Create new destination

tip

You can use @dlt.destination decorator and implement a sink function. This is a perfect way to implement reverse ETL components that push data back to REST APIs.

dlt can import destinations from external python modules. Below we show how to quickly add a dbapi based destination. dbapi is a standardized interface to access databases in Python. If you used ie. postgres (ie. psycopg2) you are already familiar with it.

🧪 This guide is not comprehensive. The internal interfaces are still evolving. Besides reading info below, you should check out source code of existing destinations

0. Prerequisites

Destinations are implemented in python packages under: dlt.destinations.impl.<destination_name>. Generally a destination consists of the following modules:

• __init__.py - this module contains the destination capabilities
• <destination_name>.py - this module contains the job client and load job implementations for the destination
• configuration.py - this module contains the destination and credentials configuration classes
• sql_client.py - this module contains the SQL client implementation for the destination, this is a wrapper over dbapi that provides consistent interface to dlt for executing queries
• factory.py - this module contains a Destination subclass that is the entry point for the destination.

1. Copy existing destination to your dlt project

Initialize a new project with dlt init

dlt init github postgres

This adds github verified source (it produces quite complicated datasets and that good for testing, does not require credentials to use) and postgres credentials (connection-string-like) that we'll repurpose later.

Clone dlt repository to a separate folder. In the repository look for dlt/destinations/impl folder and copy one of the destinations to your project. Pick your starting point:

• postgres - a simple destination without staging storage support and COPY jobs
• redshift - based on postgres, adds staging storage support and remote COPY jobs
• snowflake - a destination supporting additional authentication schemes, local and remote COPY jobs and no support for direct INSERTs

Below we'll use postgres as starting point.

2. Adjust the destination configuration and credentials

dbapi based destinations use ConnectionStringCredentials as a credentials base which accepts SQLAlchemy style connection strings. Typically you should derive from it to change the drivername and make desired properties (like host or password) mandatory.

We keep config and credentials in configuration.py. You should:

• rename the classes properly to match your destination name
• if you need more properties (ie. look at iam_role in redshift credentials) then add them, remember about typing. Behind the hood credentials and configs are dataclasses.
• adjust __init__ arguments in your Destination class in factory.py to match the new credentials and config classes
• expose the configuration type in spec attribute in factory.py

💡 Each destination implements Destination abstract class defined in reference.py.

💡 See how snowflake destination adds additional authorization methods and configuration options.

3. Set the destination capabilities

dlt needs to know a few things about the destination to correctly work with it. Those are stored in capabilities() function in __init__.py.

• supported loader file formats both for direct and staging loading (see below)
• escape_identifier a function that escapes database identifiers ie. table or column name. Look in dlt.common.data_writers.escape module to see how this is implemented for existing destinations.
• escape_literal a function that escapes string literal. it is only used if destination supports insert-values loader format (also see existing implementations in dlt.common.data_writers.escape)
• decimal_precision precision and scale of decimal/numeric types. also used to create right decimal types in loader files ie. parquet
• wei_precision precision and scale of decimal/numeric to store very large (up to 2**256) integers. specify maximum precision for scale 0
• max_identifier_length max length of table and schema/dataset names
• max_column_identifier_length max length of column name
• naming_convention a name or naming convention module that maps the input alphabet (ie. JSON identifiers) to destination alphabet. leave the default - it is very conservative
• max_query_length, is_max_query_length_in_bytes, max_text_data_type_length, is_max_text_data_type_length_in_bytes - tells dlt the maximum length of text query and of text data types.
• supports_transactions tells if destination supports transactions
• timestamp_precision sets fidelity of timestamp/datetime type: 0 - 9 (from seconds to nanoseconds), default is 6
• supports_ddl_transactions tells if the destination supports ddl transactions.
• alter_add_multi_column tells if destination can add multiple columns in ALTER statement
• supports_truncate_command tells dlt if truncate command is used, otherwise it will use DELETE to clear tables.
• schema_supports_numeric_precision whether numeric data types support precision/scale configuration
• max_rows_per_insert max number of rows supported per insert statement, used with insert-values loader file format (set to None for no limit). E.g. MS SQL has a limit of 1000 rows per statement, but most databases have no limit and the statement is divided according to max_query_length.

Supported loader file formats

Specify which loader file formats your destination will support directly and via storage staging. Direct support means that destination is able to load a local file or supports INSERT command. Loading via staging is using filesystem to send load package to a (typically) bucket storage and then load from there.

💡 the insert-values data format generates large INSERT statement that are executed on the destination. If you have any other option for local loading, avoid using this format. It is typically slower and requires the use of bullet-proof escape_literal function.

• preferred_loader_file_format - a file format that will be used by default to load data from local file system. Set to None if direct loading is not supported.
• supported_loader_file_formats - file formats that can be loaded from local file system to destination. Set to [] if direct loading is not supported.
• preferred_staging_file_format - a file format that will be used by default when staging is enabled. Set to None if destination can't load from staging.
• supported_staging_file_formats - file formats that are supported to be loaded staging storage. Set to [] if destination can't load from staging.

💡 Mind that for each file type you'll need to implement a load job (which in most cases is a COPY command to which you pass a file path and file type)

💡 Postgres does not support staging and any other file format beyond insert-values. Check the snowflake capabilities for a destination that supports all possible formats.

Escape identifiers and literals

The default escape_identifier function identifier escapes " and '\' and quotes identifier with ". This is standard SQL behavior. Mind that if you use default naming convention, dlt normalizes identifiers to an alphabet that does not accept any special characters. Users are able to change the naming convention in the configuration so correct escape function is still important.

💡 postgres destination that you modify is using standard implementation that you may keep.

You should avoid providing a custom escape_literal function by not enabling insert-values for your destination.

4. Adjust the SQL client

sql client is a wrapper over dbapi and its main role is to provide consistent interface for executing SQL statements, managing transactions and (probably the most important) to help handling errors via classifying exceptions. Here's a few things you should pay attention to:

• When opening the connection: add current dataset name to search path, set session timezone to UTC.
• Transactions: typically to begin a transaction, you need to disable the auto-commit (like postgres implementation does)
• execute_query: dlt uses %s to represent dbi api query parameters. see duckdb sql_client for a crude way to align your dbapi` client if it uses other parameter placeholders.
• execute_fragments: if your dbapi client does not provide a method to join SQL fragments without full string copy, just delete postgres override. The base class just joins strings.

Fully qualified names

When created, sql_client is bound to particular dataset name (which typically corresponds to a database schema). Most of the database engines follow usual rules of qualifying and quoting ("schema"."table"."column") but there are exceptions like BigQuery or Motherduck. You have full control over generating identifiers via:

• fully_qualified_dataset_name returns a fully qualified dataset name.
• make_qualified_table_name same but for a given table name

dbapi exceptions

dlt must be able to distinct a few error cases for the loading to work properly. Unfortunately error reporting is not very well defined by dbapi and even the existing exception tree is not used consistently across implementations.

_make_database_exception method wraps incoming Exception in one of exception types required by dlt:

• DatabaseUndefinedRelation: raised when schema or table that dlt tries to reference is undefined. It is important to detect this case exactly: via specific dbapi exceptions (like in case of postgres and duckdb) or via detecting proper category of exceptions and inspecting the error codes or messages (see. redshift and snowflake)
• DatabaseTerminalException: errors during loading that will permanently fail a job and should not retry. IntegrityError, ProgrammingError and most of the DataError belong to this class. (example: decimal value out of range, insert NULL in non NULL columns)
• DatabaseTransientException: all other exceptions. we also include SyntaxError (if exists in particular dbapi implementation) here

💡 How this works in practice: we have a set of tests for all relevant error cases in test_sql_client.py, this way we make sure that new sql_client behaves correctly.

What base class assumes

• that INFORMATION_SCHEMA exists from which we can take basic information on SCHEMATA and COLUMNS
• CREATE SCHEMA and DROP SCHEMA (see how BigQuery overrides that)
• DELETE or TRUNCATE is available to clear tables without dropping
• DROP TABLE only for CLI command (pipeline drop)

5. Adjust the job client

Job client is responsible for creating/starting load jobs and managing the schema updates. Here we'll adjust the SqlJobClientBase base class which uses the sql_client to manage the destination. Typically only a few methods needs to be overridden by a particular implementation. The job client code customarily resides in a file with name <destination_name>.py ie. postgres.py and is exposed in factory.py by the client_class property on the destination class.

Database type mappings

You must map dlt data types to destination data types. For this you can implement a subclass of TypeMapper. You can specify there dicts to map dlt data types to destination data types, with or without precision. A few tricks to remember:

• the database types must be exactly those as used in INFORMATION_SCHEMA.COLUMNS
• decimal precision and scale are filled from the capabilities (in all our implementations)
• until now all destinations could handle binary types
• we always try to map the complex type into JSON type in the destination. if that does not work you can try mapping into a string. See how we do that for various destinations.
• the reverse mapping of types is sometimes tricky ie. you may not able to detect complex types (your destination lacks JSON support). this is not really needed during schema updates and loading (just for testing) so in general you should be fine.

Table and column hints

You can map hints present for tables and columns (ie. cluster, sort, partition) to generate specific DDL for columns and tables. See _get_column_def_sql in various destinations. You can also add hints (ie indexes, partition clauses) to tables via _get_table_update_sql - see BigQuery implementation for a good example.

Participate in staging dataset merge and replace

dlt supports merging and transactional replace via staging dataset living along the destination dataset. SqlJobClientBase participates in this mechanism by default. In essence: each time when a job is completed, dlt checks which table got updated and if there are no remaining jobs for that table and its child and parent tables (all together called table chain). If table chain is fully loaded, dlt executes SQL transformations that move/merge data from staging dataset to destination dataset (that, as you can expect, happens also via jobs, of type sql that are dynamically created).

Generated SQL is quite simple and we were able to run it on all existing destinations (we may introduce sqlglot to handle future cases). The SQL used requires:

• SELECT, INSERT, DELETE/TRUNCATE statements
• WINDOW functions for merge.

In case of destinations that do not allow the data modifications you can opt out from both replace and merge:

• override get_truncate_destination_table_dispositions method and return empty list so your tables are never truncated
• override get_stage_dispositions and return empty list to opt out from any operations on staging dataset.

What base class assumes

• DDL to create and add column to tables is available
• it is possible to SELECT data
• it is possible to INSERT data (in order to complete package and store the updated schema)

💡 talk to us on slack if your destination is fully read only.

6. Implement load jobs

Load jobs make sure that all files in load package are loaded to destination. dlt creates single job per file and makes sure that it transitions to completed state. (look for LoadJob)

The file name of the job is used as the job id and both sync and async execution is supported. The executor is multi-threaded. Each job starts in separate thread and its completion status is checked from the main thread.

Jobs are typically very simple and just execute INSERT or COPY commands. They do not replace nor merge data themselves.

Enable insert jobs

If you use insert-values loader file format then derive your job client from InsertValuesJobClient. postgres.py does exactly that.

Look at snowflake.py for a destination that does not use insert-values.

Copy jobs from local and remote files

dlt allows to chain two destinations to create a storage stage (typically on a bucket). The staging destination (currently filesystem) will copy new files, complete the corresponding jobs and for each of them it will create reference job that will be passed to a destination to execute.

The postgres destination does not implement any copy jobs.

• See RedshiftCopyFileLoadJob in redshift.py how we create and start a copy job from a bucket. It uses CopyRemoteFileLoadJob base to handle the references and creates a COPY SQL statement in execute() method.
• See SnowflakeLoadJob in snowflake.py how to implement a job that can load local and reference files. It also forwards AWS credentials from staging destination. At the end the code just generates a COPY command for various loader file formats.

7. Expose your destination to dlt

The Destination subclass in dlt.destinations.impl.<destination_name>.factory module is the entry point for the destination. Add an import to your factory in dlt.destinations.__init__. dlt looks in this module when you reference a destination by name, i.e. dlt.pipeline(..., destination="postgres").

Testing

We can quickly repurpose existing github source and secrets.toml already present in the project to test new destination. Let's assume that the module name is presto, same for the destination name and config section name. Here's our testing script github_pipeline.py

import dlt

from github import github_repo_events
from presto import presto  # importing destination factory

def load_airflow_events() -> None:
    """Loads airflow events. Shows incremental loading. Forces anonymous access token"""
    pipeline = dlt.pipeline(
        "github_events", destination=presto(), dataset_name="airflow_events"
    )
    data = github_repo_events("apache", "airflow", access_token="")
    print(pipeline.run(data))

if __name__ == "__main__":
    load_airflow_events()

Here's secrets.toml:

[destination.presto]
# presto config
[destination.presto.credentials]
database = "dlt_data"
password = "loader"
username = "loader"
host = "localhost"
port = 5432

Mind that in the script above we import the presto module and then pass it in destination argument to dlt.pipeline. Github pipeline will load the events in append mode. You may force replace and merge modes in pipeline.run to check more advanced behavior of the destination.

After executing the pipeline script:

python github_pipeline.py
got page https://api.github.com/repos/apache/airflow/events?per_page=100, requests left: 59
got page https://api.github.com/repositories/33884891/events?per_page=100&page=2, requests left: 58
got page https://api.github.com/repositories/33884891/events?per_page=100&page=3, requests left: 57
Pipeline github_events completed in 4.56 seconds
1 load package(s) were loaded to destination presto and into dataset airflow_events
The presto destination used postgres://loader:***@localhost:5432/dlt_data location to store data
Load package 1690628947.953597 is LOADED and contains no failed jobs

you can use dlt pipeline show github_events to view data in the destination.