# Buelon
A scripting language to simply manage a very large amount of i/o heavy workloads. Such as API calls for your ETL, ELT or any program needing Python and/or SQL
## Table of Contents
<!--
- [Features](#features)
-->
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Supported Languages](#supported-languages) <!-- - [Configuration](#configuration) - [Usage](#usage) -->
- [Learn by Example](#learn-by-example) <!-- - [Performance](#performance) - [Contributing](#contributing) -->
- [Future of Buelon](#plans)
- [License](#license)
<!--
## Features
- Asynchronous execution of code across multiple servers
- Custom scripting language for defining ETL pipelines
- Support for Python, SQLite3, and PostgreSQL
- Efficient handling of APIs with long wait times
- Optimized for I/O-heavy workloads
- Scalable architecture for processing large amounts of data
-->
## Installation
`pip install buelon` That's it!
This will install the cli command `bue`. Check install by running `bue --version` or `bue -v`
### Note:
This package uses Cython and you may need to install `python3-dev` using
`sudo apt-get install python3-dev` [[more commands and information](https://stackoverflow.com/a/21530768/19907524)].
If you would like to use this repository without Cython,
you may `git clone` since it is not technically dependent on
these scripts, but they do provide a significant performance boost.
## Quick Start
1. Run bucket server: `bue bucket -b 0.0.0.0:61535`
2. Run hub: `bue hub -b 0.0.0.0:65432 -k localhost:61535`
3. Run n worker(s): `bue worker -b localhost:65432 -k localhost:61535`
4. Upload code: `bue upload -b localhost:65432 -f path/to/file.bue`
## Production Start
**Security:** Make sure bucket, hub and workers are under
a private network **only**
(you will need a web server or something similar
under the same private network
to access this tool using `bue upload -f path/to/file.bue`)
### With Postgres (Under 1,000,000 Jobs at once)
1. Create a `.env` file
```properties
PIPE_WORKER_SCOPES=production-very-heavy,production-heavy,production-medium,production-small,testing-heavy,testing-medium,testing-small,default
PIPE_WORKER_SUBPROCESS_JOBS=false
N_WORKER_PROCESSES="25"
USING_POSTGRES_HUB=true
USING_POSTGRES_BUCKET="true"
POSTGRES_HOST="123.45.67.89"
POSTGRES_PORT="5432"
POSTGRES_USER="daniel"
POSTGRES_PASSWORD="Password123"
POSTGRES_DATABASE="my_db"
```
2. Run n worker(s): `bue worker -b localhost:65432 -k localhost:61535`
3. Upload code: `bue upload -b localhost:65432 -f ./example.bue`
### Without Postgres (Under 10,000 jobs at once)
1. Create a `.env` file
```properties
PIPE_WORKER_SCOPES=production-very-heavy,production-heavy,production-medium,production-small,testing-heavy,testing-medium,testing-small,default
PIPE_WORKER_SUBPROCESS_JOBS=false
N_WORKER_PROCESSES="15"
PIPE_WORKER_HOST="123.45.67.89"
PIPE_WORKER_PORT="65432"
PIPELINE_HOST="0.0.0.0"
PIPELINE_PORT="65432"
BUCKET_SERVER_HOST="0.0.0.0"
BUCKET_SERVER_PORT="61535"
BUCKET_CLIENT_HOST="123.45.67.89"
BUCKET_CLIENT_PORT="61535"
```
1. Run bucket server: `bue bucket`
2. Run hub: `bue hub`
3. Run n worker(s): `bue worker`
4. Upload code: `bue upload -f ./example.bue`
## Supported Languages
- Python
- SQLite3
- PostgreSQL
## Learn by Example
(see below for `example.py` contents)
```python
# IMPORTANT: tabs are 4 spaces. white_space == " "
# [Optional] change tab sizes like this
TAB = ' '
# set config values globally
!scope production-small # job scope [see bellow]
!priority 0 # higher priority jobs are run first
!timeout 20 * 60 # job's max time to run in seconds
!retries 0 # how many times a job can run after error
# setting scopes is how you make new jobs with errors
# not interfere with all servers job queues
# and/or how you handle running heavy processes on large machine
# and small process on small machines
# define a single job called `accounts`
accounts:
python # <-- select the language to be run. currently only python, sqlite3 and postgres are available
accounts # select the function(for python) or table(for sql) name that will be used
example.py # either provide a file or write code directly using the "`" char (see below example)
# or
# define multiple jobs with:
import python (
request_report
as request,
get_status
as status
!scope testing-small,
get_report
as download
!priority 9
!timeout 60**2 * 5 / (1 % 2) // (1 + 1 - 1), # 5 hrs
transform_data
as py_transform
!scope production-heavy,
upload_to_db as upload
) example.py # <-- file path or using "`" like sql below
manipulate_data:
sqlite3
some_table # *vvvv* see below for writing code directly *vvvv*
`
SELECT
*,
CASE
WHEN sales = 0
THEN 0.0
ELSE spend / sales
END AS acos
FROM some_table
`
## this one's just to show postgres as well
#manipulate_data_again:
# postgres
# another_table
# `
#select
# *,
# case
# when spend = 0
# then 0.0
# else sales / spend
# end AS roas
#from another_table
#`
# these are pipes and what will tell the server what order to run the steps
# and also transfer the returned data between steps
# each step will be run individually and could be run on a different computer each time
accounts_pipe = | accounts # single pipes currently need a `|` before or behind the value
# api_pipe = request | status | download | manipulate_data | py_transform | upload
# # or
api_pipe = (
request | status | download
| manipulate_data | py_transform | upload
)
# currently there are only two syntax's for "running" pipes.
# either by itself:
# pipe()
#
# or in a loop:
# for value in pipe1():
# pipe2(value)
# # Another Example:
# v = pipe() # <-- single call
# pipe2(v)
for account in accounts_pipe():
api_pipe(account)
```
#### example.py
```python
import time
import random
import uuid
import logging
from typing import List, Dict, Union
from buelon.core.step import Result, StepStatus
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def accounts(*args) -> List[Dict[str, Union[int, str]]]:
"""Returns a list of sample account dictionaries.
Returns:
List[Dict[str, Union[int, str]]]: A list of dictionaries containing account information.
"""
account_list = [
{'id': 0, 'name': 'Account 1'},
{'id': 2, 'name': 'Account 2'},
{'id': 3, 'name': 'Account 4'},
]
logger.info(f"Retrieved {len(account_list)} accounts")
return account_list
def request_report(config: Dict[str, Union[int, str]]) -> Dict[str, Union[Dict, uuid.UUID, float]]:
"""Simulates a report request for a given account.
Args:
config (Dict[str, Union[int, str]]): A dictionary containing account information.
Returns:
Dict[str, Union[Dict, uuid.UUID, float]]: A dictionary with account data and request details.
"""
account_id = config['id']
request = {
'report_id': uuid.uuid4(),
'time': time.time(),
'account_id': account_id
}
logger.info(f"Requested report for account ID: {account_id}, Report ID: {request['report_id']}")
return {
'account': config,
'request': request
}
def get_status(config: Dict[str, Union[Dict, uuid.UUID, float]]) -> Union[Dict, Result]:
"""Checks the status of a report request.
Args:
config (Dict[str, Union[Dict, uuid.UUID, float]]): A dictionary containing request information.
Returns:
Union[Dict, Result]: Either the input config if successful, or a Result object if pending.
"""
requested_time = config['request']['time']
account_id = config['account']['id']
status = 'success' if requested_time + random.randint(10, 15) < time.time() else 'pending'
if status == 'pending':
logger.info(f"Report status for account ID {account_id} is pending")
return Result(status=StepStatus.pending)
logger.info(f"Report status for account ID {account_id} is success")
return config
def get_report(config: Dict[str, Union[Dict, uuid.UUID, float]]) -> Union[Dict, Result]:
"""Retrieves a report or simulates an error.
Args:
config (Dict[str, Union[Dict, uuid.UUID, float]]): A dictionary containing request configuration.
Returns:
Union[Dict, Result]: Either a dictionary with report data or a Result object for reset.
Raises:
ValueError: If an unexpected error occurs.
"""
account_id = config['account']['id']
if random.randint(0, 10) == 0:
report_data = {'status': 'error', 'msg': 'timeout error'}
else:
report_data = [
{'sales': i * 10, 'spend': i % 10, 'clicks': i * 13}
for i in range(random.randint(25, 100))
]
if not isinstance(report_data, list):
if isinstance(report_data, dict):
if (report_data.get('status') == 'error'
and report_data.get('msg') == 'timeout error'):
logger.warning(f"Timeout error for account ID {account_id}. Resetting.")
return Result(status=StepStatus.reset)
error_msg = f'Unexpected error: {report_data}'
logger.error(f"Error getting report for account ID {account_id}: {error_msg}")
raise ValueError(error_msg)
logger.info(f"Successfully retrieved report for account ID {account_id} with {len(report_data)} rows")
return {
'config': config,
'table_data': report_data
}
def transform_data(data: Dict[str, Union[Dict, List[Dict]]]) -> None:
"""Transforms the report data by adding account information to each row.
Args:
data (Dict[str, Union[Dict, List[Dict]]]): A dictionary containing config and table data.
"""
config = data['config']
table_data = data['table_data']
account_name = config['account']['name']
for row in table_data:
row['account'] = account_name
logger.info(f"Transformed {len(table_data)} rows of data for account: {account_name}")
def upload_to_db(data: Dict[str, Union[Dict, List[Dict]]]) -> None:
"""Handles table upload to database.
Args:
data (Dict[str, Union[Dict, List[Dict]]]): A dictionary containing table data to be uploaded.
"""
table_data = data['table_data']
account_name = data['config']['account']['name']
# Implementation for database upload
logger.info(f"Uploaded {len(table_data)} rows to the database for account: {account_name}")
```
## Known Defects
Error handling and logging are currently lacking
## Future Plans
If this projects sees some love,
or I just find more free time,
I'd like to support more languages like `node` or `deno` and
even compiled languages such as
`rust`, `go` and `c++`.
Allowing teams that write different
languages to work on the same program.
Web app for logging, execution and worker management
Add a scheduler process to allow scheduled pipelines
<!---
your comment goes here
and here
## Contributing
[Contributing guidelines]
-->
## License
* MIT License
Raw data
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"description": "# Buelon\n\nA scripting language to simply manage a very large amount of i/o heavy workloads. Such as API calls for your ETL, ELT or any program needing Python and/or SQL\n\n## Table of Contents\n<!--\n- [Features](#features)\n-->\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Supported Languages](#supported-languages) <!-- - [Configuration](#configuration) - [Usage](#usage) -->\n- [Learn by Example](#learn-by-example) <!-- - [Performance](#performance) - [Contributing](#contributing) -->\n- [Future of Buelon](#plans)\n- [License](#license)\n\n<!--\n## Features\n- Asynchronous execution of code across multiple servers\n- Custom scripting language for defining ETL pipelines\n- Support for Python, SQLite3, and PostgreSQL\n- Efficient handling of APIs with long wait times\n- Optimized for I/O-heavy workloads\n- Scalable architecture for processing large amounts of data\n-->\n\n## Installation\n\n`pip install buelon` That's it!\n\nThis will install the cli command `bue`. Check install by running `bue --version` or `bue -v`\n\n### Note:\n\nThis package uses Cython and you may need to install `python3-dev` using \n`sudo apt-get install python3-dev` [[more commands and information](https://stackoverflow.com/a/21530768/19907524)]. \nIf you would like to use this repository without Cython, \nyou may `git clone` since it is not technically dependent on \nthese scripts, but they do provide a significant performance boost. \n\n\n\n## Quick Start\n\n1. Run bucket server: `bue bucket -b 0.0.0.0:61535`\n2. Run hub: `bue hub -b 0.0.0.0:65432 -k localhost:61535`\n3. Run n worker(s): `bue worker -b localhost:65432 -k localhost:61535`\n4. Upload code: `bue upload -b localhost:65432 -f path/to/file.bue`\n\n## Production Start\n\n**Security:** Make sure bucket, hub and workers are under \na private network **only** \n(you will need a web server or something similar\nunder the same private network\nto access this tool using `bue upload -f path/to/file.bue`)\n\n### With Postgres (Under 1,000,000 Jobs at once)\n\n1. Create a `.env` file\n```properties\nPIPE_WORKER_SCOPES=production-very-heavy,production-heavy,production-medium,production-small,testing-heavy,testing-medium,testing-small,default\nPIPE_WORKER_SUBPROCESS_JOBS=false\nN_WORKER_PROCESSES=\"25\"\n\nUSING_POSTGRES_HUB=true\nUSING_POSTGRES_BUCKET=\"true\"\nPOSTGRES_HOST=\"123.45.67.89\"\nPOSTGRES_PORT=\"5432\"\nPOSTGRES_USER=\"daniel\"\nPOSTGRES_PASSWORD=\"Password123\"\nPOSTGRES_DATABASE=\"my_db\"\n```\n\n2. Run n worker(s): `bue worker -b localhost:65432 -k localhost:61535`\n3. Upload code: `bue upload -b localhost:65432 -f ./example.bue`\n\n### Without Postgres (Under 10,000 jobs at once)\n\n1. Create a `.env` file\n```properties\nPIPE_WORKER_SCOPES=production-very-heavy,production-heavy,production-medium,production-small,testing-heavy,testing-medium,testing-small,default\nPIPE_WORKER_SUBPROCESS_JOBS=false\nN_WORKER_PROCESSES=\"15\"\nPIPE_WORKER_HOST=\"123.45.67.89\"\nPIPE_WORKER_PORT=\"65432\"\n\nPIPELINE_HOST=\"0.0.0.0\"\nPIPELINE_PORT=\"65432\"\n\nBUCKET_SERVER_HOST=\"0.0.0.0\"\nBUCKET_SERVER_PORT=\"61535\"\nBUCKET_CLIENT_HOST=\"123.45.67.89\"\nBUCKET_CLIENT_PORT=\"61535\"\n```\n1. Run bucket server: `bue bucket`\n2. Run hub: `bue hub`\n3. Run n worker(s): `bue worker`\n4. Upload code: `bue upload -f ./example.bue`\n\n## Supported Languages\n- Python\n- SQLite3\n- PostgreSQL\n\n## Learn by Example\n\n(see below for `example.py` contents)\n\n```python\n# IMPORTANT: tabs are 4 spaces. white_space == \" \"\n# [Optional] change tab sizes like this\nTAB = ' '\n\n# set config values globally\n!scope production-small # job scope [see bellow]\n!priority 0 # higher priority jobs are run first\n!timeout 20 * 60 # job's max time to run in seconds\n!retries 0 # how many times a job can run after error\n\n# setting scopes is how you make new jobs with errors\n# not interfere with all servers job queues\n# and/or how you handle running heavy processes on large machine\n# and small process on small machines\n\n# define a single job called `accounts`\naccounts:\n python # <-- select the language to be run. currently only python, sqlite3 and postgres are available\n accounts # select the function(for python) or table(for sql) name that will be used\n example.py # either provide a file or write code directly using the \"`\" char (see below example)\n\n# or\n\n# define multiple jobs with:\nimport python (\n request_report \n as request,\n get_status \n as status \n !scope testing-small,\n get_report \n as download \n !priority 9\n !timeout 60**2 * 5 / (1 % 2) // (1 + 1 - 1), # 5 hrs\n transform_data \n as py_transform \n !scope production-heavy,\n upload_to_db as upload\n) example.py # <-- file path or using \"`\" like sql below\n\n\nmanipulate_data:\n sqlite3\n some_table # *vvvv* see below for writing code directly *vvvv*\n `\nSELECT\n *,\n CASE\n WHEN sales = 0\n THEN 0.0\n ELSE spend / sales\n END AS acos\nFROM some_table\n`\n\n## this one's just to show postgres as well\n#manipulate_data_again:\n# postgres\n# another_table\n# `\n#select\n# *,\n# case\n# when spend = 0\n# then 0.0\n# else sales / spend\n# end AS roas\n#from another_table\n#`\n\n# these are pipes and what will tell the server what order to run the steps\n# and also transfer the returned data between steps\n# each step will be run individually and could be run on a different computer each time\naccounts_pipe = | accounts # single pipes currently need a `|` before or behind the value\n# api_pipe = request | status | download | manipulate_data | py_transform | upload\n# # or\napi_pipe = (\n request | status | download \n | manipulate_data | py_transform | upload\n)\n\n\n# currently there are only two syntax's for \"running\" pipes.\n# either by itself:\n# pipe()\n#\n# or in a loop:\n# for value in pipe1():\n# pipe2(value)\n\n# # Another Example:\n# v = pipe() # <-- single call\n# pipe2(v)\n\nfor account in accounts_pipe():\n api_pipe(account)\n```\n\n#### example.py\n```python\nimport time\nimport random\nimport uuid\nimport logging\nfrom typing import List, Dict, Union\n\nfrom buelon.core.step import Result, StepStatus\n\n# Set up logging\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\n\n\ndef accounts(*args) -> List[Dict[str, Union[int, str]]]:\n \"\"\"Returns a list of sample account dictionaries.\n\n Returns:\n List[Dict[str, Union[int, str]]]: A list of dictionaries containing account information.\n \"\"\"\n account_list = [\n {'id': 0, 'name': 'Account 1'},\n {'id': 2, 'name': 'Account 2'},\n {'id': 3, 'name': 'Account 4'},\n ]\n logger.info(f\"Retrieved {len(account_list)} accounts\")\n return account_list\n\n\ndef request_report(config: Dict[str, Union[int, str]]) -> Dict[str, Union[Dict, uuid.UUID, float]]:\n \"\"\"Simulates a report request for a given account.\n\n Args:\n config (Dict[str, Union[int, str]]): A dictionary containing account information.\n\n Returns:\n Dict[str, Union[Dict, uuid.UUID, float]]: A dictionary with account data and request details.\n \"\"\"\n account_id = config['id']\n \n request = {\n 'report_id': uuid.uuid4(),\n 'time': time.time(),\n 'account_id': account_id\n }\n \n logger.info(f\"Requested report for account ID: {account_id}, Report ID: {request['report_id']}\")\n return {\n 'account': config,\n 'request': request\n }\n\n\ndef get_status(config: Dict[str, Union[Dict, uuid.UUID, float]]) -> Union[Dict, Result]:\n \"\"\"Checks the status of a report request.\n\n Args:\n config (Dict[str, Union[Dict, uuid.UUID, float]]): A dictionary containing request information.\n\n Returns:\n Union[Dict, Result]: Either the input config if successful, or a Result object if pending.\n \"\"\"\n requested_time = config['request']['time']\n account_id = config['account']['id']\n \n status = 'success' if requested_time + random.randint(10, 15) < time.time() else 'pending'\n \n if status == 'pending':\n logger.info(f\"Report status for account ID {account_id} is pending\")\n return Result(status=StepStatus.pending)\n \n logger.info(f\"Report status for account ID {account_id} is success\")\n return config\n \n\ndef get_report(config: Dict[str, Union[Dict, uuid.UUID, float]]) -> Union[Dict, Result]:\n \"\"\"Retrieves a report or simulates an error.\n\n Args:\n config (Dict[str, Union[Dict, uuid.UUID, float]]): A dictionary containing request configuration.\n\n Returns:\n Union[Dict, Result]: Either a dictionary with report data or a Result object for reset.\n\n Raises:\n ValueError: If an unexpected error occurs.\n \"\"\"\n account_id = config['account']['id']\n \n if random.randint(0, 10) == 0:\n report_data = {'status': 'error', 'msg': 'timeout error'}\n else:\n report_data = [\n {'sales': i * 10, 'spend': i % 10, 'clicks': i * 13}\n for i in range(random.randint(25, 100))\n ]\n \n if not isinstance(report_data, list):\n if isinstance(report_data, dict):\n if (report_data.get('status') == 'error' \n and report_data.get('msg') == 'timeout error'):\n logger.warning(f\"Timeout error for account ID {account_id}. Resetting.\")\n return Result(status=StepStatus.reset)\n error_msg = f'Unexpected error: {report_data}'\n logger.error(f\"Error getting report for account ID {account_id}: {error_msg}\")\n raise ValueError(error_msg)\n \n logger.info(f\"Successfully retrieved report for account ID {account_id} with {len(report_data)} rows\")\n return {\n 'config': config,\n 'table_data': report_data\n }\n\n\ndef transform_data(data: Dict[str, Union[Dict, List[Dict]]]) -> None:\n \"\"\"Transforms the report data by adding account information to each row.\n\n Args:\n data (Dict[str, Union[Dict, List[Dict]]]): A dictionary containing config and table data.\n \"\"\"\n config = data['config']\n table_data = data['table_data']\n account_name = config['account']['name']\n \n for row in table_data:\n row['account'] = account_name\n \n logger.info(f\"Transformed {len(table_data)} rows of data for account: {account_name}\")\n\n \ndef upload_to_db(data: Dict[str, Union[Dict, List[Dict]]]) -> None:\n \"\"\"Handles table upload to database.\n\n Args:\n data (Dict[str, Union[Dict, List[Dict]]]): A dictionary containing table data to be uploaded.\n \"\"\" \n table_data = data['table_data']\n account_name = data['config']['account']['name']\n # Implementation for database upload\n logger.info(f\"Uploaded {len(table_data)} rows to the database for account: {account_name}\")\n```\n\n## Known Defects\n\nError handling and logging are currently lacking\n\n\n## Future Plans\n\nIf this projects sees some love, \nor I just find more free time, \nI'd like to support more languages like `node` or `deno` and\neven compiled languages such as \n`rust`, `go` and `c++`. \nAllowing teams that write different \nlanguages to work on the same program.\n\nWeb app for logging, execution and worker management\n\nAdd a scheduler process to allow scheduled pipelines\n\n<!---\nyour comment goes here\nand here\n\n## Contributing\n[Contributing guidelines]\n-->\n\n## License\n* MIT License\n",
"bugtrack_url": null,
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"summary": "A scripting language to simply manage a very large amount of i/o heavy workloads. Such as API calls for your ETL, ELT or any program needing Python and/or SQL",
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