Spark RDD Concepts Quiz

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Questions and Answers

Which of the following best describes an RDD in the Spark framework?

A process that triggers the computation of transformations.

A set of transformations that define how to transform HDFS data.

A structure that encapsulates dependencies, partitions and a compute function defining how to process partition data. (correct)

A collection of data stored on HDFS.

How does Spark ensure the reproducibility of results when using RDDs?

By saving intermediate results to HDFS.

By automatically backing up created RDDs to multiple executors.

By using the compute function of an RDD to recalculate data based on dependencies. (correct)

By caching each transformations result.

What is the primary purpose of partitions in an RDD?

To define the transformation to be applied on the data.

To define how data is stored on disk.

To split workload for parallel computation across executors. (correct)

To divide a parent RDD into smaller logical units that can be operated on sequentially.

What does lazy evaluation of transformations mean in the context of Spark RDDs?

Transformations are queued to be computed until an action is performed. Signup and view all the answers

Which of the following is an example of an action in the context of RDDs?

count() Signup and view all the answers

According to the provided data, what is the approximate access time for data stored on a tape?

2,000 years Signup and view all the answers

What was the original motivation behind the 'five-minute rule'?

To address the administrators dilemma on how to improve performance. Signup and view all the answers

What is the cost of a Tandem disk of 180 MB?

$15,000 Signup and view all the answers

According to the provided context, how does the cost of accessing data from disk compare to the cost of keeping 1KB of data in memory, based on a single access per second?

Accessing from disk costs $2,000, while storing in memory is $5. Signup and view all the answers

When accessing data from a Tandem system, the cost of accessing data from disk is equivalent to which of the following?

The cost of 1 Tandem CPU Signup and view all the answers

What is the main consideration when deciding to cache data based on the information provided?

Whether the data is accessed multiple times within a set time period. Signup and view all the answers

Based on the 'five-minute rule' concept, if data is accessed once every 10 seconds, how much money can be saved by keeping the data in 1KB memory, compared to disk?

$200 Signup and view all the answers

According to the content, what is the performance of the Tandem disk system?

15 accesses per second. Signup and view all the answers

What is the primary function of a schema in the context of DataFrames?

To outline column names and the types of data they contain. Signup and view all the answers

Which of the following is NOT a method for defining a DataFrame schema?

Using a procedural programming approach to define data types. Signup and view all the answers

When should someone typically consider using RDDs over DataFrames?

When fine-grained control over query execution is required. Signup and view all the answers

According to the 'five-minute rule', what is the approximate break-even point for accessing data from disk versus RAM in 1987?

1 access every 5 minutes Signup and view all the answers

Which API in DataFrames is used for performing relational projections?

select() Signup and view all the answers

What does the DataSource API enable within the DataFrame context?

To read and write data to a DataFrame from various formats. Signup and view all the answers

Why is Hadoop considered misaligned with the 'five-minute rule'?

It stores all data on disk, neglecting potential memory caching. Signup and view all the answers

Which of these SQL queries would be closest to what is achievable using the DataFrame API (using `groupBy` and aggregation)?

<code>SELECT name, avg(age) FROM people GROUP BY name;</code> Signup and view all the answers

What is a significant limitation of the MapReduce computational model, according to the provided content?

Algorithm design using only map and reduce functions can be non-trivial. Signup and view all the answers

What is the primary purpose of the Spark Driver?

To transform Spark operations into a DAG, communicate with the cluster manager, and schedule computations. Signup and view all the answers

Which DataFrame API allows you to filter rows based on a specified condition?

where() Signup and view all the answers

Which of the following is a characteristic of DataFrames as opposed to RDDs?

Automatic code optimization and space efficiency. Signup and view all the answers

What is the role of the SparkSession in the Spark architecture?

It serves as a unified access point for all Spark operations and data. Signup and view all the answers

What is the main challenge with traditional Distributed Shared Memory (DSM) in data-intensive applications?

Fault tolerance, which requires replication or logging, is too expensive. Signup and view all the answers

What is a key characteristic of a Resilient Distributed Dataset (RDD)?

It is an immutable, partitioned collection of objects. Signup and view all the answers

Which action returns an array containing all elements of an RDD?

collect() Signup and view all the answers

Which of the following best explains the concept of an RDD being built through 'coarse-grained deterministic transformations'?

Data can only be updated in a precise, well-defined manner. Signup and view all the answers

Which of the following is NOT a goal when designing an in-memory abstraction for data-intensive applications?

High memory usage Signup and view all the answers

What is the key role of the Spark driver in application execution?

Transforming Spark applications into jobs and generating execution plans Signup and view all the answers

How are stages created during the logical execution planning of a Spark DAG?

Based on what operations can be performed in parallel Signup and view all the answers

Which of these options is NOT a benefit of Spark compared to Hadoop?

Spark has specialized systems with a unified vision. Signup and view all the answers

What is the smallest unit of execution in Spark, that maps to a single core and one partition of data, called?

Task Signup and view all the answers

What is a characteristic of narrow dependencies in RDDs?

Each partition of the parent RDD is used by at most one partition of the child RDD. Signup and view all the answers

Which of the following is an example of an RDD operation that results in a narrow dependency?

map Signup and view all the answers

When a wide dependency occurs between RDDs, what is a necessary consequence?

Data shuffling is required Signup and view all the answers

How is data stored in RDDs from a physical perspective?

Data is stored across multiple nodes as input partitions. Signup and view all the answers

What is the primary function of the Catalyst optimizer within SparkSQL?

To convert SQL queries into an optimized execution plan. Signup and view all the answers

What is the main purpose of Tungsten in the SparkSQL engine?

To perform full stage code generation from the optimized physical plan. Signup and view all the answers

What benefit does 'full stage code generation' provide in SparkSQL, as implemented by Tungsten?

Minimizes virtual function calls and leverages CPU registers. Signup and view all the answers

How does `DataFrame.cache()` function in Spark?

It hints to store as many of the read partitions in memory across Spark executors. Signup and view all the answers

What is the purpose of the `DataFrame.persist(StorageLevel)` method in Spark?

It provides a way to control how cached data is stored. Signup and view all the answers

What is the main purpose of `DataFrame.unpersist()` in Spark?

To remove any cached data associated with a DataFrame. Signup and view all the answers

What does it mean that `cache/persist` are hints in Spark?

The DataFrame is cached only when an action is invoked. Signup and view all the answers

According to the content, how has the evolution of DBMSs impacted their architecture?

They have developed from ‘One-size-fits-all’ to custom engines specialized for different use cases. Signup and view all the answers

Study Notes

Spark Lecture 6: Study Notes

Spark is a flexible, in-memory data processing framework written in Scala.
Spark leverages memory caching to enable fast data sharing.
The framework generalizes the two-stage MapReduce model to a Directed Acyclic Graph (DAG)-based model, supporting richer APIs.
The introduction of Spark significantly improved data processing compared to systems like Hadoop MapReduce.

Recap of MapReduce

MapReduce, introduced by Google, provides a simple programming model for distributed applications processing massive datasets.
It offers runtime environments for reliable, fault-tolerant jobs on large clusters.
Hadoop popularized MapReduce, making it widely available.
Hadoop Distributed File System (HDFS) became the central data repository.
MapReduce became a de facto standard for batch processing.
Some sources criticize MapReduce as a significant step backwards from previous approaches.

New Applications & Workloads

Modern applications are increasingly utilizing iterative computations to extract insights from vast datasets.
Apache Mahout is a popular framework for machine learning on Hadoop.
The traditional K-Means algorithm has limitations when implemented with MapReduce due to high overhead and poor performance.

K-Means MapReduce Algorithm

The K-Means algorithm's MapReduce implementation involves configuring centroid files.
Mappers calculate the distance of data points from centroids to assign clusters.
Mappers produce key-value pairs (cluster ID, data ID).
Reducers compute new cluster centroids based on assigned data points.
Reducers output key-value pairs (cluster ID, cluster centroid).
This iterative process continues until convergence.
Implementing K-Means with MapReduce has high overhead, leading to poor performance.

MapReduce & Iterative Computations

MapReduce is fundamentally designed for batch processing, operating on disk-based data (HDFS).
Iterative computations like K-Means require repeated read-write operations to HDFS, leading to poor performance due to the disk I/O bottleneck.
A single iteration of the K-Means algorithm on HDFS involves reading data, shuffling based on the closest centroid and computing new centroids.
This process is repeated on disk based storage which is very inefficient.

Memory Hierarchy Overview

Data access times vary drastically across levels of the memory hierarchy.
Registers provide the fastest access, while data on disk takes significantly longer to access.
The five-minute rule illustrates the trade-off between memory access time and the cost savings by keeping data in memory vs. disk.
This rule shows that keeping data cached in memory is more efficient than storing it on slow disks.

1980 Database Administrator's Dilemma

Balancing memory caching and disk storage is key for database server performance.
Caching frequently accessed data in memory significantly improves performance.

Tandem Computers: Price/Performance

The cost of accessing data from disk is comparatively much higher, highlighting the advantage of storing data in memory.
Memory is very expensive compared to disk space.

Five-Minute Rule

Keeping data in memory saves significant costs if the access frequency (at least once every 5 minutes) is higher than disk access frequency.
In the current systems DRAM pricing has improved making it cheaper for memory.

Five-Minute Rule: Then and Now

The "five-minute rule" highlights the increasing cost-effectiveness of memory over disk, especially considering the significant price drop in DRAM.
This rule suggests that data access should be prioritized in memory rather than disk to avoid performance bottlenecks.

MapReduce/Hadoop and Memory Hierarchy

Hadoop's design is not well suited for iterative computations as it is primarily focused on batch processing and is not ideal for in-memory data processing.
This approach bottlenecks for iterative and interactive applications.
Traditional, disk-based approaches are inadequate for modern workloads.

Hadoop Ecosystem

Specialized systems have emerged to address limitations in Hadoop (e.g., for streaming, iterative computations, etc.).
Different APIs and modules exist within the Hadoop ecosystem, leading to high operational costs and a fragmented ecosystem.

Lightning a Spark

Spark leverages in-memory data processing to offer efficient data sharing.
The introduction of Spark improved data management.

Spark Distributed Architecture

Spark's architecture is composed of a Spark Application, Spark Driver, SparkSession, Cluster Manager, and Spark Executors.
The Spark Driver's role is critical in managing Spark operations and their transformations into Directed Acyclic Graphs(DAG), coordinating resources from the cluster manager to the executors, instantiating SparkSession.

Resilient Distributed Dataset (RDD)

RDDs are a fundamental abstraction in Spark, defining a distributed collection of partitioned and immutable objects.
RDD transformations are operations that create new RDDs based on the existing ones.
RDD actions execute transformations and return values to the driver program.

RDD Transformations

Transformations are lazy operations that define data transformations in Spark.
Example transformations are 'map', 'filter', 'join', etc., yielding new RDDs.
RDDs are immutable, allowing lazy evaluation by storing operations rather than immediate execution.

RDD Actions

Actions trigger computation, unlike transformations which are lazy evaluation operations.
Actions return values to the driver.

Spark Execution

Spark converts a user application into a Directed Acyclic Graph (DAG) of jobs for optimized task execution.
Jobs are broken down into stages, enabling parallel execution.
Spark executes tasks in each stage based on data dependencies and data shuffle requirements.

RDD vs DataFrame

Choose RDDs when precise control over computational logic is required for custom operations and when performance gains from code optimization and efficient space utilization of DataFrames is not desired.
Use DataFrames for more efficient data management.

SparkSQL Engine

SparkSQL engine is the substrate on which various structured APIs are built in Spark.
Core components of the engine are Catalyst and Tungsten.

Catalyst Optimizer

The Catalyst Optimizer resembles traditional database systems' optimizers, focusing on transforming SQL queries into execution plans.
This optimization involves analyzing various factors to form appropriate plans for the fastest possible execution.
Logical and physical plans are crucial for the optimizer.

Tungsten & Code Generation

Tungsten takes the optimized physical plan and translates it into code using CPU registers, generating performant code.
Collapsing the whole query into a function helps efficiently execute plans.

Spark Caching

The .cache() and .persist() methods in Spark allow caching data in memory to improve performance for frequently read data.
DataFrames handle distributed data, caching partitions across the Spark executors.

Spark & RDBMS: Summary

Spark and RDBMS systems have evolved, with Spark rapidly adopting RDBMS concepts.
This evolution has created optimized SQL operations, and other advanced processing methods to support machine learning and graph analytics.
The core concept behind this evolution is to make structured query languages (SQL) much more efficient on distributed data.

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Description

Test your knowledge about Resilient Distributed Datasets (RDDs) in the Spark framework. This quiz covers key concepts such as lazy evaluation, partitions, actions, and data access costs. Perfect for those studying Spark and big data processing.