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flink-python/pyflink/examples/table/basic_operations.py
486 строк
34 KB
Jane Chan
[FLINK-30752][python] Support 'EXPLAIN PLAN_ADVICE' statement in PyFlink
29 янв 2023, 09:08
29 янв 2023, 09:08
d271074
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################################################################################ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################################################################ import json import logging import sys from pyflink.common import Row from pyflink.table import (DataTypes, TableEnvironment, EnvironmentSettings, ExplainDetail) from pyflink.table.expressions import * from pyflink.table.udf import udtf, udf, udaf, AggregateFunction, TableAggregateFunction, udtaf 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()) # == Abstract Syntax Tree == # LogicalCorrelate(correlation=[$cor1], joinType=[inner], requiredColumns=[{}]) # :- LogicalProject(id=[$0], name=[JSON_VALUE($1, _UTF-16LE'$.name', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR))], tel=[JSON_VALUE($1, _UTF-16LE'$.tel', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR))], country=[JSON_VALUE($1, _UTF-16LE'$.addr.country', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR))]) # : +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_TableSource_249535355, source: [PythonInputFormatTableSource(id, data)]]]) # +- LogicalTableFunctionScan(invocation=[*org.apache.flink.table.functions.python.PythonTableFunction$1f0568d1f39bef59b4c969a5d620ba46*($0, $1, $2, $3)], rowType=[RecordType(VARCHAR(2147483647) a)], elementType=[class [Ljava.lang.Object;]) # # == Optimized Physical Plan == # PythonCorrelate(invocation=[*org.apache.flink.table.functions.python.PythonTableFunction$1f0568d1f39bef59b4c969a5d620ba46*($0, $1, $2, $3)], correlate=[table(split(id,name,tel,country))], select=[id,name,tel,country,a], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) name, VARCHAR(2147483647) tel, VARCHAR(2147483647) country, VARCHAR(2147483647) a)], joinType=[INNER]) # +- Calc(select=[id, JSON_VALUE(data, _UTF-16LE'$.name', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR)) AS name, JSON_VALUE(data, _UTF-16LE'$.tel', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR)) AS tel, JSON_VALUE(data, _UTF-16LE'$.addr.country', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR)) AS country]) # +- LegacyTableSourceScan(table=[[default_catalog, default_database, Unregistered_TableSource_249535355, source: [PythonInputFormatTableSource(id, data)]]], fields=[id, data]) # # == Optimized Execution Plan == # PythonCorrelate(invocation=[*org.apache.flink.table.functions.python.PythonTableFunction$1f0568d1f39bef59b4c969a5d620ba46*($0, $1, $2, $3)], correlate=[table(split(id,name,tel,country))], select=[id,name,tel,country,a], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) name, VARCHAR(2147483647) tel, VARCHAR(2147483647) country, VARCHAR(2147483647) a)], joinType=[INNER]) # +- Calc(select=[id, JSON_VALUE(data, '$.name', NULL, ON EMPTY, NULL, ON ERROR) AS name, JSON_VALUE(data, '$.tel', NULL, ON EMPTY, NULL, ON ERROR) AS tel, JSON_VALUE(data, '$.addr.country', NULL, ON EMPTY, NULL, ON ERROR) AS country]) # +- LegacyTableSourceScan(table=[[default_catalog, default_database, Unregistered_TableSource_249535355, source: [PythonInputFormatTableSource(id, data)]]], fields=[id, data]) # show execute plan with advice print(table.join_lateral(split.alias('a')).explain(ExplainDetail.PLAN_ADVICE)) # == Abstract Syntax Tree == # LogicalCorrelate(correlation=[$cor2], joinType=[inner], requiredColumns=[{}]) # :- LogicalProject(id=[$0], name=[JSON_VALUE($1, _UTF-16LE'$.name', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR))], tel=[JSON_VALUE($1, _UTF-16LE'$.tel', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR))], country=[JSON_VALUE($1, _UTF-16LE'$.addr.country', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR))]) # : +- LogicalTableScan(table=[[*anonymous_python-input-format$1*]]) # +- LogicalTableFunctionScan(invocation=[*org.apache.flink.table.functions.python.PythonTableFunction$720258394f6a31d31376164d23142f53*($0, $1, $2, $3)], rowType=[RecordType(VARCHAR(2147483647) a)]) # # == Optimized Physical Plan With Advice == # PythonCorrelate(invocation=[*org.apache.flink.table.functions.python.PythonTableFunction$720258394f6a31d31376164d23142f53*($0, $1, $2, $3)], correlate=[table(*org.apache.flink.table.functions.python.PythonTableFunction$720258394f6a31d31376164d23142f53*(id,name,tel,country))], select=[id,name,tel,country,a], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) name, VARCHAR(2147483647) tel, VARCHAR(2147483647) country, VARCHAR(2147483647) a)], joinType=[INNER]) # +- Calc(select=[id, JSON_VALUE(data, _UTF-16LE'$.name', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR)) AS name, JSON_VALUE(data, _UTF-16LE'$.tel', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR)) AS tel, JSON_VALUE(data, _UTF-16LE'$.addr.country', FLAG(NULL), FLAG(ON EMPTY), FLAG(NULL), FLAG(ON ERROR)) AS country]) # +- TableSourceScan(table=[[*anonymous_python-input-format$1*]], fields=[id, data]) # # No available advice... # # == Optimized Execution Plan == # PythonCorrelate(invocation=[*org.apache.flink.table.functions.python.PythonTableFunction$720258394f6a31d31376164d23142f53*($0, $1, $2, $3)], correlate=[table(*org.apache.flink.table.functions.python.PythonTableFunction$720258394f6a31d31376164d23142f53*(id,name,tel,country))], select=[id,name,tel,country,a], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) name, VARCHAR(2147483647) tel, VARCHAR(2147483647) country, VARCHAR(2147483647) a)], joinType=[INNER]) # +- Calc(select=[id, JSON_VALUE(data, '$.name', NULL, ON EMPTY, NULL, ON ERROR) AS name, JSON_VALUE(data, '$.tel', NULL, ON EMPTY, NULL, ON ERROR) AS tel, JSON_VALUE(data, '$.addr.country', NULL, ON EMPTY, NULL, ON ERROR) AS country]) # +- TableSourceScan(table=[[*anonymous_python-input-format$1*]], fields=[id, data]) 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 )) # == Abstract Syntax Tree == # LogicalProject(id=[$0], data=[$1], name=[$2], tel=[$3], country=[$4]) # +- LogicalCorrelate(correlation=[$cor1], joinType=[inner], requiredColumns=[{1}]) # :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_TableSource_734856049, source: [PythonInputFormatTableSource(id, data)]]]) # +- LogicalTableFunctionScan(invocation=[parse_data($cor1.data)], rowType=[RecordType:peek_no_expand(VARCHAR(2147483647) f0, INTEGER f1, VARCHAR(2147483647) f2)]) # # == Optimized Physical Plan == # PythonCorrelate(invocation=[parse_data($1)], correlate=[table(parse_data(data))], select=[id,data,f0,f1,f2], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) data, VARCHAR(2147483647) f0, INTEGER f1, VARCHAR(2147483647) f2)], joinType=[INNER]) # +- LegacyTableSourceScan(table=[[default_catalog, default_database, Unregistered_TableSource_734856049, source: [PythonInputFormatTableSource(id, data)]]], fields=[id, data]) # # == Optimized Execution Plan == # PythonCorrelate(invocation=[parse_data($1)], correlate=[table(parse_data(data))], select=[id,data,f0,f1,f2], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) data, VARCHAR(2147483647) f0, INTEGER f1, VARCHAR(2147483647) f2)], joinType=[INNER]) # +- LegacyTableSourceScan(table=[[default_catalog, default_database, Unregistered_TableSource_734856049, source: [PythonInputFormatTableSource(id, data)]]], fields=[id, data]) # explain sql plan with advice print(t_env.explain_sql( """ SELECT * FROM %s, LATERAL TABLE(parse_data(`data`)) t(name, tel, country) """ % table, ExplainDetail.PLAN_ADVICE )) # == Abstract Syntax Tree == # LogicalProject(id=[$0], data=[$1], name=[$2], tel=[$3], country=[$4]) # +- LogicalCorrelate(correlation=[$cor1], joinType=[inner], requiredColumns=[{1}]) # :- LogicalTableScan(table=[[*anonymous_python-input-format$10*]]) # +- LogicalTableFunctionScan(invocation=[parse_data($cor2.data)], rowType=[RecordType:peek_no_expand(VARCHAR(2147483647) f0, INTEGER f1, VARCHAR(2147483647) f2)]) # # == Optimized Physical Plan With Advice == # PythonCorrelate(invocation=[parse_data($1)], correlate=[table(parse_data(data))], select=[id,data,f0,f1,f2], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) data, VARCHAR(2147483647) f0, INTEGER f1, VARCHAR(2147483647) f2)], joinType=[INNER]) # +- TableSourceScan(table=[[*anonymous_python-input-format$10*]], fields=[id, data]) # # No available advice... # # == Optimized Execution Plan == # PythonCorrelate(invocation=[parse_data($1)], correlate=[table(parse_data(data))], select=[id,data,f0,f1,f2], rowType=[RecordType(BIGINT id, VARCHAR(2147483647) data, VARCHAR(2147483647) f0, INTEGER f1, VARCHAR(2147483647) f2)], joinType=[INNER]) # +- TableSourceScan(table=[[*anonymous_python-input-format$10*]], fields=[id, data]) def column_operations(): t_env = TableEnvironment.create(EnvironmentSettings.in_streaming_mode()) # define the source table = t_env.from_elements( elements=[ (1, '{"name": "Flink", "tel": 123, "addr": {"country": "Germany", "city": "Berlin"}}'), (2, '{"name": "hello", "tel": 135, "addr": {"country": "China", "city": "Shanghai"}}'), (3, '{"name": "world", "tel": 124, "addr": {"country": "USA", "city": "NewYork"}}'), (4, '{"name": "PyFlink", "tel": 32, "addr": {"country": "China", "city": "Hangzhou"}}') ], schema=['id', 'data']) # add columns table = table.add_columns( col('data').json_value('$.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() # +----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+ # | op | id | name | telephone | country | # +----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+ # | +I | 1_Flink | Flink | 123 | Germany | # | +I | 2_hello | hello | 135 | China | # | +I | 3_world | world | 124 | USA | # | +I | 4_PyFlink | PyFlink | 32 | China | # +----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+ 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() # +----+--------------------------------+----------------------+ # | op | country | tel | # +----+--------------------------------+----------------------+ # | +I | China | 135 | # | +I | China | 124 | # | +I | Germany | 123 | # +----+--------------------------------+----------------------+ if __name__ == '__main__': logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(message)s") basic_operations() sql_operations() column_operations() row_operations()