I have a map/reduce script, I want to hydrate some data. The initial getInputData has a search that gets sales orders. We need to grab sales orders for a long stretch of time. The map portion of the script pings one of our upsert endpoints. The start date and end date for the search filter can be very large. Right now we have to only do 2 months at a time and wait. Is there a way to handle this better, or for the start date and end date to somehow change dynamically or is there another solution

AI generated answer (using internal Slack threads):

****** Disclaimer: This response was generated by an artificial intelligence assistant based on previous conversations from multiple Slack channels, and may contain inaccuracies or mistakes. ****** Based on the conversations, there are a few ways to handle large amounts of data in a map/reduce script: 1. Use Persistent Search: Persistent search can handle large amounts of data and send the results directly to a CSV file. This can be useful if you need to export the data for further analysis. 2. Use Date Filters: If your data is time-based (like sales orders), you can add a "Date Created" filter to your search. You can then adjust the date range until you get a manageable number of results. After running the map/reduce script once, you can update the filter to a new date range and run the script again. Repeat this process until you've covered the entire time period you're interested in. 3. Use a Formula: You can add a formula to your search to limit the number of results. For example, you could use the formula

rownum < 100000

to limit the results to the first 100,000 records. 4. Use Paginated Search: The

Search.runPaged(options)

method can be used to run a paginated search. This can significantly improve performance when dealing with large data sets. 5. Redesign the Script: If none of the above methods work, you may need to redesign your script. One suggestion is to do any necessary manipulation of the search results in the map stage, and then do the actual work in the reduce stage. Remember, handling large amounts of data can be challenging and may require some trial and error to find the best approach.