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There are some limitations to the FeatureLayerManager.truncate method, which are shown below (REST docs found here), one of them being sync enabled feature layers.
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Tuesday
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I can see scenarios where you may just want to delete certain features using a where clause. For example, we have a hosted feature layer that stores point-in-time data and has data written to it every 5 minutes. Once the data is written, it's not going to change, so there's not really a point in deleting all rows and replacing them. However, at some point we do want to trim the data within the feature layer, so we use delete_features to delete those feature older than a certain date. The graph below shows the item size for thisparticular feature layer. It's still growing over time, but at a much slower rate...possibly (likely?) because we aren't removing ALL features when it's updated, but rather deleting features once a day based on the date value. If you're going to be replacing ALL features within a hosted feature layer, I think this makes the case to use truncate over delete_features.
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Tuesday
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45
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Yea, when we're doing anything with internal datasets (ex: file gdb or enterprise gdb), we use truncate over deleting features. Makes sense to use it with feature layers too.
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Tuesday
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I was aware of the truncate method within the FeatureLayerManager and was hesitant to use it due to its restrictions, outlined here. However, I did change one of our scripts to use this rather than FeatureLayer.delete_features to see if it provided any change. And...it seems to have done the job! I made the change to this script around 11:30AM, and by about 1PM the feature layer showed a substantial reduction in item size, see first graph below. The item size looks to have remained consistent since making the change to use FeatureLayerManager.truncate. In the second graph below is one of our hosted feature layers that's still using FeatureLayer.delete_features. You can see this feature layer continues to grow in item size. I won't speculate on what's being done behind the scenes by Esri, but it does seem that using the truncate method flushes/resets/removes something that the delete_features method is not. Example 1: Hosted feature layer changed to using FeatureLayerManager.truncate at around 11:30AM on 12/8/2025. Substantial drop in item size noticed around 1PM and item size looks to remain consistent. Example 2: Hosted feature layer still using FeatureLayer.delete_features and the item size continues to grow.
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Tuesday
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I was curious about the same, so I created a script that pulls the size of some of our hosted feature layers. It includes a mix of those that we update via publishing a file geodatabase, and those that we update via deleting/adding features. The feature layers that are published from a file geodatabase do NOT show increased item size. Those that are updated using delete/add features DO show increased item size even though the number of features is NOT increasing proportional to their shown size. We ARE seeing this ballooning reflected in our credit usage. Below are some charts displaying what we're seeing. Example 1: This feature layer is updated every 30 minutes using delete/add features. On Friday afternoon the size of the feature layer was about 17-18MB. It is now showing about 45MB. The number of features and populated fields in this feature layer remains relatively consistent. Example 2: This feature layer is updated every 2 minutes using delete/add features. On Friday afternoon the size of the feature layer was about 2MB. It is now showing about 7MB. The number of features in this feature layer does oscillate but is likely never above 40 features. Example 3: This feature layer is updated once a day by publishing a file geodatabase. The size remains consistent. Example 4: Below is snapshot of our available credits near the end of the day for the past few days (the DateValue field is set to noon UTC for our time zone). The "CreditsUsed" field is calculated from the previous day's value. We first noticed one of our hosted feature layers ballooning in size on Friday, 12/5/2025. Once we noticed it, I changed this feature layer to be updated via publishing a file geodatabase. You can see the noticeable bump in credit usage on Thursday, 12/4, and Friday, 12/5. Once the feature layer in question was changed to be updated via a file geodatabase, the credit usage remained steady on Saturday, 12/6 and Sunday, 12/7. I would really like an explanation from someone at Esri as to why this is happening (apologies to the Esri staff I tagged, you all showed up as top collaborators). @EmilyGeo @RussRoberts @George_Thompson
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Monday
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We have a feature layer that over the past couple days has ballooned in size with no reasonable explanation I can find. This feature layer contains approximately 11k line features and less than 10 fields. Every 10 minutes the feature layer has all rows deleted and replaced using a feature set and the Python API. This morning I manually overwrote the feature layer, and the item size was around 5MB. A few hours later, the item has ballooned in size to 250MB!?! The feature layer does NOT have editing enabled, no associated views, no extra indexes, does NOT have optimize layer drawing enabled, and no attachments. Is there any reasonable explanation for why this feature layer continues to balloon in size?
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Friday
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I'm wondering why usage values are not returned for certain item types, specifically hosted tile layers, when using the Python API? When viewing the Usage tab for these items, counts appear. However, when using the Python API, either the counts are way off, or not even returned. Is this a known limitation based on item type? Below are a few examples: Example 1: Hosted Tile Layer Item usage via the item page Item usage via the Python API. The usage values ("num") are not close. Example 2: Hosted Vector Tile Layer Item usage via the item page Item usage via the Python API. The data key is an empty list. Example 3: Hosted Feature Layer Item usage via the item page Item usage via the Python API --- matches the item page
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10-21-2025
09:46 AM
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IDEA
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@SSWoodward To the best of my knowledge, having the ability to define precision and scale, specifically on the Create New Feature Class/Table interface has NEVER been available in Pro. This IS available in ArcMap/Catalog, and as many commentors have mentioned, having this ability seems rather simple given that it's available in ArcMap/Catalog and for certain Pro tools (ex: Add Field tool), and its omission is frustrating, especially going on 10 years of having Pro. Pro Create New Feature Class Interface --- this is an enterprise geodatabase --- no ability to define precision and scale in the field properties ArcCatalog Create New Feature Class Interface --- same enterprise geodatabase as above --- ability to define precision and scale Pro Add Field tool --- Precision and Scale ARE available
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08-07-2025
02:02 PM
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POST
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I'm already handling the dates if they're prior to 1900 (code below). I'm really wondering why I'm getting an error at all when trying to insert a feature with a date prior to 1900...are these dates not supported by the Python API? def format_date(input_value):
if isinstance(input_value, datetime.datetime):
if input_value.year <= 1900:
return None
else:
return input_value
else:
return None
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02-28-2025
06:02 AM
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I'm getting the errors shown in the first screenshot below when trying to add features to a hosted feature layer using the Python API. The features throwing the error all seem to have dates at or prior to 1/1/1900. When constructing the JSON for the features to add, I'm assigning our local time zone to the datetime object. Is there a different approach I should be taking to avoid these errors? The second screenshot shows a sample of the dictionary key and value being used when trying to add features. If I set any dates at or prior to the year 1900 to NoneType objects, the features are written to the hosted feature layer without errors. 'DateField': datetime.datetime(1900, 1, 1, 12, 0, tzinfo=tzlocal())
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02-27-2025
10:41 AM
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BLOG
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Hi @MollyWalkner_1, The URL parameters page you reference is what I eventually found. Below is an example URL using the minimal number of parameters to accomplish zooming the map to the searched feature. The text in bold will need to be replaced with actual values. The "widget_number" corresponds to a search widget. https://...experience_url...#widget_number=search_status:%7B"searchText"%3A"search_string"%7D Thanks!
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01-16-2025
11:33 AM
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POST
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Hi @ShengdiZhang , Thanks for pointing me in the right direction. For any future users, I referenced the "Search widget parameters" section of the Esri documentation below. https://doc.arcgis.com/en/experience-builder/latest/build-apps/url-parameters.htm
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01-15-2025
02:50 PM
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513
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POST
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Does the "zoom_to_selection" URL parameter support a search widget using a locator service? ... or, does the search widget have to be configured to search a feature layer? If a locator service is NOT supported, are there plans to support this? We have several of our WAB apps where users make use of the "find?" URL parameter for zooming an app to a particular feature, and our search widget for those apps use a composite locator service so we don't have to publish or configure multiple feature layers.
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01-13-2025
06:12 AM
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BLOG
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Hi @Jianxia , For the URL parameters that support zooming to selected features, does this support a search widget configured with a locator service? ... or, does the search widget have to be configured to search a feature layer? If a locator service is NOT supported, are there plans to support this? We have several of our WAB apps where users make use of the "find?" URL parameter for zooming an app to a particular feature, and our search widget for those apps use locator services. Thanks!
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01-13-2025
06:08 AM
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| Title | Kudos | Posted |
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| 1 | Tuesday | |
| 1 | 01-16-2025 11:33 AM | |
| 1 | 01-13-2025 06:12 AM | |
| 1 | 01-13-2025 06:08 AM | |
| 1 | 03-19-2021 09:08 AM |
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