kovid-r

select * from cast_ex.table_1 where val_int = 1;
select * from cast_ex.table_1 where val_int = '1';
select * from cast_ex.table_1 where val_int_char = 1;
select * from cast_ex.table_1 where val_int_char = '1';

kovid-r

create database if not exists cast_ex;
create table if not exists cast_ex.table_1 (
  val_int int(10) not null,
  val_int_char varchar(10) not null,
  val_str varchar(10) not null
);

insert into cast_ex.table_1
with recursive seq 
  as (

kovid-r

select a, b, a = b
from (
     select 13 a, '13dfgs' b union all
     select 13 a, '130dfgs' b union all 
     select 13 a, '13' b union all
     select 13 a, '0013dfgs' b union all
     select 13 a, ' 13dfgs' b union all
     select 13 a, 'dfgs13'
     ) as t;

kovid-r

with dates 
as
  (
   select '2019-12-12' as _date union all
   select '2019/12/12' as _date union all
   select '2019.12.12' as _date union all
   select '2019_12_12' as _date union all
   select '2019\12\12' as _date union all
   select '2012#12#12' as _date union all
   select '2019$12$12' as _date union all

kovid-r

alter table cast_ex.table_1 
  add index idx_val_int (val_int),
  add index idx_val_int_char(val_int_char);

explain select * from cast_ex.table_1 where val_int = '1';
explain select * from cast_ex.table_1 where val_int_char = 1;

explain select * from cast_ex.table_1 force index (idx_val_int_char) where val_int_char = 1;

kovid-r

select null is null null_is_null, 
       null is not null null_is_not_null,
       1 = 0 1_0,
       0 is null 0_is_null,
       1 is null 1_is_null,
       0 = 1 is null 0_1_is_null,
       null = null null_equals_null,
       null <> null null_not_equals_null,
       null != null null_not_equals_null_2,
       null < null null_less_than_null,

kovid-r

SELECT t1.*, 
       (SELECT t2.field_name 
          FROM TABLE_2 AS t2
         WHERE t1.id = t2.id
         LIMIT 1
       ) 
  FROM TABLE_1 AS t1

kovid-r

SELECT city, 
  FROM TABLE_1 AS t1 
  LEFT JOIN TABLE_2 as t2
    ON t1.id = t2.id;

kovid-r

import pyspark
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext

# create a SparkSession instance with the name moviedb with Hive support enabled
# https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html
spark = SparkSession.builder.appName("moviedb").enableHiveSupport().getOrCreate()

# create a SparkContext instance which allows the Spark Application to access 

kovid-r

# set the file_path variable in the beginning of the file
# or if your Spark application interacts with other applications, parameterize it
file_path = '/Users/kovid-r/datasets/moviedb/movies_metadata.csv'

# method 1 for reading a CSV file
df = spark.read.csv(file_path, header=True)

# method 2 for reading a CSV file
df = spark.read.format(csv_plugin).options(header='true', inferSchema='true').load(file_path)