spyql 0.2.0

SpyQL: SQL with Python in the midle

SpyQL

SQL with Python in the middle

Concept

SpyQL is a query language that combines:

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

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

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

SpyQL command-line tool

With the SpyQL command-line tool you can make SQL-like SELECTs powered by Python on top of text data (e.g. CSV and 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 iterator! Take a look at the examples section to see how to query parquet, process API calls, transverse directories of zipped JSONs, among many other things.

SpyQL also allows you to easily convert between text data formats:

• FROM: CSV, JSON, TEXT and Python iterators (YES, you can use a list comprehension as the data source)

• TO: CSV, JSON, SQL (INSERT statments), pretty terminal printing, and terminal plotting.

The JSON format is JSON lines, where each line has a valid JSON object or array. Piping with jq allows SpyQL to handle any JSON input (more on the examples section).

You can leverage command line tools to process other file types like Parquet and XML (more on the examples section).

Instalation

To install SpyQL, run this command in your terminal:

pip install spyql

Hello world

To test your installation run in the terminal:

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

Output:

Message
-----------
Hello world

Try replacing the output format to json and csv, and try adding more columns. e.g. run in the terminal:

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

Output:

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

Principles

Right now, the focus is on building a command-line tool that follows these core principles:

• Simple: simple to use with a straifghtforward 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

Syntax

[ IMPORT python_module [ AS identifier ] [, ...] ]
SELECT
    [ * | python_expression [ AS output_column_name ] [, ...] ]
    [ FROM csv | spy | text | python_expression | json [ EXPLODE path ] ]
    [ WHERE python_expression ]
    [ ORDER BY output_column_number | python_expression
        [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [, ...] ]
    [ LIMIT row_count ]
    [ OFFSET num_rows_to_skip ]
    [ TO csv | json | spy | sql | pretty | plot ]

Comming next: ORDER BY, GROUP BY, EXECUTE

Notable differences to SQL

In SpyQL:

• there is guarantee that the order of the output rows is the same as in the input
• the AS keyword must precede a column alias definition (it is not optional as in SQL)
• you can always access the nth column by using the default column names colN (e.g. col1 for the first column)
• currently only a small subset of SQL is supported, namely SELECT statements without: sub-queries, joins, set operations, etc (check the Syntax section)
• sub-queries are achieved by piping (see the [Command line examples](#command line examples) section)
• expressions are pure Python:

SQL	SpySQL
x = y	x == y
x BETWEEN a AND b	a <= x <= b
CAST(x AS INTEGER)	int(x)
CASE WHEN x > 0 THEN 1 ELSE -1 END	1 if x > 0 else -1
upper('hello')	'hello'.upper()

Notable differences to Python

Additional syntax

We added additional syntax for making querying easier:

Python	SpySQL shortcut	Purpose
json['hello']['planet earth']	json->hello->'planet earth'	Easy access of elements in dicts (e.g. JSONs)

NULL datatype

Python's None generates exceptions when making operations on missing data, breaking query execution (e.g. None + 1 throws a TypeError). To overcome this, we created a NULL type that has the same behavior as in SQL (e.g. NULL + 1 returns NULL), allowing for queries to continue processing data.

Operation	Native Python throws	SpySQL returns	SpySQL warning
NULL + 1	NameError	NULL
adict['inexisting_key']	KeyError	NULL	yes
int('')	ValueError	NULL	yes
int('abc')	ValueError	NULL	yes

The above dictionary key access only returns NULL if the dict is an instance of NullSafeDict. SpyQL adds NullSafeDict, which extends python's native dict. JSONs are automatically loaded as NullSafeDict. Unless you are creating dictionaries on the fly you do not need to worry about this.

Importing python modules and user-defined functions

By default, spyql always imports some commonly used stuff:

• everything from the math module
• datetime, date and timezone from the datetime module
• the re module

SpyQL queries support a single import statment at the beginning of the query where several modules can be imported (e.g. IMPORT numpy AS np, sys SELECT ...). Note that the python syntax from module import identifier is not supported in queries.

In addition, you can create a python file that is loaded before executing queries. Here you can define imports, functions, variables, etc using regular python code. Everything defined in this file is available to all your spyql queries. The file should be located at XDG_CONFIG_HOME/sqyql/init.py. If the environment variable XDG_CONFIG_HOME is not defined, it defaults to HOME/.config (e.g. /Users/janedoe/.config/spyql/init.py).

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)

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

Convert CSV to a flat JSON

SELECT * FROM csv TO json

Convert from CSV to a hierachical JSON

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

or

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

JSON to CSV, filtering out NULLs

SELECT json->client->id AS id, json->client->name AS name, json->price AS price
FROM json
WHERE json->client->name is not NULL
TO csv

Explode JSON to CSV

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

Sample input:

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

Output:

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

Python iterator/list/comprehension to JSON

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

or

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).

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

Getting the top 5 records

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

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.

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

Querying multiple json.gz files

gzcat *.json.gz |
jq -c |
spyql "
	SELECT json->a_field, json->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:

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

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

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

Kafka to PostegreSQL pipeline

Read data from a kafka topic and write to postgres.

NOTE: sql options not yet implemented, i.e. output table name and number of records per insert are hardcoded

kafkacat -b the.broker.com -t the.topic |
spyql "
	SELECT
		json->customer->id AS id,
		json->customer->name AS name
	FROM json
	TO sql('customer_table_name', chunk_size=1)
" |
psql -U an_user_name -h a.host.com a_database_name

Sub-queries (piping)

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

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

Queries over APIs

curl https://reqres.in/api/users?page=2 |
spyql "
	SELECT
		json->data->email AS email,
		'Dear {}, thank you for being a great customer!'.format(json->data->first_name) AS msg
	FROM json
	EXPLODE json->data
	TO json
"

Plotting to the terminal

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

Plotting with gnuplot

To the terminal:

spyql "
    SELECT col1
    FROM [10 * cos(i * ((pi * 4) / 90)) for i in range(80)]
    TO csv
" |
sed 1d |
feedgnuplot --terminal 'dumb 80,30' --exit --lines

To GUI:

spyql "
    SELECT col1
    FROM [10 * cos(i * ((pi * 4) / 90)) for i in range(80)]
    TO csv
" |
sed 1d |
feedgnuplot --lines --points --exit

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