{"id":929,"date":"2019-04-10T14:08:35","date_gmt":"2019-04-10T11:08:35","guid":{"rendered":"https:\/\/djangostars.com\/blog\/?p=929"},"modified":"2025-09-10T17:17:07","modified_gmt":"2025-09-10T17:17:07","slug":"the-python-celery-cookbook-small-tool-big-possibilities","status":"publish","type":"post","link":"https:\/\/djangostars.com\/blog\/the-python-celery-cookbook-small-tool-big-possibilities\/","title":{"rendered":"The Python Celery Cookbook: Small Tool, Big Possibilities"},"content":{"rendered":"<p>Everyone in the Python community has heard about Celery at least once, and maybe even already worked with it\u2014especially when navigating <a href=\"https:\/\/djangostars.com\/blog\/python-2-to-python-3-migration\/\">Python 2 to 3 migration<\/a> for background workers. In short, it lets you offload scheduled or specialized tasks to separate processes or servers, which boosts <a href=\"https:\/\/djangostars.com\/blog\/python-performance-improvement\/\">Python performance<\/a> while saving time and effort.<\/p>\n<p>Our tutorial is based on many years of development experience that we have implemented in our projects. Therefore, all information about Celery in this article is based only on real projects.<br \/>\n<div class=\"info_box_shortcode_holder\" style=\"background-image: url(https:\/\/djangostars.com\/blog\/wp-content\/uploads\/2023\/08\/Web-Development_2.png)\">\n    <div class=\"info_box_label\">\n    Services\n    <\/div>\n    <div class=\"info_box_logo\">\n    \n    <\/div>\n    \n    <div class=\"info_box_title font_size_\">\n   <span class=\"info_box_title_inner\">Explore our Software Architrcture Services.&lt;br \/&gt;\n<\/span>\n    <\/div>\n    <div class=\"info_box_link\">\n        <a href=\"https:\/\/djangostars.com\/services\/digital-product-development\/#software_architecture\" target=\"_blank\" >\n            <span>Learn More<\/span>\n            <div class=\"button_animated\">\n                <svg width=\"24\" height=\"12\" viewBox=\"0 0 24 12\" fill=\"none\"\n                     xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M23.725 5.33638C23.7248 5.3361 23.7245 5.33577 23.7242 5.33549L18.8256 0.460497C18.4586 0.0952939 17.865 0.096653 17.4997 0.463684C17.1345 0.830668 17.1359 1.42425 17.5028 1.7895L20.7918 5.06249H0.9375C0.419719 5.06249 0 5.48221 0 5.99999C0 6.51777 0.419719 6.93749 0.9375 6.93749H20.7917L17.5029 10.2105C17.1359 10.5757 17.1345 11.1693 17.4998 11.5363C17.865 11.9034 18.4587 11.9046 18.8256 11.5395L23.7242 6.66449C23.7245 6.66421 23.7248 6.66388 23.7251 6.6636C24.0923 6.29713 24.0911 5.70163 23.725 5.33638Z\"\n                          fill=\"#282828\"><\/path>\n                <\/svg>\n                <div class=\"shape\"><\/div>\n            <\/div>\n        <\/a>\n    <\/div>\n<\/div><\/p>\n<h2 id=\"header0\">An Introduction to the Celery Python Guide<\/h2>\n<p>Celery tool decreases performance load by running part of the functionality as postponed tasks either on the same server as other tasks, or on a different server. Most commonly, developers use it for sending emails. However, Celery has a lot more to offer. In this article, I\u2019ll show you some Celery basics, as well as a couple of Python-Celery best practices.<\/p>\n<h2 id=\"header1\">Celery Basics<\/h2>\n<p>If you have worked with Celery before, feel free to skip this chapter. But if Celery is new to you, here you will learn <a href=\"https:\/\/docs.celeryq.dev\/en\/latest\/getting-started\/first-steps-with-celery.html#application\" rel=\"nofollow\">how to enable Celery<\/a> in your project, and participate in a separate <a href=\"https:\/\/docs.celeryq.dev\/en\/latest\/django\/first-steps-with-django.html\" rel=\"nofollow\">tutorial<\/a> on using Celery with Django. Basically, you need to create a Celery instance and use it to mark Python functions as tasks.<br \/>\nIt\u2019s better to create the instance in a separate file, as it will be necessary to run Celery the same way it works with WSGI in Django. For example, if you create two instances, Flask and Celery, in one file in a Flask application and run it, you\u2019ll have two instances, but use only one. It\u2019s the same when you run Celery Python.<\/p>\n<h2 id=\"header2\">Primary Python Celery Examples<\/h2>\n<p>As I mentioned before, the go-to case of using Celery is sending email. I will use this example to show you the basics of using Celery. Here\u2019s a quick Celery Python tutorial:<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/7be985f38235e5795cdd9786f38f9c89.js\"><\/script><br \/>\nThis code uses Django, as it\u2019s our main framework for web applications. By using Celery, we reduce the time of response to customer, as we separate the sending process from the main code responsible for returning the response.<br \/>\nThe simplest way to execute this task is to call <code>delay<\/code> method of function that is provided by <code>app.task<\/code> decorator.<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/1343695d3a77b981cceb9ceae9ef61cc.js\"><\/script><br \/>\nNot only this \u2013 Celery provides more benefits. For example, we could set up retries upon failing.<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/4c8bd2c5e75631c069759443026d2e09.js\"><\/script><br \/>\nNow the task will be restarted after ten minutes if sending fails. Also, you\u2019ll be able to set the number of retries.<br \/>\nSome of you may wonder why I moved the template rendering outside of the <code>send_mail call<\/code>. It\u2019s because we wrap the call of <code>send_mail<\/code> into <code>try\/except<\/code>, and it\u2019s better to have as little code in <code>try\/except<\/code> as possible.<\/p>\n<h2 id=\"header3\">Celery for Advanced Users<\/h2>\n<h3>Celery Django Scheduled Tasks<\/h3>\n<p>Celery makes it possible to run tasks by schedulers like crontab in Linux.<br \/>\nFirst of all, if you want to use periodic tasks, you have to run the Celery worker with &#8211;beat flag, otherwise Celery will ignore the scheduler. Your next step would be to create a config that says what task should be executed and when. Here\u2019s an example:<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/2d6a3d10004f0390d72ff743916ca42b.js\"><\/script><br \/>\n*if you don\u2019t use Django, you should use <code>celery_app.conf.beat_schedule<\/code> instead of <code>CELERY_BEAT_SCHEDULE<\/code><br \/>\nWhat we have in this configuration is only one task that will be executed every Monday at 7 a.m.. The root key is a name or a cronjob, not a task.<br \/>\nYou can add arguments to tasks and choose what should be done in case the same task should run at different times with different arguments. The <code>crontab<\/code> method supports the syntax of the system crontab \u2013 such as <code>crontab(minute=\u2019*\/15\u2019)<\/code>\u2013 to run the task every 15 minutes.<\/p>\n<h3>Postponed Task Execution In Celery<\/h3>\n<p>You can also set tasks in a Python Celery queue with timeout before execution. (For example, when you need to send a notification after an action.) To do this, use the <code>apply_async method<\/code> with an <code>eta<\/code> or <code>countdown<\/code> argument.<br \/>\n<div class=\"supsystic-table-loader spinner\"style=\"background-color:#000000\"><\/div><div id=\"supsystic-table-13_8476\" class=\"supsystic-tables-wrap\" style=\" background: url(); visibility: hidden; \" data-table-width-fixed=\"100%\" data-table-width-mobile=\"100%\" ><table id=\"supsystic-table-13\" data-border-spacing=\"\" class=\"supsystic-tableborder lightboxImgcell-border\" data-id=\"13\" data-view-id=\"13_8476\" data-title=\"apply_async method with an eta or countdown argument\" data-currency-format=\"$1,000.00\" data-percent-format=\"10.00%\" data-date-format=\"DD.MM.YYYY\" data-time-format=\"HH:mm\" data-features=\"[&quot;after_table_loaded_script&quot;]\" data-search-value=\"\" data-lightbox-img=\"\" data-head-rows-count=\"1\" data-pagination-length=\"50,100,All\" data-auto-index=\"off\" data-searching-settings=\"{&quot;columnSearchPosition&quot;:&quot;bottom&quot;,&quot;minChars&quot;:&quot;0&quot;}\" data-lang=\"default\" data-override=\"{&quot;emptyTable&quot;:&quot;&quot;,&quot;info&quot;:&quot;&quot;,&quot;infoEmpty&quot;:&quot;&quot;,&quot;infoFiltered&quot;:&quot;&quot;,&quot;lengthMenu&quot;:&quot;&quot;,&quot;search&quot;:&quot;&quot;,&quot;zeroRecords&quot;:&quot;&quot;,&quot;exportLabel&quot;:&quot;&quot;,&quot;file&quot;:&quot;default&quot;}\" data-merged=\"[]\" data-responsive-mode=\"0\" data-from-history=\"0\" style=\"width:100%;\" ><thead><tr><th class=\"\" style=\"width:50%; padding: 0 !important;\"><\/th><th class=\"\" style=\"width:50%; padding: 0 !important;\"><\/th><\/tr><\/thead><tbody><tr ><td data-cell-id=\"A1\" data-x=\"0\" data-y=\"1\" class=\"\" data-cell-type=\"text\" data-original-value=\"eta\" data-order=\"eta\" style=\"min-width:50%; \" >eta <\/td><td data-cell-id=\"B1\" data-x=\"1\" data-y=\"1\" class=\"\" data-cell-type=\"text\" data-original-value=\"execute task at exact time\" data-order=\"execute task at exact time\" style=\"min-width:50%; \" >execute task at exact time <\/td><\/tr><tr ><td data-cell-id=\"A2\" data-x=\"0\" data-y=\"2\" class=\"\" data-cell-type=\"text\" data-original-value=\"countdown\" data-order=\"countdown\" >countdown <\/td><td data-cell-id=\"B2\" data-x=\"1\" data-y=\"2\" class=\"\" data-cell-type=\"text\" data-original-value=\"execute task in N seconds\" data-order=\"execute task in N seconds\" >execute task in N seconds <\/td><\/tr><\/tbody><\/table><!-- \/#supsystic-table-13.supsystic-table --><\/div><!-- \/.supsystic-tables-wrap --><!-- Tables Generator by Supsystic --><!-- Version:1.9.77 --><!-- http:\/\/supsystic.com\/ --><br \/>\nIn the first example, the email will be sent in 15 minutes, while in the second it will be sent at 7 a.m. on May 20.Let\u2019s look at what it might look like in code:<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/dcaf6020e5d484f03e88ae951dd6b90e.js\"><\/script><\/p>\n<h3>Setting Up Python Celery Queues<\/h3>\n<p>Celery can be distributed when you have several workers on different servers that use one message queue for task planning. You can configure an additional queue for your task\/worker. For example, sending emails is a critical part of your system and you don\u2019t want any other tasks to affect the sending. Then you can add a new queue, let\u2019s call it <code>mail<\/code>, and use this queue for sending emails.<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/041abd8e1e4fbc76678284bfbd0c2d30.js\"><\/script><br \/>\n*if you don\u2019t use Django, use <code>celery_app.conf.task_routes<\/code> instead of <code>CELERY_TASK_ROUTES<\/code><br \/>\nRun two separate celery workers for the default queue and the new queue:<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/62110e56b508c901ff1584dc2607adaa.js\"><\/script><br \/>\nThe first line will run the worker for the default queue called <code>celery<\/code>, and the second line will run the worker for the <code>mail<\/code> queue. You can use the first worker without the <code>-Q<\/code> argument, then this worker will use all configured queues.<\/p>\n<h3>Python Celery Long Running Tasks<\/h3>\n<p>Sometimes, I have to deal with tasks written to go through database records and perform some operations. Quite often, developers forget about data growth, which can lead to a very long task running time. It\u2019s always better to write tasks like these in a way that allows working with data chunks. The easiest way is to add an offset and limit parameters to a task. This will allow you to indicate the size of the chunk, and the cursor to get a new chunk of data.<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/a8fc28a8ec4c27b281acbed25fb90fe9.js\"><\/script><br \/>\nThis is a very simple example of how a task like this can be implemented. At the end of the task, we check how many users we found in the database. If the number equals the limit, then we\u2019ve probably got new users to process. So we run the task again, with a new offset. If the user count is less than the limit, it means it\u2019s the last chunk and we don\u2019t have to continue. Beware, though: this task implementation needs to have the same ordering for records every time.<!--[cta-banner title=\"<u>Python &amp; Django<\/u> development\" image=\"https:\/\/djangostars.com\/blog\/wp-content\/uploads\/2019\/05\/2_1_bg.png\" button=\"Learn more\" link=\"https:\/\/djangostars.com\/services\/python-django-development\/\" small_text=\"Your chance to enter the market faster\"]--><br \/>\n<div class=\"info_box_shortcode_holder\" style=\"background-image: url(https:\/\/djangostars.com\/blog\/wp-content\/uploads\/2023\/08\/Python-Development.png)\">\n    <div class=\"info_box_label\">\n    Services\n    <\/div>\n    <div class=\"info_box_logo\">\n    \n    <\/div>\n    \n    <div class=\"info_box_title font_size_\">\n   <span class=\"info_box_title_inner\">Python: Battle-tested solutions for any needs.&lt;br \/&gt;\n<\/span>\n    <\/div>\n    <div class=\"info_box_link\">\n        <a href=\"https:\/\/djangostars.com\/services\/python-development\/\" target=\"_blank\" >\n            <span>Learn More<\/span>\n            <div class=\"button_animated\">\n                <svg width=\"24\" height=\"12\" viewBox=\"0 0 24 12\" fill=\"none\"\n                     xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                    <path d=\"M23.725 5.33638C23.7248 5.3361 23.7245 5.33577 23.7242 5.33549L18.8256 0.460497C18.4586 0.0952939 17.865 0.096653 17.4997 0.463684C17.1345 0.830668 17.1359 1.42425 17.5028 1.7895L20.7918 5.06249H0.9375C0.419719 5.06249 0 5.48221 0 5.99999C0 6.51777 0.419719 6.93749 0.9375 6.93749H20.7917L17.5029 10.2105C17.1359 10.5757 17.1345 11.1693 17.4998 11.5363C17.865 11.9034 18.4587 11.9046 18.8256 11.5395L23.7242 6.66449C23.7245 6.66421 23.7248 6.66388 23.7251 6.6636C24.0923 6.29713 24.0911 5.70163 23.725 5.33638Z\"\n                          fill=\"#282828\"><\/path>\n                <\/svg>\n                <div class=\"shape\"><\/div>\n            <\/div>\n        <\/a>\n    <\/div>\n<\/div><\/p>\n<h3>Celery: Getting Task Results<\/h3>\n<p>Most developers don\u2019t record the results they get after running the task. Imagine that you can take a part of code, assign it to a task and execute this task independently as soon as you receive a user request. When we need the results of the task, we either get the results right away (if the task is completed), or wait for it to complete. Then we include the result to the general response. Using this approach, you can decrease response time, which is very good for your users and site rank.<br \/>\nWe use this feature to run simultaneous operations. In one of our projects, we have a lot of user data and a lot of service providers. To find the best service provider, we do heavy calculations and checks. To do it faster, we create tasks for user with each service provider, run them and collect results to show to the user. It\u2019s very easy to do with Celery task groups.<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/cd41095804b94fe458a15330f9dfe4e6.js\"><\/script><br \/>\nFirst, why do we even run two tasks? We use the second task to form calculation task groups, launch and return them. On top of that, the second task is where you can assign project filtration \u2013 like service providers that need to be calculated for a given user. All this can be done while Celery is doing other work. When the task group returns, the result of the first task is actually the calculation we are interested in.<br \/>\nHere\u2019s an example of how to use this approach in code:<br \/>\n<script src=\"https:\/\/gist.github.com\/Quard\/8538d286b95451377ad22decd7627f48.js\"><\/script><br \/>\nHere, we run calculations as soon as possible, wait for the results at the end of the method, then prepare the response and send it to the user.<!--[mailchimp-form]--><\/p>\n<h2 id=\"header4\">Python Celery Best Practices<\/h2>\n<h3>Tiny Data<\/h3>\n<p>I\u2019ve probably already mentioned that I use database record IDs as task arguments instead of full objects. This is a good way to decrease the message queue size. But what\u2019s more important is that when a task is executed, the data in the database can be changed. And when you have only IDs, you will get fresh data as opposed to outdated data you get when passing objects.<\/p>\n<h3>Transactions<\/h3>\n<p>Another best practice of Celery. Sometimes, issues may arise when an executed task can\u2019t find an object in a database. Why does this happen? In Django, for instance, you want to run tasks after a user is registered, like sending a greeting email, and your Django settings wrap all requests into a transaction. In Celery, however, tasks are executed fast, before the transaction is even finished. So if you use Celery when working in Django, you might see that the user doesn\u2019t exist in the database (yet).<br \/>\nTo deal with this, you can Google \u201ctask transaction implementation\u201d. In general, it\u2019s an overwritten <code>apply_async<\/code> method in task, a class that sets up a task in <code>transaction.on_commit<\/code> signal instead of doing it immediately.<\/p>\n<h2 id=\"header5\">Conclusion<\/h2>\n<p>As you see, Celery has a lot more uses than just sending emails. You can run different tasks simultaneously using the main process, and while you do your job, Celery will complete the smaller tasks at hand. You can set up queues, work with data chunks on long-running tasks, and set up times for your tasks to be executed. This will allow you to better plan your work progress, plan development time more efficiently, and spend your precious time working on the bigger things while Celery task groups work their magic. <b><a href=\"https:\/\/djangostars.com\/get-in-touch\/\">Contact Django Stars<\/a> to discuss with the experienced <a href=\"https:\/\/djangostars.com\/services\/python-development\/\">Python custom software development<\/a> team how to add some Celery to your project.<\/b><div class=\"lead-form-wrapper lets_disqus\">\n    <div class=\"lead-form transparent-footer\">\n        <p class=\"discuss-title paragraph-discuss col-md-12\">Have an idea? 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But it can be a handy tool for managing task queues, for example, when you work with Django and want to add background processing of long-running tasks.<\/dd>\n\t\t\t<\/dl><dl>\n\t\t\t\t<dt>Why use Celery Python \n\t\t\t\t<div class=\"cross\">\n\t\t\t\t<span><\/span>\n\t\t\t\t<span><\/span>\n\t\t\t\t<\/div>\n\t\t\t\t<\/dt>\n\t\t\t\t<dd>Celery decreases performance load by running part of the functionality as postponed tasks.  Basically, it\u2019s a handy tool that helps run postponed or dedicated code in a separate process or even on a separate computer or server. This saves time and effort on many levels.<\/dd>\n\t\t\t<\/dl><dl>\n\t\t\t\t<dt>When to use Celery Python? \n\t\t\t\t<div class=\"cross\">\n\t\t\t\t<span><\/span>\n\t\t\t\t<span><\/span>\n\t\t\t\t<\/div>\n\t\t\t\t<\/dt>\n\t\t\t\t<dd>Most commonly, developers use it for sending emails. However, Celery has a lot more to offer. It makes it possible to run periodic tasks by schedulers like crontab in Linux. You can also set tasks in a Python Celery queue with timeouts before execution. Celery helps run different tasks simultaneously using the main process, and while you do your job, Celery will complete the smaller tasks at hand. You can set up queues, work with data chunks on long-running tasks, and set up times for your tasks to be executed. This will allow you to better plan your work progress, plan development time more efficiently, and focus on the bigger things.<\/dd>\n\t\t\t<\/dl><dl>\n\t\t\t\t<dt>Can I Use Celery Without Django? \n\t\t\t\t<div class=\"cross\">\n\t\t\t\t<span><\/span>\n\t\t\t\t<span><\/span>\n\t\t\t\t<\/div>\n\t\t\t\t<\/dt>\n\t\t\t\t<dd>Yes. The use of Celery is not limited to Django projects. You can create the Celery object and use it as a generic asynchronous task queue with or without web frameworks like Django and Flask.<\/dd>\n\t\t\t<\/dl><dl>\n\t\t\t\t<dt>What are some alternatives to Celery Python? \n\t\t\t\t<div class=\"cross\">\n\t\t\t\t<span><\/span>\n\t\t\t\t<span><\/span>\n\t\t\t\t<\/div>\n\t\t\t\t<\/dt>\n\t\t\t\t<dd>Celery has long been the most popular asynchronous task queue for Python. It provides excellent opportunities for working with tasks, launching them within a certain period, and fine-tuning. However, this is not the only such tool. For Python alternatives, you can also look into pro and cons of utilizing arq, Huey, Dramatiq, APScheduler, or RQ (Redis Queue).<\/dd>\n\t\t\t<\/dl><dl>\n\t\t\t\t<dt>How does Celery integrate with Django? \n\t\t\t\t<div class=\"cross\">\n\t\t\t\t<span><\/span>\n\t\t\t\t<span><\/span>\n\t\t\t\t<\/div>\n\t\t\t\t<\/dt>\n\t\t\t\t<dd>Since version 3.1, Celery supports Django out of the box and no longer requires a separate library to work with Django. To learn more about how to integrate Celery and Django, check out Celery's <a href=\"https:\/\/docs.celeryq.dev\/en\/stable\/django\/index.html\" rel=\"nofollow\">documentation<\/a>.<\/dd>\n\t\t\t<\/dl><\/div>\n\t\t\t<\/div>\n\t\t<\/div><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Everyone in the Python community has heard about Celery at least once, and maybe even already worked with it\u2014especially when navigating Python 2 to 3 migration for background workers. In short, it lets you offload scheduled or specialized tasks to separate processes or servers, which boosts Python performance while saving time and effort. Our tutorial [&hellip;]<\/p>\n","protected":false},"author":35,"featured_media":3548,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[68,44],"tags":[30],"class_list":["post-929","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-engineering","category-python-django","tag-backend"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Software Development Blog &amp; IT Tech Insights | Django Stars<\/title>\n<meta name=\"description\" content=\"Many developers know Celery, but few explore its full potential. The Celery Cookbook helps you efficiently manage queues and task groups for smoother workflows.\" \/>\n<link rel=\"canonical\" href=\"https:\/\/djangostars.com\/blog\/wp-json\/wp\/v2\/posts\/929\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Python Celery Cookbook: Small Tool, Big Possibilities\" \/>\n<meta property=\"og:description\" content=\"Most developers are familiar with Celery, but not everyone knows the possibilities this tool opens up. Use the Celery Cookbook to find out how to efficiently organize your work with Celery queues and task groups.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/djangostars.com\/blog\/the-python-celery-cookbook-small-tool-big-possibilities\/\" \/>\n<meta property=\"og:site_name\" content=\"Software Development Blog &amp; IT Tech Insights | Django Stars\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/djangostars\/\" \/>\n<meta property=\"article:published_time\" content=\"2019-04-10T11:08:35+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-10T17:17:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/djangostars.com\/blog\/uploads\/2019\/04\/cover-16.png\" \/>\n<meta name=\"author\" content=\"Vadym Zakovinko\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"The Python Celery Cookbook: Small Tool, Big Possibilities\" \/>\n<meta name=\"twitter:description\" content=\"Most developers are familiar with Celery, but not everyone knows the possibilities this tool opens up. 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