def execute(self, context):
hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)
logging.info("Extracting data from Hive")
hive_table = 'druid.' + context['task_instance_key_str']
sql = self.sql.strip().strip(';')
hql = """\
set mapred.output.compress=false;
set hive.exec.compress.output=false;
DROP TABLE IF EXISTS {hive_table};
CREATE TABLE {hive_table}
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE AS
{sql};
""".format(**locals())
hive.run_cli(hql)
m = HiveMetastoreHook(self.metastore_conn_id)
t = m.get_table(hive_table)
columns = [col.name for col in t.sd.cols]
hdfs_uri = m.get_table(hive_table).sd.location
pos = hdfs_uri.find('/user')
static_path = hdfs_uri[pos:]
druid = DruidHook(druid_ingest_conn_id=self.druid_ingest_conn_id)
logging.info("Inserting rows into Druid")
druid.load_from_hdfs(datasource=self.druid_datasource,
intervals=self.intervals,
static_path=static_path,
ts_dim=self.ts_dim,
columns=columns,
metric_spec=self.metric_spec)
logging.info("Load seems to have succeeded!")
def execute(self, context):
hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)
logging.info("Extracting data from Hive")
hive_table = 'druid.' + context['task_instance_key_str']
sql = self.sql.strip().strip(';')
hql = """\
set mapred.output.compress=false;
set hive.exec.compress.output=false;
DROP TABLE IF EXISTS {hive_table};
CREATE TABLE {hive_table}
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE AS
{sql};
""".format(**locals())
#hive.run_cli(hql)
m = HiveMetastoreHook(self.metastore_conn_id)
t = m.get_table(hive_table)
columns = [col.name for col in t.sd.cols]
hdfs_uri = m.get_table(hive_table).sd.location
pos = hdfs_uri.find('/user')
static_path = hdfs_uri[pos:]
druid = DruidHook(druid_ingest_conn_id=self.druid_ingest_conn_id)
logging.info("Inserting rows into Druid")
druid.load_from_hdfs(
datasource=self.druid_datasource,
intervals=self.intervals,
static_path=static_path, ts_dim=self.ts_dim,
columns=columns, metric_spec=self.metric_spec)
logging.info("Load seems to have succeeded!")
def execute(self, context):
hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)
logging.info("Extracting data from Hive")
hive_table = 'druid.' + context['task_instance_key_str']
sql = self.sql.strip().strip(';')
hql = """\
set mapred.output.compress=false;
set hive.exec.compress.output=false;
DROP TABLE IF EXISTS {hive_table};
CREATE TABLE {hive_table}
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE
TBLPROPERTIES ('serialization.null.format' = '')
AS
{sql}
""".format(**locals())
hive.run_cli(hql)
#hqls = hql.split(';')
#logging.info(str(hqls))
#from airflow.hooks import HiveServer2Hook
#hive = HiveServer2Hook(hiveserver2_conn_id="hiveserver2_silver")
#hive.get_results(hqls)
m = HiveMetastoreHook(self.metastore_conn_id)
t = m.get_table(hive_table)
columns = [col.name for col in t.sd.cols]
hdfs_uri = m.get_table(hive_table).sd.location
pos = hdfs_uri.find('/user')
static_path = hdfs_uri[pos:]
schema, table = hive_table.split('.')
druid = DruidHook(druid_ingest_conn_id=self.druid_ingest_conn_id)
logging.info("Inserting rows into Druid")
logging.info("HDFS path: " + static_path)
druid.load_from_hdfs(
datasource=self.druid_datasource,
intervals=self.intervals,
static_path=static_path, ts_dim=self.ts_dim,
columns=columns, metric_spec=self.metric_spec,
hadoop_dependency_coordinates=self.hadoop_dependency_coordinates)
logging.info("Load seems to have succeeded!")
logging.info(
"Cleaning up by dropping the temp "
"Hive table {}".format(hive_table))
hql = "DROP TABLE IF EXISTS {}".format(hive_table)
def execute(self, context):
hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)
logging.info("Extracting data from Hive")
hive_table = 'druid.' + context['task_instance_key_str']
sql = self.sql.strip().strip(';')
hql = """\
set mapred.output.compress=false;
set hive.exec.compress.output=false;
DROP TABLE IF EXISTS {hive_table};
CREATE TABLE {hive_table}
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE
TBLPROPERTIES ('serialization.null.format' = '')
AS
{sql}
""".format(**locals())
hive.run_cli(hql)
#hqls = hql.split(';')
#logging.info(str(hqls))
#from airflow.hooks import HiveServer2Hook
#hive = HiveServer2Hook(hiveserver2_conn_id="hiveserver2_silver")
#hive.get_results(hqls)
m = HiveMetastoreHook(self.metastore_conn_id)
t = m.get_table(hive_table)
columns = [col.name for col in t.sd.cols]
hdfs_uri = m.get_table(hive_table).sd.location
pos = hdfs_uri.find('/user')
static_path = hdfs_uri[pos:]
schema, table = hive_table.split('.')
druid = DruidHook(druid_ingest_conn_id=self.druid_ingest_conn_id)
logging.info("Inserting rows into Druid")
logging.info("HDFS path: " + static_path)
druid.load_from_hdfs(
datasource=self.druid_datasource,
intervals=self.intervals,
static_path=static_path,
ts_dim=self.ts_dim,
columns=columns,
metric_spec=self.metric_spec,
hadoop_dependency_coordinates=self.hadoop_dependency_coordinates)
logging.info("Load seems to have succeeded!")
logging.info("Cleaning up by dropping the temp "
"Hive table {}".format(hive_table))
hql = "DROP TABLE IF EXISTS {}".format(hive_table)
def table(self):
table_name = request.args.get("table")
m = HiveMetastoreHook(METASTORE_CONN_ID)
table = m.get_table(table_name)
return self.render(
"metastore_browser/table.html",
table=table, table_name=table_name, datetime=datetime, int=int)
def table(self):
table_name = request.args.get("table")
m = HiveMetastoreHook(METASTORE_CONN_ID)
table = m.get_table(table_name)
return self.render("metastore_browser/table.html",
table=table,
table_name=table_name,
datetime=datetime,
int=int)
def execute(self, context=None):
metastore = HiveMetastoreHook(metastore_conn_id=self.metastore_conn_id)
table = metastore.get_table(table_name=self.table)
field_types = {col.name: col.type for col in table.sd.cols}
exprs = {
('', 'count'): 'COUNT(*)'
}
for col, col_type in field_types.items():
d = {}
if self.assignment_func:
d = self.assignment_func(col, col_type)
if d is None:
d = self.get_default_exprs(col, col_type)
else:
d = self.get_default_exprs(col, col_type)
exprs.update(d)
exprs.update(self.extra_exprs)
exprs = OrderedDict(exprs)
exprs_str = ",\n ".join([
v + " AS " + k[0] + '__' + k[1]
for k, v in exprs.items()])
where_clause = [
"{0} = '{1}'".format(k, v) for k, v in self.partition.items()]
where_clause = " AND\n ".join(where_clause)
sql = """
SELECT
{exprs_str}
FROM {self.table}
WHERE
{where_clause};
""".format(**locals())
hook = PrestoHook(presto_conn_id=self.presto_conn_id)
logging.info('Executing SQL check: ' + sql)
row = hook.get_first(hql=sql)
logging.info("Record: " + str(row))
if not row:
raise Exception("The query returned None")
part_json = json.dumps(self.partition, sort_keys=True)
logging.info("Deleting rows from previous runs if they exist")
mysql = MySqlHook(self.mysql_conn_id)
sql = """
SELECT 1 FROM hive_stats
WHERE
table_name='{self.table}' AND
partition_repr='{part_json}' AND
dttm='{self.dttm}'
LIMIT 1;
""".format(**locals())
if mysql.get_records(sql):
sql = """
DELETE FROM hive_stats
WHERE
table_name='{self.table}' AND
partition_repr='{part_json}' AND
dttm='{self.dttm}';
""".format(**locals())
mysql.run(sql)
logging.info("Pivoting and loading cells into the Airflow db")
rows = [
(self.ds, self.dttm, self.table, part_json) +
(r[0][0], r[0][1], r[1])
for r in zip(exprs, row)]
mysql.insert_rows(
table='hive_stats',
rows=rows,
target_fields=[
'ds',
'dttm',
'table_name',
'partition_repr',
'col',
'metric',
'value',
]
)
def execute(self, context=None):
metastore = HiveMetastoreHook(metastore_conn_id=self.metastore_conn_id)
table = metastore.get_table(table_name=self.table)
field_types = {col.name: col.type for col in table.sd.cols}
exprs = {('', 'count'): 'COUNT(*)'}
for col, col_type in field_types.items():
d = {}
if self.assignment_func:
d = self.assignment_func(col, col_type)
if d is None:
d = self.get_default_exprs(col, col_type)
else:
d = self.get_default_exprs(col, col_type)
exprs.update(d)
exprs.update(self.extra_exprs)
exprs = OrderedDict(exprs)
exprs_str = ",\n ".join(
[v + " AS " + k[0] + '__' + k[1] for k, v in exprs.items()])
where_clause = [
"{0} = '{1}'".format(k, v) for k, v in self.partition.items()
]
where_clause = " AND\n ".join(where_clause)
sql = """
SELECT
{exprs_str}
FROM {self.table}
WHERE
{where_clause};
""".format(**locals())
hook = PrestoHook(presto_conn_id=self.presto_conn_id)
logging.info('Executing SQL check: ' + sql)
row = hook.get_first(hql=sql)
logging.info("Record: " + str(row))
if not row:
raise AirflowException("The query returned None")
part_json = json.dumps(self.partition, sort_keys=True)
logging.info("Deleting rows from previous runs if they exist")
mysql = MySqlHook(self.mysql_conn_id)
sql = """
SELECT 1 FROM hive_stats
WHERE
table_name='{self.table}' AND
partition_repr='{part_json}' AND
dttm='{self.dttm}'
LIMIT 1;
""".format(**locals())
if mysql.get_records(sql):
sql = """
DELETE FROM hive_stats
WHERE
table_name='{self.table}' AND
partition_repr='{part_json}' AND
dttm='{self.dttm}';
""".format(**locals())
mysql.run(sql)
logging.info("Pivoting and loading cells into the Airflow db")
rows = [(self.ds, self.dttm, self.table, part_json) +
(r[0][0], r[0][1], r[1]) for r in zip(exprs, row)]
mysql.insert_rows(table='hive_stats',
rows=rows,
target_fields=[
'ds',
'dttm',
'table_name',
'partition_repr',
'col',
'metric',
'value',
])