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 max_partition(table,
schema="default",
field=None,
filter=None,
metastore_conn_id='metastore_default'):
'''
Gets the max partition for a table.
:param schema: The hive schema the table lives in
:type schema: string
:param table: The hive table you are interested in, supports the dot
notation as in "my_database.my_table", if a dot is found,
the schema param is disregarded
:type table: string
:param hive_conn_id: The hive connection you are interested in.
If your default is set you don't need to use this parameter.
:type hive_conn_id: string
:param filter: filter on a subset of partition as in
`sub_part='specific_value'`
:type filter: string
:param field: the field to get the max value from. If there's only
one partition field, this will be inferred
>>> max_partition('airflow.static_babynames_partitioned')
'2015-01-01'
'''
from airflow.hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
return hh.max_partition(schema=schema,
table_name=table,
field=field,
filter=filter)
def max_partition(
table, schema="default", field=None, filter=None,
metastore_conn_id='metastore_default'):
'''
Gets the max partition for a table.
:param schema: The hive schema the table lives in
:type schema: string
:param table: The hive table you are interested in, supports the dot
notation as in "my_database.my_table", if a dot is found,
the schema param is disregarded
:type table: string
:param hive_conn_id: The hive connection you are interested in.
If your default is set you don't need to use this parameter.
:type hive_conn_id: string
:param filter: filter on a subset of partition as in
`sub_part='specific_value'`
:type filter: string
:param field: the field to get the max value from. If there's only
one partition field, this will be inferred
>>> max_partition('airflow.static_babynames_partitioned')
'2015-01-01'
'''
from airflow.hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
return hh.max_partition(
schema=schema, table_name=table, field=field, filter=filter)
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 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):
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 closest_ds_partition(table,
ds,
before=True,
schema="default",
metastore_conn_id='metastore_default'):
'''
This function finds the date in a list closest to the target date.
An optional parameter can be given to get the closest before or after.
:param table: A hive table name
:type table: str
:param ds: A datestamp ``%Y-%m-%d`` e.g. ``yyyy-mm-dd``
:type ds: datetime.date list
:param before: closest before (True), after (False) or either side of ds
:type before: bool or None
:returns: The closest date
:rtype: str or None
>>> tbl = 'airflow.static_babynames_partitioned'
>>> closest_ds_partition(tbl, '2015-01-02')
'2015-01-01'
'''
from airflow.hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
partitions = hh.get_partitions(schema=schema, table_name=table)
if not partitions:
return None
part_vals = [list(p.values())[0] for p in partitions]
if ds in part_vals:
return ds
else:
parts = [
datetime.datetime.strptime(pv, '%Y-%m-%d') for pv in part_vals
]
target_dt = datetime.datetime.strptime(ds, '%Y-%m-%d')
closest_ds = _closest_date(target_dt, parts, before_target=before)
return closest_ds.isoformat()
def closest_ds_partition(
table, ds, before=True, schema="default",
metastore_conn_id='metastore_default'):
'''
This function finds the date in a list closest to the target date.
An optional paramter can be given to get the closest before or after.
:param table: A hive table name
:type table: str
:param ds: A datestamp ``%Y-%m-%d`` i.e. ``yyyy-mm-dd``
:type ds: datetime.date list
:param before: closest before (True), after (False) or either side of ds
:type before: bool or None
:returns: The closest date
:rtype: str or None
>>> tbl = 'airflow.static_babynames_partitioned'
>>> closest_ds_partition(tbl, '2015-01-02')
'2015-01-01'
'''
from airflow.hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
partitions = hh.get_partitions(schema=schema, table_name=table)
if not partitions:
return None
part_vals = [p.values()[0] for p in partitions]
if ds in part_vals:
return ds
else:
parts = [datetime.datetime.strptime(pv, '%Y-%m-%d')
for pv in part_vals]
target_dt = datetime.datetime.strptime(ds, '%Y-%m-%d')
closest_ds = _closest_date(target_dt, parts, before_target=before)
return closest_ds.isoformat()
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 db(self):
db = request.args.get("db")
m = HiveMetastoreHook(METASTORE_CONN_ID)
tables = sorted(m.get_tables(db=db), key=lambda x: x.tableName)
return self.render(
"metastore_browser/db.html", tables=tables, db=db)
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',
])
def db(self):
db = request.args.get("db")
m = HiveMetastoreHook(METASTORE_CONN_ID)
tables = sorted(m.get_tables(db=db), key=lambda x: x.tableName)
return self.render("metastore_browser/db.html", tables=tables, db=db)
