Source code for airflow.hooks.hive_hooks

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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 as utils


[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.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', '{}' .format(self.mapred_queue), '-hiveconf', '{}' .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', '{}' .format(self.mapred_job_name)]) hive_cmd.extend(hive_conf_params) hive_cmd.extend(['-f',]) if verbose:" ".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:'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]"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] error_loc ='(\d+):(\d+)', message) if error_loc and lst = int( begin = max(lst - 2, 0) end = min(lst + 3, len(query.split('\n'))) context = '\n'.join(query.split('\n')[begin:end])"Context :\n %s", context) else:"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(, 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.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.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.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", 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' >>> [ for col in] ['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 = [ 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( 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(, 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:"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)"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