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PySpark
- DataFrame
 1. PySpark RDD Communication
 2. Catalyst Optimizer
 3. DataFrame을 이용한 PySpark Speed-up
- 실습 -
 4. 데이터프레임 생성하기
 5. 데이터프레임 쿼리
 6. RDD와 같이 작업
 7. 데이터프레임 API로 쿼리
 8. 스파크 SQL로 쿼리
 9. 비행기록(On-time flight) 데이터프레임 사용하기
1. PySpark RDD Communication
RDD에서 쿼리를 실행하는 것은 자바 JVM 과 Py4J 사이의 Context switching과
Communications overhead를 필요로 함.
1. PySpark RDD Communication
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
2. Catalyst Optimizer
https://www.slideshare.net/databricks/deep-dive-into-catalyst-apache-spark-20s-optimizer
• A DataFrame is a distributed collection of data organized into named
columns. It is conceptually equivalent to a table in a relational
database or a data frame in R/Python, but with richer optimizations
under the hood.
DataFrames can be constructed from a wide array of sources such as:
structured data files, tables in Hive, external databases, or existing
RDDs.
3. DataFrame
• A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets
When to use them and why
3. DataFrame
3. DataFrame
https://www.slideshare.net/databricks/largescale-data-science-in-apache-spark-20/10
이제부터는 Jupyter Notebook 에서 실습하기
WIKI LINK()에서 실습코드 Download
4. DataFrame 생성하기
5. DataFrame Query
6. RDD와 같이 작업
7. DataFrame API Query
8. Spark SQL Query
9. 비행기록(On-time flight) DataFrame 사용하기
https://github.com/drabastomek/learningPySpark/blob/master/Chapter03/LearningPySpark_Chapter03.ipynb
https://github.com/donwany/Databricks/blob/master/notebooks/Users/theophilus.siameh.consultant%40nielsen.com/Master/Lesson-3.py
• References
‘[Spark] 데이터프레임’ http://12bme.tistory.com/307
‘IPython/Jupyter SQL Magic Functions for PySpark’ https://db-blog.web.cern.ch/blog/luca-canali/2016-11-ipythonjupyter-sql-magic-functions-pyspark
‘IPython magic functions for Pyspark Examples of shortcuts for executing SQL in Spark’
https://github.com/LucaCanali/Miscellaneous/blob/master/Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb

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PySpark dataframe