slate

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gleam add slate

slate supports Gleam's Erlang target. DETS is part of OTP; slate does not support the JavaScript target.

What it looks like

Open a table, insert a value, and look it up. Reads use your decoders to check the stored data.

Create the parent directory before you run the example:

mkdir -p data

import gleam/dynamic/decode
import gleam/result
import slate
import slate/set

pub fn main() {
  use users <- set.with_table(
    "data/users.dets",
    repair: slate.AutoRepair,
    access: slate.ReadWrite,
    key_decoder: decode.string,
    value_decoder: decode.int,
  )
  use Nil <- result.try(
    set.insert(users, "alice", 42),
  )
  set.lookup(users, key: "alice")
}
// Ok(42), and the table is closed

Gleam's use value <- function(...) syntax passes the rest of the block as a callback. Here, with_table supplies users and attempts to close the table when the callback returns. The decoders define the key and value types expected from disk; result.try stops early if the insert fails.

Stored data persists across node restarts. An abnormal node shutdown can lose pending writes and leave the file needing repair. The quick start explains this example. See the table cleanup guide for cleanup and repair options.

When to use DETS

Storage approaches at a glance

Approach	Setup	Persistence	Query capability
JSON file	File I/O and serialization	Yes	None
DETS (slate)	Included in OTP; add slate for Gleam	Yes	Key lookup, fold
SQLite	Embedded database library	Yes	Full SQL
Postgres	Database server	Yes	Full SQL
Mnesia	Included in OTP; configure a schema	Yes	Key lookup, query expressions

Use DETS when you need persistent key lookup without a separate database service. Each table has a 2 GB file size limit. Disk-backed access is slower than ETS for frequent reads. See Limitations.

Pick a table type

The table modules share a core API. Set and bag tables also provide insert_new, with different duplicate rules. Only set tables provide update_counter.

Set tablesOne value per key. Inserting with an existing key replaces its value. Use for caches, configuration, and key lookup.

Bag tablesMultiple distinct values per key. For tags, categories, and one-to-many relationships.

Duplicate bag tablesStore repeated key-value pairs. Useful for repeated events or measurements. Results are not ordered.

Next steps

slate follows Semantic Versioning. Continue with the quick start to create a table, write values, and read them back.