You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Apache Spark is considered a powerful complement to Hadoop, the original big data technology. Apache Spark is a more accessible, powerful and capable tool for solving various tasks related to big data. It has become the mainstream and the most demanded framework for big data in all major industries. Since 2.0 Spark has become part of Hadoop. And it is one of the most useful technologies for Python Big Data Engineers.

This series of articles is a single resource that gives an overview of Spark architecture and is useful for people who want to learn how to work with Spark.

Whole series:

What is Spark?

Apache Spark is an open-source cloud computing framework for batch and stream processing which was designed for fast in-memory data processing.

Spark is framework and is mainly used on top of other systems. You can run Spark using its standalone cluster mode on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. I will tell you about the most popular build — Spark with Hadoop Yarn.

So, before we go deeper into Apache Spark, let's take a quick look at the Hadoop platform and what YARN does there.

The OS analogy

To understand what Hadoop is, I will draw an analogy with the operating system. The traditional operating system at a high level consists of several parts: a file system and a processing component.

On one machine there is file system, it can be different: FAT32, HPFS, ext2, NFS, ZFS, etc. The operating system can be different. It is a system for data storage and management.

In addition, the OS has a processor component: a kernel, a scheduler, and some threads and a process that allows programs to run on data.

When we move this storage/processing concept to the cluster level and place it inside Hadoop, we get basically the same separation, the same two components. But the storage layer instead of the single-node file system will be HDFS — Hadoop distributed file system. And YARN (Yet Another Resource Negotiator) takes on the role of the processing component: execution, scheduling, deciding what can be done and where.

Let us take a closer look.

YARN architecture

So how the YARN works?

YARN has some basic process — ResourceManager. ResourceManager is the ultimate authority that decides the allocation of resources between all applications in the system. It has minions that run on all nodes of the cluster called NodeManager. Also, ResourceManager has a plug-in component scheduler that is responsible for allocating resources for various running applications, taking into account familiar limitations on capacity, queues and other factors.

All nodes of the cluster have a certain number of containers. Containers are computing units, a kind of wrappers for node resources to perform tasks of a user application. They are the main computing units that are managed by YARN. Containers have their own parameters that can be configured on-demand (e.g. ram, CPU, etc.).

Containers on each node are controlled by NodeManager daemon. When launching a new application on a cluster, ResourceManager allocates one container for ApplicationMaster. ApplicationMaster for each application is a framework-specific entity that is tasked with negotiating resources with ResourceManager and working with NodeManager(s) to perform and monitor component tasks.

Once launched, ApplicationMaster will be responsible for the entire lifecycle of the distributed application. First of all, it will send resource requests to ResourceManager to get the containers needed to perform the application tasks. A resource request is simply a request for a number of containers that meets some resource requirements, for example:

A number of resources, expressed in megabytes of memory and processor shares

Preferred container location, indicated by hostname, rackname, or * to indicate no preference

to indicate no preference Priority within the application, not for multiple applications.

The ApplicationMaster is run in a container like any other application. If ApplicationMaster crashes or becomes unavailable, ResourceManager can create another container and restart ApplicationMaster on it. It makes the whole application highly available. ResourceManager stores information about running applications and tasks performed in HDFS. When restarted, ResourceManager recreates the state of applications and restarts only tasks that have not been completed.

ResourceManager, NodeManager and the container do not care about the type of application or task. All application framework code is simply transferred to the ApplicationMaster so that any distributed framework can be supported by YARN — as long as someone implements a suitable ApplicationMaster for it. With this common approach, the dream of a Hadoop YARN cluster with many various workloads comes true.

Submit application

Equipped with knowledge from previous sections, it will be useful to learn how applications themselves work in YARN.

Let's go through a sequence of steps of the application launch:

A client program submits the application, including the necessary specifications to run the application-specific ApplicationMaster. ResourceManager gets responsibility for the allocation of a necessary container in which ApplicationMaster will be started. Then ResourceManager starts ApplicationMaster. ApplicationMaster registers itself in ResourceManager. Registration allows the Customer program to request specific information from ResourceManager that allows it to directly interact with its ApplicationMaster. In normal operation ApplicationMaster asks for suitable containers from ResourceManager for the application to run. After successfully receiving the containers, ApplicationMaster launches them, providing NodeManager(s) their configurations. Inside the containers, it runs the user application code. The NodeManager(s) then provides the information (execution phase, status) for ApplicationMaster. During the runtime of the user application, the client interacts with ApplicationMaster to obtain the application status. When the application completes and all necessary work is completed, ApplicationMaster deregisters from ResourceManager and terminates, releasing the container for other purposes.

Interesting facts and features

YARN offers a number of other great features. It is beyond the scope of this post to describe them all, but I have included some noteworthy features:

Uberization is the ability to run all MapReduce tasks in ApplicationMaster's JVM if the tasks are small enough. This way, you avoid the overhead associated with requesting containers from ResourceManager and asking NodeManagers to run (presumably small) tasks.

Simplified management and access to application log files. Application-generated logs do not remain on individual slave nodes (as in MRv1) but are moved to a central repository, such as HDFS. They can later be used for debugging or for historical analysis to detect performance problems.

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