Spark Tutorial For Beginners | Big Data Spark Tutorial | Apache Spark Tutorial | Simplilearn
Summary
TLDRApache Spark, an open-source cluster computing framework, was developed to overcome the limitations of Hadoop's MapReduce. It excels in real-time processing, trivial operations, and handling large data on networks, offering up to 100 times faster performance for certain applications. Spark's components, including Spark Core, RDDs, Spark SQL, Streaming, MLlib, and GraphX, provide a unified platform for diverse data processing tasks, from batch to real-time analytics, machine learning, and graph processing. Its in-memory processing capabilities and support for multiple languages enhance developer experience and enable versatile data analysis.
Takeaways
- 🚀 Apache Spark was developed at UC Berkeley's AMP Lab in 2009 and became an open-source project in 2010 under the Berkeley Software Distribution license.
- 🔄 In 2013, the project was donated to the Apache Software Foundation and the license was changed to Apache 2.0, with Spark becoming an Apache top-level project in 2014.
- 🏆 Databricks, founded by the creators of Apache Spark, used Spark to set a world record in large-scale sorting in November 2014 and now provides commercial support and certification for Spark.
- 🔍 Spark is a next-generation real-time and batch processing framework that can be compared with MapReduce, another data processing framework in Hadoop.
- 📈 Batch processing in Spark involves processing large amounts of data in a single run over a time period, typically used for heavy data load, generating reports, and managing data workflow offline.
- 🔥 Real-time processing in Spark occurs instantaneously on data entry or command receipt, with applications like fraud detection requiring stringent response time constraints.
- 🚧 The limitations of MapReduce, such as its suitability primarily for batch processing and not for real-time processing, lack of support for trivial operations, and issues with large data on the network, led to the creation of Spark.
- 💻 Spark is an open-source cluster computing framework that addresses the limitations of MapReduce, offering real-time processing, support for trivial operations, and efficient handling of large data on a network.
- 🌐 Spark's performance is significantly faster than MapReduce for certain applications, thanks to its in-memory processing capabilities, making it suitable for machine learning algorithms.
- 🛠️ A Spark project includes components like Spark Core and RDDs, Spark SQL, Spark Streaming, MLlib (Machine Learning Library), and GraphX, each serving different computational needs from basic I/O to advanced analytics.
- 🔑 In-memory processing in Spark allows for faster data access and improved performance, reducing the need for disk-based storage and enabling more efficient data compression and query execution.
Q & A
What was the original purpose behind the development of Spark?
-Spark was developed at UC Berkeley's AMP Lab in 2009 to address the limitations of the MapReduce framework and to provide a more efficient data processing framework for both batch and real-time processing.
When did Spark become an open source project?
-Spark became an open source project in 2010 under the Berkeley Software Distribution license.
What significant change occurred in 2013 regarding the Spark project?
-In 2013, the project was donated to the Apache Software Foundation and its license was changed to Apache 2.0.
Why did Spark become an Apache Top-Level Project in February 2014?
-Spark became an Apache Top-Level Project in February 2014 due to its growing popularity and the recognition of its capabilities in the big data processing domain.
What is the difference between batch processing and real-time processing as mentioned in the script?
-Batch processing involves processing a large amount of data in a single run over a time period without manual intervention, typically used for offline data workflows like generating reports. Real-time processing, on the other hand, occurs instantaneously on data entry or command receipt and requires stringent response time constraints, such as in fraud detection.
What limitations of MapReduce did Spark aim to overcome?
-Spark aimed to overcome limitations such as the slow processing time for large data sets, the complexity of writing trivial operations like filter and join, issues with large data on the network, unsuitability for online transaction processing (OLTP), and the inability to handle iterative program execution and graph processing efficiently.
What are the main components of a Spark project?
-The main components of a Spark project include Spark Core and Resilient Distributed Data Sets (RDDs), Spark SQL, Spark Streaming, the Machine Learning Library (MLlib), and GraphX.
How does Spark Core and its RDDs simplify the complexity of programming?
-Spark Core and RDDs simplify programming by providing basic input/output functionalities, distributed task dispatching, and scheduling. RDDs abstract the complexity by allowing data to be partitioned across machines and manipulated through transformations similar to local data collections.
What is Spark SQL and how does it support data manipulation?
-Spark SQL is a component that resides on top of Spark Core, introducing Schema RDD, a new data abstraction that supports semi-structured and structured data. It allows data to be manipulated using domain-specific languages like Java, Scala, and Python and supports SQL queries through JDBC or ODBC interfaces.
How does Spark Streaming differ from traditional batch processing?
-Spark Streaming leverages the fast scheduling capability of Spark Core for streaming analytics by ingesting data in small batches and performing RDD transformations on them. This design allows the same application code set written for batch analytics to be used for streaming analytics.
What advantages does Spark offer over MapReduce in terms of performance and versatility?
-Spark offers up to 100 times faster performance for certain applications due to its in-memory processing capabilities, making it suitable for machine learning algorithms. It is also more versatile, being suitable for real-time processing, trivial operations, processing larger data on a network, OLTP, graphs, and iterative execution.
Outlines
🚀 Introduction to Apache Spark and Its Evolution
Apache Spark is a data processing framework that originated at UC Berkeley's AMP Lab in 2009. It was open-sourced in 2010 under the Berkeley Software Distribution license. In 2013, it was donated to the Apache Software Foundation, transitioning to the Apache 2.0 license. By February 2014, Spark had become an Apache top-level project. It is recognized for its ability to handle both real-time and batch processing, distinguishing it from MapReduce, which is limited to batch processing. Spark's creators used it to set a world record in large-scale sorting by November 2014. Now, it is supported commercially by Databricks, the company founded by Spark's creators, and is a next-generation framework that overcomes the limitations of MapReduce, such as its inability to handle real-time processing, complex operations, large data on the network, online transaction processing, and iterative program execution.
🌟 Core Components and Capabilities of Apache Spark
Apache Spark's core components include Spark Core and Resilient Distributed Datasets (RDDs), which form the foundation of the project by providing basic I/O functionalities, distributed task dispatching, and scheduling. RDDs are a fundamental programming abstraction, offering a simplified complexity of programming by allowing data manipulation similar to local data collections. Spark SQL introduces Schema RDD, a new data abstraction for semi-structured and structured data, supporting SQL and various domain-specific languages. Spark Streaming enables real-time streaming analytics by leveraging Spark Core's fast scheduling capability. MLlib, the machine learning library, applies common statistical and machine learning algorithms, and GraphX is a distributed graph processing framework that provides an API and runtime for graph computations. In-memory processing is highlighted as a key feature, allowing for faster performance and enabling Spark to handle large datasets efficiently.
🛠️ Spark's Advantages and Its Place in the Hadoop Ecosystem
Spark is favored over MapReduce for its performance and versatility, offering a rewarding development experience with support for multiple languages like Java, Scala, and Python. It simplifies the development process by allowing the use of lambda functions and closures. In contrast, the Hadoop ecosystem, which uses MapReduce for batch analytics, is limited to batch processing and requires extensive setup for different types of data processing. Spark, however, supports a variety of workloads, including streaming, iterative algorithms, and batch applications, all on the same engine. It also integrates with Hadoop by creating distributed datasets from files stored in Hadoop's file systems or other supported storage systems. The unification provided by Spark simplifies the learning curve for developers and allows for easy management of applications across different systems.
📈 The Impact of In-Memory Processing and Spark's Future Prospects
In-memory processing allows for faster data access and improved performance, which is crucial for interactive data exploration and analysis. Spark's in-memory capabilities provide speed and efficiency, making it suitable for complex applications and various processing types. It also supports the development of distributed applications that combine different processing models, such as real-time data categorization using machine learning. The IT team benefits from maintaining a single system, as Spark integrates tightly with various components for different workloads. For those aspiring to become big data experts, the Simply Learn channel offers educational content and certification opportunities in big data, highlighting the growing importance and demand for expertise in this field.
Mindmap
Keywords
💡Apache Spark
💡Batch Processing
💡Real-time Processing
💡MapReduce
💡Databricks
💡Resilient Distributed Datasets (RDDs)
💡Spark SQL
💡Spark Streaming
💡MLlib
💡GraphX
💡In-memory Processing
Highlights
Spark was developed at UC Berkeley's AMP Lab in 2009 and became an open-source project in 2010.
In 2013, Spark was donated to the Apache Software Foundation and its license changed to Apache 2.0.
Spark became an Apache Top-Level Project in February 2014.
Databricks, founded by Spark creators, used Spark to set a world record in large-scale sorting in 2014.
Spark supports both real-time and batch processing, unlike MapReduce which is limited to batch processing.
Batch processing in Spark is used for operations like generating reports and managing data workflows offline.
Real-time processing in Spark is instantaneous and crucial for applications like fraud detection.
MapReduce's limitations include its unsuitability for real-time processing and complex operations like filter and join.
Spark addresses MapReduce's limitations, offering superior performance for real-time processing and network data processing.
Spark provides up to 100 times faster performance in certain applications due to its in-memory processing capabilities.
Spark's components include Spark Core, RDDs, Spark SQL, Spark Streaming, MLlib, and GraphX.
RDDs are Spark's fundamental programming abstraction, simplifying distributed data processing.
Spark SQL introduces Schema RDD for structured and semi-structured data manipulation.
Spark Streaming enables the use of the same batch analytics code for streaming analytics.
MLlib is Spark's distributed machine learning framework, offering performance improvements over other frameworks.
GraphX is a distributed graph processing framework in Spark, providing APIs and runtime for graph computations.
In-memory processing in Spark allows for faster data access and reduced memory requirements compared to disk-based systems.
Spark's in-memory capabilities provide a significant speed advantage for machine learning algorithms.
Spark supports multiple development languages, including Java, Scala, and Python, enhancing developer experience.
Spark's lambda functions and closures allow for inline function definitions, simplifying code comprehension.
Spark can replace Hadoop's MapReduce for batch processing, offering speed and versatility advantages.
Spark's unification feature simplifies development by allowing the use of one platform for various processing types.
Spark's integration with Hadoop systems provides flexibility and support for various data storage formats.
Spark's performance and ease of use make it a preferred choice for big data processing over MapReduce.
Transcripts
[Music]
spark as a data processing framework was
developed at uc berkeley's amp lab by
mate
in 2009
in 2010 it became an open source project
under a berkeley software distribution
license in the year 2013 the project was
donated to the apache software
foundation and the license was changed
to apache 2.0
in february 2014
spark became an apache top level project
by november 2014
spark was used by the engineering team
at databricks a company founded by the
creators of apache spark
to set a world record in large scale
sorting
now databricks provides commercial
support and certification for taking the
spark programming test
at present spark exists as a next
generation real time and batch
processing framework
let's try to understand what batch and
real-time processing mean
we will use this information in the
subsequent slides to compare spark with
mapreduce both of which are data
processing framework in hadoop
in case of batch processing a large
amount of data or transactions are
processed in a single run over a time
period
the associated jobs generally run
entirely without any manual intervention
additionally the entire data is
pre-selected and fed using command line
parameters and scripts
in typical cases batch processing is
used to execute multiple operations
handle heavy data load
generate reports and manage data
workflow which is offline an example is
to create daily or hourly reports to aid
decision making on the other hand
real-time processing occurs
instantaneously on data entry or command
receipt
it needs to execute within stringent
response time constraints
an example is fraud detection
the need for spark was created by the
limitations of mapreduce which is
another data processing framework in
hadoop let's see what these limitations
are
mapreduce is suitable for batch
processing where data is processed as a
periodic job thus it takes time to
process data and provide results if the
data is high depending on the amount of
data and the number of nodes in the
cluster to complete a job
it just takes minutes to process the
data however it is not a good choice for
real-time processing
mapreduce is also not suitable for
writing trivial operations such as
filter and join
to write such operations you might need
to rewrite the jobs using the mapreduce
framework which becomes complex because
of the key value pattern
this pattern is required to be followed
in reducer and mapper codes
mapreduce doesn't work so well with
large data on the network
the reason is that it takes a lot of
time to copy the data which may cause
network bandwidth issues it works on the
data locality principle and hence works
well on the node where the data resides
mapreduce is also unsuitable for online
transaction processing or oltp which
includes a large number of short
transactions
since it works on the batch-oriented
framework it lacks latency of seconds or
sub-seconds
additionally mapreduce is unfit for
processing graphs
graphs represent the structures to
explore relationships between various
points
for example finding common friends in
social media sites like facebook hadoop
has the apache giraffe library for such
cases
it runs on top of mapreduce and adds to
the complexity another important
limitation is its unsuitability for
iterative program execution
some use cases like k-means need such
execution where data needs to be
processed again and again to refine
results
mapreduce runs from the start every time
as it is stateless executor
spark is an open source cluster
computing framework which addresses all
of the limitations of mapreduce
it is suitable for real-time processing
trivial operations and processing larger
data on a network
it is also suitable for oltp
graphs and iterative execution
compared to the disk based two-stage
mapreduce of hadoop
spark provides up to 100 times faster
performance for a few applications
within memory primitives
fast performance makes it suitable for
machine learning algorithms as it allows
programs to load data into the memory of
a cluster and create the data constantly
a spark project comprises various
components such as spark core and
resilient distributed data sets or rdds
spark sql spark streaming machine
learning library or ml lib and graphics
let's discuss the components of spark
the first component spark core and rdds
are the foundation of the entire spark
project
they provide basic input output
functionalities distributed task
dispatching and scheduling
let's look at rdd closely rdds are the
basic programming abstraction and is a
collection of data that is partitioned
across machines logically
rdds can be created by applying
coarse-grained transformations on the
existing rdds
or by referencing external data sets the
examples of these transformations are
reduce
join filter and map
the abstraction of rdds is exposed
similarly as in process and local
collections through a language
integrated application programming
interface or api
in python java and scala as a result of
the rdd abstraction the complexity of
programming is simplified as the manner
in which applications change rdds is
similar to changing local data
collections
the second component is spark sql which
resides on the top of spark core
it introduces schema rdd which is a new
data abstraction and supports
semi-structured and structured data
schema rdd can be manipulated in any of
the provided domain specific languages
such as java scala and python by the
spark sql spark sql also supports sql
with open database connectivity or java
database connectivity commonly known as
odbc or jdbc server and command line
interfaces
the third component is spark streaming
spark streaming leverages the fast
scheduling capability of spark core for
streaming analytics ingesting data in
small batches
and performing rdd transformations on
them
with this design the same application
code set written for batch analytics can
be used on a single engine for streaming
analytics
the fourth component of spark is machine
learning library
also known as ml lib
it lies on top of spark and is a
distributed machine learning framework
ml lib applies various common
statistical and machine learning
algorithms
with its memory-based architecture it is
nine times faster than the apache
mahouts hadoop disk based version
in addition the library performs even
better than valpal wabit or vw
the vw project is a fast out of core
learning system sponsored by microsoft
the last component graphics also lies on
the top of spark and is a distributed
graph processing framework
for the computation of graphs it
provides an api and an optimized runtime
for the pregal abstraction
pragel is a system for large-scale graph
processing
the api can also model the pragal
abstraction we discussed earlier that
spark provides up to 100 times faster
performance for a few applications
within memory primitives
let's discuss the application of
in-memory processing using
column-centric databases
in column-centric databases similar
information can be stored together and
hence data can be stored with more
compression and efficiency
it also permits the storage of large
amounts of data in the same space
thereby reducing the amount of memory
required for performing a query
it also increases the speed of
processing
in an in-memory database the entire
information is loaded into memory
eliminating the need for indices
aggregates
optimized databases star schemas and
cubes
with the use of in-memory tools
compression algorithms can be
implemented that decrease the in-memory
size even beyond what is required for
hard disks
users querying data loaded in memory is
different from caching
in memory processing also helps to avoid
performance bottlenecks and slow
database access
caching is a popular method for speeding
up the performance of a query where
caches are subsets of a very particular
organized data which are already defined
within memory tools
data analysis can be flexible in size
and can be accessed within seconds by
concurrent users with an excellent
analytics potential this is possible as
data lies completely in memory
in theoretical terms this leads to data
access improvement that is 10 000 to one
million times fast when compared to a
disc
in addition it reduces the performance
tuning needed by i t professionals and
therefore provides faster data access
for end users
with in-memory processing it is possible
to access visually rich dashboards and
existing data sources
this ability is provided by several
vendors
in turn in memory processing allows end
users and business analytics to create
customized queries and reports without
any need of extensive expertise or
training
we have already discussed that spark
provides performance which in turn
offers developers a rewarding experience
spark is chosen over mapreduce mainly
for its performance advantages and
versatility
apart from these another critical
advantage is its development experience
along with language flexibility
spark provides support to various
development languages like java scala
and python and will likely support r as
well
in addition spark has the capability to
define functions in line
with the temporary exception of java a
common element in these languages is
that they provide methods to express
operations using lambda functions and
closures
using lambda closures
you can use the application core logic
to define the functions inline which
helps to create easy to comprehend codes
and preserve application flow
let's look at mapreduce in the hadoop
ecosystem the hadoop ecosystem which
allows you to store large files on
various machines
uses mapreduce for batch analytics that
is as easy as it is distributed in
nature
on the other hand apache spark supports
both real-time and batch processing
in hadoop third-party support is also
available
for example by using etl talented tools
various batch oriented workflows can be
designed
in addition it supports pig and hive
queries that enable non-java developers
to use and prepare batch workflows using
sql scripts
you can perform every type of data
processing using spark that you can
execute in hadoop
for batch processing spark batch can be
used over hadoop mapreduce
for structured data analysis spark sql
can be implemented using sql
for machine learning analysis the
machine learning library can be used for
clustering recommendation and
classification
for interactive sql analysis spark sql
can be used instead of impala in
addition for real-time streaming data
analysis spark streaming can be used in
place of a specialized library like
storm
spark has three main advantages which
are
provide speed capability
combines a various processing types
and supports hadoop
the feature of speed is critical to
process large data sets as this implies
the difference of waiting for hours or
minutes and exploring the data
interactively
spark has extended the mapreduce model
to support computations like stream
processing and interactive queries
supporting run computations in memory
with respect to speed
also its related system is more
effective when compared to mapreduce to
run complex applications on a disk
this adds to the speed capability of
spark
spark covers various workloads that
require different distributed systems
such as streaming iterative algorithms
and batch applications
as these workloads are supported on the
same engine combining different
processing types is easy
spark is normally required in the
production of data analysis pipelines
the combination feature
also allows easy management of separate
tools
spark is capable of creating distributed
data sets from any file that is stored
in the hadoop distributed file system or
any other supported storage systems
you must note that spark does not need
hadoop
it just supports the storage systems
that implement the apis of hadoop and
sequence files parquet avro text files
and all other input output formats of
hadoop
now the question is why unification
matters
unification not only provides developers
with the advantage of learning only one
platform but also allows users to take
their apps everywhere
the graphic shows the apps and the
systems that can be combined with spark
a spark project includes various closely
integrated components for distributing
scheduling and monitoring applications
with many computational tasks across a
computing cluster or various worker
machines
the spark core engine is general purpose
and fast
as a result it empowers various higher
level components that are specialized
for different workloads like machine
learning or sql these components can
inter-operate closely
another important advantage is that it
integrates tightly allowing you to
create applications that easily combine
different processing models
an example is the ability to write an
application using machine learning to
categorize data in real time as it is
ingested from sources of streaming
additionally it allows analysts to query
the data which results through sql
moreover data scientists and engineers
can access the same data through the
python shell for ad-hoc analysis and in
standalone batch applications
for all this the it team needs to
maintain one system only
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