select substring(page_type,1,7) as page_type, visiturl, count(*) as count_num from law where visiturl like '%faguizt%' and page_type like '%199%' ;改成sparksql
时间: 2024-03-05 17:55:01 浏览: 10
可以将该SQL语句转换成Spark SQL语句如下:
```
SELECT SUBSTRING(page_type, 1, 7) AS page_type, visiturl, COUNT(*) AS count_num
FROM law
WHERE visiturl LIKE '%faguizt%' AND page_type LIKE '%199%'
GROUP BY SUBSTRING(page_type, 1, 7), visiturl;
```
在Spark SQL中,GROUP BY子句中的所有列都必须在SELECT子句中指定,因此需要将SUBSTRING(page_type, 1, 7)和visiturl都包含在SELECT子句中。
相关问题
将select substring(page_type,1,7) as page_type, visiturl, count(*) as count_num from law where visiturl like '%faguizt%' and page_type like '%199%' ; 改成sparksql 代码
可以使用以下Spark SQL代码将Hive SQL语句转换为Spark SQL语句:
```python
from pyspark.sql.functions import substring
# 读取Hive表数据
df = spark.table("law")
# 执行查询
result = df.filter(df.visiturl.like("%faguizt%") & df.page_type.like("%199%")) \
.groupBy(substring(df.page_type, 1, 7), df.visiturl) \
.count() \
.withColumnRenamed("count", "count_num") \
.select(substring(df.page_type, 1, 7).alias("page_type"), "visiturl", "count_num")
# 显示查询结果
result.show()
```
这段代码首先使用`spark.table`函数读取Hive表`law`的数据,然后使用`filter`函数设置查询条件,使用`groupBy`函数对`page_type`字段进行截取并按照`page_type`和`visiturl`字段进行分组,使用`count`函数统计符合条件的记录数,并使用`withColumnRenamed`函数将结果列重命名为`count_num`,最后使用`select`函数选择需要的列,并将`page_type`字段再次进行截取并重命名为`page_type`。最后调用`show`函数显示查询结果。
select substring(page_type,1,6) as page_type, count(*) as count_num, round((count(*)/411665.0)*100,4) as weights from law_part where page_type_part=101 group by substring(page_type,1,6) order by count_num desc;改成sparksql
下面是将该SQL语句转换为Spark SQL的代码:
```
SELECT SUBSTRING(page_type,1,6) AS page_type, COUNT(*) AS count_num, ROUND((COUNT(*)/411665.0)*100,4) AS weights
FROM law_part
WHERE page_type_part=101
GROUP BY SUBSTRING(page_type,1,6)
ORDER BY count_num DESC;
```
转换为Spark SQL后的代码:
```scala
import org.apache.spark.sql.functions._
val resultDF = law_part
.filter($"page_type_part" === 101)
.groupBy(substring($"page_type", 1, 6).as("page_type"))
.agg(count("*").as("count_num"))
.withColumn("weights", round((col("count_num")/411665.0)*100, 4))
.orderBy(desc("count_num"))
```
其中,`law_part`是你的数据表,可以通过`spark.read.jdbc()`或其他方式加载数据。