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> 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**).
# 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:
```sql theme={null}
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:
```python theme={null}
#!/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`:
```sql theme={null}
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:
```sql theme={null}
SELECT * FROM my_executable_table
```
```response theme={null}
┌─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](/reference/engines/table-engines/mergetree-family/textindexes#hacker-news-dataset). The `hackernews` table includes an `id` column of type `UInt64` and a `String` column named `comment`. Let's start by defining the `Executable` table:
```sql theme={null}
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`:
```python theme={null}
#!/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:
```sql theme={null}
SELECT *
FROM sentiment
```
The response looks like:
```response theme={null}
┌───────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`:
```sql theme={null}
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.