spark sql split
时间: 2023-08-19 18:07:33 浏览: 54
在Spark SQL中,可以使用split函数来分割字符串。split函数接受两个参数:要分割的字符串和分隔符。
下面是一个示例代码,展示如何在Spark SQL中使用split函数进行字符串分割:
```scala
import org.apache.spark.sql.functions._
// 创建一个DataFrame
val data = Seq(("John,Doe"), ("Jane,Smith"), ("Tom,Hanks"))
val df = spark.createDataFrame(data).toDF("name")
// 使用split函数分割字符串
val splitDF = df.select(split(col("name"), ",").as("split_name"))
// 展示分割后的结果
splitDF.show()
```
运行以上代码,会得到以下输出:
```
+----------+
|split_name|
+----------+
| [John, Doe]|
|[Jane, Smith]|
| [Tom, Hanks]|
+----------+
```
在上面的示例中,我们使用split函数将"name"列中的字符串按照逗号进行分割,并将结果存储在新的列"split_name"中。你可以根据实际需求修改代码来适应你的数据。
相关问题
saprk sql split
Spark SQL Split function is used to split a string into an array of substrings based on a specified delimiter. The syntax for split function in Spark SQL is as follows:
```
split(string, delimiter)
```
Where,
- `string` is the input string that needs to be split.
- `delimiter` is the character or string used as a separator to split the input string.
For example, if we have a string "Hello,World" and we want to split it into an array of substrings based on the comma separator, we can use the split function as follows:
```
SELECT split("Hello,World", ",") as words
```
This will return an array of two strings - "Hello" and "World".
We can also use the split function with column names in a table to split the values in that column. For example, if we have a table `employee` with a column `name` containing full names separated by spaces, we can split the names into first and last name using the split function as follows:
```
SELECT split(name, " ")[0] as first_name, split(name, " ")[1] as last_name FROM employee
```
This will return a table with two columns - `first_name` and `last_name` containing the first and last names of all employees in the `employee` table.
spark sql建表
使用Spark SQL建表有多种方法,以下是三种常用的方法:
方法一,使用case class定义表:
1. 首先,使用case class定义表的结构,例如:
case class Emp(empno: Int, ename: String, job: String, mgr: String, hiredate: String, sal: Int, comm: String, deptno: Int)
2. 在main函数中,创建一个SparkSession:
val spark = SparkSession.builder().master("local").appName("sql").getOrCreate()
3. 导入隐式转换:
import spark.sqlContext.implicits._
4. 导入数据,这里以导入emp.csv文件为例:
val lines = spark.sparkContext.textFile("G:/emp.csv").map(_.split(","))
5. 将导入的数据映射为表的结构:
val allEmp = lines.map(x=>Emp(x(0).toInt,x(1),x(2),x(3),x(4),x(5).toInt,x(6),x(7).toInt))
6. 将映射后的数据转换为DataFrame:
val empDF = allEmp.toDF()
7. 打印DataFrame:
println(empDF.show())
方法二,使用SparkSession对象:
1. 在main函数中,创建一个SparkSession:
val spark = SparkSession.builder().master("local").appName("sql").getOrCreate()
2. 导入隐式转换:
import spark.sqlContext.implicits._
3. 导入数据,这里以导入emp.csv文件为例:
val lines = spark.sparkContext.textFile("G:/emp.csv").map(_.split(","))
4. 定义表的结构,这里以StructType为例:
val myschema = StructType(List(
StructField("empno", DataTypes.IntegerType),
StructField("ename", DataTypes.StringType),
StructField("job", DataTypes.StringType),
StructField("mgr", DataTypes.StringType),
StructField("hiredate", DataTypes.StringType),
StructField("sal", DataTypes.IntegerType),
StructField("comm", DataTypes.StringType),
StructField("deptno", DataTypes.IntegerType)
))
5. 将导入的每一行数据映射为Row:
val rowRDD = lines.map(x => Row(x(0).toInt, x(1), x(2), x(3), x(4), x(5).toInt, x(6), x(7).toInt))
6. 使用创建DataFrame的方法将Row转换为DataFrame:
val df = spark.createDataFrame(rowRDD, myschema)
7. 打印DataFrame:
println(df.show())
方法三,直接读取带格式的文件(如json文件):
1. 在main函数中,创建一个SparkSession:
val spark = SparkSession.builder().master("local").appName("sql").getOrCreate()
2. 导入隐式转换:
import spark.sqlContext.implicits._
3. 直接读取一个带格式的文件,这里以读取json文件为例:
val df = spark.read.json("G:/person.json")
4. 打印DataFrame:
println(df.show())
以上是使用Spark SQL建表的三种方法,具体使用哪种方法取决于你的数据格式和需求。