# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
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
"""
template_fields = ('table', 'partition', 'ds', 'dttm')
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() }}'
def get_default_exprs(self, col, col_type):
if col in self.col_blacklist:
return {}
d = {}
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.name: col.type for col in table.sd.cols}
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 {self.table}
WHERE
{where_clause};
""".format(**locals())
hook = PrestoHook(presto_conn_id=self.presto_conn_id)
self.log.info('Executing SQL check: %s', sql)
row = hook.get_first(hql=sql)
self.log.info("Record: %s", row)
if not row:
raise AirflowException("The query returned None")
part_json = json.dumps(self.partition, sort_keys=True)
self.log.info("Deleting rows from previous runs if they exist")
mysql = MySqlHook(self.mysql_conn_id)
sql = """
SELECT 1 FROM hive_stats
WHERE
table_name='{self.table}' AND
partition_repr='{part_json}' AND
dttm='{self.dttm}'
LIMIT 1;
""".format(**locals())
if mysql.get_records(sql):
sql = """
DELETE FROM hive_stats
WHERE
table_name='{self.table}' AND
partition_repr='{part_json}' AND
dttm='{self.dttm}';
""".format(**locals())
mysql.run(sql)
self.log.info("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',
]
)