join

问题 I would like to combine different results from the same table as one big result. SELECT host_name,stats_avgcpu,stats_avgmem,stats_avgswap,stats_avgiowait FROM sar_stats,sar_hosts,sar_appgroups,sar_environments WHERE stats_host = host_id AND host_environment = env_id AND env_name = 'Staging 2' AND host_appgroup = group_id AND group_name = 'Pervasive' AND DATE(stats_report_time) = DATE_SUB(curdate(), INTERVAL 1 DAY) SELECT AVG(stats_avgcpu),AVG(stats_avgmem),AVG(stats_avgswap),AVG(stats

问题 I would like to combine different results from the same table as one big result. SELECT host_name,stats_avgcpu,stats_avgmem,stats_avgswap,stats_avgiowait FROM sar_stats,sar_hosts,sar_appgroups,sar_environments WHERE stats_host = host_id AND host_environment = env_id AND env_name = 'Staging 2' AND host_appgroup = group_id AND group_name = 'Pervasive' AND DATE(stats_report_time) = DATE_SUB(curdate(), INTERVAL 1 DAY) SELECT AVG(stats_avgcpu),AVG(stats_avgmem),AVG(stats_avgswap),AVG(stats

问题 I have a Spark program that reads a relatively big dataframe (~3.2 terabyte) that contains 2 columns: id, name and another relatively small dataframe (~20k entries) that contain a single column: id What I'm trying to do is take both the id and the name from the big dataframe if they appear in the small dataframe I was wondering what would be an efficient solution to get this working and why? Several options I had in mind: Broadcast join the 2 dataframes Broadcast the small dataframe and

问题 I have a Spark program that reads a relatively big dataframe (~3.2 terabyte) that contains 2 columns: id, name and another relatively small dataframe (~20k entries) that contain a single column: id What I'm trying to do is take both the id and the name from the big dataframe if they appear in the small dataframe I was wondering what would be an efficient solution to get this working and why? Several options I had in mind: Broadcast join the 2 dataframes Broadcast the small dataframe and

问题 I am trying to merge 2 dataframes together. Ironically, they started out as part of the same dataframe, but I am making baby steps -- sometimes in the wrong direction. Frame 1 looks like this: Int64Index: 10730 entries, 0 to 10729 Data columns (total 6 columns): RegionID 10730 non-null int64 RegionName 10730 non-null object State 10730 non-null object Metro 10259 non-null object CountyName 10730 non-null object SizeRank 10730 non-null int64 dtypes: int64(2), object(4) Frame 2 looks like this:

问题 I am trying to merge 2 dataframes together. Ironically, they started out as part of the same dataframe, but I am making baby steps -- sometimes in the wrong direction. Frame 1 looks like this: Int64Index: 10730 entries, 0 to 10729 Data columns (total 6 columns): RegionID 10730 non-null int64 RegionName 10730 non-null object State 10730 non-null object Metro 10259 non-null object CountyName 10730 non-null object SizeRank 10730 non-null int64 dtypes: int64(2), object(4) Frame 2 looks like this:

问题 I've created a view to join two tables in Big Query. When I use that view as a data source in Data Studio I get a configuration error. The query used to create the view looks like this: SELECT emp.name, emp.gender, emp.age, pay.salary FROM [project:doug.employees] as emp JOIN [project:doug.payrates] as pay on emp.name = pay.name Within Big Query the view works as expected. I can query it without any problems. If I try to use the view directly as a data source in Data Studio, I get the

问题 I've created a view to join two tables in Big Query. When I use that view as a data source in Data Studio I get a configuration error. The query used to create the view looks like this: SELECT emp.name, emp.gender, emp.age, pay.salary FROM [project:doug.employees] as emp JOIN [project:doug.payrates] as pay on emp.name = pay.name Within Big Query the view works as expected. I can query it without any problems. If I try to use the view directly as a data source in Data Studio, I get the

问题 Here's a minimal setup with 2 tables a and b each with 3 rows: CREATE TABLE a ( id SERIAL PRIMARY KEY, value TEXT ); CREATE INDEX ON a (value); CREATE TABLE b ( id SERIAL PRIMARY KEY, value TEXT ); CREATE INDEX ON b (value); INSERT INTO a (value) VALUES ('x'), ('y'), (NULL); INSERT INTO b (value) VALUES ('y'), ('z'), (NULL); Here is a LEFT JOIN that works fine as expected: SELECT * FROM a LEFT JOIN b ON a.value IS NOT DISTINCT FROM b.value; with output: id | value | id | value ----+-------+--

问题 I'm trying to join two dataframes - one with multiindex columns and the other with a single column name. They have similar index. I get the following warning: "UserWarning: merging between different levels can give an unintended result (3 levels on the left, 1 on the right)" For example: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] tuples = list(zip(*arrays)) index = pd.MultiIndex.from_tuples(tuples, names=[