Array Metadata

Learn how to vacuum array metadata after array metadata consolidation.

How to run this tutorial

You can run this tutorial in two ways:

Locally on your machine.
On TileDB Cloud.

However, since TileDB Cloud has a free tier, we strongly recommend that you sign up and run everything there, as that requires no installations or deployment.

This tutorial demonstrates how to vacuum array metadata, after consolidation takes place. Before running this tutorial, it is recommended that you read the following sections:

First, import the necessary libraries, set the array URI (i.e., its path, which in this tutorial will be on local storage), and delete any previously created arrays with the same name.

Python
R

# Import necessary libraries
import tiledb
import numpy as np
import shutil
import os.path

# Set array URI
array_uri = os.path.expanduser("~/vacuuming_array_meta")

# Delete array if it already exists
if os.path.exists(array_uri):
    shutil.rmtree(array_uri)

# Import necessary libraries
library(tiledb)

# Set array URI
array_uri <- path.expand("~/vacuuming_array_meta_r")

# Delete array if it already exists
if (file.exists(array_uri)) {
  unlink(array_uri, recursive = TRUE)
}

Next, create the array by specifying its schema. This example uses a sparse array, but the functionality is similar for dense arrays.

Python
R

# Create the two dimensions
d1 = tiledb.Dim(name="d1", domain=(0, 3), tile=2, dtype=np.int32)
d2 = tiledb.Dim(name="d2", domain=(0, 3), tile=2, dtype=np.int32)

# Create a domain using the two dimensions
dom = tiledb.Domain(d1, d2)

# Create an attribute
a = tiledb.Attr(name="a", dtype=np.int32)

# Create the array schema with `sparse=True`.
sch = tiledb.ArraySchema(domain=dom, sparse=True, attrs=[a])

# Create the array on disk (it will initially be empty)
tiledb.Array.create(array_uri, sch)

# Create the two dimensions
d1 <- tiledb_dim("d1", c(0L, 3L), 2L, "INT32")
d2 <- tiledb_dim("d2", c(0L, 3L), 2L, "INT32")

# Create a domain using the two dimensions
dom <- tiledb_domain(dims = c(d1, d2))

# Create an attribute
a <- tiledb_attr("a", type = "INT32")

# Create the array schema, setting `sparse = TRUE`
sch <- tiledb_array_schema(dom, a, sparse = TRUE)

# Create the array on disk (it will initially be empty)
arr <- tiledb_array_create(array_uri, sch)

Write some array metadata to the array.

Python
R

# Write some metadata
with tiledb.open(array_uri, "w") as A:
    A.meta["a"] = 1

arr <- tiledb_array(
  array_uri,
  query_type = "WRITE",
  keep_open = TRUE,
  return_as = "data.frame"
)

tiledb_put_metadata(arr, "a", 1L)

Perform a second write, so that two fragments are generated.

Python
R

# Write some metadata
with tiledb.open(array_uri, "w") as A:
    A.meta["b"] = 2

tiledb_put_metadata(arr, "b", 2L)

arr <- tiledb_array_close(arr)

Now consolidate the array, passing array_meta as the value of configuration parameter sm.consolidation.mode.

Python
R

# Consolidate
config = tiledb.Config({"sm.consolidation.mode": "array_meta"})
tiledb.consolidate(array_uri, config=config)

# Consolidate
cfg <- tiledb_config(
  config = c(
    "sm.consolidation.mode" = "array_meta"
  )
)

array_consolidate(array_uri, cfg = cfg)

Inspecting the file hierarchy of the array, observe the four files inside the __meta directory: two from the earlier writes, and two by consolidation.

/Users/stavrospapadopoulos/vacuuming_array_meta
├── __commits
├── __fragment_meta
├── __fragments
├── __labels
├── __meta
│   ├── __1716155432802_1716155432802_72a70b6c183fe7217996b0ddbdd00761
│   ├── __1716155432802_1716155432807_1af008ea64b46312cb4b23322e6f82d5
│   ├── __1716155432802_1716155432807_1af008ea64b46312cb4b23322e6f82d5.vac
│   └── __1716155432807_1716155432807_6cdc6f6046a28291eef30f065cb35912
└── __schema
    ├── __1716155432798_1716155432798_000000023b730ef32a21b969d611203c
    └── __enumerations

8 directories, 5 files

Read the array metadata and observe that all the metadata from the two writes are in the result, as expected.

Python
R

# Read all metadata
with tiledb.open(array_uri, "r") as A:
    for key, value in A.meta.items():
        print(f"{key}: {value}")

a: 1
b: 2

arr <- tiledb_array_open(arr, type = "READ")

print(tiledb_get_all_metadata(arr))

arr <- tiledb_array_close(arr)

a:  1
b:  2

Now perform a vacuuming operation, by passing array_meta as the value of configuration parameter sm.vacuum.mode.

Python
R

# Vacuum
config = tiledb.Config({"sm.vacuum.mode": "array_meta"})
tiledb.vacuum(array_uri, config=config)

# Vacuum
cfg <- tiledb_config(
  config = c(
    "sm.vacuum.mode" = "array_meta"
  )
)

array_vacuum(array_uri, cfg = cfg)

Inspecting the file hierarchy of the array again, observe the single file inside the __meta directory, which accounts for the consolidated metadata containing all the metadata from the two writes.

/Users/stavrospapadopoulos/vacuuming_array_meta
├── __commits
├── __fragment_meta
├── __fragments
├── __labels
├── __meta
│   └── __1716155432802_1716155432807_1af008ea64b46312cb4b23322e6f82d5
└── __schema
    ├── __1716155432798_1716155432798_000000023b730ef32a21b969d611203c
    └── __enumerations

8 directories, 2 files

Reading the array again, all the metadata from the two writes is returned, again as expected. Vacuuming (in combination with consolidation), helps significantly boost performance in the presence of multiple write operations. For more details, visit the Performance: Tuning Writes section.

Python
R

# Read all metadata
with tiledb.open(array_uri, "r") as A:
    for key, value in A.meta.items():
        print(f"{key}: {value}")

a: 1
b: 2

arr <- tiledb_array_open(arr, type = "READ")

print(tiledb_get_all_metadata(arr))

arr <- tiledb_array_close(arr)

a:  1
b:  2

Clean up in the end by deleting the array.

Python
R

# Delete the array
if os.path.exists(array_uri):
    shutil.rmtree(array_uri)

# Delete the array
if (file.exists(array_uri)) {
  unlink(array_uri, recursive = TRUE)
}