DataStoryTelling

 

Exploring the Art of Data Communication: Tips and Tricks for Effective Data Storytelling (youtube.com)

Claus Grand Bang

polymorphic pattern

 let's walk through the implementation of a polymorphic pattern in MongoDB with an example of a content management system where different types of content (e.g., articles, videos, and images) are stored in a single collection.

Step 1: Identify Different Document Types

• Determine the types of documents you want to store in the collection. In our example, we have articles, videos, and images.

Step 2: Design Schema

• Define a schema that accommodates different document types using fields to indicate the type or structure. Include common fields shared by all document types, as well as type-specific fields.
• Example schema:

  json
  

• {
    "type": "article" | "video" | "image",
    "title": <string>,
    "content": <string>,
    "url": <string> // Only for video and image types
    // Additional fields specific to each type
  }
  

Step 3: Insert Documents of Different Types

• Insert documents of different types into the MongoDB collection, ensuring they adhere to the specified schema.
• Example documents:

  json
  

• {
    "type": "article",
    "title": "Introduction to MongoDB Polymorphic Pattern",
    "content": "This article provides an overview of implementing a polymorphic pattern in MongoDB.",
    // Additional fields specific to articles
  }
  {
    "type": "video",
    "title": "MongoDB Tutorial",
    "content": "A tutorial on using MongoDB.",
    "url": "https://example.com/mongodb-tutorial"
    // Additional fields specific to videos
  }
  {
    "type": "image",
    "title": "MongoDB Logo",
    "content": "The official MongoDB logo.",
    "url": "https://example.com/mongodb-logo"
    // Additional fields specific to images
  }
  

Step 4: Query Data by Type

• Use MongoDB queries to retrieve documents based on their type field value.
• Example query to retrieve all articles:

  javascript
  

• db.content.find({ "type": "article" })
  

Step 5: Handle Different Document Types

• Implement conditional logic in queries and application code to handle different document types appropriately. This might involve different processing or rendering logic based on the document type.

By following these steps and adjusting them to fit your specific use case, you can effectively implement a polymorphic pattern in MongoDB to store and query documents of different types within a single collection.

MongoDB Patterns

Certainly! Here's a concise cheat sheet covering various MongoDB data modeling patterns with schema design and a retail domain example for each:

---

Embedded Data Pattern

• Description: Store related data within a single document using nested structures.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "order_id": "ORD123",
    "customer": {
      "name": "John Doe",
      "email": "john@example.com",
      "address": {
        "street": "123 Main St",
        "city": "Anytown",
        "country": "USA"
      }
    },
    "products": [
      {
        "name": "Product 1",
        "quantity": 2,
        "price": 50
      },
      {
        "name": "Product 2",
        "quantity": 1,
        "price": 75
      }
    ]
  }
  

• Retail Domain Example: Order document containing customer details and ordered products.

---

Normalized Data Pattern

• Description: Organize related data across multiple collections and establish relationships using references.
• Schema Design:

  json
  

• // Customers collection
  {
    "_id": ObjectId("..."),
    "name": "John Doe",
    "email": "john@example.com"
  }
  
  // Orders collection
  {
    "_id": ObjectId("..."),
    "customer_id": ObjectId("..."),
    "order_id": "ORD123",
    // Other order fields...
  }
  
  // Products collection
  {
    "_id": ObjectId("..."),
    "name": "Product 1",
    "price": 50
    // Other product fields...
  }
  

• Retail Domain Example: Separate collections for customers, orders, and products with references between them.

---

Array of Objects Pattern

• Description: Store related data as an array of objects within a document.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "customer": "John Doe",
    "orders": [
      {
        "order_id": "ORD123",
        "products": [
          {
            "name": "Product 1",
            "quantity": 2,
            "price": 50
          },
          {
            "name": "Product 2",
            "quantity": 1,
            "price": 75
          }
        ]
      }
    ]
  }
  

• Retail Domain Example: Customer document with an array of orders, each containing ordered products.

---

Bucketing Pattern

• Description: Group related data into buckets or categories within a single collection.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "timestamp": ISODate("..."),
    "category": "sales",
    "order_id": "ORD123",
    // Other sales-related fields...
  }
  

• Retail Domain Example: Sales data bucketed by categories like orders, returns, discounts, etc.

---

Polymorphic Pattern

• Description: Accommodate different types of data within a single collection.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "entity_type": "customer",
    // Customer fields...
  }
  {
    "_id": ObjectId("..."),
    "entity_type": "product",
    // Product fields...
  }
  {
    "_id": ObjectId("..."),
    "entity_type": "order",
    // Order fields...
  }
  

• Retail Domain Example: Documents representing customers, products, and orders stored in a single collection.

---

Shredding Pattern

• Description: Decompose complex, nested structures into simpler, flatter documents.
• Schema Design:
  • Decompose nested structures into separate collections and establish relationships using references.
• Retail Domain Example: Decompose order documents into separate collections for customers, orders, and products.

---

Document Versioning Pattern

• Description: Track changes to documents over time.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "order_id": "ORD123",
    "status": "shipped",
    "__v": 1 // Version number
  }
  

• Retail Domain Example: Order documents with a versioning field to track status changes.

---

By utilizing these patterns with appropriate schema designs in a retail domain context, you can effectively model your data in MongoDB to handle various aspects of a retail business, such as orders, customers, products, and sales data.

mongodb bucketing pattern

 

Let's walk through the implementation of a bucketing pattern in MongoDB with an example of time-series data. In this scenario, we'll create buckets representing different time intervals (e.g., days) for storing sensor data.

Step 1: Identify Data to Bucket

• We have sensor data that records temperature readings every minute.

Step 2: Define Bucketing Criteria

• We'll bucket the sensor data by day, meaning each bucket will represent a single day's worth of temperature readings.

Step 3: Design Schema

• Our schema will include fields for the temperature reading, the timestamp, and a bucketing field to represent the day.
• Example schema:

  json
  

• {
    "temperature": <value>,
    "timestamp": <timestamp>,
    "day_bucket": <date>
  }
  

Step 4: Insert Documents with Bucketing Field

• Insert documents into the MongoDB collection, ensuring each document includes the day_bucket field representing the day it belongs to.
• Example document:

  json
  

• {
    "temperature": 25.5,
    "timestamp": ISODate("2024-05-20T12:30:00Z"),
    "day_bucket": ISODate("2024-05-20")
  }
  

Step 5: Query Data by Bucket

• Use MongoDB's query capabilities to retrieve data based on the bucketing criteria.
• Example query to retrieve temperature readings for May 20, 2024:

  javascript
  

• db.sensor_data.find({ "day_bucket": ISODate("2024-05-20") })
  

Step 6: Aggregate Data Across Buckets

• Utilize MongoDB's aggregation framework to perform calculations across multiple buckets.
• Example aggregation pipeline to calculate the average temperature for each day:

  javascript
  

• db.sensor_data.aggregate([
    {
      $group: {
        _id: "$day_bucket",
        average_temperature: { $avg: "$temperature" }
      }
    }
  ])
  

Step 7: Optimize Performance

• Monitor data distribution across buckets and create indexes on the day_bucket field to optimize query performance.

Step 8: Handle Bucket Growth

• Implement strategies to manage bucket growth, such as archiving or partitioning buckets further, as needed.

By following these steps and adjusting them to fit your specific use case, you can effectively implement a bucketing pattern in MongoDB to organize and query time-series data.

MongoDB modeling techniques

 

Expanding the list to the top 10 modeling techniques in MongoDB provides a broader perspective on the various strategies available for data modeling:

1. Embedded Data Models: Store related data within a single document using nested or embedded structures. Suitable for one-to-one and one-to-many relationships where the embedded data logically belongs to the parent document.

2. Normalized Data Models: Organize related data across multiple collections and establish relationships using references or foreign keys. Ideal for many-to-many relationships or scenarios requiring data integrity and consistency.

3. Array of Objects: Utilize arrays within documents to store related data as a collection of objects. Suitable for scenarios with one-to-many relationships and small, relatively static arrays.

4. Bucketing or Bucketing Patterns: Group related data into "buckets" or categories within a single collection, often used for partitioning data such as time-series or event-based data.

5. Polymorphic Patterns: Accommodate diverse data types within a single collection by using a field to indicate document types or by storing documents with varying structures but similar attributes. Offers flexibility for evolving schemas or heterogeneous data.

6. Tree Structures: Model hierarchical relationships such as organizational charts or category hierarchies using tree structures like parent references or materialized path patterns.

7. Schema Versioning: Implement techniques to manage schema evolution over time, such as versioning documents or using flexible schema designs like the "attribute pattern" or "schemaless" modeling.

8. Sharding and Data Partitioning: Scale out MongoDB deployments by distributing data across multiple shards based on a shard key, partitioning data to improve performance and scalability.

9. Materialized Views: Precompute and store aggregated or derived data in separate collections to improve query performance for frequently accessed data or complex aggregations.

10. Document Versioning: Implement versioning within documents to track changes over time, allowing for historical analysis or data rollback capabilities.

Each modeling technique offers specific advantages and trade-offs, and the selection depends on factors such as data access patterns, query requirements, scalability needs, and data consistency requirements. It's essential to evaluate the characteristics of your data and application to choose the most appropriate modeling approach.

Prompt Engineering

Prompt engineering involves designing and crafting prompts that effectively communicate the desired task or question to a language model like ChatGPT. 

The key components of prompt engineering include:

1. Task Definition: Clearly defining the task or problem you want the language model to solve. This involves specifying the input format, expected output format, and any constraints or requirements.

2. Context and Examples: Providing relevant context and examples to guide the language model's understanding of the task. This can include giving it sample inputs and corresponding outputs, demonstrating different cases or scenarios, and providing additional information or constraints.

3. Prompt Structure: Designing the structure and format of the prompt to ensure clarity and consistency. This includes using appropriate language, specifying placeholders or variables, and organizing the prompt in a logical and coherent manner.

4. Few-Shot Learning: Leveraging few-shot learning techniques to train the language model on a small number of examples. This helps the model generalize and adapt to new tasks or variations of existing tasks.

5. Prompt Patterns: Utilizing prompt patterns or templates that capture common patterns or structures in prompt writing. These patterns provide a framework for constructing prompts and can help improve efficiency and effectiveness in generating desired outputs.

By focusing on these key components, prompt engineering contributes to improving prompt writing skills in several ways:

• Precision: Prompt engineering helps in generating precise prompts that clearly communicate the desired task or question to the language model. This improves the accuracy and relevance of the model's responses.

• Consistency: By designing consistent prompt structures and formats, prompt engineering ensures that the language model receives consistent inputs, making it easier to interpret and generate desired outputs.

• Adaptability: Through few-shot learning, prompt engineering enables the language model to learn and generalize from a small number of examples. This enhances its ability to handle new tasks or variations of existing tasks.

• Efficiency: Prompt patterns provide a systematic approach to prompt writing, saving time and effort by reusing proven structures and formats. This allows prompt engineers to focus on customizing prompts for specific tasks rather than starting from scratch.

• Effectiveness: Well-engineered prompts improve the overall performance and reliability of the language model, leading to more accurate and useful responses. This enhances the user experience and the value derived from using the model.

By honing their prompt engineering skills, individuals can effectively harness the capabilities of language models and achieve better outcomes in various applications, such as natural language understanding, problem-solving, and content generation.

There are various types of prompt patterns that can be used to enhance prompt engineering with large language models like ChatGPT. Here are some examples:

1. Input Prompt Patterns:

• Asking for user input: Prompting the user to provide specific information or answer a question.
• Providing alternatives: Offering multiple options for the user to choose from.

2. Persona Prompt Patterns:

• Adopting a persona: Writing prompts from the perspective of a specific character or persona.
• Role-playing: Engaging in a conversation or interaction with the model as a specific persona.

3. Instruction Prompt Patterns:

   • Asking for clarification: Requesting the model to provide more details or clarify a certain topic.
   • Asking for examples: Prompting the model to provide examples or demonstrate a concept.

4. Formatting Prompt Patterns:

   • Specifying output format: Instructing the model to generate output in a specific format or structure.
   • Controlling verbosity: Guiding the model to be more concise or elaborate in its responses.

5. Contextual Prompt Patterns:

   • Providing context: Including relevant background information or previous conversation history in the prompt.
   • Referring to previous responses: Referring to the model's previous answers or statements in the prompt.

6. Goal-oriented Prompt Patterns:

   • Setting goals: Explicitly stating the desired outcome or objective in the prompt.
   • Requesting step-by-step instructions: Asking the model to provide a sequence of actions or steps to achieve a specific goal.

These are just a few examples of prompt patterns that can be used to structure prompts and guide the behavior of large language models. By leveraging these patterns effectively, users can achieve more accurate and desired responses from the models.

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

We explore how generating a chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain-of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting.

Refinement pattern 

This text outlines the concept of using the question refinement pattern to enhance interactions with large language models like ChatGPT. It suggests that by refining initial questions with the model's assistance, users can obtain more precise and contextually relevant queries. The process involves prompting the model to suggest improvements to questions and then deciding whether to use the refined version. The text emphasizes the importance of continuously striving for better questions to optimize interactions with the language model. Through an example involving a decision about attending Vanderbilt University, it illustrates how refining questions can lead to more informative and tailored inquiries. Additionally, it highlights how this pattern fosters reflection on the clarity and completeness of questions, helping users identify missing information and refine their queries accordingly. Overall, the text underscores the value of leveraging question refinement to generate better questions, enhance learning from model refinements, and address missing contextual elements for improved outputs.