def sql_operations():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# define the source
table = t_env.from_elements(elements=[
(1,
'{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'
),
(2,
'{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'
),
(3,
'{"name": "world", "tel": 124, "addr": {"country": "USA", "city": "NewYork"}}'
),
(4,
'{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}'
)
],
schema=['id', 'data'])
t_env.sql_query("SELECT * FROM %s" % table) \
.execute().print()
# +----+----------------------+--------------------------------+
# | op | id | data |
# +----+----------------------+--------------------------------+
# | +I | 1 | {"name": "Flink", "tel": 12... |
# | +I | 2 | {"name": "hello", "tel": 13... |
# | +I | 3 | {"name": "world", "tel": 12... |
# | +I | 4 | {"name": "PyFlink", "tel": ... |
# +----+----------------------+--------------------------------+
# execute sql statement
@udtf(
result_types=[DataTypes.STRING(),
DataTypes.INT(),
DataTypes.STRING()])
def parse_data(data: str):
json_data = json.loads(data)
yield json_data['name'], json_data['tel'], json_data['addr']['country']
t_env.create_temporary_function('parse_data', parse_data)
t_env.execute_sql("""
SELECT *
FROM %s, LATERAL TABLE(parse_data(`data`)) t(name, tel, country)
""" % table).print()
# +----+----------------------+--------------------------------+--------------------------------+-------------+--------------------------------+
# | op | id | data | name | tel | country |
# +----+----------------------+--------------------------------+--------------------------------+-------------+--------------------------------+
# | +I | 1 | {"name": "Flink", "tel": 12... | Flink | 123 | Germany |
# | +I | 2 | {"name": "hello", "tel": 13... | hello | 135 | China |
# | +I | 3 | {"name": "world", "tel": 12... | world | 124 | USA |
# | +I | 4 | {"name": "PyFlink", "tel": ... | PyFlink | 32 | China |
# +----+----------------------+--------------------------------+--------------------------------+-------------+--------------------------------+
# explain sql plan
print(
t_env.explain_sql("""
SELECT *
FROM %s, LATERAL TABLE(parse_data(`data`)) t(name, tel, country)
""" % table))
def process_json_data():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# define the source
table = t_env.from_elements(elements=[
(1,
'{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'
),
(2,
'{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'
),
(3,
'{"name": "world", "tel": 124, "addr": {"country": "USA", "city": "NewYork"}}'
),
(4,
'{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}'
)
],
schema=['id', 'data'])
# define the sink
t_env.create_temporary_table(
'sink',
TableDescriptor.for_connector('print').schema(
Schema.new_builder().column('id', DataTypes.BIGINT()).column(
'data', DataTypes.STRING()).build()).build())
table = table.select(
col('id'),
col('data').json_value('$.addr.country', DataTypes.STRING()))
# execute
table.execute_insert('sink') \
.wait()
def setUp(self):
super(StreamDependencyTests, self).setUp()
origin_execution_mode = os.environ['_python_worker_execution_mode']
os.environ['_python_worker_execution_mode'] = "loopback"
try:
self.st_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
finally:
if origin_execution_mode is not None:
os.environ['_python_worker_execution_mode'] = origin_execution_mode
def conversion_from_dataframe():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
t_env.get_config().set("parallelism.default", "1")
# define the source with watermark definition
pdf = pd.DataFrame(np.random.rand(1000, 2))
table = t_env.from_pandas(pdf,
schema=DataTypes.ROW([
DataTypes.FIELD("a", DataTypes.DOUBLE()),
DataTypes.FIELD("b", DataTypes.DOUBLE())
]))
print(table.to_pandas())
def test_add_python_file(self):
import uuid
env = self.env
python_file_dir = os.path.join(self.tempdir,
"python_file_dir_" + str(uuid.uuid4()))
os.mkdir(python_file_dir)
python_file_path = os.path.join(python_file_dir, "test_dep1.py")
with open(python_file_path, 'w') as f:
f.write("def add_two(a):\n return a + 2")
def plus_two_map(value):
from test_dep1 import add_two
return add_two(value)
get_j_env_configuration(env._j_stream_execution_environment).\
setString("taskmanager.numberOfTaskSlots", "10")
env.add_python_file(python_file_path)
ds = env.from_collection([1, 2, 3, 4, 5])
ds = ds.map(plus_two_map, Types.LONG()) \
.slot_sharing_group("data_stream") \
.map(lambda i: i, Types.LONG()) \
.slot_sharing_group("table")
python_file_path = os.path.join(python_file_dir, "test_dep2.py")
with open(python_file_path, 'w') as f:
f.write("def add_three(a):\n return a + 3")
def plus_three(value):
from test_dep2 import add_three
return add_three(value)
t_env = StreamTableEnvironment.create(
stream_execution_environment=env,
environment_settings=EnvironmentSettings.in_streaming_mode())
env.add_python_file(python_file_path)
from pyflink.table.udf import udf
from pyflink.table.expressions import col
add_three = udf(plus_three, result_type=DataTypes.BIGINT())
tab = t_env.from_data_stream(ds, col('a')) \
.select(add_three(col('a')))
t_env.to_append_stream(tab, Types.ROW([Types.LONG()])) \
.map(lambda i: i[0]) \
.add_sink(self.test_sink)
env.execute("test add_python_file")
result = self.test_sink.get_results(True)
expected = ['6', '7', '8', '9', '10']
result.sort()
expected.sort()
self.assertEqual(expected, result)
def word_count(input_path, output_path):
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# write all the data to one file
t_env.get_config().get_configuration().set_string("parallelism.default",
"1")
# define the source
if input_path is not None:
t_env.create_temporary_table(
'source',
TableDescriptor.for_connector('filesystem').schema(
Schema.new_builder().column(
'word', DataTypes.STRING()).build()).option(
'path', input_path).format('csv').build())
tab = t_env.from_path('source')
else:
print("Executing word_count example with default input data set.")
print("Use --input to specify file input.")
tab = t_env.from_elements(
map(lambda i: (i, ), word_count_data),
DataTypes.ROW([DataTypes.FIELD('line', DataTypes.STRING())]))
# define the sink
if output_path is not None:
t_env.create_temporary_table(
'sink',
TableDescriptor.for_connector('filesystem').schema(
Schema.new_builder().column('word', DataTypes.STRING()).column(
'count',
DataTypes.BIGINT()).build()).option('path', output_path).
format(FormatDescriptor.for_format('canal-json').build()).build())
else:
print(
"Printing result to stdout. Use --output to specify output path.")
t_env.create_temporary_table(
'sink',
TableDescriptor.for_connector('print').schema(
Schema.new_builder().column('word', DataTypes.STRING()).column(
'count', DataTypes.BIGINT()).build()).build())
@udtf(result_types=[DataTypes.STRING()])
def split(line: Row):
for s in line[0].split():
yield Row(s)
# compute word count
tab.flat_map(split).alias('word') \
.group_by(col('word')) \
.select(col('word'), lit(1).count) \
.execute_insert('sink') \
.wait()
def test_to_retract_stream(self):
self.env.set_parallelism(1)
t_env = StreamTableEnvironment.create(
self.env,
environment_settings=EnvironmentSettings.in_streaming_mode())
table = t_env.from_elements([(1, "Hi", "Hello"), (1, "Hi", "Hello")], ["a", "b", "c"])
new_table = table.group_by("c").select("a.sum, c as b")
ds = t_env.to_retract_stream(table=new_table, type_info=Types.ROW([Types.LONG(),
Types.STRING()]))
test_sink = DataStreamTestSinkFunction()
ds.map(lambda x: x).add_sink(test_sink)
self.env.execute("test_to_retract_stream")
result = test_sink.get_results(True)
expected = ["(True, Row(f0=1, f1='Hello'))", "(False, Row(f0=1, f1='Hello'))",
"(True, Row(f0=2, f1='Hello'))"]
self.assertEqual(result, expected)
def test_to_append_stream(self):
self.env.set_parallelism(1)
t_env = StreamTableEnvironment.create(
self.env,
environment_settings=EnvironmentSettings.in_streaming_mode())
table = t_env.from_elements([(1, "Hi", "Hello"), (2, "Hello", "Hi")], ["a", "b", "c"])
new_table = table.select("a + 1, b + 'flink', c")
ds = t_env.to_append_stream(table=new_table, type_info=Types.ROW([Types.LONG(),
Types.STRING(),
Types.STRING()]))
test_sink = DataStreamTestSinkFunction()
ds.add_sink(test_sink)
self.env.execute("test_to_append_stream")
result = test_sink.get_results(False)
expected = ['+I[2, Hiflink, Hello]', '+I[3, Helloflink, Hi]']
self.assertEqual(result, expected)
def multi_sink():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
table = t_env.from_elements(elements=[(1, 'Hello'), (2, 'World'),
(3, "Flink"), (4, "PyFlink")],
schema=['id', 'data'])
# define the sink tables
t_env.execute_sql("""
CREATE TABLE first_sink (
id BIGINT,
data VARCHAR
) WITH (
'connector' = 'print'
)
""")
t_env.execute_sql("""
CREATE TABLE second_sink (
id BIGINT,
data VARCHAR
) WITH (
'connector' = 'print'
)
""")
# create a statement set
statement_set = t_env.create_statement_set()
# emit the data with id <= 3 to the "first_sink" via sql statement
statement_set.add_insert_sql(
"INSERT INTO first_sink SELECT * FROM %s WHERE id <= 3" % table)
# emit the data which contains "Flink" to the "second_sink"
@udf(result_type=DataTypes.BOOLEAN())
def contains_flink(data):
return "Flink" in data
second_table = table.where(contains_flink(table.data))
statement_set.add_insert("second_sink", second_table)
# execute the statement set
# remove .wait if submitting to a remote cluster, refer to
# https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/faq/#wait-for-jobs-to-finish-when-executing-jobs-in-mini-cluster
# for more details
statement_set.execute().wait()
def test_mode_selection(self):
builder = EnvironmentSettings.new_instance()
# test the default behaviour to make sure it is consistent with the python doc
environment_settings = builder.build()
self.assertTrue(environment_settings.is_streaming_mode())
# test in_streaming_mode
environment_settings = builder.in_streaming_mode().build()
self.assertTrue(environment_settings.is_streaming_mode())
environment_settings = EnvironmentSettings.in_streaming_mode()
self.assertTrue(environment_settings.is_streaming_mode())
# test in_batch_mode
environment_settings = builder.in_batch_mode().build()
self.assertFalse(environment_settings.is_streaming_mode())
environment_settings = EnvironmentSettings.in_batch_mode()
self.assertFalse(environment_settings.is_streaming_mode())
def process_json_data_with_udf():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# define the source
table = t_env.from_elements(elements=[
(1,
'{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'
),
(2,
'{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'
),
(3,
'{"name": "world", "tel": 124, "addr": {"country": "USA", "city": "NewYork"}}'
),
(4,
'{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}'
)
],
schema=['id', 'data'])
# define the sink
t_env.create_temporary_table(
'sink',
TableDescriptor.for_connector('print').schema(
Schema.new_builder().column('id', DataTypes.BIGINT()).column(
'data', DataTypes.STRING()).build()).build())
# update json columns
@udf(result_type=DataTypes.STRING())
def update_tel(data):
json_data = json.loads(data)
json_data['tel'] += 1
return json.dumps(json_data)
table = table.select(table.id, update_tel(table.data))
# execute
table.execute_insert('sink') \
.wait()
def setUp(self):
super(StreamDependencyTests, self).setUp()
self.st_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
self.st_env._execution_mode = "loopback"
def row_operations():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# define the source
table = t_env.from_elements(elements=[
(1,
'{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'
),
(2,
'{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'
),
(3,
'{"name": "world", "tel": 124, "addr": {"country": "China", "city": "NewYork"}}'
),
(4,
'{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}'
)
],
schema=['id', 'data'])
# map operation
@udf(result_type=DataTypes.ROW([
DataTypes.FIELD("id", DataTypes.BIGINT()),
DataTypes.FIELD("country", DataTypes.STRING())
]))
def extract_country(input_row: Row):
data = json.loads(input_row.data)
return Row(input_row.id, data['addr']['country'])
table.map(extract_country) \
.execute().print()
# +----+----------------------+--------------------------------+
# | op | id | country |
# +----+----------------------+--------------------------------+
# | +I | 1 | Germany |
# | +I | 2 | China |
# | +I | 3 | China |
# | +I | 4 | China |
# +----+----------------------+--------------------------------+
# flat_map operation
@udtf(result_types=[DataTypes.BIGINT(), DataTypes.STRING()])
def extract_city(input_row: Row):
data = json.loads(input_row.data)
yield input_row.id, data['addr']['city']
table.flat_map(extract_city) \
.execute().print()
# +----+----------------------+--------------------------------+
# | op | f0 | f1 |
# +----+----------------------+--------------------------------+
# | +I | 1 | Berlin |
# | +I | 2 | Shanghai |
# | +I | 3 | NewYork |
# | +I | 4 | Hangzhou |
# +----+----------------------+--------------------------------+
# aggregate operation
class CountAndSumAggregateFunction(AggregateFunction):
def get_value(self, accumulator):
return Row(accumulator[0], accumulator[1])
def create_accumulator(self):
return Row(0, 0)
def accumulate(self, accumulator, input_row):
accumulator[0] += 1
accumulator[1] += int(input_row.tel)
def retract(self, accumulator, input_row):
accumulator[0] -= 1
accumulator[1] -= int(input_row.tel)
def merge(self, accumulator, accumulators):
for other_acc in accumulators:
accumulator[0] += other_acc[0]
accumulator[1] += other_acc[1]
def get_accumulator_type(self):
return DataTypes.ROW([
DataTypes.FIELD("cnt", DataTypes.BIGINT()),
DataTypes.FIELD("sum", DataTypes.BIGINT())
])
def get_result_type(self):
return DataTypes.ROW([
DataTypes.FIELD("cnt", DataTypes.BIGINT()),
DataTypes.FIELD("sum", DataTypes.BIGINT())
])
count_sum = udaf(CountAndSumAggregateFunction())
table.add_columns(
col('data').json_value('$.name', DataTypes.STRING()).alias('name'),
col('data').json_value('$.tel', DataTypes.STRING()).alias('tel'),
col('data').json_value('$.addr.country', DataTypes.STRING()).alias('country')) \
.group_by(col('country')) \
.aggregate(count_sum.alias("cnt", "sum")) \
.select(col('country'), col('cnt'), col('sum')) \
.execute().print()
# +----+--------------------------------+----------------------+----------------------+
# | op | country | cnt | sum |
# +----+--------------------------------+----------------------+----------------------+
# | +I | China | 3 | 291 |
# | +I | Germany | 1 | 123 |
# +----+--------------------------------+----------------------+----------------------+
# flat_aggregate operation
class Top2(TableAggregateFunction):
def emit_value(self, accumulator):
for v in accumulator:
if v:
yield Row(v)
def create_accumulator(self):
return [None, None]
def accumulate(self, accumulator, input_row):
tel = int(input_row.tel)
if accumulator[0] is None or tel > accumulator[0]:
accumulator[1] = accumulator[0]
accumulator[0] = tel
elif accumulator[1] is None or tel > accumulator[1]:
accumulator[1] = tel
def get_accumulator_type(self):
return DataTypes.ARRAY(DataTypes.BIGINT())
def get_result_type(self):
return DataTypes.ROW([DataTypes.FIELD("tel", DataTypes.BIGINT())])
top2 = udtaf(Top2())
table.add_columns(
col('data').json_value('$.name', DataTypes.STRING()).alias('name'),
col('data').json_value('$.tel', DataTypes.STRING()).alias('tel'),
col('data').json_value('$.addr.country', DataTypes.STRING()).alias('country')) \
.group_by(col('country')) \
.flat_aggregate(top2) \
.select(col('country'), col('tel')) \
.execute().print()
def basic_operations():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# define the source
table = t_env.from_elements(elements=[
(1,
'{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'
),
(2,
'{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'
),
(3,
'{"name": "world", "tel": 124, "addr": {"country": "USA", "city": "NewYork"}}'
),
(4,
'{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}'
)
],
schema=['id', 'data'])
right_table = t_env.from_elements(elements=[(1, 18), (2, 30), (3, 25),
(4, 10)],
schema=['id', 'age'])
table = table.add_columns(
col('data').json_value('$.name', DataTypes.STRING()).alias('name'),
col('data').json_value('$.tel', DataTypes.STRING()).alias('tel'),
col('data').json_value('$.addr.country', DataTypes.STRING()).alias('country')) \
.drop_columns(col('data'))
table.execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | name | tel | country |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | Flink | 123 | Germany |
# | +I | 2 | hello | 135 | China |
# | +I | 3 | world | 124 | USA |
# | +I | 4 | PyFlink | 32 | China |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# limit the number of outputs
table.limit(3).execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | name | tel | country |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | Flink | 123 | Germany |
# | +I | 2 | hello | 135 | China |
# | +I | 3 | world | 124 | USA |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# filter
table.filter(col('id') != 3).execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | name | tel | country |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | Flink | 123 | Germany |
# | +I | 2 | hello | 135 | China |
# | +I | 4 | PyFlink | 32 | China |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# aggregation
table.group_by(col('country')) \
.select(col('country'), col('id').count, col('tel').cast(DataTypes.BIGINT()).max) \
.execute().print()
# +----+--------------------------------+----------------------+----------------------+
# | op | country | EXPR$0 | EXPR$1 |
# +----+--------------------------------+----------------------+----------------------+
# | +I | Germany | 1 | 123 |
# | +I | USA | 1 | 124 |
# | +I | China | 1 | 135 |
# | -U | China | 1 | 135 |
# | +U | China | 2 | 135 |
# +----+--------------------------------+----------------------+----------------------+
# distinct
table.select(col('country')).distinct() \
.execute().print()
# +----+--------------------------------+
# | op | country |
# +----+--------------------------------+
# | +I | Germany |
# | +I | China |
# | +I | USA |
# +----+--------------------------------+
# join
# Note that it still doesn't support duplicate column names between the joined tables
table.join(right_table.rename_columns(col('id').alias('r_id')), col('id') == col('r_id')) \
.execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+----------------------+----------------------+
# | op | id | name | tel | country | r_id | age |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+----------------------+----------------------+
# | +I | 4 | PyFlink | 32 | China | 4 | 10 |
# | +I | 1 | Flink | 123 | Germany | 1 | 18 |
# | +I | 2 | hello | 135 | China | 2 | 30 |
# | +I | 3 | world | 124 | USA | 3 | 25 |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+----------------------+----------------------+
# join lateral
@udtf(result_types=[DataTypes.STRING()])
def split(r: Row):
for s in r.name.split("i"):
yield s
table.join_lateral(split.alias('a')) \
.execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | name | tel | country | a |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | Flink | 123 | Germany | Fl |
# | +I | 1 | Flink | 123 | Germany | nk |
# | +I | 2 | hello | 135 | China | hello |
# | +I | 3 | world | 124 | USA | world |
# | +I | 4 | PyFlink | 32 | China | PyFl |
# | +I | 4 | PyFlink | 32 | China | nk |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
# show schema
table.print_schema()
# (
# `id` BIGINT,
# `name` STRING,
# `tel` STRING,
# `country` STRING
# )
# show execute plan
print(table.join_lateral(split.alias('a')).explain())
def column_operations():
t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode())
# define the source
table = t_env.from_elements(elements=[
(1,
'{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'
),
(2,
'{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'
),
(3,
'{"name": "world", "tel": 124, "addr": {"country": "USA", "city": "NewYork"}}'
),
(4,
'{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}'
)
],
schema=['id', 'data'])
# add columns
table = table.add_columns(
col('data').json_value('$.name', DataTypes.STRING()).alias('name'),
col('data').json_value('$.tel', DataTypes.STRING()).alias('tel'),
col('data').json_value('$.addr.country',
DataTypes.STRING()).alias('country'))
table.execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | data | name | tel | country |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | {"name": "Flink", "tel": 12... | Flink | 123 | Germany |
# | +I | 2 | {"name": "hello", "tel": 13... | hello | 135 | China |
# | +I | 3 | {"name": "world", "tel": 12... | world | 124 | USA |
# | +I | 4 | {"name": "PyFlink", "tel": ... | PyFlink | 32 | China |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
# drop columns
table = table.drop_columns(col('data'))
table.execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | name | tel | country |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | Flink | 123 | Germany |
# | +I | 2 | hello | 135 | China |
# | +I | 3 | world | 124 | USA |
# | +I | 4 | PyFlink | 32 | China |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# rename columns
table = table.rename_columns(col('tel').alias('telephone'))
table.execute().print()
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | op | id | name | telephone | country |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# | +I | 1 | Flink | 123 | Germany |
# | +I | 2 | hello | 135 | China |
# | +I | 3 | world | 124 | USA |
# | +I | 4 | PyFlink | 32 | China |
# +----+----------------------+--------------------------------+--------------------------------+--------------------------------+
# replace columns
table = table.add_or_replace_columns(
concat(col('id').cast(DataTypes.STRING()), '_',
col('name')).alias('id'))
table.execute().print()
