Optimus's transformation plugins are implementations of Task and Hook interfaces that allows execution of arbitrary jobs in optimus.
brew tap odpf/taps
brew install optimus-plugins-odpf
curl -sL ${PLUGIN_RELEASE_URL} | tar xvz
chmod +x optimus-*
mv optimus-* /usr/bin/
Hello, Currently, for any query, we try to find the dependancy and ignoredependancy with FindDependenciesWithRegex and then we again pull the Refereced table with big query dry run.
If query contains view which is marked with /* @ignoreupstream */ helper, then ignoredependancy will contain the view name but not the table referenced by view.
The change here is to revise ignoredependancy list with table referenced by view.
I kept the loop execution in sequential manner, please let me know if should add concurrency here
enhancementWe have a query referencing to table as well as view.
select * from proj.dataset.table t1 left join proj.dataset.view v1 on t1.date = v1.date and t1.id = v1.id
proj.dataset.table then it correctly ignores to create upstream dependancy for this table.proj.dataset.view ( note the view query refers to 2 more tables ) then it does not ignore view or table referenced by view.As per Optimus PR, the executor boot process is standardised and maintained at optimus. Plugin devs need no longer have to wrap the executor image. closes odpf/optimus#405
Hi team,
I have a bq2bq job with window configuration
window:
size: 720h
offset: -48h
truncate_to: M
I expect to have transformation for date 01 to last day of the month, e.g on April, I expect got transformation from date 01 - 30. but currently only got transformation from date 01 - 29
[2022-06-13 15:08:12,323] {pod_launcher.py:149} INFO - [2022-06-13 15:08:12] INFO:bumblebee.transformation: create transformation for partition: 2022-04-26 00:00:00+00:00
[2022-06-13 15:08:12,323] {pod_launcher.py:149} INFO - [2022-06-13 15:08:12] INFO:bumblebee.transformation: create transformation for partition: 2022-04-27 00:00:00+00:00
[2022-06-13 15:08:12,323] {pod_launcher.py:149} INFO - [2022-06-13 15:08:12] INFO:bumblebee.transformation: create transformation for partition: 2022-04-28 00:00:00+00:00
[2022-06-13 15:08:12,323] {pod_launcher.py:149} INFO - [2022-06-13 15:08:12] INFO:bumblebee.transformation: create transformation for partition: 2022-04-29 00:00:00+00:00
[2022-06-13 15:08:12,324] {pod_launcher.py:149} INFO - [2022-06-13 15:08:12] INFO:bumblebee.transformation: start transformation job
[2022-06-13 15:08:12,324] {pod_launcher.py:149} INFO - [2022-06-13 15:08:12] INFO:bumblebee.transformation: sql transformation query:
after checking, I suspect the logic may related to this line, where the last day generated by windows class not included as the transformation partition.
https://github.com/odpf/transformers/blob/ea1de4f0de3d17d9be7ccefb1e2f3beab1a685f1/task/bq2bq/executor/bumblebee/transformation.py#L393
please kindly check it, and release the fix. thank you
With this we are adding support for using secrets in macros, we do not want to print the env vars in the logs, so exporting them as a separate file from optimus.
Plugins can export this extra file to get env vars.
As per https://github.com/odpf/optimus/pull/425, the executor boot process is standardised and maintained at optimus. Plugin devs need no longer have to wrap the executor image. closes https://github.com/odpf/optimus/issues/405
The most intuitive way is to parse the query and hit the metadata apis instead of going through the dry run which should be definitly costly then the metadata fetch apis.
enhancement performanceOptions
aa14f19 feat: load method for partition replace using aggregation queries (#15)
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.9cb26e8f feat: add destination type when generating destination of bq2bq task to support backup feature (#7)
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.882d75fb build(deps): bump sqlparse from 0.4.1 to 0.4.2 in /task/bq2bq/executor (#6) 7b6ea2d feat: add execution_project configuration (#9)
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.7047c3ba fix(bq2bq): handle BQ dry run error caused by self table SQL reference (#5)
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.6631527a refactor: implement plugin mods 621dc7d refactor: update help for bigquery prompts to ask for destination (#3)
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.52122157 build(deps): bump rsa from 4.6 to 4.7 in /task/bq2bq/executor (#1) 5f4d0d2 build(deps): bump urllib3 from 1.25.9 to 1.26.5 in /task/bq2bq/executor (#2) db8307c feat(bq2bq): support default values inferred from job name
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.4365796e fix: dataset/table name passed during config gen
docker pull docker.io/odpf/optimus-task-bq2bq:0.0.3
jiant is an NLP toolkit The multitask and transfer learning toolkit for natural language processing research Why should I use jiant? jiant supports mu
About A simple implementation of N-gram language model. Requirements numpy Data preparation Corpus Training data for the N-gram model, a text file lik
PyStanfordDependencies Python interface for converting Penn Treebank trees to Universal Dependencies and Stanford Dependencies. Example usage Start by
spaCy: Industrial-strength NLP spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest researc
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"
Pretrain and Fine-tune a T5 model with Flax on GCP This tutorial details how pretrain and fine-tune a FlaxT5 model from HuggingFace using a TPU VM ava
🤖 Coeus - EARIST A.C.E 💬 Coeus is an Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology,
Wisdomify A BERT-based reverse-dictionary of Korean proverbs. 김유빈 : 모델링 / 데이터 수집 / 프로젝트 설계 / back-end 김종윤 : 데이터 수집 / 프로젝트 설계 / front-end / back-end 임용
天池中药说明书实体识别挑战冠军方案；中文命名实体识别；NER; BERT-CRF & BERT-SPAN & BERT-MRC；Pytorch
Project: Text Analysis - This project aims to conduct a text information retrieval and text mining on medical research publication regarding Covid19 -
whylogs Workshop The code from the whylogs workshop in DataTalks.Club on 29 March 2022 whylogs - The open source standard for data logging (Don't forg
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoenc
✨ NeuralCoref 4.0: Coreference Resolution in spaCy with Neural Networks. NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolv
tacotron_pytorch PyTorch implementation of Tacotron speech synthesis model. Inspired from keithito/tacotron. Currently not as much good speech quality
Allennlp Integration for Shiba allennlp-shiab-model is a Python library that provides AllenNLP integration for shiba-model. SHIBA is an approximate re
Large-scale Knowledge Graph Construction with Prompting across tasks (predictive and generative), and modalities (language, image, vision + language, etc.)
✏️ rpunct - Restore Punctuation This repo contains code for Punctuation restoration. This package is intended for direct use as a punctuation restorat
English | 中文说明 TextPruner是一个为预训练语言模型设计的模型裁剪工具包，通过轻量、快速的裁剪方法对模型进行结构化剪枝，从而实现压缩模型体积、提升模型速度。 其他相关资源： 知识蒸馏工具TextBrewer：https://github.com/airaria/TextBrewe
HuggingTweets - Train a model to generate tweets Create in 5 minutes a tweet generator based on your favorite Tweeter Make my own model with the demo
Content What is deepaudio-speaker? Installation Get Started Model Architecture How to contribute to deepaudio-speaker? Acknowledge What is deepaudio-s
1.5k Jan 04, 2023
4 Nov 24, 2021
64 May 08, 2022
24.9k Jan 02, 2023
105 Jan 03, 2023
41 Nov 18, 2022
3 Oct 14, 2022
94 Dec 08, 2022
751 Dec 30, 2022
1 Mar 14, 2022
12 Sep 05, 2022
1.8k Dec 31, 2022
2.6k Jan 04, 2023
279 Dec 09, 2022
12 Feb 16, 2022
161 Dec 28, 2022
72 Dec 30, 2022
231 Jan 08, 2023
318 Jan 04, 2023
20 Dec 29, 2022