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what are the most common causes of timeouts for workflow steps?
The most common causes for timeouts in workflow steps are: 1. Your code is running for longer than expected. This is often due to making a request to a third-party API that doesn’t respond in the time you expect, or you’re processing a large amount of data in your workflow, and it doesn’t complete before you hit the execution limit. 2. Your workflow exceeds the default execution limit. For HTTP and Email-triggered workflows, the default is 30 seconds per execution. For Cron-triggered workflows, the default is 60 seconds per execution. If your code exceeds these limits, a Timeout error will be thrown and your workflow will be stopped. Remember, timeouts are not an issue with Pipedream — they are specific to your workflow. If you’re consistently hitting the execution limit, you can raise the execution limit of a workflow in your workflow’s settings. If you need to change the execution limit for an event source, please reach out to the Pipedream team. For more details, you can refer to the Pipedream troubleshooting guide and the Pipedream limits documentation.
How can I check the memory usage for a given step?
Pipedream does not currently expose a built-in feature to check the memory usage for a specific workflow step. However, you can use Node.js’s process.memoryUsage() within your code to get information about the memory usage of the process at that point in time. Here’s an example: ```
export default defineComponent({ async run({ steps, $ }) { const used = process.memoryUsage(); for (let key in used) { console.log(${key} ${Math.round((used[key] / 1024 / 1024) * 100) / 100} MB); } // Your code here }, });
can you show me how to use destructuring with a data store’s this.data.set functionality?
Sure, you can use destructuring with the this.data.set method. Here’s an example: export default defineComponent({ props: { data: { type: "data_store" }, }, async run({ steps, $ }) { // Assume you have an object with properties you want to store const obj = { prop1: 'value1', prop2: 'value2', prop3: 'value3' }; // Destructure the object const { prop1, prop2, prop3 } = obj; // Store each property in the data store await this.data.set('prop1', prop1); await this.data.set('prop2', prop2); await this.data.set('prop3', prop3); }, }) In this example, we’re destructuring the obj object into prop1, prop2, and prop3 variables, then storing each of these in the data store using this.data.set.
how would you recommend structuring a workflow that is repeatedly calling a rate-limited API and has to wait for the rate limits to reset in order to optimize for minimal compute time and thus credit usage?
To structure a workflow that calls a rate-limited API while optimizing for minimal compute time and credit usage, you can follow these recommendations: 1. Use Pipedream’s Execution Controls: Set execution controls to limit the rate at which your workflow executes to match the rate limit of the API. This helps to avoid hitting the rate limits and unnecessary executions that could consume credits. 2. Implement Retries with Exponential Backoff: If you hit a rate limit, implement a retry mechanism with exponential backoff within your workflow. This allows your workflow to wait for an increasing amount of time before retrying the API call, giving the rate limit time to reset. 3. Use Built-in Delay: Utilize Pipedream’s built-in $end function to end the workflow early if you detect that the rate limit has been reached. You can then use the pd.flow.rerun function to schedule the workflow to run again after a certain delay, which should be longer than the rate limit reset time. 4. Monitor API Usage: Keep track of your API usage by storing timestamps or counters in a Pipedream data store. This can help you determine when to pause or resume workflow execution based on the API’s rate limit window. 5. Optimize Workflow Logic: Ensure that your workflow logic is efficient and only makes API calls when necessary. Avoid unnecessary processing or API calls that do not contribute to the workflow’s main purpose. 6. Handle Webhook Events Properly: If your workflow is triggered by webhooks, make sure to handle and acknowledge the events quickly to avoid unnecessary retries from the sender, which could lead to additional compute time. Here’s an example of how you might implement some of these strategies in a Pipedream workflow: ``` export default defineComponent({ async run({ steps, $ }) { try { // Make an API call const response = await axios($, { method: “GET”, url: “https://api.rate-limited.com/data”, // Include necessary headers, params, etc. }); // Process the response // … } catch (error) { if (error.response && error.response.status === 429) { // We’ve hit the rate limit, so we end
is using $.flow.delay basically equivalent to something like JavaScript’s setTimeout() in a custom code step?
Yes, using $.flow.delay in a Pipedream workflow is conceptually similar to using setTimeout() in JavaScript. Both are used to introduce a delay in the execution of code. However, there are some differences in how they are used within the context of Pipedream workflows: - $.flow.delay is a method provided by Pipedream’s workflow builder that allows you to delay the execution of the next step in your workflow. It’s designed to be used within Pipedream’s Node.js environment and is part of the platform’s flow control features. - setTimeout() is a standard JavaScript function that sets a timer which executes a function or specified piece of code once the timer expires. In Pipedream, $.flow.delay is preferred for delaying steps in a workflow because it’s optimized for the platform’s execution model and billing. It allows you to pause the workflow without using compute time, which can save on execution credits. Here’s an example of how to use $.flow.delay in a Pipedream workflow: export default defineComponent({ async run({ steps, $ }) { // Delay the workflow for 1000 milliseconds (1 second) await $.flow.delay(1000); // The code after the delay will run after the specified delay period // ... }, }); Remember that when using $.flow.delay, the delay is specified in milliseconds.