Source code for airflow.hooks.hive_hooks
# -*- coding: utf-8 -*-
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# Licensed to the Apache Software Foundation (ASF) under one
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# 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
#
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#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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from __future__ import print_function, unicode_literals
import contextlib
import os
from six.moves import zip
from past.builtins import basestring, unicode
import unicodecsv as csv
import re
import six
import subprocess
import time
from collections import OrderedDict
from tempfile import NamedTemporaryFile
import hmsclient
from airflow import configuration as conf
from airflow.exceptions import AirflowException
from airflow.hooks.base_hook import BaseHook
from airflow.utils.helpers import as_flattened_list
from airflow.utils.file import TemporaryDirectory
from airflow import configuration
import airflow.security.utils as utils
HIVE_QUEUE_PRIORITIES = ['VERY_HIGH', 'HIGH', 'NORMAL', 'LOW', 'VERY_LOW']
[docs]class HiveCliHook(BaseHook):
"""Simple wrapper around the hive CLI.
It also supports the ``beeline``
a lighter CLI that runs JDBC and is replacing the heavier
traditional CLI. To enable ``beeline``, set the use_beeline param in the
extra field of your connection as in ``{ "use_beeline": true }``
Note that you can also set default hive CLI parameters using the
``hive_cli_params`` to be used in your connection as in
``{"hive_cli_params": "-hiveconf mapred.job.tracker=some.jobtracker:444"}``
Parameters passed here can be overridden by run_cli's hive_conf param
The extra connection parameter ``auth`` gets passed as in the ``jdbc``
connection string as is.
:param mapred_queue: queue used by the Hadoop Scheduler (Capacity or Fair)
:type mapred_queue: string
:param mapred_queue_priority: priority within the job queue.
Possible settings include: VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW
:type mapred_queue_priority: string
:param mapred_job_name: This name will appear in the jobtracker.
This can make monitoring easier.
:type mapred_job_name: string
"""
def __init__(
self,
hive_cli_conn_id="hive_cli_default",
run_as=None,
mapred_queue=None,
mapred_queue_priority=None,
mapred_job_name=None):
conn = self.get_connection(hive_cli_conn_id)
self.hive_cli_params = conn.extra_dejson.get('hive_cli_params', '')
self.use_beeline = conn.extra_dejson.get('use_beeline', False)
self.auth = conn.extra_dejson.get('auth', 'noSasl')
self.conn = conn
self.run_as = run_as
if mapred_queue_priority:
mapred_queue_priority = mapred_queue_priority.upper()
if mapred_queue_priority not in HIVE_QUEUE_PRIORITIES:
raise AirflowException(
"Invalid Mapred Queue Priority. Valid values are: "
"{}".format(', '.join(HIVE_QUEUE_PRIORITIES)))
self.mapred_queue = mapred_queue or conf.get('hive',
'default_hive_mapred_queue')
self.mapred_queue_priority = mapred_queue_priority
self.mapred_job_name = mapred_job_name
def _prepare_cli_cmd(self):
"""
This function creates the command list from available information
"""
conn = self.conn
hive_bin = 'hive'
cmd_extra = []
if self.use_beeline:
hive_bin = 'beeline'
jdbc_url = "jdbc:hive2://{conn.host}:{conn.port}/{conn.schema}"
if configuration.conf.get('core', 'security') == 'kerberos':
template = conn.extra_dejson.get(
'principal', "hive/_HOST@EXAMPLE.COM")
if "_HOST" in template:
template = utils.replace_hostname_pattern(
utils.get_components(template))
proxy_user = "" # noqa
if conn.extra_dejson.get('proxy_user') == "login" and conn.login:
proxy_user = "hive.server2.proxy.user={0}".format(conn.login)
elif conn.extra_dejson.get('proxy_user') == "owner" and self.run_as:
proxy_user = "hive.server2.proxy.user={0}".format(self.run_as)
jdbc_url += ";principal={template};{proxy_user}"
elif self.auth:
jdbc_url += ";auth=" + self.auth
jdbc_url = jdbc_url.format(**locals())
cmd_extra += ['-u', jdbc_url]
if conn.login:
cmd_extra += ['-n', conn.login]
if conn.password:
cmd_extra += ['-p', conn.password]
hive_params_list = self.hive_cli_params.split()
return [hive_bin] + cmd_extra + hive_params_list
def _prepare_hiveconf(self, d):
"""
This function prepares a list of hiveconf params
from a dictionary of key value pairs.
:param d:
:type d: dict
>>> hh = HiveCliHook()
>>> hive_conf = {"hive.exec.dynamic.partition": "true",
... "hive.exec.dynamic.partition.mode": "nonstrict"}
>>> hh._prepare_hiveconf(hive_conf)
["-hiveconf", "hive.exec.dynamic.partition=true",\
"-hiveconf", "hive.exec.dynamic.partition.mode=nonstrict"]
"""
if not d:
return []
return as_flattened_list(
zip(["-hiveconf"] * len(d),
["{}={}".format(k, v) for k, v in d.items()])
)
[docs] def run_cli(self, hql, schema=None, verbose=True, hive_conf=None):
"""
Run an hql statement using the hive cli. If hive_conf is specified
it should be a dict and the entries will be set as key/value pairs
in HiveConf
:param hive_conf: if specified these key value pairs will be passed
to hive as ``-hiveconf "key"="value"``. Note that they will be
passed after the ``hive_cli_params`` and thus will override
whatever values are specified in the database.
:type hive_conf: dict
>>> hh = HiveCliHook()
>>> result = hh.run_cli("USE airflow;")
>>> ("OK" in result)
True
"""
conn = self.conn
schema = schema or conn.schema
if schema:
hql = "USE {schema};\n{hql}".format(**locals())
with TemporaryDirectory(prefix='airflow_hiveop_') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir) as f:
f.write(hql.encode('UTF-8'))
f.flush()
hive_cmd = self._prepare_cli_cmd()
hive_conf_params = self._prepare_hiveconf(hive_conf)
if self.mapred_queue:
hive_conf_params.extend(
['-hiveconf',
'mapreduce.job.queuename={}'
.format(self.mapred_queue),
'-hiveconf',
'mapred.job.queue.name={}'
.format(self.mapred_queue),
'-hiveconf',
'tez.job.queue.name={}'
.format(self.mapred_queue)
])
if self.mapred_queue_priority:
hive_conf_params.extend(
['-hiveconf',
'mapreduce.job.priority={}'
.format(self.mapred_queue_priority)])
if self.mapred_job_name:
hive_conf_params.extend(
['-hiveconf',
'mapred.job.name={}'
.format(self.mapred_job_name)])
hive_cmd.extend(hive_conf_params)
hive_cmd.extend(['-f', f.name])
if verbose:
self.log.info(" ".join(hive_cmd))
sp = subprocess.Popen(
hive_cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=tmp_dir,
close_fds=True)
self.sp = sp
stdout = ''
while True:
line = sp.stdout.readline()
if not line:
break
stdout += line.decode('UTF-8')
if verbose:
self.log.info(line.decode('UTF-8').strip())
sp.wait()
if sp.returncode:
raise AirflowException(stdout)
return stdout
[docs] def test_hql(self, hql):
"""
Test an hql statement using the hive cli and EXPLAIN
"""
create, insert, other = [], [], []
for query in hql.split(';'): # naive
query_original = query
query = query.lower().strip()
if query.startswith('create table'):
create.append(query_original)
elif query.startswith(('set ',
'add jar ',
'create temporary function')):
other.append(query_original)
elif query.startswith('insert'):
insert.append(query_original)
other = ';'.join(other)
for query_set in [create, insert]:
for query in query_set:
query_preview = ' '.join(query.split())[:50]
self.log.info("Testing HQL [%s (...)]", query_preview)
if query_set == insert:
query = other + '; explain ' + query
else:
query = 'explain ' + query
try:
self.run_cli(query, verbose=False)
except AirflowException as e:
message = e.args[0].split('\n')[-2]
self.log.info(message)
error_loc = re.search('(\d+):(\d+)', message)
if error_loc and error_loc.group(1).isdigit():
lst = int(error_loc.group(1))
begin = max(lst - 2, 0)
end = min(lst + 3, len(query.split('\n')))
context = '\n'.join(query.split('\n')[begin:end])
self.log.info("Context :\n %s", context)
else:
self.log.info("SUCCESS")
[docs] def load_df(
self,
df,
table,
field_dict=None,
delimiter=',',
encoding='utf8',
pandas_kwargs=None, **kwargs):
"""
Loads a pandas DataFrame into hive.
Hive data types will be inferred if not passed but column names will
not be sanitized.
:param df: DataFrame to load into a Hive table
:type df: DataFrame
:param table: target Hive table, use dot notation to target a
specific database
:type table: str
:param field_dict: mapping from column name to hive data type.
Note that it must be OrderedDict so as to keep columns' order.
:type field_dict: OrderedDict
:param delimiter: field delimiter in the file
:type delimiter: str
:param encoding: string encoding to use when writing DataFrame to file
:type encoding: str
:param pandas_kwargs: passed to DataFrame.to_csv
:type pandas_kwargs: dict
:param kwargs: passed to self.load_file
"""
def _infer_field_types_from_df(df):
DTYPE_KIND_HIVE_TYPE = {
'b': 'BOOLEAN', # boolean
'i': 'BIGINT', # signed integer
'u': 'BIGINT', # unsigned integer
'f': 'DOUBLE', # floating-point
'c': 'STRING', # complex floating-point
'M': 'TIMESTAMP', # datetime
'O': 'STRING', # object
'S': 'STRING', # (byte-)string
'U': 'STRING', # Unicode
'V': 'STRING' # void
}
d = OrderedDict()
for col, dtype in df.dtypes.iteritems():
d[col] = DTYPE_KIND_HIVE_TYPE[dtype.kind]
return d
if pandas_kwargs is None:
pandas_kwargs = {}
with TemporaryDirectory(prefix='airflow_hiveop_') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir, mode="w") as f:
if field_dict is None:
field_dict = _infer_field_types_from_df(df)
df.to_csv(path_or_buf=f,
sep=(delimiter.encode(encoding)
if six.PY2 and isinstance(delimiter, unicode)
else delimiter),
header=False,
index=False,
encoding=encoding,
date_format="%Y-%m-%d %H:%M:%S",
**pandas_kwargs)
f.flush()
return self.load_file(filepath=f.name,
table=table,
delimiter=delimiter,
field_dict=field_dict,
**kwargs)
[docs] def load_file(
self,
filepath,
table,
delimiter=",",
field_dict=None,
create=True,
overwrite=True,
partition=None,
recreate=False,
tblproperties=None):
"""
Loads a local file into Hive
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 tables 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 filepath: local filepath of the file to load
:type filepath: str
:param table: target Hive table, use dot notation to target a
specific database
:type table: str
:param delimiter: field delimiter in the file
:type delimiter: str
:param field_dict: A dictionary of the fields name in the file
as keys and their Hive types as values.
Note that it must be OrderedDict so as to keep columns' order.
:type field_dict: OrderedDict
:param create: whether to create the table if it doesn't exist
:type create: bool
:param overwrite: whether to overwrite the data in table or partition
:type overwrite: bool
:param partition: target partition as a dict of partition columns
and values
:type partition: dict
:param recreate: whether to drop and recreate the table at every
execution
:type recreate: bool
:param tblproperties: TBLPROPERTIES of the hive table being created
:type tblproperties: dict
"""
hql = ''
if recreate:
hql += "DROP TABLE IF EXISTS {table};\n"
if create or recreate:
if field_dict is None:
raise ValueError("Must provide a field dict when creating a table")
fields = ",\n ".join(
[k + ' ' + v for k, v in field_dict.items()])
hql += "CREATE TABLE IF NOT EXISTS {table} (\n{fields})\n"
if partition:
pfields = ",\n ".join(
[p + " STRING" for p in partition])
hql += "PARTITIONED BY ({pfields})\n"
hql += "ROW FORMAT DELIMITED\n"
hql += "FIELDS TERMINATED BY '{delimiter}'\n"
hql += "STORED AS textfile\n"
if tblproperties is not None:
tprops = ", ".join(
["'{0}'='{1}'".format(k, v) for k, v in tblproperties.items()])
hql += "TBLPROPERTIES({tprops})\n"
hql += ";"
hql = hql.format(**locals())
self.log.info(hql)
self.run_cli(hql)
hql = "LOAD DATA LOCAL INPATH '{filepath}' "
if overwrite:
hql += "OVERWRITE "
hql += "INTO TABLE {table} "
if partition:
pvals = ", ".join(
["{0}='{1}'".format(k, v) for k, v in partition.items()])
hql += "PARTITION ({pvals});"
# As a workaround for HIVE-10541, add a newline character
# at the end of hql (AIRFLOW-2412).
hql += '\n'
hql = hql.format(**locals())
self.log.info(hql)
self.run_cli(hql)
def kill(self):
if hasattr(self, 'sp'):
if self.sp.poll() is None:
print("Killing the Hive job")
self.sp.terminate()
time.sleep(60)
self.sp.kill()
[docs]class HiveMetastoreHook(BaseHook):
""" Wrapper to interact with the Hive Metastore"""
# java short max val
MAX_PART_COUNT = 32767
def __init__(self, metastore_conn_id='metastore_default'):
self.metastore_conn = self.get_connection(metastore_conn_id)
self.metastore = self.get_metastore_client()
def __getstate__(self):
# This is for pickling to work despite the thirft hive client not
# being pickable
d = dict(self.__dict__)
del d['metastore']
return d
def __setstate__(self, d):
self.__dict__.update(d)
self.__dict__['metastore'] = self.get_metastore_client()
[docs] def get_metastore_client(self):
"""
Returns a Hive thrift client.
"""
from thrift.transport import TSocket, TTransport
from thrift.protocol import TBinaryProtocol
ms = self.metastore_conn
auth_mechanism = ms.extra_dejson.get('authMechanism', 'NOSASL')
if configuration.conf.get('core', 'security') == 'kerberos':
auth_mechanism = ms.extra_dejson.get('authMechanism', 'GSSAPI')
kerberos_service_name = ms.extra_dejson.get('kerberos_service_name', 'hive')
socket = TSocket.TSocket(ms.host, ms.port)
if configuration.conf.get('core', 'security') == 'kerberos' \
and auth_mechanism == 'GSSAPI':
try:
import saslwrapper as sasl
except ImportError:
import sasl
def sasl_factory():
sasl_client = sasl.Client()
sasl_client.setAttr("host", ms.host)
sasl_client.setAttr("service", kerberos_service_name)
sasl_client.init()
return sasl_client
from thrift_sasl import TSaslClientTransport
transport = TSaslClientTransport(sasl_factory, "GSSAPI", socket)
else:
transport = TTransport.TBufferedTransport(socket)
protocol = TBinaryProtocol.TBinaryProtocol(transport)
return hmsclient.HMSClient(iprot=protocol)
def get_conn(self):
return self.metastore
[docs] def check_for_partition(self, schema, table, partition):
"""
Checks whether a partition exists
:param schema: Name of hive schema (database) @table belongs to
:type schema: string
:param table: Name of hive table @partition belongs to
:type schema: string
:partition: Expression that matches the partitions to check for
(eg `a = 'b' AND c = 'd'`)
:type schema: string
:rtype: boolean
>>> hh = HiveMetastoreHook()
>>> t = 'static_babynames_partitioned'
>>> hh.check_for_partition('airflow', t, "ds='2015-01-01'")
True
"""
with self.metastore as client:
partitions = client.get_partitions_by_filter(
schema, table, partition, 1)
if partitions:
return True
else:
return False
[docs] def check_for_named_partition(self, schema, table, partition_name):
"""
Checks whether a partition with a given name exists
:param schema: Name of hive schema (database) @table belongs to
:type schema: string
:param table: Name of hive table @partition belongs to
:type schema: string
:partition: Name of the partitions to check for (eg `a=b/c=d`)
:type schema: string
:rtype: boolean
>>> hh = HiveMetastoreHook()
>>> t = 'static_babynames_partitioned'
>>> hh.check_for_named_partition('airflow', t, "ds=2015-01-01")
True
>>> hh.check_for_named_partition('airflow', t, "ds=xxx")
False
"""
with self.metastore as client:
return client.check_for_named_partition(schema, table, partition_name)
[docs] def get_table(self, table_name, db='default'):
"""Get a metastore table object
>>> hh = HiveMetastoreHook()
>>> t = hh.get_table(db='airflow', table_name='static_babynames')
>>> t.tableName
'static_babynames'
>>> [col.name for col in t.sd.cols]
['state', 'year', 'name', 'gender', 'num']
"""
if db == 'default' and '.' in table_name:
db, table_name = table_name.split('.')[:2]
with self.metastore as client:
return client.get_table(dbname=db, tbl_name=table_name)
[docs] def get_tables(self, db, pattern='*'):
"""
Get a metastore table object
"""
with self.metastore as client:
tables = client.get_tables(db_name=db, pattern=pattern)
return client.get_table_objects_by_name(db, tables)
[docs] def get_databases(self, pattern='*'):
"""
Get a metastore table object
"""
with self.metastore as client:
return client.get_databases(pattern)
[docs] def get_partitions(
self, schema, table_name, filter=None):
"""
Returns a list of all partitions in a table. Works only
for tables with less than 32767 (java short max val).
For subpartitioned table, the number might easily exceed this.
>>> hh = HiveMetastoreHook()
>>> t = 'static_babynames_partitioned'
>>> parts = hh.get_partitions(schema='airflow', table_name=t)
>>> len(parts)
1
>>> parts
[{'ds': '2015-01-01'}]
"""
with self.metastore as client:
table = client.get_table(dbname=schema, tbl_name=table_name)
if len(table.partitionKeys) == 0:
raise AirflowException("The table isn't partitioned")
else:
if filter:
parts = client.get_partitions_by_filter(
db_name=schema, tbl_name=table_name,
filter=filter, max_parts=HiveMetastoreHook.MAX_PART_COUNT)
else:
parts = client.get_partitions(
db_name=schema, tbl_name=table_name,
max_parts=HiveMetastoreHook.MAX_PART_COUNT)
pnames = [p.name for p in table.partitionKeys]
return [dict(zip(pnames, p.values)) for p in parts]
@staticmethod
def _get_max_partition_from_part_specs(part_specs, partition_key, filter_map):
"""
Helper method to get max partition of partitions with partition_key
from part specs. key:value pair in filter_map will be used to
filter out partitions.
:param part_specs: list of partition specs.
:type part_specs: list
:param partition_key: partition key name.
:type partition_key: string
:param filter_map: partition_key:partition_value map used for partition filtering,
e.g. {'key1': 'value1', 'key2': 'value2'}.
Only partitions matching all partition_key:partition_value
pairs will be considered as candidates of max partition.
:type filter_map: map
:return: Max partition or None if part_specs is empty.
"""
if not part_specs:
return None
# Assuming all specs have the same keys.
if partition_key not in part_specs[0].keys():
raise AirflowException("Provided partition_key {} "
"is not in part_specs.".format(partition_key))
if filter_map:
is_subset = set(filter_map.keys()).issubset(set(part_specs[0].keys()))
if filter_map and not is_subset:
raise AirflowException("Keys in provided filter_map {} "
"are not subset of part_spec keys: {}"
.format(', '.join(filter_map.keys()),
', '.join(part_specs[0].keys())))
candidates = [p_dict[partition_key] for p_dict in part_specs
if filter_map is None or
all(item in p_dict.items() for item in filter_map.items())]
if not candidates:
return None
else:
return max(candidates).encode('utf-8')
[docs] def max_partition(self, schema, table_name, field=None, filter_map=None):
"""
Returns the maximum value for all partitions with given field in a table.
If only one partition key exist in the table, the key will be used as field.
filter_map should be a partition_key:partition_value map and will be used to
filter out partitions.
:param schema: schema name.
:type schema: string
:param table_name: table name.
:type table_name: string
:param field: partition key to get max partition from.
:type field: string
:param filter_map: partition_key:partition_value map used for partition filtering.
:type filter_map: map
>>> hh = HiveMetastoreHook()
>>> filter_map = {'ds': '2015-01-01', 'ds': '2014-01-01'}
>>> t = 'static_babynames_partitioned'
>>> hh.max_partition(schema='airflow',\
... table_name=t, field='ds', filter_map=filter_map)
'2015-01-01'
"""
with self.metastore as client:
table = client.get_table(dbname=schema, tbl_name=table_name)
key_name_set = set(key.name for key in table.partitionKeys)
if len(table.partitionKeys) == 1:
field = table.partitionKeys[0].name
elif not field:
raise AirflowException("Please specify the field you want the max "
"value for.")
elif field not in key_name_set:
raise AirflowException("Provided field is not a partition key.")
if filter_map and not set(filter_map.keys()).issubset(key_name_set):
raise AirflowException("Provided filter_map contains keys "
"that are not partition key.")
part_names = \
client.get_partition_names(schema,
table_name,
max_parts=HiveMetastoreHook.MAX_PART_COUNT)
part_specs = [client.partition_name_to_spec(part_name)
for part_name in part_names]
return HiveMetastoreHook._get_max_partition_from_part_specs(part_specs,
field,
filter_map)
[docs] def table_exists(self, table_name, db='default'):
"""
Check if table exists
>>> hh = HiveMetastoreHook()
>>> hh.table_exists(db='airflow', table_name='static_babynames')
True
>>> hh.table_exists(db='airflow', table_name='does_not_exist')
False
"""
try:
self.get_table(table_name, db)
return True
except Exception:
return False
[docs]class HiveServer2Hook(BaseHook):
"""
Wrapper around the pyhive library
Note that the default authMechanism is PLAIN, to override it you
can specify it in the ``extra`` of your connection in the UI as in
"""
def __init__(self, hiveserver2_conn_id='hiveserver2_default'):
self.hiveserver2_conn_id = hiveserver2_conn_id
def get_conn(self, schema=None):
db = self.get_connection(self.hiveserver2_conn_id)
auth_mechanism = db.extra_dejson.get('authMechanism', 'NONE')
if auth_mechanism == 'NONE' and db.login is None:
# we need to give a username
username = 'airflow'
kerberos_service_name = None
if configuration.conf.get('core', 'security') == 'kerberos':
auth_mechanism = db.extra_dejson.get('authMechanism', 'KERBEROS')
kerberos_service_name = db.extra_dejson.get('kerberos_service_name', 'hive')
# pyhive uses GSSAPI instead of KERBEROS as a auth_mechanism identifier
if auth_mechanism == 'GSSAPI':
self.log.warning(
"Detected deprecated 'GSSAPI' for authMechanism "
"for %s. Please use 'KERBEROS' instead",
self.hiveserver2_conn_id
)
auth_mechanism = 'KERBEROS'
from pyhive.hive import connect
return connect(
host=db.host,
port=db.port,
auth=auth_mechanism,
kerberos_service_name=kerberos_service_name,
username=db.login or username,
database=schema or db.schema or 'default')
def _get_results(self, hql, schema='default', fetch_size=None):
from pyhive.exc import ProgrammingError
if isinstance(hql, basestring):
hql = [hql]
previous_description = None
with contextlib.closing(self.get_conn(schema)) as conn, \
contextlib.closing(conn.cursor()) as cur:
cur.arraysize = fetch_size or 1000
for statement in hql:
cur.execute(statement)
# we only get results of statements that returns
lowered_statement = statement.lower().strip()
if (lowered_statement.startswith('select') or
lowered_statement.startswith('with')):
description = [c for c in cur.description]
if previous_description and previous_description != description:
message = '''The statements are producing different descriptions:
Current: {}
Previous: {}'''.format(repr(description),
repr(previous_description))
raise ValueError(message)
elif not previous_description:
previous_description = description
yield description
try:
# DB API 2 raises when no results are returned
# we're silencing here as some statements in the list
# may be `SET` or DDL
for row in cur:
yield row
except ProgrammingError:
self.log.debug("get_results returned no records")
def get_results(self, hql, schema='default', fetch_size=None):
results_iter = self._get_results(hql, schema, fetch_size=fetch_size)
header = next(results_iter)
results = {
'data': list(results_iter),
'header': header
}
return results
def to_csv(
self,
hql,
csv_filepath,
schema='default',
delimiter=',',
lineterminator='\r\n',
output_header=True,
fetch_size=1000):
results_iter = self._get_results(hql, schema, fetch_size=fetch_size)
header = next(results_iter)
message = None
with open(csv_filepath, 'wb') as f:
writer = csv.writer(f,
delimiter=delimiter,
lineterminator=lineterminator,
encoding='utf-8')
try:
if output_header:
self.log.debug('Cursor description is %s', header)
writer.writerow([c[0] for c in header])
for i, row in enumerate(results_iter):
writer.writerow(row)
if i % fetch_size == 0:
self.log.info("Written %s rows so far.", i)
except ValueError as exception:
message = str(exception)
if message:
# need to clean up the file first
os.remove(csv_filepath)
raise ValueError(message)
self.log.info("Done. Loaded a total of %s rows.", i)
[docs] def get_records(self, hql, schema='default'):
"""
Get a set of records from a Hive query.
>>> hh = HiveServer2Hook()
>>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
>>> len(hh.get_records(sql))
100
"""
return self.get_results(hql, schema=schema)['data']
[docs] def get_pandas_df(self, hql, schema='default'):
"""
Get a pandas dataframe from a Hive query
>>> hh = HiveServer2Hook()
>>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
>>> df = hh.get_pandas_df(sql)
>>> len(df.index)
100
"""
import pandas as pd
res = self.get_results(hql, schema=schema)
df = pd.DataFrame(res['data'])
df.columns = [c[0] for c in res['header']]
return df