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23 posts tagged with "2022"

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· 8 min read
Doug Sillars

In our previous post on image processing workflows, we built a Netflix Conductor workflow that took an image input, and then ran 2 tasks: The first task resizes and reformats the image, and the second task uploads the image to an AWS S3 bucket.

With today's varied screen sizes, and varied browser support, it is a common requirement that the image processing pipeline must create multiple images with different sizes and formats of each image.

To do this with a Conductor workflow, we'll utilize the FORK operation to create parallel processes to generate multiple versions of the same image. The FORK task creates multiple parallel processes, so each image will be created asynchronously - ensuring a fast and efficient process.

In this post, our workflow will create 2 versions of the same image - a jpg and webp.

· 2 min read

What is Conductor

Conductor is a Microservices orchestration platform from Netflix, released under Apache 2.0 Open Source License.

Design for failures

Failures and service degradation are the fact of any system, this is especially true with large interconnected systems running in cloud. Conductor is designed with principles that systems can and will go down, degrade in performance and any dependencies should be able to handle such failures.

Tasks in Conductor

Conductor workflows are orchestration of many activities known as task. Each task represents a (ideally) stateless worker that given a specific input does the work and produces output. The tasks are typically running outside of Conductor server and there are many factors that could effect their availability.

Designing for failures

Conductor allows you to define your stateful applications that can handle failures and temporary degradation of services and without having to write code for that.

Configuring tasks to handle failures

Each task in Conductor can be configured how it responds to availability events such as: 1) Failures 2) Timeouts and 3) Rate limits.

Here is a sample task definition:

"createdBy": "user",
"name": "sample_task_name_1",
"description": "This is a sample task for demo",
"responseTimeoutSeconds": 10,
"timeoutSeconds": 30,
"timeoutPolicy": "TIME_OUT_WF",
"retryCount": 3,
"retryLogic": "FIXED",
"retryDelaySeconds": 5,
"rateLimitPerFrequency": 0,
"rateLimitFrequencyInSeconds": 1

retry* parameters specify how to handle cases where the task execution fails and retries can be configured to be with fixed delay or exponential backoff. Similarly timeout* parameters specify how much time to give for task to complete execution and if the task should be marked as 'Timed Out' if it runs longer than that.

More Details

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· 9 min read
Doug Sillars

There are many tools available to work with images - resizing, changing the format, cropping, changing colors, etc. Tools like Photoshop require a lot of manual work to create image. Online tools for image processing are also extremely popular. But, rather than doing the work manually, or paying for a service to modify your images, wouldn't it be cool to have a workflow that does image resizing for you automatically? In this post, we'll build just this using Conductor to orchestrate the microservices involved, and to create an API-like surface for image processing.

In this post, we'll run Conductor locally on your computer. The Conductor workflow consists of two tasks. The first task reads in an image and resizes it according to the parameters provided (labeled "image_convert_resize_ref" in the image below). The second task ("image_toS3_ref" below) takes the resized image and saves the it to an Amazon S3 bucket.

Diagram of our image processing workflow

Using a microservice architecture for this process allows for easy swapping of components, and allows for easy extension of the workflow - easily adding additional image processing steps (or even swapping in and out different processes for different workflows). We could also easily change the location of the saved file based on different parameters.