def f(arr, num):
          data = {}
          for i in arr:
              if num - i in data:
                  return True
              data[i] = data.setdefault(i, 0)
      
          return False
      
      print(f([1,7,4,9,7,8,2], 15))
      print(f([1,7,4,9,7,9,2], 15))
      
      print(f([1,7,4,9,7,9,2], 14))
      print(f([1,7,4,9,1,9,2], 14))

      SELECT
            b."name" AS Department, 
            a."name" AS Employeer, 
            a."salary" AS Salary
      FROM (
            SELECT 
                  "department_id", 
                  "name", 
                  "salary", 
                  dense_rank() OVER (PARTITION BY "department_id" ORDER BY "salary" DESC) as "runks"
            FROM "Employee" 
            ) a 
            INNER JOIN "Departments" b on(a."department_id"=b."id")
     WHERE a."runks" <= 3

| department | employeer | salary |

| IT	     | Priya   | 80000 |
| IT       | Vikas   | 75000 |
| IT       | Priyna  | 75000 |
| IT       | Anil    | 65000 |
| IT       | Santosh | 65000 |
| Siles    | Rajesh  | 90000 |
| Siles    | Nidhi   | 60000 |
| Siles    | Raman   | 55000 | 

      DROP SEQUENCE IF EXISTS "Departments_id_seq";
      CREATE SEQUENCE "Departments_id_seq" INCREMENT  MINVALUE  MAXVALUE  CACHE ;
      
      CREATE TABLE "public"."Departments" (
          "id" integer DEFAULT nextval('"Departments_id_seq"') NOT NULL,
          "name" character varying NOT NULL,
          CONSTRAINT "Departments_name" UNIQUE ("name"),
          CONSTRAINT "Departments_pkey" PRIMARY KEY ("id")
      ) WITH (oids = false);
      
      INSERT INTO "Departments" ("id", "name") VALUES
      (1,	'IT'),
      (2,	'Siles');
      
      DROP TABLE IF EXISTS "Employee";
      DROP SEQUENCE IF EXISTS "Employee_id_seq";
      CREATE SEQUENCE "Employee_id_seq" INCREMENT  MINVALUE  MAXVALUE  CACHE ;
      
      CREATE TABLE "public"."Employee" (
          "id" integer DEFAULT nextval('"Employee_id_seq"') NOT NULL,
          "name" character varying NOT NULL,
          "salary" integer NOT NULL,
          "department_id" integer NOT NULL,
          CONSTRAINT "Employee_name" UNIQUE ("name"),
          CONSTRAINT "Employee_pkey" PRIMARY KEY ("id")
      ) WITH (oids = false);
      
      INSERT INTO "Employee" ("id", "name", "salary", "department_id") VALUES
      (2,	'Vikas',	75000,	1),
      (3,	'Nisha',	40000,	1),
      (4,	'Nidhi',	60000,	2),
      (6,	'Priya',	80000,	1),
      (7,	'Mohit',	45000,	1),
      (8,	'Rajesh',	90000,	2),
      (9,	'Raman',	55000,	2),
      (10,	'Santosh',	65000,	1),
      (12,	'Mlohit',	40000,	1),
      (11,	'Priyna',	75000,	1),
      (1,	'Anil',	65000,	1);
      
      ALTER TABLE ONLY "public"."Employee" ADD CONSTRAINT "Employee_department_id_fkey" FOREIGN KEY (department_id) REFERENCES "Departments"(id) ON UPDATE CASCADE ON DELETE CASCADE NOT DEFERRABLE;

make install-requirements

make run-infra

make create-database (postgres:12)
  connect to DB postgres://docker:docker@localhost:5333/meteo_data


make load-wind-data
  extracting and inserting data from meteo-data/archive.zip file


make update-wind-data
service run every 300 sec and check directory
    ${HOME}/meteo-data-storage/data
so 1) change permission ${HOME}/meteo-data-storage/data
    sudo chmod (775 or 777) -R ${HOME}/meteo-data-storage
    copy csv files to ${HOME}/meteo-data-storage/data upsert DB


make query

    SELECT column, CASE WHEN UPPER(column) = LOWER(column) THEN 'Fizz' ELSE 'Buzz' END AS FizzBuzz FROM table;      

    |  x    |
    |-------|
    | 2     |
    |-2     |
    | 4     |
    |-4     |
    |-3     |
    | 0     |
    | 2     |



   SELECT SUM(CASE WHEN x>0 THEN x ELSE 0 END) sum_a, SUM(CASE WHEN x<0 THEN x ELSE 0 END) sum_b
   FROM A;

            SELECT salary FROM employee_table ORDER BY salary DESC LIMIT 1 OFFSET 2;

| 10 | Anil | 50000 | 18 |
| 11 |  Vikas |  75000 |  16 |
| 12 |  Nisha |  40000 |  18 |
| 13 |  Nidhi |  60000 |  17 |
| 14 |  Priya |  80000 |  18 |
| 15 |  Mohit |  45000 |  18 |
| 16 |  Rajesh |  90000 |  – |
| 17 |  Raman |  55000 |  16 |
| 18 |  Santosh |  65000 |  17 |

      SELECT b.emp_id AS Manager_Id, b.emp_name AS Manager, AVG(a.salary) AS Average_Salary_Under_Manager
                    FROM Employee a, Employee b
                    WHERE a.manager_id = b.emp_id
                    GROUP BY b.emp_id, b.emp_name
                    ORDER BY b.emp_id,

            UNION performs a DISTINCT on the result set, eliminating any duplicate rows.
            UNION ALL does not remove duplicates, and it is therefore faster than UNION.

            If the query does not use GROUP BY, then the WHERE and HAVING clauses are equivalent.
            But when using GROUP BY: The HAVING clause is used to filter values from a group (that is, to test conditions after aggregation into groups). The WHERE clause is used to filter records from the result before any grouping is triggered.

        SELECT values
        FROM TestTable1 a LEFT JOIN TestTable2 b ON (a.value=b.value)
        WHERE b.value IS NULL

SELECT Id, YEAR(BillingDate) AS BillingYear
FROM Invoices
WHERE BillingYear >= 2010;

        SELECT Id, YEAR(BillingDate) AS BillingYear
        FROM Invoices
        WHERE YEAR(BillingDate) >= 2010;

    1.Inner join: returns only those records that match in both the tables.

        SELECT a.*, b.* FROM A a
        INNER JOIN B b
        ON a.key=b.key;

    2.Left outer: join returns all records from left table and only matching records from right.

    3.Right outer: join returns all records from right table and only matching records from left.

    4.Full outer join: combines left outer join and right outer join. This join returns all records/rows from both the tables.

    5.Cross join: is a cartesian join means cartesian product of both the tables. This join does not need any condition to join two tables.

    6.Self join: is used to join a database table to itself