Apache Storm Overview and Architecture

<p>The native scheduler may become a bottleneck. (C)</p> Signup and view all the answers

<p>Both Storm and Hadoop offer high scalability. (D)</p> Signup and view all the answers

<p>Real-time stream processing (B)</p> Signup and view all the answers

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

<p>Ongoing calculations that update results dynamically (C)</p> Signup and view all the answers

<p>It is essential for real-time decision-making (B)</p> Signup and view all the answers

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

<p>Extract-Transform-Load (C)</p> Signup and view all the answers

<p>Unbounded data streams with no predefined end (A)</p> Signup and view all the answers

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

<p>Assign tasks and monitor worker nodes (C)</p> Signup and view all the answers

<p>Execute data processing tasks, such as running spouts and bolts (D)</p> Signup and view all the answers

<p>It allows for distributed coordination and configuration management (C)</p> Signup and view all the answers

<p>Nimbus running in a cluster (B)</p> Signup and view all the answers

<p>Spouts provide input data, and bolts process that data (A)</p> Signup and view all the answers

<p>Highly reliable distributed coordination service (B)</p> Signup and view all the answers

<p>Nimbus assigns new tasks and monitors existing tasks (D)</p> Signup and view all the answers

<p>Adding or removing Supervisor nodes (A)</p> Signup and view all the answers

<p>Pull data from external sources and emit it as tuples (B)</p> Signup and view all the answers

<p>Aggregation of values from streams (A)</p> Signup and view all the answers

<p>Generating reports or saving processed data (B)</p> Signup and view all the answers

<p>Through a directed acyclic graph structure (C)</p> Signup and view all the answers

<p>It manages and assigns tasks to worker nodes (D)</p> Signup and view all the answers

<p>Emitting data tuples (A)</p> Signup and view all the answers

<p>It prevents cycles in the data processing flow (D)</p> Signup and view all the answers

<p>A Twitter Spout reading tweets from the Twitter Streaming API (C)</p> Signup and view all the answers

<p>A stream of sentences as tuples (C)</p> Signup and view all the answers

<p>Splits the sentence into individual words (D)</p> Signup and view all the answers

<p>WordCountBolt (C)</p> Signup and view all the answers

<p>It updates the word count table and prints the contents (A)</p> Signup and view all the answers

<p>To connect to dynamic data sources (D)</p> Signup and view all the answers

<p>It increments the count for the corresponding word (B)</p> Signup and view all the answers

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

<p>Streams of sentence tuples (B)</p> Signup and view all the answers

<p>Real-time content analysis and trending topic detection (A)</p> Signup and view all the answers

<p>An ordered list of values that must be serializable (D)</p> Signup and view all the answers

<p>They generate tuples from external sources. (D)</p> Signup and view all the answers

<p>Bolts encapsulate application logic and process input streams. (C)</p> Signup and view all the answers

<p>To process user listening data for real-time recommendations. (C)</p> Signup and view all the answers

<p>They are a sequence of tuples that flow through the system. (D)</p> Signup and view all the answers

<p>The computation processes in the data flow. (A)</p> Signup and view all the answers

<p>Streams are an ordered sequence of tuples that are unlimited. (B)</p> Signup and view all the answers

Apache Storm is an open-source framework for real-time stream processing. It handles continuous data flow, making it ideal for tasks like analytics and monitoring.

Apache Storm processes data continuously without a defined end point. The data flow is unlimited, and Storm handles it in real-time as it arrives.

Apache Storm processes data with minimal delay, crucial for applications that need immediate decisions, like fraud detection or financial transactions.

Apache Storm can scale by adding more processing nodes to distribute the workload, handling larger data volumes effectively.

Real-time analytics involves processing data immediately upon arrival, making it ideal for applications that require instant insights and action.

Continuous computation involves ongoing calculations on a data stream, dynamically updating results as fresh data enters the system.

Companies like Twitter use Storm to process their vast data streams, demonstrating its real-world application.

Storm's ability to process data in real-time makes it suitable for tasks like extracting, transforming, and loading data, streamlining data workflows.

An ordered list of values or objects, where each value can be of any data type, but they must all be serializable. Think of it like a container holding different pieces of information in a specific order.

An unbounded sequence of tuples, constantly flowing. It's a fundamental concept in Apache Storm because it's what Storm is designed to work with.

A network of interconnected nodes, called 'vertices', that process data in a stream format. Think of it like a complex pipeline where data flows and gets manipulated.

Special vertices in Storm that generate the streams of tuples. They pull data from external sources, like Twitter APIs or log files, and inject them into the topology for processing.

Verticies in Storm that perform operations on streams of tuples. Think of them as filters, sorters, aggregators – processing data as it flows.

A framework for building distributed, real-time computation systems that processes data as fast as incoming events arrive. It's commonly used for applications like real-time analytics, social media monitoring, fraud detection.

It's the brain of the Storm cluster, responsible for assigning tasks to worker nodes and monitoring their health.

Nimbuse can run in a cluster for fault tolerance, meaning if one node fails, another can take over without service interruption.

They execute the actual data processing tasks, such as running spouts and bolts, and communicate health and status reports to Nimbus.

A Storm cluster can easily scale up or down by adding or removing supervisor nodes as needed, making it flexible for handling varying workloads.

ZooKeeper handles cluster coordination and configuration management, ensuring all nodes have access to the same information.

Storm uses ZooKeeper to keep track of worker availability and task assignments, enabling efficient resource allocation.

Storm uses ZooKeeper to store configuration details and topology definitions, ensuring consistent settings across all nodes.

ZooKeeper's highly distributed and reliable nature contributes to the overall reliability and fault tolerance of the Storm cluster.

A network of spouts and bolts that work together to process data streams. It's represented as a graph where each node is a spout or bolt, and edges indicate data flow between them.

A data source in Apache Storm responsible for ingesting raw data from external sources, like Twitter API or log files, and converting it into tuples. These tuples are emitted to bolts in the topology for further processing.

In Apache Storm, bolts are the processing units that receive tuples from spouts or other bolts. They perform various operations like filtering, transforming, aggregating, and joining data. The result of bolt processing is sent to downstream bolts for further actions or towards output destinations.

The process of assigning tasks from a Storm topology to worker nodes. It's handled by the Nimbus component, which manages the overall cluster and task distribution.

The data flow in a Storm topology starts with spouts, which emit data to bolts. Bolts process the data and may pass it to other bolts. Finally, a dedicated bolt acts as the output mechanism, storing processed data or displaying results in real-time.

A directed acyclic graph (DAG) that represents a Storm topology. Spouts feed data to bolts, which can then pass on processed data to downstream bolts. The topology runs continuously, processing real-time data streams.

The central node in a Storm cluster, responsible for managing the overall system. It handles tasks such as assigning tasks to worker nodes, monitoring the topology, and managing the cluster.

Worker nodes are the execution units in a Storm cluster. They run tasks assigned by Nimbus and handle the actual processing of data. These nodes work together to process the continuous data flow.

Storm topologies run perpetually, continuously processing data streams. This contrasts with Hadoop jobs that process data in batches and finish.

Storm excels at handling large data volumes by distributing processing across multiple nodes. This allows it to scale effectively, handling more data as needed.

Unlike Hadoop's stateful nodes, Storm uses stateless nodes. This means that each node doesn't retain data from previous processing tasks, making it more agile.

Storm emphasizes reliability by ensuring that each data segment is processed at least once. This minimizes data loss during processing.

A Storm component that continuously emits tuples containing sentences as strings.

A Storm bolt that processes each received sentence by splitting it into individual words and emitting tuples containing each word.

A Storm bolt that counts the occurrences of each word in the incoming stream. It keeps track of the count and emits tuples for each word and its count.

A Storm bolt that receives word counts, updates a table of words and their counts, and then finally prints the results to the console.

Apache Storm is designed to reliably process data streams by ensuring that each tuple is processed at least once and, if necessary, re-processed if there's a failure. This ensures that no data is lost during processing.

Apache Storm can seamlessly scale to handle larger data volumes by adding more processing nodes to distribute the workload across them.