Support `default-target-schema`

`target-snowflake`

Target for Snowflake.

Capabilities

about
stream-maps
schema-flattening
target-schema

Settings

Setting	Required	Default	Description
user	True	None	The login name for your Snowflake user.
password	False	None	The password for your Snowflake user.
private_key_path	False	None	Path to file containing private key.
private_key_passphrase	False	None	Passphrase to decrypt private key if encrypted.
account	True	None	Your account identifier. See Account Identifiers.
database	True	None	The initial database for the Snowflake session.
schema	False	None	The initial schema for the Snowflake session.
warehouse	False	None	The initial warehouse for the session.
role	False	None	The initial role for the session.
add_record_metadata	False	1	Whether to add metadata columns.
clean_up_batch_files	False	1	Whether to remove batch files after processing.
default_target_schema	False	None	The default target database schema name to use for all streams.
hard_delete	False	0	Hard delete records.
load_method	False	TargetLoadMethods.APPEND_ONLY	The method to use when loading data into the destination. `append-only` will always write all input records whether that records already exists or not. `upsert` will update existing records and insert new records. `overwrite` will delete all existing records and insert all input records.
batch_size_rows	False	None	Maximum number of rows in each batch.
validate_records	False	1	Whether to validate the schema of the incoming streams.
stream_maps	False	None	Config object for stream maps capability. For more information check out Stream Maps.
stream_map_config	False	None	User-defined config values to be used within map expressions.
faker_config	False	None	Config for the `Faker` instance variable `fake` used within map expressions. Only applicable if the plugin specifies `faker` as an addtional dependency (through the `singer-sdk` `faker` extra or directly).
faker_config.seed	False	None	Value to seed the Faker generator for deterministic output: https://faker.readthedocs.io/en/master/#seeding-the-generator
faker_config.locale	False	None	One or more LCID locale strings to produce localized output for: https://faker.readthedocs.io/en/master/#localization
flattening_enabled	False	None	'True' to enable schema flattening and automatically expand nested properties.
flattening_max_depth	False	None	The max depth to flatten schemas.
use_browser_authentication	False	False	If authentication should be done using SSO (via external browser). See See SSO browser authentication.

A full list of supported settings and capabilities is available by running: target-snowflake --about

Initializing a Snowflake Account

This target has an interactive feature that will help you get a Snowflake account initialized with everything needed to get started loading data.

User
Role
Warehouse
Database
Proper grants

The CLI will ask you to provide information about the new user/role/etc. you want to create but it will also need SYSADMIN credentials to execute the queries. You should prepare the following inputs:

Account
User that has SYSADMIN and SECURITYADMIN access. These comes default with the user that created the Snowflake account.
The password for your SYSADMIN user.

Run the following command to get started with the interactive CLI. Note - the CLI will print the SQL queries it is planning to run and confirm with you before it makes any changes.

poetry run target-snowflake --initialize

# Alternatively using Meltano CLI
meltano invoke target-snowflake --initialize

The CLI also has a "dry run" mode that will print the queries without executing them.

Check out the demo of this on YouTube.

Configure using environment variables

This Singer target will automatically import any environment variables within the working directory's .env if the --config=ENV is provided, such that config values will be considered if a matching environment variable is set either in the terminal context or in the .env file.

Usage

You can easily run target-snowflake by itself or in a pipeline using Meltano.

Executing the Target Directly

target-snowflake --version
target-snowflake --help
# Test using the "Carbon Intensity" sample:
tap-carbon-intensity | target-snowflake --config /path/to/target-snowflake-config.json

Developer Resources

Initialize your Development Environment

pipx install poetry
poetry install

Create and Run Tests

Create tests within the target_snowflake/tests subfolder and then run:

poetry run pytest

You can also test the target-snowflake CLI interface directly using poetry run:

poetry run target-snowflake --help

Testing with Meltano

Note: This target will work in any Singer environment and does not require Meltano. Examples here are for convenience and to streamline end-to-end orchestration scenarios.

Your project comes with a custom meltano.yml project file already created.

Next, install Meltano (if you haven't already) and any needed plugins:

# Install meltano
pipx install meltano
# Initialize meltano within this directory
cd target-snowflake
meltano install

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano invoke target-snowflake --version
# OR run a test `elt` pipeline with the Carbon Intensity sample tap:
meltano run tap-carbon-intensity target-snowflake

SDK Dev Guide

See the dev guide for more instructions on how to use the Meltano SDK to develop your own Singer taps and targets.

	@staticmethod
	def to_sql_type(jsonschema_type: dict) -> sqlalchemy.types.TypeEngine:
	"""Return a JSON Schema representation of the provided type.

	Uses custom Snowflake types from [snowflake-sqlalchemy](https://github.com/snowflakedb/snowflake-sqlalchemy/blob/main/src/snowflake/sqlalchemy/custom_types.py)

	Args:
	jsonschema_type: The JSON Schema representation of the source type.

	Returns:
	The SQLAlchemy type representation of the data type.
	"""
	# start with default implementation
	target_type = SQLConnector.to_sql_type(jsonschema_type)
	# snowflake max and default varchar length
	# https://docs.snowflake.com/en/sql-reference/intro-summary-data-types.html
	maxlength = jsonschema_type.get("maxLength", 16777216)
	# define type maps
	string_submaps = [
	TypeMap(eq, sct.TIMESTAMP_NTZ(), "date-time"),
	TypeMap(contains, sqlalchemy.types.TIME(), "time"),
	TypeMap(eq, sqlalchemy.types.DATE(), "date"),
	TypeMap(eq, sqlalchemy.types.VARCHAR(maxlength), None),
	]
	type_maps = [
	TypeMap(th._jsonschema_type_check, sct.NUMBER(), ("integer",)),
	TypeMap(th._jsonschema_type_check, sct.VARIANT(), ("object",)),
	TypeMap(th._jsonschema_type_check, sct.VARIANT(), ("array",)),
	]
	# apply type maps
	if th._jsonschema_type_check(jsonschema_type, ("string",)):
	datelike_type = th.get_datelike_property_type(jsonschema_type)
	target_type = evaluate_typemaps(string_submaps, datelike_type, target_type)
	else:
	target_type = evaluate_typemaps(type_maps, jsonschema_type, target_type)

	return cast(sqlalchemy.types.TypeEngine, target_type)

	class TypeMap:
	def __init__(self, operator, map_value, match_value=None):
	self.operator = operator
	self.map_value = map_value
	self.match_value = match_value

	def match(self, compare_value):
	try:
	if self.match_value:
	return self.operator(compare_value, self.match_value)
	return self.operator(compare_value)
	except TypeError:
	return False


	def evaluate_typemaps(type_maps, compare_value, unmatched_value):
	for type_map in type_maps:
	if type_map.match(compare_value):
	return type_map.map_value
	return unmatched_value

meltanolabs / target-snowflake Goto Github PK

target-snowflake's Introduction

target-snowflake

Capabilities

Settings

Initializing a Snowflake Account

Configure using environment variables

Usage

Executing the Target Directly

Developer Resources

Initialize your Development Environment

Create and Run Tests

Testing with Meltano

SDK Dev Guide

target-snowflake's People

Contributors

Stargazers

Watchers

Forkers

target-snowflake's Issues

What is the Issue?

Steps to Reproduce

The problem

The error

MRE

Runtime details

Relevant code snippets from this target-snowflake implementation

to_sql_type()

_adapt_column_type()

Custom TypeMap logic:

Recommend Projects

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`target-snowflake`

Relevant code snippets from this `target-snowflake` implementation

`to_sql_type()`

`_adapt_column_type()`

Custom `TypeMap` logic: