Resilient Distributed Datasets Quiz

<p>filter (B)</p> Signup and view all the answers

<p>Groups elements from an RDD into multiple RDDs based on a grouping key. (A)</p> Signup and view all the answers

<p>They require multiple iterations until a stopping condition is met. (D)</p> Signup and view all the answers

<p>K-Means clustering (C)</p> Signup and view all the answers

<p>They require mapping over all data each time a query is modified. (C)</p> Signup and view all the answers

<p>Data can be kept in memory to avoid slow disk reads. (B)</p> Signup and view all the answers

<p>Map/Reduce model (C)</p> Signup and view all the answers

<p>To provide a shared memory abstraction. (C)</p> Signup and view all the answers

<p>Allows for a pipeline of queries utilizing different models. (A)</p> Signup and view all the answers

<p>Disk read/write operations can be resource-intensive. (A)</p> Signup and view all the answers

<p>getTags(status) (A)</p> Signup and view all the answers

<p>To perform actions on processed data (B)</p> Signup and view all the answers

<p>Counts occurrences of each unique hashtag (A)</p> Signup and view all the answers

<p>The time period of data to aggregate (A)</p> Signup and view all the answers

<p>Every five seconds (D)</p> Signup and view all the answers

<p>A continuous stream of data (D)</p> Signup and view all the answers

<p>Transforms a DStream into multiple output streams (B)</p> Signup and view all the answers

<p>Initialize Twitter streaming (D)</p> Signup and view all the answers

<p>To efficiently manage distributed snapshots and state information (B)</p> Signup and view all the answers

<p>Limited lifetime due to the number of writes to each block (B)</p> Signup and view all the answers

<p>HashMapStateBackend (D)</p> Signup and view all the answers

<p>Log-Structured-Merge Tree (D)</p> Signup and view all the answers

<p>Incremental snapshots that capture only changes (D)</p> Signup and view all the answers

<p>To maintain a summary of the data seen so far (A)</p> Signup and view all the answers

<p>It encapsulates the full status of the computation at any time (C)</p> Signup and view all the answers

<p>In parallel and distributed processing scenarios (B)</p> Signup and view all the answers

<p>It's an example of a purely data-parallel stream operation (A)</p> Signup and view all the answers

<p>They provide a complete view of all active computations (A)</p> Signup and view all the answers

<p>It allows continuous updates of the computation state (C)</p> Signup and view all the answers

<p>It can be logically associated with streams and computations (D)</p> Signup and view all the answers

<p>It ensures the computations' state can be consistently updated (C)</p> Signup and view all the answers

<p>To allow partition at the dataflow sources and recover from failures (D)</p> Signup and view all the answers

<p>It enables tasks to prioritize inputs from pending epochs without interruption (D)</p> Signup and view all the answers

<p>HDFS and other logging options (C)</p> Signup and view all the answers

<p>New epochs are being initiated in distributed tasks (C)</p> Signup and view all the answers

<p>To provide new features without interrupting existing tasks (C)</p> Signup and view all the answers

<p>It removes the necessity for full data consumption before proceeding (A)</p> Signup and view all the answers

<p>It limits logging to each dataflow cycle for efficiency (D)</p> Signup and view all the answers

<p>Maintaining continuity in processing during disruption (C)</p> Signup and view all the answers

Algorithms that repeatedly execute a process until a specific condition is met. These algorithms often involve modifying data structures or performing calculations iteratively to reach a desired outcome.

A type of algorithm or process that involves multiple iterations, where each iteration modifies the input data or state, continuously refining the solution until a specific condition is met. This process can be used for various tasks, such as clustering data points, ranking webpages, or exploring graphs.

A group of algorithms that use iterative computations to cluster data points into groups based on their similarity. The goal is to minimize the distance between data points within the same cluster while maximizing the distance between clusters.

An algorithm that assigns a ranking score to websites based on the quantity and quality of incoming links, where a website with more and higher- quality links receives a higher ranking.

Computations where a user can modify and submit new queries based on the results of previous queries. This allows for interactive data analysis and exploration, making it easier to refine queries and get more specific results.

A distributed computing framework that simplifies the process of building real-time applications by allowing you to directly interact with data. It allows you to build applications that process data as it arrives, enabling fast response times and real-time decision-making.

Spark's core engine, built to overcome the limitations of traditional map/reduce approaches. It enables efficient processing of massive amounts of data by minimizing data movement and providing in-memory processing, leading to faster query execution.

The data structure that allows for distributed and parallel processing of data in Spark. It provides a mechanism for handling data across multiple nodes in a cluster, making it possible to process large datasets efficiently.

A way for a stream operation to remember information about the data it has processed. This information can be used to calculate things like averages or to keep track of unique values.

The ability to take a snapshot of the entire state of a running stream operation, including all the gathered information and data.

The process of taking snapshots of the state of a streaming operation, sending them to storage, and restoring them later if needed.

A resilient distributed dataset (RDD) is a fault-tolerant abstraction for a large, shared-memory object that is partitioned across a cluster and manipulated in parallel. RDDs are immutable, which means they cannot be changed once they are created.

RDDs can be created using a variety of operations, including loading data from a file or database, applying transformations to existing RDDs, and combining RDDs together. Transformations are operations that create new RDDs from existing ones.

A map operation takes each element of an RDD and applies a function to it, creating a new RDD with the same number of elements. Filter operations select elements from an RDD based on a condition, creating a new RDD with fewer elements.

The groupBy operation splits an RDD into multiple smaller RDDs based on a grouping key, allowing for parallel processing of grouped elements.

RDDs are a fundamental concept in distributed computing, allowing for efficient parallel processing of large datasets across multiple machines. RDDs provide a powerful abstraction that simplifies working with large datasets in a distributed environment.

A transformation operation in Spark Streaming that modifies the data within a DStream to create a new DStream. Examples include flatMap.

A function within Spark Streaming that processes each element in a DStream, creating a new DStream with the result. It modifies the data in the stream.

A method in Spark Streaming used to perform actions on processed data within a DStream. It doesn't create a new DStream. Examples include foreach.

A function within Spark Streaming that takes processed data and allows you to perform operations such as writing to a database, updating analytics UI, or other custom actions.

A mechanism in Spark Streaming that groups data into time-based windows. It allows processing data based on a fixed period of time, rather than single batches. Examples include window.

A parameter in Spark Streaming's windowing operations, representing the size of the window.

A parameter in Spark Streaming's windowing operations, representing the interval between subsequent windows. The window slides forward by this interval.

An operation in Spark Streaming performed on a windowed DStream, to count the occurrence of each element in the window, producing a DStream. Examples include countByValue.

A data storage backend for Flink that stores data in the Java heap. It's fast and efficient but isn't suitable for storing large amounts of state.

A data storage backend for Flink that utilizes RocksDB, a persistent key-value store for fast and efficient storage of state on disk.

A measure of how frequently data is rewritten to storage. It's a key factor in SSD lifetime as excessive writes can wear down the SSD.

A data storage technique where recent data is stored in RAM and periodically flushed to an SSD. As data ages, it undergoes compaction for better storage efficiency.

A process where only the changes since the last snapshot are captured, saving storage space and making the process more efficient.

A mechanism for capturing the complete state of a running dataflow pipeline at a specific moment in time.

They enable restarting computations in case of failures, reconfiguring parallelism for optimized performance, and upgrading software without interrupting continuous data processing.

A mechanism that marks specific points along the dataflow graph, signifying the completion of distinct processing stages known as epochs.

A form of distributed checkpointing where the dataflow runtime coordinates the capture of local states across all participating tasks.

For each task within the dataflow graph, local states are stored, representing the current processing state for each task.

Stored in systems like HDFS, snapshots represent the entire processing state, allowing for restoration and resumption of computations without restarting the entire process.

In cyclic dataflow, this refers to logging only the minimal necessary data within each cyclical loop, preserving state without storing the entire dataflow history.

A phase that synchronizes processing progress across different tasks in a dataflow, ensuring that no task is significantly lagging behind the others.