Big data: new features of spark, shuffle,
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Big data: new features of spark
Hash shuffle is easy to handle. It is very laborious to play shuffle
with memory and disk. It is time-consuming and space-consuming.
sort shuffle
sortshufflemanager
Play in batches
Index is a guide to pull files
Without sorting, the space-time performance will be improved.
Wide dependencies cannot avoid the transmission of IO.
Try not to shuffle.
The current focus of spark is RDD and SQL
SQL is very simple and easy to learn, so sparkSQL is very important.
Try to balance the data and
dynamically merge
spark. Now the bottom layer of
pyspark and koalas database
can realize ordinary python development,
and pandas can also be used to realize data development
After a wave of operations, you get distributed data.
When you use pandas to operate, it is all distributed computing.
You can also create data through spark, and then convert it into pandas distributed data
. ks can also build distributed pandas data objects
anyway. Feel free to use those functions of native pandas
All kinds of show operations
Summarize
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1)
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