Naming convention

dlt creates table and column identifiers from the data. The data source, i.e., a stream of JSON documents, may have identifiers (i.e., key names in a dictionary) with any Unicode characters, of any length, and naming style. On the other hand, destinations require that you follow strict rules when you name tables, columns, or collections. A good example is Redshift that accepts case-insensitive alphanumeric identifiers with a maximum of 127 characters.

dlt groups tables from a single source in a schema. Each schema defines a naming convention that tells dlt how to translate identifiers to the namespace that the destination understands. Naming conventions are, in essence, functions that map strings from the source identifier format into the destination identifier format. For example, our snake_case (default) naming convention will translate the DealFlow source identifier into the deal_flow destination identifier.

You can pick which naming convention to use. dlt provides a few to choose from. You can easily add your own as well.

tip

The standard behavior of dlt is to use the same naming convention for all destinations so users always see the same table and column names in their databases.

Use default naming convention (snake_case)

snake_case is a case-insensitive naming convention, converting source identifiers into lower-case snake case identifiers with a reduced alphabet.

• Spaces around identifiers are trimmed.
• Keeps ASCII alphanumerics and underscores, replaces all other characters with underscores (with the exceptions below).
• Replaces + and * with x, - with _, @ with a, and | with l.
• Prepends _ if the name starts with a number.
• Multiples of _ are converted into a single _.
• Replaces all trailing _ with x.

Uses __ as a nesting separator for tables and flattened column names.

tip

If you do not like snake_case, your next safe option is sql_ci, which generates SQL-safe, lowercase, case-insensitive identifiers without any other transformations. To permanently change the default naming convention on a given machine:

1. Set an environment variable SCHEMA__NAMING to sql_ci_v1 OR
2. Add the following line to your global config.toml (the one in your home dir, i.e., ~/.dlt/config.toml):

[schema]
naming="sql_ci_v1"

Source identifiers vs destination identifiers

Pick the right identifier form when defining resources

dlt keeps source (not normalized) identifiers during data extraction and translates them during normalization. For you, it means:

1. If you write a transformer or a mapping/filtering function, you will see the original data, without any normalization. Use the source identifiers to access the dicts!
2. If you define a primary_key or cursor that participates in cursor field incremental loading, use the source identifiers (dlt uses them to inspect source data, Incremental class is just a filtering function).
3. When defining any other hints, i.e., columns or merge_key, you can pick source or destination identifiers. dlt normalizes all hints together with your data.
4. The Schema object (i.e., obtained from the pipeline or from dlt source via discover_schema) always contains destination (normalized) identifiers.

Understand the identifier normalization

Identifiers are translated from source to destination form in the normalize step. Here's how dlt picks the naming convention:

• The default naming convention is snake_case.
• Each destination may define a preferred naming convention in destination capabilities. Some destinations (i.e., Weaviate) need a specialized naming convention and will override the default.
• You can configure a naming convention explicitly. Such configuration overrides the destination settings.
• This naming convention is used when new schemas are created. It happens when the pipeline is run for the first time.
• Schemas preserve the naming convention when saved. Your running pipelines will maintain existing naming conventions if not requested otherwise.
• dlt applies the final naming convention in the normalize step. Jobs (files) in the load package now have destination identifiers. The pipeline schema is duplicated, locked, and saved in the load package and will be used by the destination.

caution

If you change the naming convention and dlt detects a change in the destination identifiers for tables/collections/files that already exist and store data, the normalize process will fail. This prevents an unwanted schema migration. New columns and tables will be created for identifiers that changed.

Case-sensitive and insensitive destinations

Naming conventions declare if the destination identifiers they produce are case-sensitive or insensitive. This helps dlt to generate case-sensitive / insensitive identifiers for the destinations that support both. For example, if you pick a case-insensitive naming like snake_case or sql_ci_v1, with Snowflake, dlt will generate all uppercase identifiers that Snowflake sees as case-insensitive. If you pick a case-sensitive naming like sql_cs_v1, dlt will generate quoted case-sensitive identifiers that preserve identifier capitalization.

Note that many destinations are exclusively case-insensitive, of which some preserve the casing of identifiers (i.e., duckdb) and some will case-fold identifiers when creating tables (i.e., Redshift, Athena do lowercase on the names). dlt is able to detect resulting identifier collisions and stop the load process before data is mangled.

Identifier shortening

Identifier shortening happens during normalization. dlt takes the maximum length of the identifier from the destination capabilities and will trim the identifiers that are too long. The default shortening behavior generates short deterministic hashes of the source identifiers and places them in the middle of the destination identifier. This (with a high probability) avoids shortened identifier collisions.

🚧 [WIP] Name convention changes are lossy

dlt does not store the source identifiers in the schema so when the naming convention changes (or we increase the maximum identifier length), it is not able to generate a fully correct set of new identifiers. Instead, it will re-normalize already normalized identifiers. We are currently working to store the full identifier lineage - source identifiers will be stored and mapped to the destination in the schema.

Pick your own naming convention

Configure naming convention

You can use config.toml, environment variables, or any other configuration provider to set the naming convention name. The configured naming convention overrides all other settings:

• Changes the naming convention stored in the already created schema.
• Overrides the destination capabilities preference.

[schema]
naming="sql_ci_v1"

The configuration above will request sql_ci_v1 for all pipelines (schemas). An environment variable SCHEMA__NAMING set to sql_ci_v1 has the same effect.

You have the option to set the naming convention per source:

[sources.zendesk]
config="prop"
[sources.zendesk.schema]
naming="sql_cs_v1"
[sources.zendesk.credentials]
password="pass"

The snippet above demonstrates how to apply a certain naming for an example zendesk source.

You can use naming conventions that you created yourself or got from other users. In that case, you should pass a full Python import path to the module that contains the naming convention:

[schema]
naming="tests.common.cases.normalizers.sql_upper"

dlt will import tests.common.cases.normalizers.sql_upper and use the NamingConvention class found in it as the naming convention.

Available naming conventions

You can pick from a few built-in naming conventions.

• snake_case - the default.
• duck_case - case-sensitive, allows all Unicode characters like emoji 💥.
• direct - case-sensitive, allows all Unicode characters, does not contract underscores.
• sql_cs_v1 - case-sensitive, generates SQL-safe identifiers.
• sql_ci_v1 - case-insensitive, generates SQL-safe lowercase identifiers.

Ignore naming convention for dataset_name

You control the dataset naming normalization separately. Set enable_dataset_name_normalization to false to ignore the naming convention for dataset_name:

[destination.snowflake]
enable_dataset_name_normalization=false

In that case, the dataset_name would be preserved the same as it was set in the pipeline:

import dlt

pipeline = dlt.pipeline(dataset_name="MyCamelCaseName")

The default value for the enable_dataset_name_normalization configuration option is true.

note

The same setting would be applied to staging dataset. Thus, if you set enable_dataset_name_normalization to false, the staging dataset name would also not be normalized.

caution

Depending on the destination, certain names may not be allowed. To ensure your dataset can be successfully created, use the default normalization option.

Avoid identifier collisions

dlt detects various types of identifier collisions and ignores the others.

1. dlt detects collisions if a case-sensitive naming convention is used on a case-insensitive destination.
2. dlt detects collisions if a change of naming convention changes the identifiers of tables already created in the destination.
3. dlt detects collisions when the naming convention is applied to column names of arrow tables.

dlt will not detect a collision when normalizing source data. If you have a dictionary, keys will be merged if they collide after being normalized. You can create a custom naming convention that does not generate collisions on data, see examples below.

Write your own naming convention

Custom naming conventions are classes that derive from NamingConvention, which you can import from dlt.common.normalizers.naming. We recommend the following module layout:

1. Each naming convention resides in a separate Python module (file).
2. The class is always named NamingConvention.

In that case, you can use a fully qualified module name in schema configuration or pass the module explicitly.

We include two examples of naming conventions that you may find useful:

1. A variant of sql_ci that generates identifier collisions with a low (user-defined) probability by appending a deterministic tag to each name.
2. A variant of sql_cs that allows for LATIN (i.e., umlaut) characters.

note

Note that the fully qualified name of your custom naming convention will be stored in the schema, and dlt will attempt to import it when the schema is loaded from storage. You should distribute your custom naming conventions with your pipeline code or via a pip package from which it can be imported.