> ## Documentation Index > Fetch the complete documentation index at: https://docs-old.tilebox.com/llms.txt > Use this file to discover all available pages before exploring further. # Querying data > Access and filter data stored in your datasets by time, location, and custom fields, with built-in pagination and progress tracking. Tilebox offers a powerful and flexible querying API to access and filter data from your datasets. When querying, you can [filter by time](https://docs-old.tilebox.com/datasets/query/filter-by-time.md), [filter by custom fields](https://docs-old.tilebox.com/datasets/query/filter-by-fields.md), and for [Spatio-temporal datasets](https://docs-old.tilebox.com/datasets/types/spatiotemporal.md), optionally [filter by a location in the form of a geometry](https://docs-old.tilebox.com/datasets/query/filter-by-location.md). ## Running a query You can query data from either a specific collection of a dataset, from a selected set of collections of a dataset, or from all collections of a dataset at once. Below is a simple example showcasing those options by querying Sentinel-2 data for April 2025 over the state of Colorado. ```python Python theme={"system"} from shapely import Polygon from tilebox.datasets import Client area = Polygon( # area roughly covering the state of Colorado ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)), ) client = Client() sentinel2_msi = client.dataset("open_data.copernicus.sentinel2_msi") # query data from a specific collection collection = sentinel2_msi.collection("S2A_S2MSI2A") data = collection.query( temporal_extent=("2025-04-01", "2025-05-01"), spatial_extent=area, show_progress=True, ) # query data from a selected set of collections data = sentinel2_msi.query( collections=["S2A_S2MSI2A", "S2B_S2MSI2A", "S2C_S2MSI2A"], temporal_extent=("2025-04-01", "2025-05-01"), spatial_extent=area, show_progress=True, ) # query data from all collections in the dataset data = sentinel2_msi.query( # omit the collections argument to query all collections temporal_extent=("2025-04-01", "2025-05-01"), spatial_extent=area, show_progress=True, ) ``` ```go Go theme={"system"} startDate := time.Date(2025, 4, 1, 0, 0, 0, 0, time.UTC) endDate := time.Date(2025, 5, 1, 0, 0, 0, 0, time.UTC) timeInterval := query.NewTimeInterval(startDate, endDate) area := orb.Polygon{ // area roughly covering the state of Colorado {{-109.05, 41.00}, {-109.045, 37.0}, {-102.05, 37.0}, {-102.05, 41.00}, {-109.05, 41.00}}, } collectionA, err := client.Collections.Get(ctx, dataset.ID, "S2A_S2MSI2A") if err != nil { log.Fatalf("Failed to get collection: %v", err) } collectionB, err := client.Collections.Get(ctx, dataset.ID, "S2B_S2MSI2A") if err != nil { log.Fatalf("Failed to get collection: %v", err) } collectionC, err := client.Collections.Get(ctx, dataset.ID, "S2C_S2MSI2A") if err != nil { log.Fatalf("Failed to get collection: %v", err) } // query data from a specific collection var fromCollection []*v1.Sentinel2Msi err = client.Datapoints.QueryInto(ctx, dataset.ID, &fromCollection, datasets.WithCollectionIDs(collectionA.ID), datasets.WithTemporalExtent(timeInterval), datasets.WithSpatialExtent(area), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } // query data from a selected set of collections var datapoints []*v1.Sentinel2Msi err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapoints, datasets.WithCollectionIDs(collectionA.ID, collectionB.ID, collectionC.ID), datasets.WithTemporalExtent(timeInterval), datasets.WithSpatialExtent(area), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } // query data from all collections in the dataset var datapointsFromAllCollections []*v1.Sentinel2Msi err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapointsFromAllCollections, datasets.WithTemporalExtent(timeInterval), datasets.WithSpatialExtent(area), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } ``` Some datasets include assets that point to files in object storage. After selecting one datapoint, use an [asset collection and the storage client](https://docs-old.tilebox.com/datasets/assets-and-storage/read-and-download.md) to read or download those files. To learn more about how to narrow down query results, see the following pages about filtering by time, geometry, custom fields, or datapoint ID. > [card] [Filter by time](https://docs-old.tilebox.com/datasets/query/filter-by-time.md) > > Learn how to run queries for datapoints within a given temporal extent. > [card] [Filter by geometry](https://docs-old.tilebox.com/datasets/query/filter-by-location.md) > > Learn how to filter your query results by a certain geographical extent. > [card] [Filter by custom fields](https://docs-old.tilebox.com/datasets/query/filter-by-fields.md) > > Filter datapoints by queryable fields in the dataset schema. > [card] [Filter by Datapoint ID](https://docs-old.tilebox.com/datasets/query/filter-by-id.md) > > Find individual datapoints by their unique ID. ## Automatic pagination Querying large datasets can result in a large number of data points. For those cases Tilebox automatically handles pagination for you by sending paginated requests to the server. > [!TIP] > > When using the python SDK in an interactive notebook environment, you can additionally also display a > progress bar to keep track of the progress of the query by setting the `show_progress` parameter to `True`. ## Skipping data fields Sometimes, only the ID or timestamp associated with a datapoint is required. In those cases, you can speed up querying by skipping downloading of all dataset fields except of the `time`, the `id` and the `ingestion_time` by setting the `skip_data` parameter to `True`. For example, when checking how many datapoints exist in a given time interval, you can use `skip_data=True` to avoid loading the data fields. This works the same way on both collection-level and dataset-level queries. ```python Python theme={"system"} interval = ("2023-01-01", "2023-02-01") data = collection.query(temporal_extent=interval, skip_data=True) print(f"Found {data.sizes['time']} data points.") ``` ```go Go theme={"system"} startDate := time.Date(2023, time.January, 1, 0, 0, 0, 0, time.UTC) endDate := time.Date(2023, time.February, 1, 0, 0, 0, 0, time.UTC) interval := query.NewTimeInterval(startDate, endDate) var datapoints []*v1.Sentinel1Sar err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapoints, datasets.WithCollectionIDs(collection.ID), datasets.WithTemporalExtent(interval), datasets.WithSkipData(), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } log.Printf("Queried %d datapoints", len(datapoints)) log.Printf("First datapoint time: %s", datapoints[0].GetTime().AsTime()) ``` Output ```plaintext Python theme={"system"} Size: 160B Dimensions: (time: 1) Coordinates: ingestion_time (time) datetime64[ns] 8B 2024-08-01T08:53:08.450499 id (time)