Celery Task

Celery has a scheduler called beat…. See the CELERY_TASK_PUBLISH_RETRY setting. Celery is a task queue that is to built an asynchronous message passing system. What is Celery: Celery is an asynchronous task queue/job queue based on distributed message passing. Introducing Celery for Python+Django provides an introduction to the Celery task queue with Django as the intended framework for building a web application. These all have C# APIs. gz $ cd celery-0. It performs dual roles in that it defines both what happens when a task is called (sends a message), and what happens when a worker receives that message. Celery is written in Python and makes it very easy to offload work out of the synchronous request lifecycle of a web app onto a pool of task workers to perform jobs asynchronously. Viewed 6k times 14. Celery Application(or Client): It is responsible for adding tasks to the queue. 10, I have to issue python tasks from django using celery. It's best to keep them simple. The following are code examples for showing how to use celery. Background Tasks with celery¶. The first problem that presents is how to create the celery object, which provides the celery. result = celery. Celery supports linking tasks together so that one task follows another. celery内置了 celery. Introduction to Celery. This java celery integration was implemented with the help of a message broker/queue and what I chose for this was RabbitMQ. Updated on December 2015! - Now for Celery 3. To use celery_once, your tasks need to inherit from an abstract base task called QueueOnce. - (Instructor) In our previous video, we saw the…use of Celery to distribute tasks. By default the entries are taken from the CELERYBEAT_SCHEDULE setting, but custom stores can also be used, like storing the entries in an SQL database. As I soon discovered, Celery is a fast and powerful way to turn any python callable into a scheduled task, or message-processing worker. It is backed by Redis and it is designed to have a low barrier to entry. Another thing Celery is helpful for is scheduling tasks to run at specific times. We can create a file named tasks. ResultBase [源代码] ¶ Base class for all results. Set up Flower to monitor and administer Celery jobs and workers. >>> result = celery. With Celery queues, you can control which Celery workers process which tasks. 基本使用 celery. Your application just need to push messages to a broker, like RabbitMQ, and Celery workers will pop them and schedule task execution. Celery is a really good framework for doing background task processing in Python (and other languages). Strategy 1: Wait for the task to finish. Celery supports linking tasks together so that one task follows another. local # Force an specified worker to cancel consuming from a queue. We use it in a lot of the custom web apps we build at Caktus, and it's quickly becoming the standard for all variety of task scheduling work loads, from simple to highly complex. It has the reputation of being fussy; however, it’s really quite easy if you understand its specific needs. Specifically, I want to be able to define. get_task_logger function. Celery is a distributed task queue for Python projects. I guess I'd like a simple way to delay() Celery tasks, but keep them out of the task queue until after I commit the transaction that wanted them done. Putting a task on a queue just adds it to a to-do list, so to speak. celery beat is a scheduler. To create a periodic task executing at an interval you must first create the interval object: >>> from django_celery_beat. Celery makes it easy to write to the task queue, thereby delaying the task until a worker can take it from the queue. I'm using Python 3. celery, biennial plant (Apium graveolens) of the family Umbelliferae (parsley parsley,Mediterranean aromatic herb (Petroselinum crispum or Apium petroselinum) of the carrot family, cultivated since the days of the Romans for its foliage, used in cookery as a seasoning and garnish. Setting up an asynchronous task queue for Django using Celery and Redis May 18 th , 2014 Celery is a powerful, production-ready asynchronous job queue, which allows you to run time-consuming Python functions in the background. async(**opts)` to dispatch and run it asynchronously. The task requires a decorator (@celery. Simply put, Celery is a background task runner. It's happens with multiple workers for tasks with any delay (countdown, ETA). The execution units, called tasks, are executed concurrently on a single or more worker servers using multiprocessing, Eventlet , or gevent. For discussions about the usage, development, and future of celery, please join the celery-users mailing list. Celery Task Queue for Python. This also gets triggered when the task fails to run for some reason, i. There are several strategies to test this Celery task. I ended up using a different approach of creating one task type that is registered for the whole application, then writing my own Task class to manage the particulars, but I'm saving this in case that road hits a dead end. With Celery queues, you can control which Celery workers process which tasks. celery_executor Source code for airflow. periodic_task(). The fact that it provides the decorator means that it has to be created as a global variable, and that implies that the Flask application instance is not going to be around when it is created. If you have a few asynchronous tasks and you use just the celery default queue, all tasks will be going to the same queue. This is a perfect place to store our current and total progress. celery -A tasks inspect reserved. There are a bunch of options for creating a new task , the example below uses. In this video, I'll show you how to integrate Celery with Flask. Delay is preconfigured with default configurations, and only requires arguments which will be passed to task. Celery gives us two methods delay() and apply_async() to call tasks. - To enable celery to track the STARTED state of the task, put the following line to settings. Celery is an asynchronous task queue/job queue system, which is used by MiaRec web portal for different tasks (long running tasks and/or periodic jobs). Running the Examples. RabbitMQ is a message broker which implements the Advanced Message Queuing Protocol (AMQP). Custom Celery task states How to hack Celery task states Published on September 28, 2018 Estimated reading time: 8 minutes. apply_async (( 2 , 2 ), link = add. $ tar xvfz celery-tar. Whether you are interested in running a large scale distributed computation or you want to setup background jobs for your Django app, Celery is the way to go. LOGGING for ‘celery. local # Force an specified worker to cancel consuming from a queue. pid` # Send a second TERM to complete the shutdown kill -TERM `cat celery. It is focused on real-time operation, but supports scheduling as well. Now customize the name of a clipboard to store your clips. The celery is used in connexion flask. In this video, you see how to use Celery to distribute tasks. I installed Celery for my Django project following what the official tutorial / doc says. It can be integrated in your web stack easily. Open a new terminal and run celery with. Tasks have a required 'apply' method (what this task will _do_), and an optional 'rollback' method (to be executed on task failure if specified). Celery Django Scheduled Tasks. It is focused on real-time operations but supports scheduling as well. Celery itself is already installed on your system when you deployed MiaRecWeb portal. Data transferred between clients and workers needs to be serialized, so every message in Celery has a content_type header that describes the serialization method used to encode it. See the CELERY_TASK_PUBLISH_RETRY setting. Celery Best Practices by Balthazar Rouberol. Celery - Distributed Task Queue¶ Celery is a simple, flexible and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. py file and it never even gets loaded for Celery during app. The Celery task might not even be able to see the resources you've created, but not committed. test_celery __init__. This also gets triggered when the task fails to run for some reason, i. Notice how we expect as argument user_id rather than a User object. In this case, using @shared_task decorator is the right way to ensure you'll have everything in place. Your application just need to push messages to a broker, like RabbitMQ, and Celery workers will pop them and schedule task execution. Background Tasks with celery¶. I have tried everything but still not able to run the task in anyway. routing_key - Custom routing key used to route the task to a worker server. get_context_data, a method like Django Views that should provide a dictionary for passing to the template renderer, 2. Celery is a task queue system based on distributed message passing. Celery is a great tool for background task processing in Django. Celery is a readily-available such system (a task-queue to be precise) which enables this and it is easy to integrate into Django using django-celery. The periodic tasks can be managed from the Django Admin interface, where you can create, edit and delete periodic tasks and how often they should run. Handling Celery task failures in a consistent and predictable way is a prerquisite to building a resilient asynchronous system. connect decorator, make sure you use @app. task decorator. The following are code examples for showing how to use celery. celery -A celery_task beat 执行任务 celery -A celery_task worker -l info -P eventlet 将以上两条合并 celery -B -A celery_task worker 后台启动celery worker进程 celery multi start work_1 -A appcelery 停止worker进程,如果无法停止,加上-A celery multi stop WORKNAME 重启worker进程 celery multi restart WORKNAME. However, it doesn't necessarily live up to all the claims being made about its health benefits. Last released: Oct 24, 2019 No project description provided. I want different results depending on whether there is a valid task for a certain id. The state allows us to set an overall status, as well as attach arbitrary metadata to the task. Example creating interval-based periodic task. Celery Task Queue for Python. py, but I don't manage to get celery to discover them. Tutorials, tips and tricks for the Celery task queue for Python. autodiscover_tasks(). It's focused on real-time operation, but supports scheduling as well. Each task is executed within a Flask application context (notice the use of e. class celery. 这将列出工作者已经预取的所有任务,并且当前正在等待执行(不包括设置了ETA值的任务)。 1. Build Celery Tasks. I found a somewhat dated pyramic_celery module that's supposed to handle Pyramid. Delay is preconfigured with default configurations, and only requires arguments which will be passed to task. We will explore AWS SQS for scaling our parallel tasks on the cloud. Celery gives us two methods delay() and apply_async() to call tasks. Introduction ¶. result ¶ Task results/state and groups of results. py run_tasks. Tedious work such as creating database backup, reporting annual KPI, or even blasting email could be made a breeze. async(**opts)` to dispatch and run it asynchronously. I want to know whether a given task id is a real celery task id and not a random string. If you go through the work of defining tasks, you have a scalable entity (the workers) that you can use as a knob to scale with volume. Celery communicates via messages, usually using a broker to mediate between clients and workers. py # 获取结果. We’re now using it to clip the aforementioned indices, as well as perform other tasks such as injecting work payloads into a distributed queue. Often, in most practical use-cases you will be better off writing custom tasks rather than decorating functions as tasks. It is focused on real-time operation, but supports scheduling as well. I'm currently testing on the same machine but eventually the celery worker should run on a remote machine. Dealing with resource-consuming tasks on Celery by Vinta. This is -- very loosely and abstractly --- what I'm trying to do: tasks. You can start as many Spark tasks as you want by clicking the button multiple times. That can seem like a daunting task on. This will continue until the Celery task completes. celery -A celery_task beat 执行任务 celery -A celery_task worker -l info -P eventlet 将以上两条合并 celery -B -A celery_task worker 后台启动celery worker进程 celery multi start work_1 -A appcelery 停止worker进程,如果无法停止,加上-A celery multi stop WORKNAME 重启worker进程 celery multi restart WORKNAME. Celery is a task queue system based on distributed message passing. Two, this is a shortcut to send a task message, but does not support execution options. Celery is "an asynchronous task queue/job queue based on distributed message passing. Example creating interval-based periodic task. Tasks have a required 'apply' method (what this task will _do_), and an optional 'rollback' method (to be executed on task failure if specified). Now customize the name of a clipboard to store your clips. Notice how we expect as argument user_id rather than a User object. Such tasks, called periodic tasks, are easy to set up with Celery. destination – If set, a list of the hosts to send the command to, when empty broadcast to all workers. Celery is an asynchronous task queue/job queue based on distributed message passing. Celery is a task queue based on distributed message passing. It can run time-intensive tasks in the background so that your application can focus on the stuff that matters the most. Assuming a very active Twitter user signs up and we need 200 api calls to fetch all his tweets. Simply put, Celery is a background task runner. Suppose that we have another task called too_long_task and one more called quick_task and imagine that we have one single queue and four workers. By Greg Davidson March 27, 2012 I recently had the opportunity to work on a Django project that was using Celery with RabbitMQ to handle long-running server-side processing tasks. 5 months now and it's been pretty reliable. The program should be running in schedule. 基本使用 celery. Writing unit tests for celery tasks can be painful since they are asynchronous and long. A task queue’s input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform. We rely on Celery to manage all our background queues and asyncronous scheduling. If you are trying to distribute tasks to a number of worker nodes running as separate processes on the same or different boxes, then pretty much all third party messaging apis could be used. And it's working fine when I launch celery at the command line, I can see it receiving the tasks and execute them. Tedious work such as creating database backup, reporting annual KPI, or even blasting email could be made a breeze. Flask asynchronous background tasks with Celery and Redis Allyn H PyCon UK 2017 presentation Allyn H Creating a Python app for Destiny - Part 8: Displaying the Vault contents. io is a distributed messaging service platform that works with many types of task queues such as Celery. Celery is a framework for performing asynchronous tasks in your application. It is focused on real-time operation, but supports scheduling as well. 6, Django 1. t task trees/graphs/dependecies etc. get_context_data, a method like Django Views that should provide a dictionary for passing to the template renderer, 2. We will have some tasks which may take a while. task decorator. Previously, I wrote about how to customise your Celery log handlers. get_task_logger function. This java celery integration was implemented with the help of a message broker/queue and what I chose for this was RabbitMQ. celery -A tasks. Suppose that we have another task called too_long_task and one more called quick_task and imagine that we have one single queue and four workers. It's best to keep them simple. Python, PyCon, PyConAU, australia, programming, sydney. Another thing Celery is helpful for is scheduling tasks to run at specific times. This tells Celery this is a task that will be run in the task queue. task 装饰使得成为一个后台作业。这个函数唯一值得注意的就是 Flask-Mail 需要在应用的上下文中运行,因此需要在调用 send() 之前创建一个应用上下文。 重点注意在这个示例中从异步调用返回值并不保留,因此应用不能知道调用成功或者失败。. anybody figured out how to debug celery tasks inside PyCharm or IntelliJ? I could find a single, really old thread that is not giving too much details about it. Now customize the name of a clipboard to store your clips. It can run time-intensive tasks in the background so that your application can focus on the stuff that matters the most. CATMAID doesn’t talk directly to Celery, but uses a so called message broker, which Celery talks to to get new tasks. pid In another terminal send 6 tasks: python script. celery worker --loglevel=info. While it is ridiculously easy to use celery, doing complex task flow has been a challenge in celery. This can be useful if you have a slow and a fast task and you want the slow tasks not to interfere with the fast tasks. Being a Python stack, Celery felt like a natural fit to manage codecov's long running tasks. Celery is a long-season vegetable grown in the spring or fall. connect decorator, make sure you use @app. I guess I'd like a simple way to delay() Celery tasks, but keep them out of the task queue until after I commit the transaction that wanted them done. If this is for in-process tasks, then the TPL is where to look. Using Celery on Heroku. py # 添加任务 └── get_result. The following are code examples for showing how to use celery. Celery knows six built-in states:. Strategy 1: Wait for the task to finish. By default, Celery routes all tasks to a single queue and all workers consume this default queue. py run_tasks. Airflow workers are configured to listen for events(i. To stop Celery from prefetching too many tasks and free the worker's memory to our tasks, we can use the CELERYD_PREFETCH_MULTIPLIER setting, which is a setting that tells Celery how many tasks should be prefetched into memory at the same time. Celery Best Practices by Balthazar Rouberol. Celery Django Scheduled Tasks. Flask asynchronous background tasks with Celery and Redis Allyn H PyCon UK 2017 presentation Allyn H Creating a Python app for Destiny - Part 8: Displaying the Vault contents. Distributed Tasks Demystified with Celery, SQS & Python 4. In the sample diagram, you can see that i already have a task running. Invoking a celery task from java application is not hassle but not an easy one either. Celery is on the Python Package Index (PyPi), and can be easily installed with pip or easy_install and its dependencies. @auvipy if this is only one line of code to fix, then why not just fix it within celery, instead of using the docs to recommend the users implement a workaround? Why is a completely platform-breaking bug with such a simple fix still a problem after nearly 2 years?. AsyncResult(id, backend=None, task_name=None, app=None, parent=None) [源代码] ¶ Query task state. apply_async (( 2 , 2 ), link = add. This also gets triggered when the task fails to run for some reason, i. It can run time-intensive tasks in the background so that your application can focus on the stuff that matters the most. In this article, we shall see how we can setup Django and Celery to start processing our background tasks. The following are code examples for showing how to use celery. Django and Celery makes background task processing a breeze. periodic_task(). How to mock the custom task status in unit test. Periodic tasks, in layman’s terms, can be explained as tasks whose execution will happen at pre-determined time intervals and with minimal human intervention. Reply Delete. Celeryconfig contains the configurations for celery to execute the tasks, including import path, task serialization format, and of course, the schedule for which tasks should be triggered. The following are code examples for showing how to use celery. You can vote up the examples you like or vote down the ones you don't like. Celery Executor¶. Mailing list. Celery makes it possible to run tasks by schedulers like crontab in Linux. I am new to celery and want to add a periodic task. Python, PyCon, PyConAU, australia, programming, sydney. In addition to the above tornado-celery was used to connect tornado and celery. The program should be running in schedule. periodic_task(). run py3clean or pyclean command in your work directory to clear all cache. send_task ("tasks. # the use of classmethods was a hack so that it was not necessary. Introducing Celery for Python+Django provides an introduction to the Celery task queue with Django as the intended framework for building a web application. It is a Python package that makes the handling of asynchronous tasks a walk in the park. This also gets triggered when the task fails to run for some reason, i. Defining it as 1 will tell Celery that it should only reserve one task per worker process at a time. Background Tasks with celery¶. We realized that in one of our environments, Airflow scheduler picks up old task instances that were already a success (whether marked as success or completed successfully). RabbitMQ is a complete, stable, and durable message broker that can be used with Celery. Celery - the solution for those problems! Celery is a distributed system to process lots of messages. gz $ cd celery-0. It both provide us to run real-time operations and schedule some tasks to be executed later. Delay is preconfigured with default configurations, and only requires arguments which will be passed to task. 6, Django 1. October 16, 2019. I'm using Python 3. Logging what’s happening in your tasks. Celery is a Python package which implements a task queue mechanism with a foucs on real-time processing, while also supporting task scheduling. We use it in a lot of the custom web apps we build at Caktus, and it's quickly becoming the standard for all variety of task scheduling work loads, from simple to highly complex. Celery is a task queue based on distributed message passing. If you need to set any of the settings (attributes) you'd normally be able to set on a Celery Task class had you written it yourself, you may specify them in a dict in the CELERY_EMAIL_TASK_CONFIG setting:. The closest thing I could find was a module specific logger, via the celery. First of all, if you want to use periodic tasks, you have to run the Celery worker with –beat flag, otherwise Celery will ignore the scheduler. Celery Executor¶. Celery is a really good framework for doing background task processing in Python (and other languages). celery_executor # -*- coding: utf-8 -*- # # Licensed under the Apache License, Version 2. A Celery worker runs as many concurrent jobs as there are CPUs by default, so when you play with this example make sure you start a large number of tasks to see how Celery keeps jobs in PENDING state until the worker can take it. Celery is an asynchronous task queue/job queue system, which is used by MiaRec web portal for different tasks (long running tasks and/or periodic jobs). Being a Python stack, Celery felt like a natural fit to manage codecov's long running tasks. App's Local Working Screenshot and Explanation Note: To run this app locally you need to have install amqp (messaging) Local Installation & Running. Celery knows six built-in states:. The backend parameter is an optional parameter that is necessary if you wish to query the status of a background task, or retrieve its results. It's best to keep them simple. from celery import Celery app = Celery('tasks', backend='amqp', broker='amqp://') The first argument to the Celery function is the name that will be prepended to tasks to identify them. 这将列出工作者已经预取的所有任务,并且当前正在等待执行(不包括设置了ETA值的任务)。 1. Whether you are interested in running a large scale distributed computation or you want to setup background jobs for your Django app, Celery is the way to go. It is focused on real-time operation, but supports scheduling as well. Celery can be used to add color, texture, and taste. It is designed around best practices so that your product can scale and integrate with other languages, and it comes with the tools and support you need to run such a system in production. You can vote up the examples you like or vote down the ones you don't like. Using Celery on Heroku. If the distributed message processor, Celery, isn't working out for your project, you might want to try a new tool called Kuyruk. by default, celery keeps unexecuted tasks in it’s queue even when it’s restarted. If you need to set any of the settings (attributes) you'd normally be able to set on a Celery Task class had you written it yourself, you may specify them in a dict in the CELERY_EMAIL_TASK_CONFIG setting:. Celery is "an asynchronous task queue/job queue based on distributed message passing. Note you need to handle the producer/connection manually for this to work. celery -A tasks inspect reserved. RQ (http://python-rq. Your next step would be to create a config that says what task should be executed and when. Learn about why you might want a task queue (and when you definitely don't), when Celery is appropriate, and what you can do when it's not. Celery Django Scheduled Tasks. You can vote up the examples you like or vote down the ones you don't like. To create a periodic task executing at an interval you must first create the interval object: >>> from django_celery_beat. Defining it as 1 will tell Celery that it should only reserve one task per worker process at a time. Automation in Django is a developer dream. Introduction ¶. Celery is a distributed task queue for Python projects. task logger is a special logger set up by the Celery worker. This will continue until the Celery task completes. "-A celery_blog" tells that celery configuration, which includes the app and the tasks celery worker should be aware of, is kept in module celery_blog. Getting Help. It makes asynchronous task management easy. 27 November 2011 This is the second part in a series about Django in Production. Celery is perfectly suited for tasks which will take some time to execute but we don. Celery is the most advanced task queue in the Python ecosystem and usually considered as a de facto when it comes to process tasks simultaneously in the background. By default the entries are taken from the beat_schedule setting, but custom stores can also be used, like storing the entries in a SQL database. Celery is the ubiquitous python job queueing tool and jobtastic is a python library that adds useful features to your Celery tasks. tips1: clear all pycache files or folders in your project. Celery is a distributed task queue for Python. Celery is "an asynchronous task queue/job queue based on distributed message passing. Or if you need to send tasks from one microservice. run py3clean or pyclean command in your work directory to clear all cache. By default the entries are taken from the CELERYBEAT_SCHEDULE setting, but custom stores can also be used, like storing the entries in an SQL database. Below is the structure of our demo project. Reusable tasks ¶. It's best to keep them simple. Celery is the ubiquitous python job queueing tool and jobtastic is a python library that adds useful features to your Celery tasks. AsyncResult(task_id) That will get you the result of a previous task. This includes RabbitMQ, MSMQ, Active MQ, Tibco EMS, etc. Celery makes it easy to write to the task queue, thereby delaying the task until a worker can take it from the queue. How to create Periodic Tasks with Django Celery? Celery provides asynchronous job queues, which allows you to run Python functions in the background. py file removing the import of the time package since this is no longer needed and import the newly created tasks object. This can be achieved by using task_postrun signal provided by celery. It's a task queue with focus on real-time processing, while also supporting task scheduling. As I soon discovered, Celery is a fast and powerful way to turn any python callable into a scheduled task, or message-processing worker. A task queue's input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform. Celery tasks always have a state. But, if i use celery beat, the parameters passed to the external "library" function, once the task is called, are strings and not serialized dicts. When a task is ready to be run, Celery puts it on a queue, a list of tasks that are ready to be run. If we were just to import the standard logging module, Celery will patch the logger module to add process-aware information (ensure_process_aware_logger()), and then add format/handlers to both the root logger and the logger defined by the multiprocessing module (the multiprocessing get_logger() does not use process shared-logs but it allows. While it supports scheduling, its focus is on operations in real time. Celery is a distributed task queue for Python projects. Being a Python stack, Celery felt like a natural fit to manage codecov's long running tasks. If a task execution resulted in an exception, its state is FAILURE. py, do not define the custom state as in this example. local # Force an specified worker to cancel consuming from a queue. It is focused on real-time operation, but supports scheduling as well. task logger. Notice how we decorated the send_verification_email function with @app. celery -A celery_task beat 执行任务 celery -A celery_task worker -l info -P eventlet 将以上两条合并 celery -B -A celery_task worker 后台启动celery worker进程 celery multi start work_1 -A appcelery 停止worker进程,如果无法停止,加上-A celery multi stop WORKNAME 重启worker进程 celery multi restart WORKNAME. Celery - Distributed Task Queue¶ Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. To install Celery execute the following command:. A task queue's input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform. >>> result = celery. get_context_data, a method like Django Views that should provide a dictionary for passing to the template renderer, 2. Custom Celery task states How to hack Celery task states Published on September 28, 2018 Estimated reading time: 8 minutes. Logging what’s happening in your tasks. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: