numeric datatypes(数字类型) long、integer、short、byte、double、float、half_float、scaled_float。...Geo datatypes 地图数据类型。 geo_point 地图坐标;存储经纬度。
另外一个比较表现突出的是返回ORA-00932: inconsistent datatypes: expected - got CLOB错误,即不一致的数据类型,获得CLOB数据类型。...---------------------------------------------------------- 30/13 PL/SQL: ORA-00932: inconsistent datatypes...expected - got CLOB 30/13 PL/SQL: SQL Statement ignored 898/13 PL/SQL: ORA-00932: inconsistent datatypes...id,wm_concat(val) new_val from t8 group by id * ERROR at line 1: ORA-00932: inconsistent datatypes
([DataTypes.FIELD('name', DataTypes.STRING()), DataTypes.FIELD('english', DataTypes.FLOAT()), DataTypes.FIELD..."name", DataTypes.STRING()), DataTypes.FIELD("score", DataTypes.FLOAT()), DataTypes.FIELD("class", DataTypes.STRING...([DataTypes.FIELD("name", DataTypes.STRING()), DataTypes.FIELD("score", DataTypes.FLOAT()), DataTypes.FIELD...(DataTypes.ROW([DataTypes.FIELD("name", DataTypes.STRING()), DataTypes.FIELD("score", DataTypes.FLOAT...DataTypes.ROW([DataTypes.FIELD("name", DataTypes.STRING()), DataTypes.FIELD("score", DataTypes.FLOAT
([DataTypes.FIELD('word', DataTypes.STRING())]) tab_source = t_env.from_elements(map(lambda i: Row...下面我们看下入参不同时,UDF怎么写 入参并非表中一行(Row) @udf(result_type=DataTypes.ROW([DataTypes.FIELD("lower_word", DataTypes.STRING...result_type我们设置为一个DataTypes.ROW([DataTypes.FIELD(“lower_word”, DataTypes.STRING())])。...([DataTypes.FIELD('word', DataTypes.STRING())]) tab_source = t_env.from_elements(map(lambda i: Row...([DataTypes.FIELD("lower_word", DataTypes.STRING())]), input_types=[DataTypes.STRING()]) def colFunc
构建MapType的json String mapTypeJson = DataTypes.createMapType(DataTypes.StringType, DataTypes.StringType...(DataTypes.StringType).json()); String mapTypeJson = DataTypes.createMapType(DataTypes.StringType, arrayStringDataType...(DataTypes.StringType).json()); String mapTypeJson = DataTypes.createMapType(DataTypes.StringType, DataTypes.FloatType...("street", DataTypes.StringType, true)); structFieldList.add(DataTypes.createStructField("city...", DataTypes.StringType, true)); String jsonStr = DataTypes.createStructType(structFieldList)
where['createdAt'] = { [Op.between]: [createdAtFrom,createdAtTo] }) 多表查询 首先有两个表 用户表 const { DataTypes..., allowNull: true, comment: '头像' }, register_date:{ type: DataTypes.DATE..., allowNull: true, comment: '注册日期' }, login_date:{ type: DataTypes.DATE...: true, comment: '标签' }, signature:{ type: DataTypes.STRING, allowNull...: true, comment: '座右铭' }, email:{ type: DataTypes.STRING, allowNull:
" => DataTypes.IntegerType case "UInt64" => DataTypes.LongType //DataTypes.IntegerType; case "UInt32..." => DataTypes.LongType case "UInt16" => DataTypes.IntegerType case "UInt8" => DataTypes.IntegerType...> DataTypes.FloatType case "Float64" => DataTypes.DoubleType case "Decimal32" => DataTypes.createDecimalType..." => DataTypes.StringType case "FixedString" => DataTypes.StringType case "Nothing" => DataTypes.NullType...DataTypes.LongType => "Int64" case DataTypes.DateType => "DateTime" case DataTypes.TimestampType
()) def add(i, j): return i + j @udtf(result_types=[DataTypes.BIGINT(), DataTypes.BIGINT()]) def range_emit...(s, e): for i in range(e): yield s, i @udaf(result_type=DataTypes.FLOAT(), func_type="pandas") def...mean_udaf(v): return v.mean() 但是没有见到udtaf修饰function的案例,比如 # 错误的 @udtaf(result_type=DataTypes.ROW([DataTypes.FIELD...("word", DataTypes.STRING()) , DataTypes.FIELD("count", DataTypes.BIGINT())]), accumulator_type=DataTypes.ROW...([DataTypes.FIELD("word", DataTypes.STRING())]), func_type="general") def lower(line): yield Row('a'
( "a", DataTypes.StringType, true )); structFields.add(DataTypes.createStructField( "b", DataTypes.StringType..., true )); structFields.add(DataTypes.createStructField( "c", DataTypes.StringType, true ));...( "field1", DataTypes.StringType, true )); return DataTypes.createStructType( structFields );...( "a", DataTypes.StringType, true )); structFields.add(DataTypes.createStructField( "b", DataTypes.StringType...( "a", DataTypes.StringType, true )); structFields.add(DataTypes.createStructField( "b", DataTypes.StringType
([DataTypes.FIELD('name', DataTypes.STRING()), DataTypes.FIELD('score', DataTypes.FLOAT()), DataTypes.FIELD...([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("max tag", DataTypes.STRING()), DataTypes.FIELD...([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("max tag", DataTypes.STRING()), DataTypes.FIELD...([DataTypes.FIELD('name', DataTypes.STRING()), DataTypes.FIELD('score', DataTypes.FLOAT()), DataTypes.FIELD...([DataTypes.FIELD('name', DataTypes.STRING()), DataTypes.FIELD('score', DataTypes.FLOAT()), DataTypes.FIELD
("nums",DataTypes.IntegerType,true)); inputSchema=DataTypes.createStructType(inputFields);...List bufferFields = new ArrayList(); bufferFields.add(DataTypes.createStructField...("datas",DataTypes.StringType,true)); bufferSchema=DataTypes.createStructType(bufferFields);...{ return bufferSchema; } @Override public DataType dataType() { return DataTypes.DoubleType...(DataTypes.IntegerType)); sqlContext.udf().register("media",new MedianUdaf()); sqlContext.sql
(Array[StructField]( DataTypes.createStructField("name",DataTypes.StringType,true), DataTypes.createStructField...(Array[StructField]( DataTypes.createStructField("name",DataTypes.StringType,true), DataTypes.createStructField...(Array[StructField]( DataTypes.createStructField("sum",DataTypes.DoubleType,true), DataTypes.createStructField...("name",DataTypes.StringType,true), DataTypes.createStructField("age",DataTypes.IntegerType,true...("name",DataTypes.StringType,true), DataTypes.createStructField("age",DataTypes.IntegerType,true
("name", DataTypes.StringType, true)); // age structFieldList.add(DataTypes.createStructField...(DataTypes.createArrayType(AddressEntity.dataType()).json()); structFieldList.add(DataTypes.createStructField...(DataTypes.createArrayType(DataTypes.StringType).json()); structFieldList.add(DataTypes.createStructField...("street", DataTypes.StringType, true)); structFieldList.add(DataTypes.createStructField("city...", DataTypes.StringType, true)); String jsonStr = DataTypes.createStructType(structFieldList)
The following is a list of datatypes available in Oracle....Character Datatypes The following are the Character Datatypes in Oracle: Data Type Syntax Oracle 9i Oracle...(backward compatible) Numeric Datatypes The following are the Numeric Datatypes in Oracle: Data Type...For example:interval day(2) to second(6) Large Object (LOB) Datatypes The following are the LOB Datatypes...Rowid Datatypes The following are the Rowid Datatypes in Oracle: Data Type Syntax Oracle 9i Oracle 10g
@udtf(result_types=[DataTypes.STRING()], input_types=row_type_tab_source) def rowFunc(row):...pyflink.table import (EnvironmentSettings, TableEnvironment, Schema) from pyflink.table.types import DataTypes....build() t_env = TableEnvironment.create(env_settings) row_type_tab_source = DataTypes.ROW...([DataTypes.FIELD('word', DataTypes.STRING())]) tab_source = t_env.from_elements(map(lambda i: Row...().not_null()) \ .column("count", DataTypes.BIGINT()) \ .primary_key("word") \
String t1) throws Exception { return t1.length(); } }, DataTypes.IntegerType...t1, Integer t2) throws Exception { return t1.length()+t2; } } ,DataTypes.IntegerType...("name", DataTypes.StringType, true)); StructType schema = DataTypes.createStructType(fields)...(Arrays.asList(DataTypes.createStructField("bffer111", DataTypes.IntegerType, true))); }...(Arrays.asList(DataTypes.createStructField("nameeee", DataTypes.StringType, true))); }
{DataTypes, Table} import org.apache.flink.table.api.scala._ import org.apache.flink.table.descriptors...()) .field("name", DataTypes.STRING()) ).createTemporaryTable("FileInput") // 查询数据...实现代码 import org.apache.flink.streaming.api.scala._ import org.apache.flink.table.api.DataTypes import...()) .field("name", DataTypes.STRING()) ).createTemporaryTable("FileInput") // 设置kafka...()) .field("name", DataTypes.STRING()) ).createTemporaryTable("FileInput") val result
import org.apache.flink.formats.json.JsonRowDataDeserializationSchema; import org.apache.flink.table.api.DataTypes...( DataTypes.FIELD("columns", DataTypes.STRING()), DataTypes.FIELD("rows...", DataTypes.STRING()), DataTypes.FIELD("table", DataTypes.STRING())).getLogicalType(...( DataTypes.FIELD("columns", DataTypes.STRING()), DataTypes.FIELD("rows...", DataTypes.STRING()), DataTypes.FIELD("table", DataTypes.STRING())).getLogicalType(
", 2); list.add(row); List structFields = new ArrayList(); structFields.add(DataTypes.createStructField...("id", DataTypes.IntegerType, true)); structFields.add(DataTypes.createStructField("user_id",...DataTypes.StringType, true)); structFields.add(DataTypes.createStructField("user_name", DataTypes.StringType..., true)); structFields.add(DataTypes.createStructField("rule", DataTypes.IntegerType, true));...StructType structType = DataTypes.createStructType(structFields); //overwrite 会把字段覆盖掉
stream_execution_environment=stream_execute_env) 定义行结构 schame = Schema.new_builder().column('seed', DataTypes.INT...(DataTypes.INT())) \ .build() table_descriptor...(DataTypes.STRING(), DataTypes.INT())) \ .build()...(DataTypes.STRING())) \ .build()...([DataTypes.FIELD("id", DataTypes.BIGINT()), DataTypes.FIELD("data", DataTypes.STRING())])) \
领取专属 10元无门槛券
手把手带您无忧上云