretty"


.. code-block::


Hello world

You can try replacing the output format by JSON or CSV, and adding more columns. e.g. run in the terminal:

.. code-block:: sh

spyql "SELECT 'Hello world' as message, 1+2 as three TO json"


.. code-block:: json

{"message": "Hello world", "three": 3}

.. hello_end

.. recipes_start

Example queries

You can run the following example queries in the terminal: spyql "the_query" < a_data_file

Example data files are not provided on most cases.

Query a CSV (and print a pretty table) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT a_col_name, 'positive' if int(col2) >= 0 else 'negative' AS sign FROM csv TO pretty

Convert CSV to a flat JSON ^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT * FROM csv TO json

Convert from CSV to a hierarchical JSON ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT {'client': {'id': col1, 'name': col2}, 'price': 120.40} AS json FROM csv TO json


.. code-block:: sql

SELECT {'id': col1, 'name': col2} AS client, 120.40 AS price FROM csv TO json

JSON to CSV, filtering out NULLs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT AS id, AS name, .price FROM json WHERE is not NULL TO csv

Explode JSON to CSV ^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT .invoice_num AS id, AS name, .items.price AS price FROM json EXPLODE .items TO csv

Sample input:

.. code-block:: json

{"invoice_num" : 1028, "items": [{"name": "tomatoes", "price": 1.5}, {"name": "bananas", "price": 2.0}]} {"invoice_num" : 1029, "items": [{"name": "peaches", "price": 3.12}]}


.. code-block::

id, name, price 1028, tomatoes, 1.5 1028, bananas, 2.0 1029, peaches, 3.12

Python iterator/list/comprehension to JSON ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT 10 * cos(col1 * ((pi * 4) / 90)) FROM range(80) TO json


.. code-block:: sql

SELECT col1 FROM [10 * cos(i * ((pi * 4) / 90)) for i in range(80)] TO json

Importing python modules ^^^^^^^^^^^^^^^^^^^^^^^^

Here we import hashlib to calculate a md5 hash for each input line. Before running this example you need to install the hashlib package (\ pip install hashlib\ ).

.. code-block:: sql

IMPORT hashlib as hl SELECT hl.md5(col1.encode('utf-8')).hexdigest() FROM text

Getting the top 5 records ^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sql

SELECT int(score) AS score, player_name FROM csv ORDER BY 1 DESC NULLS LAST, score_date LIMIT 5

Aggregations ^^^^^^^^^^^^

Totals by player, alphabetically ordered.

.. code-block:: sql

SELECT .player_name, sum_agg(.score) AS total_score FROM json GROUP BY 1 ORDER BY 1

Partial aggregations ^^^^^^^^^^^^^^^^^^^^

Calculating the cumulative sum of a variable using the PARTIALS modifier. Also demoing the lag aggregator.

.. code-block:: sql

SELECT PARTIALS .new_entries, sum_agg(.new_entries) AS cum_new_entries, lag(.new_entries) AS prev_entries FROM json TO json

Sample input:

.. code-block:: json

{"new_entries" : 10} {"new_entries" : 5} {"new_entries" : 25} {"new_entries" : null} {} {"new_entries" : 100}


.. code-block:: json

{"new_entries" : 10, "cum_new_entries" : 10, "prev_entries": null} {"new_entries" : 5, "cum_new_entries" : 15, "prev_entries": 10} {"new_entries" : 25, "cum_new_entries" : 40, "prev_entries": 5} {"new_entries" : null, "cum_new_entries" : 40, "prev_entries": 25} {"new_entries" : null, "cum_new_entries" : 40, "prev_entries": null} {"new_entries" : 100, "cum_new_entries" : 140, "prev_entries": null}

If PARTIALS was omitted the result would be equivalent to the last output row.

Distinct rows ^^^^^^^^^^^^^

.. code-block:: sql


Command line examples

To run the following examples, type Ctrl-x Ctrl-e on you terminal. This will open your default editor (emacs/vim). Paste the code of one of the examples, save and exit.

Queries on Parquet with directories ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Here, find transverses a directory and executes parquet-tools for each parquet file, dumping each file to json format. jq -c makes sure that the output has 1 json per line before handing over to spyql. This is far from being an efficient way to query parquet files, but it might be a handy option if you need to do a quick inspection.

.. code-block:: sh

find /the/directory -name "*.parquet" -exec parquet-tools cat --json {} ; | jq -c | spyql " SELECT .a_field, .a_num_field * 2 + 1 FROM json "

Querying multiple json.gz files ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sh

gzcat *.json.gz | jq -c | spyql " SELECT .a_field, .a_num_field * 2 + 1 FROM json "

Querying YAML / XML / TOML files ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

yq <>_ converts yaml, xml and toml files to json, allowing to easily query any of these with spyql.

.. code-block:: sh

cat file.yaml | yq -c | spyql "SELECT .a_field FROM json"

.. code-block:: sh

cat file.xml | xq -c | spyql "SELECT .a_field FROM json"

.. code-block:: sh

cat file.toml | tomlq -c | spyql "SELECT .a_field FROM json"

Kafka to PostegreSQL pipeline ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Read data from a kafka topic and write to postgres table name customer.

.. code-block:: sh

kafkacat -b -t the.topic | spyql -Otable=customer -Ochunk_size=1 --unbuffered " SELECT AS id, AS name FROM json TO sql " | psql -U an_user_name -h a_database_name

Monitoring statistics in Kafka ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Read data from a kafka topic, continuously calculating statistics.

.. code-block:: sh

kafkacat -b -t the.topic | spyql --unbuffered " SELECT PARTIALS count_agg(*) AS running_count, sum_agg(value) AS running_sum, min_agg(value) AS min_so_far, value AS current_value FROM json TO csv "

Sub-queries (piping) ^^^^^^^^^^^^^^^^^^^^

A special file format (spy) is used to efficiently pipe data between queries.

.. code-block:: sh

cat a_file.json | spyql " SELECT ' '.join([.first_name, .middle_name, .last_name]) AS full_name FROM json TO spy" | spyql "SELECT full_name, full_name.upper() FROM spy"

(Equi) Joins ^^^^^^^^^^^^^

It is possible to make simple (LEFT) JOIN operations based on dictionary lookups.

Given numbers.json:

.. code-block:: json

{ "1": "One", "2": "Two", "3": "Three" }


.. code-block:: sh

spyql -Jnums=numbers.json " SELECT nums[col1] as res FROM [3,4,1,1] TO json"


.. code-block:: json

{"res": "Three"} {"res": null} {"res": "One"} {"res": "One"}

If you want a INNER JOIN instead of a LEFT JOIN, you can add a criteria to the where clause, e.g.:

.. code-block:: sql

SELECT nums[col1] as res FROM [3,4,1,1] WHERE col1 in nums TO json


.. code-block:: json

{"res": "Three"} {"res": "One"} {"res": "One"}

Queries over APIs ^^^^^^^^^^^^^^^^^

.. code-block:: sh

curl | spyql " SELECT AS email, 'Dear {}, thank you for being a great customer!'.format(.data.first_name) AS msg FROM json EXPLODE .data TO json "

Plotting to the terminal ^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sh

spyql " SELECT col1 FROM [10 * cos(i * ((pi * 4) / 90)) for i in range(80)] TO plot "

Plotting with matplotcli <>_ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: sh

spyql " SELECT col1 AS y FROM [10 * cos(i * ((pi * 4) / 90)) for i in range(80)] TO json " | plt "plot(y)"

.. image:: imgs/matplotcli_demo1.png :width: 600 :alt: matplotcli demo

.. recipes_end

This package was created with Cookiecutter <>_ and the audreyr/cookiecutter-pypackage project template <>_.



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