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Mastering flat_map in Python with List Comprehension

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Introduction In Python, when working with nested lists or iterables, one common challenge is flattening them into a single list while applying transformations. Many programming languages provide a built-in flatMap function, but Python does not have an explicit flat_map method. However, Python’s powerful list comprehensions offer an elegant way to achieve the same functionality. This article examines implementation behavior using Python’s list comprehensions and other methods. What is flat_map ? Functional programming  flatMap is a combination of map and flatten . It transforms the collection's element and flattens the resulting nested structure into a single sequence. For example, given a list of lists, flat_map applies a function to each sublist and returns a single flattened list. Example in a Functional Programming Language: List(List(1, 2), List(3, 4)).flatMap(x => x.map(_ * 2)) // Output: List(2, 4, 6, 8) Implementing flat_map in Python Using List Comprehension Python’...
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Spark SQL Query how to write it in Ten steps

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Spark SQL example
Spark SQL example
The post tells how to write SQL query in Spark and explained in ten steps.This example demonstrates how to use sqlContext.sql to create and load two tables and select rows from the tables into two DataFrames.

The next steps use the DataFrame API to filter the rows for salaries greater than 150,000 from one of the tables and shows the resulting DataFrame. Then the two DataFrames are joined to create a third DataFrame. Finally the new DataFrame is saved to a Hive table.

1. At the command line, copy the Hue sample_07 and sample_08 CSV files to HDFS:
$ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_07.csv /user/hdfs
$ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_08.csv /user/hdfs

where HUE_HOME defaultsto /opt/cloudera/parcels/CDH/lib/hue (parcel installation) or /usr/lib/hue
(package installation).

2. Start spark-shell:
$ spark-shell

3. Create Hive tables sample_07 and sample_08:

scala> sqlContext.sql("CREATE TABLE sample_07 (code string,description string,total_emp
 int,salary int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TextFile")
scala> sqlContext.sql("CREATE TABLE sample_08 (code string,description string,total_emp
 int,salary int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TextFile")

Also Read: Learn SparkSQL by your own with little money

 4. In Beeline, show the Hive tables:
[0: jdbc:hive2://hostname.com:> show tables;
+------------+--+
| tab_name |
+------------+--+
16 | Spark Guide
Developing Spark Applications
| sample_07 |
| sample_08 |
+------------+--+

Also read: The role of Spark in Hadoop eco system

5. Load the data in the CSV files into the tables:
scala> sqlContext.sql("LOAD DATA INPATH '/user/hdfs/sample_07.csv' OVERWRITE INTO TABLE
 sample_07")
scala> sqlContext.sql("LOAD DATA INPATH '/user/hdfs/sample_08.csv' OVERWRITE INTO TABLE
 sample_08")

6. Create DataFrames containing the contents of the sample_07 and sample_08 tables:
scala> val df_07 = sqlContext.sql("SELECT * from sample_07")
scala> val df_08 = sqlContext.sql("SELECT * from sample_08")

Apache Spark
7. Show all rows in df_07 with salary greater than 150,000:
scala> df_07.filter(df_07("salary") > 150000).show()
The output should be:
+-------+--------------------+---------+------+
| code| description|total_emp|salary|
+-------+--------------------+---------+------+
|11-1011| Chief executives| 299160|151370|
|29-1022|Oral and maxillof...| 5040|178440|
|29-1023| Orthodontists| 5350|185340|
|29-1024| Prosthodontists| 380|169360|
|29-1061| Anesthesiologists| 31030|192780|
|29-1062|Family and genera...| 113250|153640|
|29-1063| Internists, general| 46260|167270|
|29-1064|Obstetricians and...| 21340|183600|
|29-1067| Surgeons| 50260|191410|
|29-1069|Physicians and su...| 237400|155150|
+-------+--------------------+---------+------+

8.Create the DataFrame df_09 by joining df_07 and df_08, retaining only the code and description columns.
scala> val df_09 = df_07.join(df_08, df_07("code") ===
df_08("code")).select(df_07.col("code"),df_07.col("description"))
scala> df_09.show()

The new DataFrame looks like:
+-------+--------------------+
| code| description|
+-------+--------------------+
|00-0000| All Occupations|
|11-0000|Management occupa...|
|11-1011| Chief executives|
|11-1021|General and opera...|
|11-1031| Legislators|
|11-2011|Advertising and p...|
|11-2021| Marketing managers|
|11-2022| Sales managers|
|11-2031|Public relations ...|
|11-3011|Administrative se...|
|11-3021|Computer and info...|
|11-3031| Financial managers|
|11-3041|Compensation and ...|
|11-3042|Training and deve...|
|11-3049|Human resources m...|
|11-3051|Industrial produc...|
|11-3061| Purchasing managers|
|11-3071|Transportation, s...|
|11-9011|Farm, ranch, and ...|
+-------+--------------------+

9. Save DataFrame df_09 as the Hive table sample_09:
scala> df_09.write.saveAsTable("sample_09")

10. In Beeline, show the Hive tables:
[0: jdbc:hive2://hostname.com:> show tables;
+------------+--+
| tab_name |
+------------+--+
| sample_07 |
| sample_08 |
| sample_09 |
+------------+--+

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