airflow.hooks.hive_hooks

Module Contents

airflow.hooks.hive_hooks.HIVE_QUEUE_PRIORITIES = ['VERY_HIGH', 'HIGH', 'NORMAL', 'LOW', 'VERY_LOW'][source]
airflow.hooks.hive_hooks.get_context_from_env_var()[source]
Extract context from env variable, e.g. dag_id, task_id and execution_date,
so that they can be used inside BashOperator and PythonOperator.
Returns

The context of interest.

class airflow.hooks.hive_hooks.HiveCliHook(hive_cli_conn_id='hive_cli_default', run_as=None, mapred_queue=None, mapred_queue_priority=None, mapred_job_name=None)[source]

Bases: airflow.hooks.base_hook.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.

Parameters

  • mapred_queue (str) – queue used by the Hadoop Scheduler (Capacity or Fair)

  • mapred_queue_priority (str) – priority within the job queue. Possible settings include: VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW

  • mapred_job_name (str) – This name will appear in the jobtracker. This can make monitoring easier.

_get_proxy_user(self)[source]

This function set the proper proxy_user value in case the user overwtire the default.

_prepare_cli_cmd(self)[source]

This function creates the command list from available information

static _prepare_hiveconf(d)[source]

This function prepares a list of hiveconf params from a dictionary of key value pairs.

Parameters

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"]
run_cli(self, hql, schema=None, verbose=True, hive_conf=None)[source]

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

Parameters

hive_conf (dict) – 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
test_hql(self, hql)[source]

Test an hql statement using the hive cli and EXPLAIN

load_df(self, df, table, field_dict=None, delimiter=', ', encoding='utf8', pandas_kwargs=None, **kwargs)[source]

Loads a pandas DataFrame into hive.

Hive data types will be inferred if not passed but column names will not be sanitized.

Parameters

  • df (pandas.DataFrame) – DataFrame to load into a Hive table

  • table (str) – target Hive table, use dot notation to target a specific database

  • field_dict (collections.OrderedDict) – mapping from column name to hive data type. Note that it must be OrderedDict so as to keep columns’ order.

  • delimiter (str) – field delimiter in the file

  • encoding (str) – str encoding to use when writing DataFrame to file

  • pandas_kwargs (dict) – passed to DataFrame.to_csv

  • kwargs – passed to self.load_file

load_file(self, filepath, table, delimiter=', ', field_dict=None, create=True, overwrite=True, partition=None, recreate=False, tblproperties=None)[source]

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.

Parameters

  • filepath (str) – local filepath of the file to load

  • table (str) – target Hive table, use dot notation to target a specific database

  • delimiter (str) – field delimiter in the file

  • field_dict (collections.OrderedDict) – 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.

  • create (bool) – whether to create the table if it doesn’t exist

  • overwrite (bool) – whether to overwrite the data in table or partition

  • partition (dict) – target partition as a dict of partition columns and values

  • recreate (bool) – whether to drop and recreate the table at every execution

  • tblproperties (dict) – TBLPROPERTIES of the hive table being created

kill(self)[source]
class airflow.hooks.hive_hooks.HiveMetastoreHook(metastore_conn_id='metastore_default')[source]

Bases: airflow.hooks.base_hook.BaseHook

Wrapper to interact with the Hive Metastore

MAX_PART_COUNT = 32767[source]
__getstate__(self)[source]
__setstate__(self, d)[source]
get_metastore_client(self)[source]

Returns a Hive thrift client.

_find_valid_server(self)[source]
get_conn(self)[source]
check_for_partition(self, schema, table, partition)[source]

Checks whether a partition exists

Parameters

  • schema (str) – Name of hive schema (database) @table belongs to

  • table – Name of hive table @partition belongs to

Partition

Expression that matches the partitions to check for (eg a = ‘b’ AND c = ‘d’)

Return type

bool

>>> hh = HiveMetastoreHook()
>>> t = 'static_babynames_partitioned'
>>> hh.check_for_partition('airflow', t, "ds='2015-01-01'")
True
check_for_named_partition(self, schema, table, partition_name)[source]

Checks whether a partition with a given name exists

Parameters

  • schema (str) – Name of hive schema (database) @table belongs to

  • table – Name of hive table @partition belongs to

Partition

Name of the partitions to check for (eg a=b/c=d)

Return type

bool

>>> 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
get_table(self, table_name, db='default')[source]

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']
get_tables(self, db, pattern='*')[source]

Get a metastore table object

get_databases(self, pattern='*')[source]

Get a metastore table object

get_partitions(self, schema, table_name, filter=None)[source]

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'}]
static _get_max_partition_from_part_specs(part_specs, partition_key, filter_map)[source]

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.

Parameters

  • part_specs (list) – list of partition specs.

  • partition_key (str) – partition key name.

  • filter_map (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.

Returns

Max partition or None if part_specs is empty.

max_partition(self, schema, table_name, field=None, filter_map=None)[source]

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.

Parameters

  • schema (str) – schema name.

  • table_name (str) – table name.

  • field (str) – partition key to get max partition from.

  • filter_map (map) – partition_key:partition_value map used for partition filtering.

>>> 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'
table_exists(self, table_name, db='default')[source]

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
class airflow.hooks.hive_hooks.HiveServer2Hook(hiveserver2_conn_id='hiveserver2_default')[source]

Bases: airflow.hooks.base_hook.BaseHook

Wrapper around the pyhive library

Notes: * the default authMechanism 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

get_conn(self, schema=None)[source]

Returns a Hive connection object.

_get_results(self, hql, schema='default', fetch_size=None, hive_conf=None)[source]
get_results(self, hql, schema='default', fetch_size=None, hive_conf=None)[source]

Get results of the provided hql in target schema.

Parameters

  • hql (str or list) – hql to be executed.

  • schema (str) – target schema, default to ‘default’.

  • fetch_size (int) – max size of result to fetch.

  • hive_conf (dict) – hive_conf to execute alone with the hql.

Returns

results of hql execution, dict with data (list of results) and header

Return type

dict

to_csv(self, hql, csv_filepath, schema='default', delimiter=', ', lineterminator='rn', output_header=True, fetch_size=1000, hive_conf=None)[source]

Execute hql in target schema and write results to a csv file.

Parameters

  • hql (str or list) – hql to be executed.

  • csv_filepath (str) – filepath of csv to write results into.

  • schema (str) – target schema, default to ‘default’.

  • delimiter (str) – delimiter of the csv file, default to ‘,’.

  • lineterminator (str) – lineterminator of the csv file.

  • output_header (bool) – header of the csv file, default to True.

  • fetch_size (int) – number of result rows to write into the csv file, default to 1000.

  • hive_conf (dict) – hive_conf to execute alone with the hql.

get_records(self, hql, schema='default')[source]

Get a set of records from a Hive query.

Parameters

  • hql (str or list) – hql to be executed.

  • schema (str) – target schema, default to ‘default’.

  • hive_conf (dict) – hive_conf to execute alone with the hql.

Returns

result of hive execution

Return type

list

>>> hh = HiveServer2Hook()
>>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
>>> len(hh.get_records(sql))
100
get_pandas_df(self, hql, schema='default')[source]

Get a pandas dataframe from a Hive query

Parameters

  • hql (str or list) – hql to be executed.

  • schema (str) – target schema, default to ‘default’.

Returns

result of hql execution

Return type

DataFrame

>>> hh = HiveServer2Hook()
>>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
>>> df = hh.get_pandas_df(sql)
>>> len(df.index)
100
Returns

pandas.DateFrame

Was this entry helpful?