ClickHouse/docs/en/engines/table-engines/special/executable.md

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/en/engines/table-engines/special/executable	40	Executable

Executable and ExecutablePool Table Engines

The Executable and ExecutablePool table engines allow you to define a table whose rows are generated from a script that you define (by writing rows to stdout). The executable script is stored in the users_scripts directory and can read data from any source.

• Executable tables: the script is run on every query
• ExecutablePool tables: maintains a pool of persistent processes, and takes processes from the pool for reads

You can optionally include one or more input queries that stream their results to stdin for the script to read.

Creating an Executable Table

The Executable table engine requires two parameters: the name of the script and the format of the incoming data. You can optionally pass in one or more input queries:

Executable(script_name, format, [input_query...])

Here are the relevant settings for an Executable table:

• send_chunk_header
  • Description: Send the number of rows in each chunk before sending a chunk to process. This setting can help to write your script in a more efficient way to preallocate some resources
  • Default value: false
• command_termination_timeout
  • Description: Command termination timeout in seconds
  • Default value: 10
• command_read_timeout
  • Description: Timeout for reading data from command stdout in milliseconds
  • Default value: 10000
• command_write_timeout
  • Description: Timeout for writing data to command stdin in milliseconds
  • Default value: 10000

Let's look at an example. The following Python script is named my_script.py and is saved in the user_scripts folder. It reads in a number i and prints i random strings, with each string preceded by a number that is separated by a tab:

#!/usr/bin/python3

import sys
import string
import random

def main():

    # Read input value
    for number in sys.stdin:
        i = int(number)

        # Generate some random rows
        for id in range(0, i):
            letters = string.ascii_letters
            random_string =  ''.join(random.choices(letters ,k=10))
            print(str(id) + '\t' + random_string + '\n', end='')

        # Flush results to stdout
        sys.stdout.flush()

if __name__ == "__main__":
    main()

The following my_executable_table is built from the output of my_script.py, which will generate 10 random strings every time you run a SELECT from my_executable_table:

CREATE TABLE my_executable_table (
   x UInt32,
   y String
)
ENGINE = Executable('my_script.py', TabSeparated, (SELECT 10))

Creating the table returns immediately and does not invoke the script. Querying my_executable_table causes the script to be invoked:

SELECT * FROM my_executable_table

┌─x─┬─y──────────┐
│ 0 │ BsnKBsNGNH │
│ 1 │ mgHfBCUrWM │
│ 2 │ iDQAVhlygr │
│ 3 │ uNGwDuXyCk │
│ 4 │ GcFdQWvoLB │
│ 5 │ UkciuuOTVO │
│ 6 │ HoKeCdHkbs │
│ 7 │ xRvySxqAcR │
│ 8 │ LKbXPHpyDI │
│ 9 │ zxogHTzEVV │
└───┴────────────┘

Passing Query Results to a Script

Users of the Hacker News website leave comments. Python contains a natural language processing toolkit (nltk) with a SentimentIntensityAnalyzer for determining if comments are positive, negative, or neutral - including assigning a value between -1 (a very negative comment) and 1 (a very positive comment). Let's create an Executable table that computes the sentiment of Hacker News comments using nltk.

This example uses the hackernews table described here. The hackernews table includes an id column of type UInt64 and a String column named comment. Let's start by defining the Executable table:

CREATE TABLE sentiment (
   id UInt64,
   sentiment Float32
)
ENGINE = Executable(
    'sentiment.py',
    TabSeparated,
    (SELECT id, comment FROM hackernews WHERE id > 0 AND comment != '' LIMIT 20)
);

Some comments about the sentiment table:

• The file sentiment.py is saved in the user_scripts folder (the default folder of the user_scripts_path setting)
• The TabSeparated format means our Python script needs to generate rows of raw data that contain tab-separated values
• The query selects two columns from hackernews. The Python script will need to parse out those column values from the incoming rows

Here is the definition of sentiment.py:

#!/usr/local/bin/python3.9

import sys
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

def main():
    sentiment_analyzer = SentimentIntensityAnalyzer()

    while True:
        try:
            row = sys.stdin.readline()
            if row == '':
                break

            split_line = row.split("\t")

            id = str(split_line[0])
            comment = split_line[1]

            score = sentiment_analyzer.polarity_scores(comment)['compound']
            print(id + '\t' + str(score) + '\n', end='')
            sys.stdout.flush()
        except BaseException as x:
            break

if __name__ == "__main__":
    main()

Some comments about our Python script:

• For this to work, you will need to run nltk.downloader.download('vader_lexicon'). This could have been placed in the script, but then it would have been downloaded every time a query was executed on the sentiment table - which is not efficient
• Each value of row is going to be a row in the result set of SELECT id, comment FROM hackernews WHERE id > 0 AND comment != '' LIMIT 20
• The incoming row is tab-separated, so we parse out the id and comment using the Python split function
• The result of polarity_scores is a JSON object with a handful of values. We decided to just grab the compound value of this JSON object
• Recall that the sentiment table in ClickHouse uses the TabSeparated format and contains two columns, so our print function separates those columns with a tab

Every time you write a query that selects rows from the sentiment table, the SELECT id, comment FROM hackernews WHERE id > 0 AND comment != '' LIMIT 20 query is executed and the result is passed to sentiment.py. Let's test it out:

SELECT *
FROM sentiment

The response looks like:

┌───────id─┬─sentiment─┐
│  7398199 │    0.4404 │
│ 21640317 │    0.1779 │
│ 21462000 │         0 │
│ 25168863 │         0 │
│ 25168978 │   -0.1531 │
│ 25169359 │         0 │
│ 25169394 │   -0.9231 │
│ 25169766 │    0.4137 │
│ 25172570 │    0.7469 │
│ 25173687 │    0.6249 │
│ 28291534 │         0 │
│ 28291669 │   -0.4767 │
│ 28291731 │         0 │
│ 28291949 │   -0.4767 │
│ 28292004 │    0.3612 │
│ 28292050 │    -0.296 │
│ 28292322 │         0 │
│ 28295172 │    0.7717 │
│ 28295288 │    0.4404 │
│ 21465723 │   -0.6956 │
└──────────┴───────────┘

Creating an ExecutablePool Table

The syntax for ExecutablePool is similar to Executable, but there are a couple of relevant settings unique to an ExecutablePool table:

• pool_size
  • Description: Processes pool size. If size is 0, then there are no size restrictions
  • Default value: 16
• max_command_execution_time
  • Description: Max command execution time in seconds
  • Default value: 10

We can easily convert the sentiment table above to use ExecutablePool instead of Executable:

CREATE TABLE sentiment_pooled (
   id UInt64,
   sentiment Float32
)
ENGINE = ExecutablePool(
	'sentiment.py',
	TabSeparated,
	(SELECT id, comment FROM hackernews WHERE id > 0 AND comment != '' LIMIT 20000)
)
SETTINGS
	pool_size = 4;

ClickHouse will maintain 4 processes on-demand when your client queries the sentiment_pooled table.