astro.databases.postgres

Postgres database implementation.

Module Contents

Classes

PostgresDatabase

Handle interactions with Postgres databases. If this class is successful, we should not have any Postgres-specific

Attributes

DEFAULT_CONN_ID

astro.databases.postgres.DEFAULT_CONN_ID
class astro.databases.postgres.PostgresDatabase(conn_id=DEFAULT_CONN_ID, table=None, load_options=None)

Bases: astro.databases.base.BaseDatabase

Handle interactions with Postgres databases. If this class is successful, we should not have any Postgres-specific logic in other parts of our code-base.

Parameters:

  • conn_id (str) –

  • table (BaseTable | None) –

  • load_options (LoadOptions | None) –

property sql_type: str
Return type:

str

property default_metadata: astro.table.Metadata

Fill in default metadata values for table objects addressing Postgres databases.

Currently, Schema is not being fetched from airflow connection for Postgres because, in Postgres, databases and schema are different concepts: https://www.postgresql.org/docs/current/ddl-schemas.html

The PostgresHook only exposes schema: airflow.providers.postgres.hooks.postgres.PostgresHook

However, implementation-wise, it seems that if the PostgresHook receives a schema during initialization, but it uses it as a database in the connection to Postgres: https://github.com/apache/airflow/blob/main/airflow/providers/postgres/hooks/postgres.py#L96

Return type:

astro.table.Metadata

DEFAULT_SCHEMA
illegal_column_name_chars :list[str] = ['.']
illegal_column_name_chars_replacement :list[str] = ['_']
hook()

Retrieve Airflow hook to interface with the Postgres database.

Return type:

airflow.providers.postgres.hooks.postgres.PostgresHook

schema_exists(schema)

Checks if a schema exists in the database

Parameters:

schema – DB Schema - a namespace that contains named objects like (tables, functions, etc)

Return type:

bool

load_pandas_dataframe_to_table(source_dataframe, target_table, if_exists='replace', chunk_size=DEFAULT_CHUNK_SIZE)

Create a table with the dataframe’s contents. If the table already exists, append or replace the content, depending on the value of if_exists.

Parameters:

  • source_dataframe (pandas.DataFrame) – Local or remote filepath

  • target_table (astro.table.BaseTable) – Table in which the file will be loaded

  • if_exists (astro.constants.LoadExistStrategy) – Strategy to be used in case the target table already exists.

  • chunk_size (int) – Specify the number of rows in each batch to be written at a time.

Return type:

None

static get_table_qualified_name(table)

Return table qualified name. This is Database-specific. For instance, in Sqlite this is the table name. In Snowflake, however, it is the database, schema and table

Parameters:

table (astro.table.BaseTable) – The table we want to retrieve the qualified name for.

Return type:

str

table_exists(table)

Check if a table exists in the database

Parameters:

table (astro.table.BaseTable) – Details of the table we want to check that exists

Return type:

bool

merge_table(source_table, target_table, source_to_target_columns_map, target_conflict_columns, if_conflicts='exception')

Merge the source table rows into a destination table. The argument if_conflicts allows the user to define how to handle conflicts.

Parameters:

  • source_table (astro.table.BaseTable) – Contains the rows to be merged to the target_table

  • target_table (astro.table.BaseTable) – Contains the destination table in which the rows will be merged

  • source_to_target_columns_map (dict[str, str]) – Dict of target_table columns names to source_table columns names

  • target_conflict_columns (list[str]) – List of cols where we expect to have a conflict while combining

  • if_conflicts (astro.constants.MergeConflictStrategy) – The strategy to be applied if there are conflicts.

Return type:

None

static get_dataframe_from_file(file)

Get pandas dataframe file

Parameters:

file (astro.files.File) – File path and conn_id for object stores

openlineage_dataset_name(table)

Returns the open lineage dataset name as per https://github.com/OpenLineage/OpenLineage/blob/main/spec/Naming.md Example: schema_name.table_name

Parameters:

table (astro.table.BaseTable) –

Return type:

str

openlineage_dataset_namespace()

Returns the open lineage dataset namespace as per https://github.com/OpenLineage/OpenLineage/blob/main/spec/Naming.md Example: postgresql://localhost:5432

Return type:

str

openlineage_dataset_uri(table)

Returns the open lineage dataset uri as per https://github.com/OpenLineage/OpenLineage/blob/main/spec/Naming.md

Parameters:

table (astro.table.BaseTable) –

Return type:

str