airflow mssql_to_hive 源码

airflow mssql_to_hive 代码

文件路径：/airflow/providers/apache/hive/transfers/mssql_to_hive.py

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# to you under the Apache License, Version 2.0 (the
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"""This module contains an operator to move data from MSSQL to Hive."""
from __future__ import annotations

from collections import OrderedDict
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Sequence

import pymssql
import unicodecsv as csv

from airflow.models import BaseOperator
from airflow.providers.apache.hive.hooks.hive import HiveCliHook
from airflow.providers.microsoft.mssql.hooks.mssql import MsSqlHook
from airflow.www import utils as wwwutils

if TYPE_CHECKING:
    from airflow.utils.context import Context


class MsSqlToHiveOperator(BaseOperator):
    """
    Moves data from Microsoft SQL Server to Hive. The operator runs
    your query against Microsoft SQL Server, stores the file locally
    before loading it into a Hive table. If the ``create`` or
    ``recreate`` arguments are set to ``True``,
    a ``CREATE TABLE`` and ``DROP TABLE`` statements are generated.
    Hive data types are inferred from the cursor's metadata.
    Note that the table generated in Hive uses ``STORED AS textfile``
    which isn't the most efficient serialization format. If a
    large amount of data is loaded and/or if the table gets
    queried considerably, you may want to use this operator only to
    stage the data into a temporary table before loading it into its
    final destination using a ``HiveOperator``.

    :param sql: SQL query to execute against the Microsoft SQL Server
        database. (templated)
    :param hive_table: target Hive table, use dot notation to target a specific
        database. (templated)
    :param create: whether to create the table if it doesn't exist
    :param recreate: whether to drop and recreate the table at every execution
    :param partition: target partition as a dict of partition columns and
        values. (templated)
    :param delimiter: field delimiter in the file
    :param mssql_conn_id: source Microsoft SQL Server connection
    :param hive_cli_conn_id: Reference to the
        :ref:`Hive CLI connection id <howto/connection:hive_cli>`.
    :param tblproperties: TBLPROPERTIES of the hive table being created
    """

    template_fields: Sequence[str] = ('sql', 'partition', 'hive_table')
    template_ext: Sequence[str] = ('.sql',)
    # TODO: Remove renderer check when the provider has an Airflow 2.3+ requirement.
    template_fields_renderers = {'sql': 'tsql' if 'tsql' in wwwutils.get_attr_renderer() else 'sql'}
    ui_color = '#a0e08c'

    def __init__(
        self,
        *,
        sql: str,
        hive_table: str,
        create: bool = True,
        recreate: bool = False,
        partition: dict | None = None,
        delimiter: str = chr(1),
        mssql_conn_id: str = 'mssql_default',
        hive_cli_conn_id: str = 'hive_cli_default',
        tblproperties: dict | None = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.sql = sql
        self.hive_table = hive_table
        self.partition = partition
        self.create = create
        self.recreate = recreate
        self.delimiter = delimiter
        self.mssql_conn_id = mssql_conn_id
        self.hive_cli_conn_id = hive_cli_conn_id
        self.partition = partition or {}
        self.tblproperties = tblproperties

    @classmethod
    def type_map(cls, mssql_type: int) -> str:
        """Maps MsSQL type to Hive type."""
        map_dict = {
            pymssql.BINARY.value: 'INT',
            pymssql.DECIMAL.value: 'FLOAT',
            pymssql.NUMBER.value: 'INT',
        }
        return map_dict.get(mssql_type, 'STRING')

    def execute(self, context: Context):
        mssql = MsSqlHook(mssql_conn_id=self.mssql_conn_id)
        self.log.info("Dumping Microsoft SQL Server query results to local file")
        with mssql.get_conn() as conn:
            with conn.cursor() as cursor:
                cursor.execute(self.sql)
                with NamedTemporaryFile("w") as tmp_file:
                    csv_writer = csv.writer(tmp_file, delimiter=self.delimiter, encoding='utf-8')
                    field_dict = OrderedDict()
                    for col_count, field in enumerate(cursor.description, start=1):
                        col_position = f"Column{col_count}"
                        field_dict[col_position if field[0] == '' else field[0]] = self.type_map(field[1])
                    csv_writer.writerows(cursor)
                    tmp_file.flush()

            hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)
            self.log.info("Loading file into Hive")
            hive.load_file(
                tmp_file.name,
                self.hive_table,
                field_dict=field_dict,
                create=self.create,
                partition=self.partition,
                delimiter=self.delimiter,
                recreate=self.recreate,
                tblproperties=self.tblproperties,
            )

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