# Optimizing TileDB Query Performance for GEDI Data

**URL:** <https://forum.tiledb.com/t/optimizing-tiledb-query-performance-for-gedi-data/776>\
**Category:** Uncategorized\
**Created:** [February 27, 2025, 1:48pm UTC](https://forum.tiledb.com/t/optimizing-tiledb-query-performance-for-gedi-data/776 "2025-02-27T13:48:14Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Simon\_Besnard](https://yyz1.discourse-cdn.com/flex035/user_avatar/forum.tiledb.com/simon_besnard/32/352_2.png) [@Simon\_Besnard](https://forum.tiledb.com/u/Simon_Besnard)\
**Post date:** [February 27, 2025, 1:48pm UTC](https://forum.tiledb.com/t/optimizing-tiledb-query-performance-for-gedi-data/776/1 "2025-02-27T13:48:14Z")

</div>

Hi everyone,

I’m working with a TileDB array to store and query GEDI data. The array includes spatial (latitude, longitude) and temporal dimensions, with variables stored as attributes. I want to get feedback on two aspects:

1. **TileDB Schema:** Does the structure of my TileDB array make sense for efficient querying?
2. **Query Optimization:** Am I reading the data efficiently, or are there improvements I could make (e.g., indexing strategies, query execution optimizations, parallel reading)?

Here’s the way to look at my TileDB schema:

```auto
import tiledb
import os

# S3 TileDB context
tiledb_config = tiledb.Config(
    {
        "vfs.s3.endpoint_override": "https://s3.gfz-potsdam.de",
        "vfs.s3.region": "eu-central-1",
        "vfs.s3.no_sign_request" : "true"
    }
)

ctx = tiledb.Ctx(tiledb_config)

# Read TileDB schema
bucket = "dog.gedidb.gedi-l2-l4-v002"
array_uri = os.path.join(f"s3://{bucket}", "array_uri")

with tiledb.open(array_uri, mode="r", ctx=ctx) as array:
    print(array.schema)

```

Below is an example of how I would query the data.

```auto
import tiledb
import os

# S3 TileDB context

tiledb_config = tiledb.Config(
    {
        "vfs.s3.endpoint_override": "https://s3.gfz-potsdam.de",
        "vfs.s3.region": "eu-central-1",
        "vfs.s3.no_sign_request" : "true"
    }
)

ctx = tiledb.Ctx(tiledb_config)

# Path to the tileDB array
bucket = "dog.gedidb.gedi-l2-l4-v002"
array_uri = os.path.join(f"s3://{bucket}", "array_uri")

# Define query parameters
attr_list = ["agbd"]
lat_min = -17.140088
lat_max = -17.094909
lon_min = 145.606605 
lon_max = 145.653595
start_time = 17532
end_time = 19929

# Read the data
with tiledb.open(array_uri, mode="r", ctx=ctx) as array:
   
    query = array.query(attrs=attr_list)
    data = query.multi_index[
        lat_min:lat_max, lon_min:lon_max, start_time:end_time
    ]

```

As an indication, this is a visualisation of the fragment’s structure in my tileDB array.

 ![tileDB_fragment_structure](https://canada1.discourse-cdn.com/flex035/uploads/tiledb/original/1X/3c7579146b3291f2f2885bce540f636625be3ef0.jpeg)

I would appreciate any insights on whether my approach is well-optimized or if there are ways to improve it!

Thanks in advance!

Simon.