Resilient Distributed DataSets - Apache SPARK

Nov 3, 2014Download as pptx, pdf6 likes6,930 views

RDDs (Resilient Distributed Datasets) provide a fault-tolerant abstraction for data reuse across jobs in distributed applications. They allow data to be persisted in memory and manipulated using transformations like map and filter. This enables efficient processing of iterative algorithms. RDDs achieve fault tolerance by logging the transformations used to build a dataset rather than the actual data, enabling recovery of lost partitions through recomputation.

$RDD – Overview (Resilient Distributed Datasets*) { Nov 1st 2014 Oakland CA By Taposh Dutta Roy * Source: https://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf$

Contents
• What is RDD
• Motivation Behind RDD
• Use Cases for RDD
• Challenges for RDD
• RDD: Solve

What is RDD
“RDDs are fault tolerant, parallel data structures
that let users explicitly persist intermediate
results in memory, control their partitioning to
optimize data placement, and manipulate them
using a rich set of operations. “
In a nutshell RDDs are a level of abstraction
that enable efficient data reuse in a broad
range of applications

Motivation behind RDD
Current frameworks like MapReduce &
Dyrad provide a numerous abstractions for
accessing a cluster’s computational resources
but lack abstractions for leveraging the
distributed memory !!!
Data reuse is common in many iterative
machine learning algorithms such as – Page
Rank, K-means Clustering & Logistic
Regression.

Motivation behind RDD
Another use case is when an user runs
multiple adhoc queries on the same subset of
data.
Unfortunately in current frameworks, the
only way to reuse data between
computations i.e between two jobs is to write
to an external storage system e.g. a
distributed file system such as Amazon S3.

Use cases for RDD
1. Solving Iterative problems
Existing Solution – Slow, needs high I/O
RDD - Fast, in memory

Use cases for RDD
Example: Suppose I have to look at the
webserver access logs and look for an
error_code or certain text.

Use cases for RDD
Example (cont’d) : I run the above code on server
which returns a set of files with the words
looked for grepped, closes the cluster and puts
the file into an Amazon S3 location specified in
the script.
Now we look at the result files and need to
extract some other text from this file, we will
need to write or use another set of map-reduce
code. This might take extra time to fetch the files,
process and provide the results.

Use cases for RDD
RDD solves this problem by storing the data
in memory and providing a ability for the
user to requery the subset.

Use cases for RDD
2. Solving Interactive Problems
The second use case is its usage in interactive
algorithms such as logistic regression which need
the data to be re-used.

Challenge for RDD
The main challenge in designing RDD is
defining a programming interface that can
provide fault tolerance efficiently.

Challenges for RDD
Existing solutions such as distributed shared
memory, key value stores, & databases offer
an interface based on fine-grained updates.
With such systems, the only way to get
fault tolerance is to replicate the data across
machines or to log updates across machines. Both
of these approaches are data intensive. They
need high bandwidth to move the data over
the cluster network and large storage.

RDD: Solve
RDD solves these probems by providing an
interface based on coarse grained
transformations such as map, filter and join.
These transformations apply the same
operations to many data items.
This allows them to efficiently provide fault
tolerance by logging the transformations
used to build a dataset (i.e. lineage) rather
than actual data. If a partition of RDD is lost,
the RDD has enough information about how
it ..

RDD: Solve
(Cont’d) was derived from other RDD to
recompute just that partition. The lost data
can be recovered quickly, without costly
replication.

Applications not suitable : RDD
RDDs would be less suitable for applications
that make asynchronous fine grained updates
to shared state, such as a storage system for a
web application or an incremental web
crawller. For such applications traditional
update logging and data checkpointing
such as databases.

Conclusion RDD
RDD's goal is to provide an efficient
programming model for batch
analytics.
RDD has been implemented in a system
called SPARK.

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2. Contents • What is RDD • Motivation Behind RDD • Use Cases for RDD • Challenges for RDD • RDD: Solve

3. What is RDD “RDDs are fault tolerant, parallel data structures that let users explicitly persist intermediate results in memory, control their partitioning to optimize data placement, and manipulate them using a rich set of operations. “ In a nutshell RDDs are a level of abstraction that enable efficient data reuse in a broad range of applications

4. Motivation behind RDD Current frameworks like MapReduce & Dyrad provide a numerous abstractions for accessing a cluster’s computational resources but lack abstractions for leveraging the distributed memory !!! Data reuse is common in many iterative machine learning algorithms such as – Page Rank, K-means Clustering & Logistic Regression.

5. Motivation behind RDD Another use case is when an user runs multiple adhoc queries on the same subset of data. Unfortunately in current frameworks, the only way to reuse data between computations i.e between two jobs is to write to an external storage system e.g. a distributed file system such as Amazon S3.

6. Use cases for RDD 1. Solving Iterative problems Existing Solution – Slow, needs high I/O RDD - Fast, in memory

7. Use cases for RDD Example: Suppose I have to look at the webserver access logs and look for an error_code or certain text.

8. Use cases for RDD Example (cont’d) : I run the above code on server which returns a set of files with the words looked for grepped, closes the cluster and puts the file into an Amazon S3 location specified in the script. Now we look at the result files and need to extract some other text from this file, we will need to write or use another set of map-reduce code. This might take extra time to fetch the files, process and provide the results.

9. Use cases for RDD RDD solves this problem by storing the data in memory and providing a ability for the user to requery the subset.

10. Use cases for RDD 2. Solving Interactive Problems The second use case is its usage in interactive algorithms such as logistic regression which need the data to be re-used.

11. Challenge for RDD The main challenge in designing RDD is defining a programming interface that can provide fault tolerance efficiently.

12. Challenges for RDD Existing solutions such as distributed shared memory, key value stores, & databases offer an interface based on fine-grained updates. With such systems, the only way to get fault tolerance is to replicate the data across machines or to log updates across machines. Both of these approaches are data intensive. They need high bandwidth to move the data over the cluster network and large storage.

13. RDD: Solve RDD solves these probems by providing an interface based on coarse grained transformations such as map, filter and join. These transformations apply the same operations to many data items. This allows them to efficiently provide fault tolerance by logging the transformations used to build a dataset (i.e. lineage) rather than actual data. If a partition of RDD is lost, the RDD has enough information about how it ..

14. RDD: Solve (Cont’d) was derived from other RDD to recompute just that partition. The lost data can be recovered quickly, without costly replication.

15. Applications not suitable : RDD RDDs would be less suitable for applications that make asynchronous fine grained updates to shared state, such as a storage system for a web application or an incremental web crawller. For such applications traditional update logging and data checkpointing such as databases.

16. Conclusion RDD RDD's goal is to provide an efficient programming model for batch analytics. RDD has been implemented in a system called SPARK.

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