Source code for airflow.operators.hive_stats_operator

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from builtins import zip
from collections import OrderedDict
import json

from airflow.exceptions import AirflowException
from airflow.hooks.mysql_hook import MySqlHook
from airflow.hooks.presto_hook import PrestoHook
from airflow.hooks.hive_hooks import HiveMetastoreHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults

[docs]class HiveStatsCollectionOperator(BaseOperator): """ Gathers partition statistics using a dynamically generated Presto query, inserts the stats into a MySql table with this format. Stats overwrite themselves if you rerun the same date/partition. :: CREATE TABLE hive_stats ( ds VARCHAR(16), table_name VARCHAR(500), metric VARCHAR(200), value BIGINT ); :param table: the source table, in the format ``database.table_name``. (templated) :type table: str :param partition: the source partition. (templated) :type partition: dict of {col:value} :param extra_exprs: dict of expression to run against the table where keys are metric names and values are Presto compatible expressions :type extra_exprs: dict :param col_blacklist: list of columns to blacklist, consider blacklisting blobs, large json columns, ... :type col_blacklist: list :param assignment_func: a function that receives a column name and a type, and returns a dict of metric names and an Presto expressions. If None is returned, the global defaults are applied. If an empty dictionary is returned, no stats are computed for that column. :type assignment_func: function """
[docs] template_fields = ('table', 'partition', 'ds', 'dttm')
[docs] ui_color = '#aff7a6'
@apply_defaults def __init__( self, table, partition, extra_exprs=None, col_blacklist=None, assignment_func=None, metastore_conn_id='metastore_default', presto_conn_id='presto_default', mysql_conn_id='airflow_db', *args, **kwargs): super(HiveStatsCollectionOperator, self).__init__(*args, **kwargs) self.table = table self.partition = partition self.extra_exprs = extra_exprs or {} self.col_blacklist = col_blacklist or {} self.metastore_conn_id = metastore_conn_id self.presto_conn_id = presto_conn_id self.mysql_conn_id = mysql_conn_id self.assignment_func = assignment_func self.ds = '{{ ds }}' self.dttm = '{{ execution_date.isoformat() }}'
[docs] def get_default_exprs(self, col, col_type): if col in self.col_blacklist: return {} d = {(col, 'non_null'): "COUNT({col})"} if col_type in ['double', 'int', 'bigint', 'float', 'double']: d[(col, 'sum')] = 'SUM({col})' d[(col, 'min')] = 'MIN({col})' d[(col, 'max')] = 'MAX({col})' d[(col, 'avg')] = 'AVG({col})' elif col_type == 'boolean': d[(col, 'true')] = 'SUM(CASE WHEN {col} THEN 1 ELSE 0 END)' d[(col, 'false')] = 'SUM(CASE WHEN NOT {col} THEN 1 ELSE 0 END)' elif col_type in ['string']: d[(col, 'len')] = 'SUM(CAST(LENGTH({col}) AS BIGINT))' d[(col, 'approx_distinct')] = 'APPROX_DISTINCT({col})' return {k: v.format(col=col) for k, v in d.items()}
[docs] def execute(self, context=None): metastore = HiveMetastoreHook(metastore_conn_id=self.metastore_conn_id) table = metastore.get_table(table_name=self.table) field_types = { col.type for col in} exprs = { ('', 'count'): 'COUNT(*)' } for col, col_type in list(field_types.items()): d = {} if self.assignment_func: d = self.assignment_func(col, col_type) if d is None: d = self.get_default_exprs(col, col_type) else: d = self.get_default_exprs(col, col_type) exprs.update(d) exprs.update(self.extra_exprs) exprs = OrderedDict(exprs) exprs_str = ",\n ".join([ v + " AS " + k[0] + '__' + k[1] for k, v in exprs.items()]) where_clause = [ "{0} = '{1}'".format(k, v) for k, v in self.partition.items()] where_clause = " AND\n ".join(where_clause) sql = "SELECT {exprs_str} FROM {table} WHERE {where_clause};".format( exprs_str=exprs_str, table=self.table, where_clause=where_clause) hook = PrestoHook(presto_conn_id=self.presto_conn_id)'Executing SQL check: %s', sql) row = hook.get_first(hql=sql)"Record: %s", row) if not row: raise AirflowException("The query returned None") part_json = json.dumps(self.partition, sort_keys=True)"Deleting rows from previous runs if they exist") mysql = MySqlHook(self.mysql_conn_id) sql = """ SELECT 1 FROM hive_stats WHERE table_name='{table}' AND partition_repr='{part_json}' AND dttm='{dttm}' LIMIT 1; """.format(table=self.table, part_json=part_json, dttm=self.dttm) if mysql.get_records(sql): sql = """ DELETE FROM hive_stats WHERE table_name='{table}' AND partition_repr='{part_json}' AND dttm='{dttm}'; """.format(table=self.table, part_json=part_json, dttm=self.dttm)"Pivoting and loading cells into the Airflow db") rows = [ (self.ds, self.dttm, self.table, part_json) + (r[0][0], r[0][1], r[1]) for r in zip(exprs, row)] mysql.insert_rows( table='hive_stats', rows=rows, target_fields=[ 'ds', 'dttm', 'table_name', 'partition_repr', 'col', 'metric', 'value',
] )