/
hive.py
1062 lines (906 loc) · 41.4 KB
/
hive.py
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#
# 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 __future__ import annotations
import contextlib
import os
import re
import socket
import subprocess
import time
import warnings
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import TYPE_CHECKING, Any, Iterable, Mapping
from airflow.exceptions import AirflowProviderDeprecationWarning
if TYPE_CHECKING:
import pandas as pd
import csv
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
from airflow.providers.common.sql.hooks.sql import DbApiHook
from airflow.security import utils
from airflow.utils.helpers import as_flattened_list
from airflow.utils.operator_helpers import AIRFLOW_VAR_NAME_FORMAT_MAPPING
HIVE_QUEUE_PRIORITIES = ["VERY_HIGH", "HIGH", "NORMAL", "LOW", "VERY_LOW"]
def get_context_from_env_var() -> dict[Any, Any]:
"""
Extract context from env variable, (dag_id, task_id, etc) for use in BashOperator and PythonOperator.
:return: The context of interest.
"""
return {
format_map["default"]: os.environ.get(format_map["env_var_format"], "")
for format_map in AIRFLOW_VAR_NAME_FORMAT_MAPPING.values()
}
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 by passing ``hive_cli_params``
space separated list of parameters to add to the hive command.
The extra connection parameter ``auth`` gets passed as in the ``jdbc``
connection string as is.
:param hive_cli_conn_id: Reference to the
:ref:`Hive CLI connection id <howto/connection:hive_cli>`.
:param mapred_queue: queue used by the Hadoop Scheduler (Capacity or Fair)
:param mapred_queue_priority: priority within the job queue.
Possible settings include: VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW
:param mapred_job_name: This name will appear in the jobtracker.
This can make monitoring easier.
:param hive_cli_params: Space separated list of hive command parameters to add to the
hive command.
"""
conn_name_attr = "hive_cli_conn_id"
default_conn_name = "hive_cli_default"
conn_type = "hive_cli"
hook_name = "Hive Client Wrapper"
def __init__(
self,
hive_cli_conn_id: str = default_conn_name,
run_as: str | None = None,
mapred_queue: str | None = None,
mapred_queue_priority: str | None = None,
mapred_job_name: str | None = None,
hive_cli_params: str = "",
auth: str | None = None,
) -> None:
super().__init__()
conn = self.get_connection(hive_cli_conn_id)
self.hive_cli_params: str = hive_cli_params
self.use_beeline: bool = conn.extra_dejson.get("use_beeline", False)
self.auth = auth
self.conn = conn
self.run_as = run_as
self.sub_process: Any = None
if mapred_queue_priority:
mapred_queue_priority = mapred_queue_priority.upper()
if mapred_queue_priority not in HIVE_QUEUE_PRIORITIES:
raise AirflowException(
f"Invalid Mapred Queue Priority. Valid values are: {', '.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 _get_proxy_user(self) -> str:
"""Set the proper proxy_user value in case the user overwrite the default."""
conn = self.conn
proxy_user_value: str = conn.extra_dejson.get("proxy_user", "")
if proxy_user_value == "login" and conn.login:
return f"hive.server2.proxy.user={conn.login}"
if proxy_user_value == "owner" and self.run_as:
return f"hive.server2.proxy.user={self.run_as}"
if proxy_user_value != "": # There is a custom proxy user
return f"hive.server2.proxy.user={proxy_user_value}"
return proxy_user_value # The default proxy user (undefined)
def _prepare_cli_cmd(self) -> list[Any]:
"""Create the command list from available information."""
conn = self.conn
hive_bin = "hive"
cmd_extra = []
if self.use_beeline:
hive_bin = "beeline"
self._validate_beeline_parameters(conn)
jdbc_url = f"jdbc:hive2://{conn.host}:{conn.port}/{conn.schema}"
if 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 = self._get_proxy_user()
if ";" in template:
raise RuntimeError("The principal should not contain the ';' character")
if ";" in proxy_user:
raise RuntimeError("The proxy_user should not contain the ';' character")
jdbc_url += f";principal={template};{proxy_user}"
elif self.auth:
jdbc_url += ";auth=" + self.auth
jdbc_url = f'"{jdbc_url}"'
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 _validate_beeline_parameters(self, conn):
if ":" in conn.host or "/" in conn.host or ";" in conn.host:
raise Exception(
f"The host used in beeline command ({conn.host}) should not contain ':/;' characters)"
)
try:
int_port = int(conn.port)
if int_port <= 0 or int_port > 65535:
raise Exception(f"The port used in beeline command ({conn.port}) should be in range 0-65535)")
except (ValueError, TypeError) as e:
raise Exception(f"The port used in beeline command ({conn.port}) should be a valid integer: {e})")
if ";" in conn.schema:
raise Exception(
f"The schema used in beeline command ({conn.schema}) should not contain ';' character)"
)
@staticmethod
def _prepare_hiveconf(d: dict[Any, Any]) -> list[Any]:
"""
Prepare a list of hiveconf params from a dictionary of key value pairs.
:param d:
>>> 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), [f"{k}={v}" for k, v in d.items()]))
def run_cli(
self,
hql: str,
schema: str | None = None,
verbose: bool = True,
hive_conf: dict[Any, Any] | None = None,
) -> Any:
"""
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 hql: an hql (hive query language) statement to run with hive cli
:param schema: Name of hive schema (database) to use
:param verbose: Provides additional logging. Defaults to True.
: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.
>>> hh = HiveCliHook()
>>> result = hh.run_cli("USE airflow;")
>>> ("OK" in result)
True
"""
conn = self.conn
schema = schema or conn.schema
invalid_chars_list = re.findall(r"[^a-z0-9_]", schema)
if invalid_chars_list:
invalid_chars = "".join(invalid_chars_list)
raise RuntimeError(f"The schema `{schema}` contains invalid characters: {invalid_chars}")
if schema:
hql = f"USE {schema};\n{hql}"
with TemporaryDirectory(prefix="airflow_hiveop_") as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir) as f:
hql += "\n"
f.write(hql.encode("UTF-8"))
f.flush()
hive_cmd = self._prepare_cli_cmd()
env_context = get_context_from_env_var()
# Only extend the hive_conf if it is defined.
if hive_conf:
env_context.update(hive_conf)
hive_conf_params = self._prepare_hiveconf(env_context)
if self.mapred_queue:
hive_conf_params.extend(
[
"-hiveconf",
f"mapreduce.job.queuename={self.mapred_queue}",
"-hiveconf",
f"mapred.job.queue.name={self.mapred_queue}",
"-hiveconf",
f"tez.queue.name={self.mapred_queue}",
]
)
if self.mapred_queue_priority:
hive_conf_params.extend(
["-hiveconf", f"mapreduce.job.priority={self.mapred_queue_priority}"]
)
if self.mapred_job_name:
hive_conf_params.extend(["-hiveconf", f"mapred.job.name={self.mapred_job_name}"])
hive_cmd.extend(hive_conf_params)
hive_cmd.extend(["-f", f.name])
if verbose:
self.log.info("%s", " ".join(hive_cmd))
sub_process: Any = subprocess.Popen(
hive_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=tmp_dir, close_fds=True
)
self.sub_process = sub_process
stdout = ""
while True:
line = sub_process.stdout.readline()
if not line:
break
stdout += line.decode("UTF-8")
if verbose:
self.log.info(line.decode("UTF-8").strip())
sub_process.wait()
if sub_process.returncode:
raise AirflowException(stdout)
return stdout
def test_hql(self, hql: str) -> None:
"""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].splitlines()[-2]
self.log.info(message)
error_loc = re.search(r"(\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.splitlines()))
context = "\n".join(query.splitlines()[begin:end])
self.log.info("Context :\n %s", context)
else:
self.log.info("SUCCESS")
def load_df(
self,
df: pd.DataFrame,
table: str,
field_dict: dict[Any, Any] | None = None,
delimiter: str = ",",
encoding: str = "utf8",
pandas_kwargs: Any = None,
**kwargs: Any,
) -> None:
"""
Load 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
:param table: target Hive table, use dot notation to target a
specific database
:param field_dict: mapping from column name to hive data type.
Note that Python dict is ordered so it keeps columns' order.
:param delimiter: field delimiter in the file
:param encoding: str encoding to use when writing DataFrame to file
:param pandas_kwargs: passed to DataFrame.to_csv
:param kwargs: passed to self.load_file
"""
def _infer_field_types_from_df(df: pd.DataFrame) -> dict[Any, Any]:
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
}
order_type = {}
for col, dtype in df.dtypes.items():
order_type[col] = dtype_kind_hive_type[dtype.kind]
return order_type
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,
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
)
def load_file(
self,
filepath: str,
table: str,
delimiter: str = ",",
field_dict: dict[Any, Any] | None = None,
create: bool = True,
overwrite: bool = True,
partition: dict[str, Any] | None = None,
recreate: bool = False,
tblproperties: dict[str, Any] | None = None,
) -> None:
"""
Load 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
:param table: target Hive table, use dot notation to target a
specific database
:param delimiter: field delimiter in the file
:param field_dict: A dictionary of the fields name in the file
as keys and their Hive types as values.
Note that Python dict is ordered so it keeps columns' order.
:param create: whether to create the table if it doesn't exist
:param overwrite: whether to overwrite the data in table or partition
:param partition: target partition as a dict of partition columns
and values
:param recreate: whether to drop and recreate the table at every
execution
:param tblproperties: TBLPROPERTIES of the hive table being created
"""
hql = ""
if recreate:
hql += f"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(f"`{k.strip('`')}` {v}" for k, v in field_dict.items())
hql += f"CREATE TABLE IF NOT EXISTS {table} (\n{fields})\n"
if partition:
pfields = ",\n ".join(p + " STRING" for p in partition)
hql += f"PARTITIONED BY ({pfields})\n"
hql += "ROW FORMAT DELIMITED\n"
hql += f"FIELDS TERMINATED BY '{delimiter}'\n"
hql += "STORED AS textfile\n"
if tblproperties is not None:
tprops = ", ".join(f"'{k}'='{v}'" for k, v in tblproperties.items())
hql += f"TBLPROPERTIES({tprops})\n"
hql += ";"
self.log.info(hql)
self.run_cli(hql)
hql = f"LOAD DATA LOCAL INPATH '{filepath}' "
if overwrite:
hql += "OVERWRITE "
hql += f"INTO TABLE {table} "
if partition:
pvals = ", ".join(f"{k}='{v}'" for k, v in partition.items())
hql += f"PARTITION ({pvals})"
# As a workaround for HIVE-10541, add a newline character
# at the end of hql (AIRFLOW-2412).
hql += ";\n"
self.log.info(hql)
self.run_cli(hql)
def kill(self) -> None:
"""Kill Hive cli command."""
if hasattr(self, "sub_process"):
if self.sub_process.poll() is None:
print("Killing the Hive job")
self.sub_process.terminate()
time.sleep(60)
self.sub_process.kill()
class HiveMetastoreHook(BaseHook):
"""
Wrapper to interact with the Hive Metastore.
:param metastore_conn_id: reference to the
:ref: `metastore thrift service connection id <howto/connection:hive_metastore>`.
"""
# java short max val
MAX_PART_COUNT = 32767
conn_name_attr = "metastore_conn_id"
default_conn_name = "metastore_default"
conn_type = "hive_metastore"
hook_name = "Hive Metastore Thrift"
def __init__(self, metastore_conn_id: str = default_conn_name) -> None:
super().__init__()
self.conn = self.get_connection(metastore_conn_id)
self.metastore = self.get_metastore_client()
def __getstate__(self) -> dict[str, Any]:
# This is for pickling to work despite the thrift hive client not
# being picklable
state = dict(self.__dict__)
del state["metastore"]
return state
def __setstate__(self, d: dict[str, Any]) -> None:
self.__dict__.update(d)
self.__dict__["metastore"] = self.get_metastore_client()
def get_metastore_client(self) -> Any:
"""Return a Hive thrift client."""
import hmsclient
from thrift.protocol import TBinaryProtocol
from thrift.transport import TSocket, TTransport
host = self._find_valid_host()
conn = self.conn
if not host:
raise AirflowException("Failed to locate the valid server.")
if "authMechanism" in conn.extra_dejson:
warnings.warn(
"The 'authMechanism' option is deprecated. Please use 'auth_mechanism'.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
conn.extra_dejson["auth_mechanism"] = conn.extra_dejson["authMechanism"]
del conn.extra_dejson["authMechanism"]
auth_mechanism = conn.extra_dejson.get("auth_mechanism", "NOSASL")
if conf.get("core", "security") == "kerberos":
auth_mechanism = conn.extra_dejson.get("auth_mechanism", "GSSAPI")
kerberos_service_name = conn.extra_dejson.get("kerberos_service_name", "hive")
conn_socket = TSocket.TSocket(host, conn.port)
if 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()
sasl_client.setAttr("host", host)
sasl_client.setAttr("service", kerberos_service_name)
sasl_client.init()
return sasl_client
from thrift_sasl import TSaslClientTransport
transport = TSaslClientTransport(sasl_factory, "GSSAPI", conn_socket)
else:
transport = TTransport.TBufferedTransport(conn_socket)
protocol = TBinaryProtocol.TBinaryProtocol(transport)
return hmsclient.HMSClient(iprot=protocol)
def _find_valid_host(self) -> Any:
conn = self.conn
hosts = conn.host.split(",")
for host in hosts:
host_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.log.info("Trying to connect to %s:%s", host, conn.port)
if host_socket.connect_ex((host, conn.port)) == 0:
self.log.info("Connected to %s:%s", host, conn.port)
host_socket.close()
return host
else:
self.log.error("Could not connect to %s:%s", host, conn.port)
return None
def get_conn(self) -> Any:
return self.metastore
def check_for_partition(self, schema: str, table: str, partition: str) -> bool:
"""
Check whether a partition exists.
:param schema: Name of hive schema (database) @table belongs to
:param table: Name of hive table @partition belongs to
:param partition: Expression that matches the partitions to check for
(eg `a = 'b' AND c = 'd'`)
>>> 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, HiveMetastoreHook.MAX_PART_COUNT
)
return bool(partitions)
def check_for_named_partition(self, schema: str, table: str, partition_name: str) -> Any:
"""
Check whether a partition with a given name exists.
:param schema: Name of hive schema (database) @table belongs to
:param table: Name of hive table @partition belongs to
:param partition_name: Name of the partitions to check for (eg `a=b/c=d`)
>>> 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)
def get_table(self, table_name: str, db: str = "default") -> Any:
"""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)
def get_tables(self, db: str, pattern: str = "*") -> Any:
"""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)
def get_databases(self, pattern: str = "*") -> Any:
"""Get a metastore table object."""
with self.metastore as client:
return client.get_databases(pattern)
def get_partitions(self, schema: str, table_name: str, partition_filter: str | None = None) -> list[Any]:
"""
Return 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 table.partitionKeys:
if partition_filter:
parts = client.get_partitions_by_filter(
db_name=schema,
tbl_name=table_name,
filter=partition_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]
else:
raise AirflowException("The table isn't partitioned")
@staticmethod
def _get_max_partition_from_part_specs(
part_specs: list[Any], partition_key: str | None, filter_map: dict[str, Any] | None
) -> Any:
"""
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.
:param partition_key: partition key name.
: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.
: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(f"Provided partition_key {partition_key} is not in part_specs.")
is_subset = None
if filter_map:
is_subset = set(filter_map.keys()).issubset(set(part_specs[0].keys()))
if filter_map and not is_subset:
raise AirflowException(
f"Keys in provided filter_map {', '.join(filter_map.keys())} "
f"are not subset of part_spec 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)
def max_partition(
self,
schema: str,
table_name: str,
field: str | None = None,
filter_map: dict[Any, Any] | None = None,
) -> Any:
"""
Return 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.
:param table_name: table name.
:param field: partition key to get max partition from.
:param filter_map: partition_key:partition_value map used for partition filtering.
>>> hh = HiveMetastoreHook()
>>> filter_map = {'ds': '2015-01-01'}
>>> 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 = {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)
def table_exists(self, table_name: str, db: str = "default") -> bool:
"""
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
def drop_partitions(self, table_name, part_vals, delete_data=False, db="default"):
"""
Drop partitions from the given table matching the part_vals input.
:param table_name: table name.
:param part_vals: list of partition specs.
:param delete_data: Setting to control if underlying data have to deleted
in addition to dropping partitions.
:param db: Name of hive schema (database) @table belongs to
>>> hh = HiveMetastoreHook()
>>> hh.drop_partitions(db='airflow', table_name='static_babynames',
part_vals="['2020-05-01']")
True
"""
if self.table_exists(table_name, db):
with self.metastore as client:
self.log.info(
"Dropping partition of table %s.%s matching the spec: %s", db, table_name, part_vals
)
return client.drop_partition(db, table_name, part_vals, delete_data)
else:
self.log.info("Table %s.%s does not exist!", db, table_name)
return False
class HiveServer2Hook(DbApiHook):
"""
Wrapper around the pyhive library.
Notes:
* the default auth_mechanism is PLAIN, to override it you
can specify it in the ``extra`` of your connection in the UI
* the default for run_set_variable_statements is true, if you
are using impala you may need to set it to false in the
``extra`` of your connection in the UI
:param hiveserver2_conn_id: Reference to the
:ref: `Hive Server2 thrift service connection id <howto/connection:hiveserver2>`.
:param schema: Hive database name.
"""
conn_name_attr = "hiveserver2_conn_id"
default_conn_name = "hiveserver2_default"
conn_type = "hiveserver2"
hook_name = "Hive Server 2 Thrift"
supports_autocommit = False
def get_conn(self, schema: str | None = None) -> Any:
"""Return a Hive connection object."""
username: str | None = None
password: str | None = None
db = self.get_connection(self.hiveserver2_conn_id) # type: ignore
if "authMechanism" in db.extra_dejson:
warnings.warn(
"The 'authMechanism' option is deprecated. Please use 'auth_mechanism'.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
db.extra_dejson["auth_mechanism"] = db.extra_dejson["authMechanism"]
del db.extra_dejson["authMechanism"]
auth_mechanism = db.extra_dejson.get("auth_mechanism", "NONE")
if auth_mechanism == "NONE" and db.login is None:
# we need to give a username
username = "airflow"
kerberos_service_name = None
if conf.get("core", "security") == "kerberos":
auth_mechanism = db.extra_dejson.get("auth_mechanism", "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 auth_mechanism for %s. Please use 'KERBEROS' instead",
self.hiveserver2_conn_id, # type: ignore
)
auth_mechanism = "KERBEROS"
# Password should be set if and only if in LDAP or CUSTOM mode
if auth_mechanism in ("LDAP", "CUSTOM"):
password = db.password
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,
password=password,
database=schema or db.schema or "default",
)
def _get_results(
self,
sql: str | list[str],
schema: str = "default",
fetch_size: int | None = None,
hive_conf: Iterable | Mapping | None = None,
) -> Any:
from pyhive.exc import ProgrammingError
if isinstance(sql, str):
sql = [sql]
previous_description = None
with contextlib.closing(self.get_conn(schema)) as conn, contextlib.closing(conn.cursor()) as cur:
cur.arraysize = fetch_size or 1000
# not all query services (e.g. impala AIRFLOW-4434) support the set command
db = self.get_connection(self.hiveserver2_conn_id) # type: ignore
if db.extra_dejson.get("run_set_variable_statements", True):
env_context = get_context_from_env_var()
if hive_conf:
env_context.update(hive_conf)
for k, v in env_context.items():
cur.execute(f"set {k}={v}")
for statement in sql:
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")
or lowered_statement.startswith("show")
or (lowered_statement.startswith("set") and "=" not in lowered_statement)
):
description = cur.description
if previous_description and previous_description != description:
message = f"""The statements are producing different descriptions:
Current: {description!r}
Previous: {previous_description!r}"""
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
yield from cur
except ProgrammingError:
self.log.debug("get_results returned no records")
def get_results(
self,
sql: str | list[str],
schema: str = "default",
fetch_size: int | None = None,
hive_conf: Iterable | Mapping | None = None,
) -> dict[str, Any]:
"""
Get results of the provided hql in target schema.
:param sql: hql to be executed.
:param schema: target schema, default to 'default'.
:param fetch_size: max size of result to fetch.
:param hive_conf: hive_conf to execute alone with the hql.
:return: results of hql execution, dict with data (list of results) and header
"""
results_iter = self._get_results(sql, schema, fetch_size=fetch_size, hive_conf=hive_conf)
header = next(results_iter)
results = {"data": list(results_iter), "header": header}
return results
def to_csv(
self,
sql: str,
csv_filepath: str,
schema: str = "default",
delimiter: str = ",",
lineterminator: str = "\r\n",
output_header: bool = True,
fetch_size: int = 1000,
hive_conf: dict[Any, Any] | None = None,
) -> None:
"""
Execute hql in target schema and write results to a csv file.
:param sql: hql to be executed.
:param csv_filepath: filepath of csv to write results into.
:param schema: target schema, default to 'default'.
:param delimiter: delimiter of the csv file, default to ','.
:param lineterminator: lineterminator of the csv file.
:param output_header: header of the csv file, default to True.
:param fetch_size: number of result rows to write into the csv file, default to 1000.
:param hive_conf: hive_conf to execute alone with the hql.
"""
results_iter = self._get_results(sql, schema, fetch_size=fetch_size, hive_conf=hive_conf)
header = next(results_iter)
message = None
i = 0
with open(csv_filepath, "w", encoding="utf-8") as file:
writer = csv.writer(file, delimiter=delimiter, lineterminator=lineterminator)
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, 1):
writer.writerow(row)
if i % fetch_size == 0:
self.log.info("Written %s rows so far.", i)