TileDB With Dask Array

TileDB integrates very well with dask.array. We demonstrate with an example below where attribute attr stores an int32 value per cell:
Python
import dask
import dask.array as da
​
array = da.from_tiledb('s3://my-bucket/my-dense-array',
                        attribute='attr',
                        storage_options={'vfs.s3.aws_access_key_id': 'mykeyid',
                                         'vfs.s3.aws_secret_access_key': 'mysecret'})
print(array.mean().compute())
You can add any TileDB configuration parameter in storage_options. Moreover, storage_options accepts an additional key option, where you can pass an encryption key if your array is encrypted (see Encryption).
You can also set array chunking similar to Dask's chunking. For example, you can do the following:
Python
import dask
import dask.array as da
​
array = da.from_tiledb('s3://my-bucket/my-dense-array',
                        attribute='attr',
                        chunks=10, # or chunks=(10,) 
                        storage_options={'vfs.s3.aws_access_key_id': 'mykeyid',
                                         'vfs.s3.aws_secret_access_key': 'mysecret'})
print(array.mean().compute())
You can also write a Dask array into TileDB as follows:
Python
import tiledb
import numpy as np
import dask, dask.array as da
​
array = da.random.random((25,25))
​
array.to_tiledb("s3://my-bucket/my-uri", storage_options={'vfs.s3.aws_access_key_id': 'mykeyid',
                                      'vfs.s3.aws_secret_access_key': 'mysecret'})
Note that the TileDB array does not need to exist. The above function call will create it if it does not by inferring the schema from the Dask array. To write to an existing array, you should open the array for writing as follows, which will create new fragment(s):
Python
import numpy as np
import dask, dask.array as da
​
array = da.random.random((25,25))
​
with tiledb.open(s3://my-bucket/my-uri, 'w') as A:
    array.to_tiledb(A)
Using an existing Array object allows extra customization of the array schema beyond what is possible with the automatic array creation shown earlier. For example, to create an array with a compression filter applied to the attribute, create the schema and array first, then write to the open Array:
First Tab
import tiledb, dask, dask.array as da, numpy as np
from tiledb import Domain, Dim, Attr
​
uri = "/path/to/array"
​
# create schema with Zstd compression applied to the attribute:
schema = tiledb.ArraySchema(
    Domain([Dim("x", domain=(0,99), tile=100, dtype=np.uint64),
            Dim("y", domain=(0,99), tile=100, dtype=np.uint64)]),
    attrs=[Attr("v", dtype=np.float64, filters=[tiledb.ZstdFilter(1)])],
    sparse=False
)
​
# create empty array from the schema above:
tiledb.Array.create(uri, schema)
​
# write a 100x100 array of 1s to array created above:
with tiledb.open(uri, "w") as tdb_array:
    da.ones((100,100)).to_tiledb(tdb_array)
Quickstart
TileDB With Dask Delayed
Last modified 10mo ago