def test_to_csv_assertlogs(self):
hook = HiveServer2Hook()
query = "SELECT * FROM {}".format(self.table)
csv_filepath = 'query_results.csv'
with self.assertLogs() as cm:
hook.to_csv(query,
csv_filepath,
schema=self.database,
delimiter=',',
lineterminator='\n',
output_header=True,
fetch_size=2)
df = pd.read_csv(csv_filepath, sep=',')
self.assertListEqual(df.columns.tolist(), self.columns)
self.assertListEqual(df[self.columns[0]].values.tolist(), [1, 2])
self.assertEqual(len(df), 2)
self.assertIn(
'INFO:airflow.hooks.hive_hooks.HiveServer2Hook:'
'Written 2 rows so far.', cm.output)
def test_get_conn_with_password(self, mock_connect):
from airflow.hooks.base_hook import CONN_ENV_PREFIX
conn_id = "conn_with_password"
conn_env = CONN_ENV_PREFIX + conn_id.upper()
conn_value = os.environ.get(conn_env)
os.environ[
conn_env] = "jdbc+hive2://conn_id:conn_pass@localhost:10000/default?authMechanism=LDAP"
HiveServer2Hook(hiveserver2_conn_id=conn_id).get_conn()
mock_connect.assert_called_once_with(host='localhost',
port=10000,
auth='LDAP',
kerberos_service_name=None,
username='******',
password='******',
database='default')
if conn_value:
os.environ[conn_env] = conn_value
def get_records(self):
"""Executes a query to obtain a count of records on a table
Returns:
int -- quantity of records from a count query
"""
if self.query_engine == 'hive':
hook = HiveServer2Hook(self.query_engine_conn_id)
elif self.query_engine == 'presto':
hook = PrestoHook(self.query_engine_conn_id)
# executes query to Hive or Presto
res = hook.get_records(self.records_query)
if len(res) > 1:
raise
else:
return res[0]
def execute(self, context):
hive = HiveServer2Hook(hiveserver2_conn_id=self.hiveserver2_conn_id)
logging.info('Extracting data from Hive')
logging.info(self.hql)
data = hive.get_pandas_df(self.hql, schema=self.schema)
gcp_hook = GoogleCloudStorageHook(google_cloud_storage_conn_id=self.google_cloud_storage_conn_id)
logging.info('Inserting rows onto google cloud storage')
with tempfile.NamedTemporaryFile(suffix='.json', prefix='tmp') as tmp_file:
data = data.to_json(orient='records')
recs = json.loads(data)
for record in recs:
tmp_file.write(json.dumps(record))
tmp_file.write("\n")
tmp_file.flush()
remote_file_name = self.file_pattern.format('aa')
remote_name = os.path.join(self.subdir, remote_file_name)
gcp_hook.upload(self.bucket, remote_name, tmp_file.name)
logging.info('Done.')
def test_get_results_with_hive_conf(self):
hql = ["set key",
"set airflow.ctx.dag_id",
"set airflow.ctx.dag_run_id",
"set airflow.ctx.task_id",
"set airflow.ctx.execution_date"]
dag_id_ctx_var_name = \
AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_DAG_ID']['env_var_format']
task_id_ctx_var_name = \
AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_TASK_ID']['env_var_format']
execution_date_ctx_var_name = \
AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_EXECUTION_DATE'][
'env_var_format']
dag_run_id_ctx_var_name = \
AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_DAG_RUN_ID'][
'env_var_format']
os.environ[dag_id_ctx_var_name] = 'test_dag_id'
os.environ[task_id_ctx_var_name] = 'test_task_id'
os.environ[execution_date_ctx_var_name] = 'test_execution_date'
os.environ[dag_run_id_ctx_var_name] = 'test_dag_run_id'
hook = HiveServer2Hook()
output = '\n'.join(res_tuple[0]
for res_tuple
in hook.get_results(hql=hql,
hive_conf={'key': 'value'})['data'])
self.assertIn('value', output)
self.assertIn('test_dag_id', output)
self.assertIn('test_task_id', output)
self.assertIn('test_execution_date', output)
self.assertIn('test_dag_run_id', output)
del os.environ[dag_id_ctx_var_name]
del os.environ[task_id_ctx_var_name]
del os.environ[execution_date_ctx_var_name]
del os.environ[dag_run_id_ctx_var_name]
def execute(self, context):
hive = HiveServer2Hook(hiveserver2_conn_id=self.hiveserver2_conn_id)
self.log.info("Extracting data from Hive: %s", self.sql)
hive_conf = context_to_airflow_vars(context)
if self.hive_conf:
hive_conf.update(self.hive_conf)
if self.bulk_load:
tmp_file = NamedTemporaryFile()
hive.to_csv(self.sql,
tmp_file.name,
delimiter='\t',
lineterminator='\n',
output_header=False,
hive_conf=hive_conf)
else:
hive_results = hive.get_records(self.sql, hive_conf=hive_conf)
mysql = MySqlHook(mysql_conn_id=self.mysql_conn_id)
if self.mysql_preoperator:
self.log.info("Running MySQL preoperator")
mysql.run(self.mysql_preoperator)
self.log.info("Inserting rows into MySQL")
if self.bulk_load:
mysql.bulk_load(table=self.mysql_table, tmp_file=tmp_file.name)
tmp_file.close()
else:
mysql.insert_rows(table=self.mysql_table, rows=hive_results)
if self.mysql_postoperator:
self.log.info("Running MySQL postoperator")
mysql.run(self.mysql_postoperator)
self.log.info("Done.")
def execute(self, context):
hive = HiveServer2Hook(hiveserver2_conn_id=self.hiveserver2_conn_id)
self.log.info('Extracting data from Hive')
self.log.info(self.sql)
data = hive.get_pandas_df(self.sql, schema=self.schema)
dynamodb = AwsDynamoDBHook(aws_conn_id=self.aws_conn_id,
table_name=self.table_name,
table_keys=self.table_keys,
region_name=self.region_name)
self.log.info('Inserting rows into dynamodb')
if self.pre_process is None:
dynamodb.write_batch_data(
json.loads(data.to_json(orient='records')))
else:
dynamodb.write_batch_data(
self.pre_process(data=data,
args=self.pre_process_args,
kwargs=self.pre_process_kwargs))
self.log.info('Done.')
def add_partition(v_execution_date, v_execution_day, v_execution_hour, db_name, table_name, conn_id, hive_table_name,
server_name, hive_db, is_must_have_data, **kwargs):
# 生成_SUCCESS
"""
第一个参数true: 数据目录是有country_code分区。false 没有
第二个参数true: 数据有才生成_SUCCESS false 数据没有也生成_SUCCESS
"""
TaskTouchzSuccess().countries_touchz_success(
v_execution_day,
hive_db,
hive_table_name,
ALL_HI_OSS_PATH % hive_table_name,
"false", is_must_have_data, v_execution_hour)
sql = '''
ALTER TABLE {hive_db}.{table} ADD IF NOT EXISTS PARTITION (dt = '{ds}', hour = '{hour}')
'''.format(hive_db=hive_db, table=hive_table_name, ds=v_execution_day, hour=v_execution_hour)
hive2_conn = HiveServer2Hook().get_conn()
cursor = hive2_conn.cursor()
cursor.execute(sql)
return
def test_get_records(self):
hook = HiveServer2Hook()
query = "SELECT * FROM {}".format(self.table)
results = hook.get_pandas_df(query, schema=self.database)
self.assertEqual(len(results), 2)
def run_check_table(schema_table_db_name, schema_table_name,
target_table_db_name, target_table_name, conn_id,
hive_table_name, server_name, **kwargs):
# SHOW TABLES in oride_db LIKE 'data_aa'
check_sql = 'SHOW TABLES in %s LIKE \'%s\'' % (HIVE_DB, hive_table_name)
hive2_conn = HiveServer2Hook().get_conn()
cursor = hive2_conn.cursor()
cursor.execute(check_sql)
if len(cursor.fetchall()) == 0:
logging.info('Create Hive Table: %s.%s', HIVE_DB, hive_table_name)
# get table column
column_sql = '''
SELECT
COLUMN_NAME,
DATA_TYPE,
NUMERIC_PRECISION,
NUMERIC_SCALE,
COLUMN_COMMENT
FROM
information_schema.columns
WHERE
table_schema='{db_name}' and table_name='{table_name}'
'''.format(db_name=schema_table_db_name,
table_name=schema_table_name)
mysql_hook = MySqlHook(conn_id)
mysql_conn = mysql_hook.get_conn()
mysql_cursor = mysql_conn.cursor()
mysql_cursor.execute(column_sql)
results = mysql_cursor.fetchall()
rows = []
for result in results:
if result[0] == 'dt':
col_name = '_dt'
else:
col_name = result[0]
if result[1] == 'timestamp' or result[1] == 'varchar' or result[1] == 'char' or result[1] == 'text' or \
result[1] == 'longtext' or \
result[1] == 'mediumtext' or \
result[1] == 'json' or \
result[1] == 'datetime':
data_type = 'string'
elif result[1] == 'decimal':
data_type = result[1] + "(" + str(result[2]) + "," + str(
result[3]) + ")"
else:
data_type = result[1]
rows.append("`%s` %s comment '%s'" %
(col_name, data_type, str(result[4]).replace(
'\n', '').replace('\r', '')))
mysql_conn.close()
# hive create table
hive_hook = HiveCliHook()
sql = ODS_CREATE_TABLE_SQL.format(
db_name=HIVE_DB,
table_name=hive_table_name,
columns=",\n".join(rows),
oss_path=OSS_PATH % ("{server_name}.{db_name}.{table_name}".format(
server_name=server_name,
db_name=target_table_db_name,
table_name=target_table_name)))
logging.info('Executing: %s', sql)
hive_hook.run_cli(sql)
else:
sqoopSchema = SqoopSchemaUpdate()
response = sqoopSchema.append_hive_schema(
hive_db=HIVE_DB,
hive_table=hive_table_name,
mysql_db=schema_table_db_name,
mysql_table=schema_table_name,
mysql_conn=conn_id,
oss_path=OSS_PATH % ("{server_name}.{db_name}.{table_name}".format(
server_name=server_name,
db_name=target_table_db_name,
table_name=target_table_name)))
if response:
return True
return
def test_select_conn(self):
from airflow.hooks.hive_hooks import HiveServer2Hook
sql = "select 1"
hook = HiveServer2Hook()
hook.get_records(sql)
def test_get_results_header(self):
hook = HiveServer2Hook()
query = "SELECT * FROM {}".format(self.table)
results = hook.get_results(query, schema=self.database)
self.assertListEqual([col[0] for col in results['header']],
self.columns)
def test_get_results_data(self):
hook = HiveServer2Hook()
query = "SELECT * FROM {}".format(self.table)
results = hook.get_results(query, schema=self.database)
self.assertListEqual(results['data'], [(1, 1), (2, 2)])
def test_get_conn(self):
hook = HiveServer2Hook()
hook.get_conn()
def get_hook(self):
if self.conn_type == 'mysql':
from airflow.hooks.mysql_hook import MySqlHook
return MySqlHook(mysql_conn_id=self.conn_id)
elif self.conn_type == 'google_cloud_platform':
from airflow.gcp.hooks.bigquery import BigQueryHook
return BigQueryHook(bigquery_conn_id=self.conn_id)
elif self.conn_type == 'postgres':
from airflow.hooks.postgres_hook import PostgresHook
return PostgresHook(postgres_conn_id=self.conn_id)
elif self.conn_type == 'pig_cli':
from airflow.hooks.pig_hook import PigCliHook
return PigCliHook(pig_cli_conn_id=self.conn_id)
elif self.conn_type == 'hive_cli':
from airflow.hooks.hive_hooks import HiveCliHook
return HiveCliHook(hive_cli_conn_id=self.conn_id)
elif self.conn_type == 'presto':
from airflow.hooks.presto_hook import PrestoHook
return PrestoHook(presto_conn_id=self.conn_id)
elif self.conn_type == 'hiveserver2':
from airflow.hooks.hive_hooks import HiveServer2Hook
return HiveServer2Hook(hiveserver2_conn_id=self.conn_id)
elif self.conn_type == 'sqlite':
from airflow.hooks.sqlite_hook import SqliteHook
return SqliteHook(sqlite_conn_id=self.conn_id)
elif self.conn_type == 'jdbc':
from airflow.hooks.jdbc_hook import JdbcHook
return JdbcHook(jdbc_conn_id=self.conn_id)
elif self.conn_type == 'mssql':
from airflow.hooks.mssql_hook import MsSqlHook
return MsSqlHook(mssql_conn_id=self.conn_id)
elif self.conn_type == 'oracle':
from airflow.hooks.oracle_hook import OracleHook
return OracleHook(oracle_conn_id=self.conn_id)
elif self.conn_type == 'vertica':
from airflow.contrib.hooks.vertica_hook import VerticaHook
return VerticaHook(vertica_conn_id=self.conn_id)
elif self.conn_type == 'cloudant':
from airflow.contrib.hooks.cloudant_hook import CloudantHook
return CloudantHook(cloudant_conn_id=self.conn_id)
elif self.conn_type == 'jira':
from airflow.contrib.hooks.jira_hook import JiraHook
return JiraHook(jira_conn_id=self.conn_id)
elif self.conn_type == 'redis':
from airflow.contrib.hooks.redis_hook import RedisHook
return RedisHook(redis_conn_id=self.conn_id)
elif self.conn_type == 'wasb':
from airflow.contrib.hooks.wasb_hook import WasbHook
return WasbHook(wasb_conn_id=self.conn_id)
elif self.conn_type == 'docker':
from airflow.hooks.docker_hook import DockerHook
return DockerHook(docker_conn_id=self.conn_id)
elif self.conn_type == 'azure_data_lake':
from airflow.contrib.hooks.azure_data_lake_hook import AzureDataLakeHook
return AzureDataLakeHook(azure_data_lake_conn_id=self.conn_id)
elif self.conn_type == 'azure_cosmos':
from airflow.contrib.hooks.azure_cosmos_hook import AzureCosmosDBHook
return AzureCosmosDBHook(azure_cosmos_conn_id=self.conn_id)
elif self.conn_type == 'cassandra':
from airflow.contrib.hooks.cassandra_hook import CassandraHook
return CassandraHook(cassandra_conn_id=self.conn_id)
elif self.conn_type == 'mongo':
from airflow.contrib.hooks.mongo_hook import MongoHook
return MongoHook(conn_id=self.conn_id)
elif self.conn_type == 'gcpcloudsql':
from airflow.gcp.hooks.cloud_sql import CloudSqlDatabaseHook
return CloudSqlDatabaseHook(gcp_cloudsql_conn_id=self.conn_id)
elif self.conn_type == 'grpc':
from airflow.contrib.hooks.grpc_hook import GrpcHook
return GrpcHook(grpc_conn_id=self.conn_id)
raise AirflowException("Unknown hook type {}".format(self.conn_type))
def test_to_csv(self):
from airflow.hooks.hive_hooks import HiveServer2Hook
sql = "select 1"
hook = HiveServer2Hook()
hook.to_csv(hql=sql, csv_filepath="/tmp/test_to_csv")
def test_mysql_to_hive_verify_loaded_values(self):
mysql_conn_id = 'airflow_ci'
mysql_table = 'test_mysql_to_hive'
hive_table = 'test_mysql_to_hive'
from airflow.hooks.mysql_hook import MySqlHook
m = MySqlHook(mysql_conn_id)
try:
minmax = (
255,
65535,
16777215,
4294967295,
18446744073709551615,
-128,
-32768,
-8388608,
-2147483648,
-9223372036854775808
)
with m.get_conn() as c:
c.execute("DROP TABLE IF EXISTS {}".format(mysql_table))
c.execute("""
CREATE TABLE {} (
c0 TINYINT UNSIGNED,
c1 SMALLINT UNSIGNED,
c2 MEDIUMINT UNSIGNED,
c3 INT UNSIGNED,
c4 BIGINT UNSIGNED,
c5 TINYINT,
c6 SMALLINT,
c7 MEDIUMINT,
c8 INT,
c9 BIGINT
)
""".format(mysql_table))
c.execute("""
INSERT INTO {} VALUES (
{}, {}, {}, {}, {}, {}, {}, {}, {}, {}
)
""".format(mysql_table, *minmax))
from airflow.operators.mysql_to_hive import MySqlToHiveTransfer
t = MySqlToHiveTransfer(
task_id='test_m2h',
mysql_conn_id=mysql_conn_id,
hive_cli_conn_id='beeline_default',
sql="SELECT * FROM {}".format(mysql_table),
hive_table=hive_table,
recreate=True,
delimiter=",",
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
from airflow.hooks.hive_hooks import HiveServer2Hook
h = HiveServer2Hook()
r = h.get_records("SELECT * FROM {}".format(hive_table))
self.assertEqual(r[0], minmax)
finally:
with m.get_conn() as c:
c.execute("DROP TABLE IF EXISTS {}".format(mysql_table))
def get_hook(self):
return HiveServer2Hook(
hiveserver2_conn_id=self.hive_cli_conn_id)
