Practice Complex queries- find duplicate records

-

  1. Write a SQL Query to fetch all the duplicate records in a table.

    Table Name: USERS

    Note: Record is considered duplicate if a user name and his email is present more than once.

    Approach: Partition the data based on user name and then give a row number to each of the partitioned user name. If a user name exists more than once then it would have multiple row numbers. Using the row number which is other than 1, we can identify the duplicate records.

create table users
(
user_id int primary key,
user_name varchar(30) not null,
email varchar(50));

insert into users values (1, ‘Sumit’, ‘sumit@gmail.com’);
insert into users values (2, ‘Reshma’, ‘reshma@gmail.com’);
insert into users values (3, ‘Farhana’, ‘farhana@gmail.com’);
insert into users values (4, ‘Robin’, ‘robin@gmail.com’);
insert into users values (5, ‘Robin’, ‘robin@gmail.com’);
insert into users values (6, ‘Farhana’, ‘farhana@gmail.com’);
insert into users values (7, ‘Farhana’, ‘farhana@gmail.com’);

expected output should be Farina and Robin as rows

–Solution to find duplicate using row_number() window function
select * from(
select
user_id,
user_name,
email,
ROW_number() over(partition by USER_NAME, EMAIL order by USER_ID desc) as ROW_ID
from users)
where ROW_ID=2;

Popular posts from this blog

Window function in PySpark with Joins example using 2 Dataframes (inner join)

-
Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Solution: from pyspark.sql.functions import col # this can be done without using window function creditCardData = spark.read.json(“card_transactions.json”) useridMaxSpendDF = df.groupby(‘user_id’).max(‘amount’) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“max(amount)”,”max_amount”) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“user_id”,”m_user_id”) cond = [creditCardData.user_id == useridMaxSpen...
Read more

Complex SQL: fetch the users who logged in consecutively 3 or more times (lead perfect example)

-
From the login_details table, fetch the users who logged in consecutively 3 or more times. Table Name : LOGIN_DETAILS Approach : We need to fetch users who have appeared 3 or more times consecutively in login details table. There is a window function which can be used to fetch data from the following record. Use that window function to compare the user name in current row with user name in the next row and in the row following the next row. If it matches then fetch those records. –Table Structure: drop table login_details; create table login_details( login_id int primary key, user_name varchar(50) not null, login_date date); delete from login_details; insert into login_details values (101, ‘Michael’, current_date), (102, ‘James’, current_date), (103, ‘Stewart’, current_date+1), (104, ‘Stewart’, current_date+1), (105, ‘Stewart’, current_date+1), (106, ‘Michael’, current_date+2), (107, ‘Michael’, cur...
Read more

Credit Card Data Analysis using PySpark (how to use auto broadcast join after disabling it)

-
Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Application: Here we will disable auto broadcast and then join 2 data frames and will see execution plan which should use broadcast join Solution: from pyspark.sql import SparkSession from pyspark.sql.functions import broadcast spark = SparkSession.builder.master(‘local[2]’)\ .appName(‘RDD_Methods_Examples’)\ .getOrCreate() print(spark.conf.get(“spark.sql.autoBroadcastJoinThreshold”)) spark.conf.set(“spark.sql.autoBroadcastJoinThreshold”, -1) pr...
Read more