join

问题 I have a users table and an appointments table. In appointment table I have two user ID's (customer_id, staff_id). I want to retrieve all the appointments with customer name and the staff name. users table id name appointments table id staff_id(user_id) customer_id(user_id) datetime As you can see, I have to join the users table twice with the appointments table. Usually I do this with inner joins . Can we do the same thing with Laravel eloquent eager loading using with()? Can we do something

问题 The validator in laravel can have a customization of the exists database rule, for instance if you need to check an extra column. An example from the manual: use Illuminate\Validation\Rule; Validator::make($data, [ 'email' => [ 'required', Rule::exists('staff')->where(function ($query) { $query->where('account_id', 1); }), ], ]); The query in the closure is not typehinted, so i'm not quite positive what kind of object this is. I can see that the DatabaseRule itself only has some function

问题 I know conditions are used in table joining. But I met a specific situation and the SQL codes writes like " Table A join table B ON TRUE " What will happen based on the "ON TRUE" condition? Is that just a total cross join without any condition selection? Actually, the original expression is like: Table A LEFT outer join table B on TRUE Let's say A has m rows and B has n rows. Is there any conflict between "left outer join" and "on true"? Because it seems "on true" results a cross join. From

问题 I am fetching the id, first name, and last name of all employees that are approved and not archived. Then I am looping these results and using the ids to query other tables to collect some count data. I tried the below code, but I am not getting the expected output. $queryEmp = " SELECT id, firstname, lastname FROM tbl_employee as e WHERE is_archive=0 and is_approved=1 "; $getQuery= $this->db->query($queryEmp); $result= $getQuery->result(); foreach ($result as $key=> $value) { //echo "<pre>";

问题 I am fetching the id, first name, and last name of all employees that are approved and not archived. Then I am looping these results and using the ids to query other tables to collect some count data. I tried the below code, but I am not getting the expected output. $queryEmp = " SELECT id, firstname, lastname FROM tbl_employee as e WHERE is_archive=0 and is_approved=1 "; $getQuery= $this->db->query($queryEmp); $result= $getQuery->result(); foreach ($result as $key=> $value) { //echo "<pre>";

问题 Here are examples of the 4 tables I'm working with. Items +----+------+ | id | name | +----+------+ | 1 | abc | | 2 | def | | 3 | ghi | +----+------+ Buy Table +----+-------------+-----+---------+ | id | date | qty | item_id | +----+-------------+-----+---------+ | 1 | 2020-05-01 | 10 | 1 | | 2 | 2020-05-02 | 20 | 2 | | 3 | 2020-05-03 | 5 | 3 | +----+-----------+-------+---------+ Rent Table +----+-------------+-----+---------+ | id | date | qty | item_id | +----+-------------+-----+---------

问题 Here are examples of the 4 tables I'm working with. Items +----+------+ | id | name | +----+------+ | 1 | abc | | 2 | def | | 3 | ghi | +----+------+ Buy Table +----+-------------+-----+---------+ | id | date | qty | item_id | +----+-------------+-----+---------+ | 1 | 2020-05-01 | 10 | 1 | | 2 | 2020-05-02 | 20 | 2 | | 3 | 2020-05-03 | 5 | 3 | +----+-----------+-------+---------+ Rent Table +----+-------------+-----+---------+ | id | date | qty | item_id | +----+-------------+-----+---------

问题 For the following join between two DataFrames in Spark 1.6.0 val df0Rep = df0.repartition(32, col("a")).cache val df1Rep = df1.repartition(32, col("a")).cache val dfJoin = df0Rep.join(df1Rep, "a") println(dfJoin.count) Does this join not only co-partitioned but also co-located? I know that for RDDs if using the same partitioner and shuffled in the same operation, the join would be co-located. But what about dataframes? Thank you. 回答1: [https://medium.com/@achilleus/https-medium-com-joins-in

问题 For the following join between two DataFrames in Spark 1.6.0 val df0Rep = df0.repartition(32, col("a")).cache val df1Rep = df1.repartition(32, col("a")).cache val dfJoin = df0Rep.join(df1Rep, "a") println(dfJoin.count) Does this join not only co-partitioned but also co-located? I know that for RDDs if using the same partitioner and shuffled in the same operation, the join would be co-located. But what about dataframes? Thank you. 回答1: [https://medium.com/@achilleus/https-medium-com-joins-in

问题 For the following join between two DataFrames in Spark 1.6.0 val df0Rep = df0.repartition(32, col("a")).cache val df1Rep = df1.repartition(32, col("a")).cache val dfJoin = df0Rep.join(df1Rep, "a") println(dfJoin.count) Does this join not only co-partitioned but also co-located? I know that for RDDs if using the same partitioner and shuffled in the same operation, the join would be co-located. But what about dataframes? Thank you. 回答1: [https://medium.com/@achilleus/https-medium-com-joins-in