What is PySpark
Apache Spark is one of the most popular distributed computation framework available. Most popular for the ability it provides to perform seamless data analysis. It is also fast becoming the choice for performing Machine Learning tasks. It provides native support for Scala APIs. PySpark is the package that provides Python API interface to PySpark.
What is EMR
The central component of Amazon EMR is the cluster. A cluster is a collection of Amazon Elastic Compute Cloud (Amazon EC2) instances. Each instance in the cluster is called a node. Each node has a role within the cluster, referred to as the node type. Amazon EMR also installs different software components on each node type, giving each node a role in a distributed application like Apache Hadoop.
Why SageMaker Notebook
Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the important parts of Amazon SageMaker is the powerful Jupyter notebook interface, which can be used to build models.
How to Set this up in no time
Why would use these combo technologies
I recently had a request to crunch through a huge dataset that had some strange requirements (not so strange actually). However, this got me smacking my lips to learn about Spark and actually have a project to show for it. I decided to use the bleeding edge.
The initial set of questins had me asking - how should I allocate the number of nodes in the cluster? How should I split the work?
What are RDDs?
An RDD is a collection of elements in distributed systems context. In
Spark all the data items are in the form of RDDs. You are working with RDDs one
way of another. When you read a large data file RDDs are created, Spark then
provides mechanics to operate on these RDDs.
According to the O’Reilly’s Book
Learning Spark it is:
An RDD in Spark is simply an immutable distributed collection of objects. Each RDD is split into multiple partitions, which may be computed on different nodes of the cluster. RDDs can contain any type of Python, Java, or Scala objects, including user- defined classes.
How do you create RDDs from DataSet?
RDDs are commonly created by loading an external dataset that is stored in some persistent storage. The most common practice is to directly load from an external remote storage such as AWS S3.
Everything in Spark works within a
Spark Context which is created using the
from pyspark import SparkConf, SparkContext conf = SparkConf().setMaster("local").setAppName("My App") sc = SparkContext(conf = conf)
After creating a Spark Context, you can use it call a slew of APIs that the context object supports. You can read a dataset as follows
import csv import StringIO def read_line(line): """Parse a CSV line""" sio = StringIO.StringIO(line) csv_rd = csv.DictReader(sio, fieldnames=["col1", "col2"]) return csv_rd.next() input = sc.textFile(file_path).map(read_line)
However, if you have a local file, you can pass the path to the API for reading
external files/datasets. The fun bit is that this path
file_path below can
be a remote path such as S3 path as well. It even supports wildcards in these
remote paths too!
Passing this to the API will read all the CSV files present under
dir_name in S3’s bucket
bucket_name which is unique.
How can you run queries on them?
Spark makes it convenient to work with structured and semi-structured data using Spark SQL. Saprk SQL provides a DataFrame abstraction on the data loaded. This can be viewed as a “poor man’s” Pandas DataFrame since the access APIs are not as rich as Pandas’ interface provides. But Spark’s DataFrames can be thought of as Relational DataBase rather than a data structure in memory. However, SparkSQL supports a variety of file formats. It supports SQL queries on the DataFrames which provides a huge advantage for plugging into existing systems that already work with DataBases. Such systems can automatically leverage from the distributed nature of the RDDs.
What are the disadvantages of using a Pandas DataFrames with PySpark?
Although you can use Pandas DataFrames in your Python code which can be intermixed with PySpark, the underlying distributed-ness of the Spark Framework cannot be leveraged since Pandas doesn’t support the RDDs semantics therefore, does not yield itself to Spark’s Distributed Computing Paradigm. RDDs are distributed behind the scenes from the moment they are created from a dataset, therefore, allow for Spark’s efficiency in dealing with them. External construct like Pandas has to be explicitly operated upon to scale.
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