接下来,需要在查询中替换:min_age参数并执行查询: defrun_sql_query(spark,sql_query,min_age):# 替换参数sql_query=sql_query.replace(':min_age',str(min_age))returnspark.sql(sql_query)# 执行查询min_age_value=18result_df=run_sql_query(spark,sql_query,min_age_value)result_df.show() 1. 2...
接下来,我们可以执行多条 SQL 查询,通常是通过循环或存储在列表中的方式一次性执行。 # 定义 SQL 查询列表sql_queries=["SELECT * FROM my_table WHERE age > 30","SELECT COUNT(*) FROM my_table","SELECT name, COUNT(*) FROM my_table GROUP BY name"]# 执行 SQL 查询results=[]forqueryinsql_que...
sql(sql_hive_insert) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 DataFrame[] 读取hive表 代码语言:javascript 代码运行次数:0 运行 AI代码解释 sql_hive_query = ''' select id ,dtype ,cnt from temp.hive_mysql ''' df = spark.sql(sql_hive_query).toPandas() df.head() id dtype...
from pyspark.sql.functions import col df_that_one_customer = df_customer.filter(col("c_custkey") == 412449) To filter on multiple conditions, use logical operators. For example, & and | enable you to AND and OR conditions, respectively. The following example filters rows where the c_nati...
Spark SQL的起点: SparkSession代码: frompyspark.sqlimportSparkSessionspark=SparkSession\.builder\.appName("Python Spark SQL basic example")\.config("spark.some.config.option","some-value")\.getOrCreate() 使用SparkSession,应用程序可以从现有的RDD、Hive表或Spark数据源中创建DataFrames。
query='select x1,x2 from table where x3>20' df_2=spark.sql(query) #查询所得的df_2是一个DataFrame对象 4、数据可视化(绘图) spark中的数据可视化有三种方式,(1)自带的绘图函数,(2)转成Pandas对象绘图,(3)转成Handy绘图 #( 1)自带的绘图函数test_df=spark.read.csv("test.csv",header=True,infer...
#example from pyspark.sqlimportSparkSession spark=SparkSession\.builder\.appName('exam1')\.enableHiveSupport()\.getOrCreate() 本文参与腾讯云自媒体同步曝光计划,分享自作者个人站点/博客。 原始发表:2021/03/18 ,如有侵权请联系cloudcommunity@tencent.com删除 ...
I have written a pyspark.sql query as shown below. I would like the query results to be sent to a textfile but I get the error: AttributeError: 'DataFrame' object has no attribute 'saveAsTextFile' Can someone take a look at the code and let me know where I'm going wron...
glimpse this first example looks simple, butfilterhas a profound impact on performance on large data sets. Whenever you are reading from some external source always attempt to push the predicate. You can see whether the predicate pushed to the source system or not as shown in below query plan...
frompyspark.sql.datasourceimportDataSource, DataSourceReaderfrompyspark.sql.typesimportStructTypeclassFakeDataSource(DataSource):""" An example data source for batch query using the `faker` library. """@classmethoddefname(cls):return"fake"defschema(self):return"name string, date string, zipcode str...