Beyond Web Scraping: Building a Reddit Intelligence Engine With Airflow, DuckDB, and Ollama

Reddit offers an invaluable trove of community-driven discussions that provide rich data for computational analysis. As researchers and computer scientists, we can extract meaningful insights from these social interactions using modern data engineering and AI techniques. 

In this article, I'll demonstrate how to build a sophisticated Reddit intelligence engine that goes beyond basic web scraping to deliver actionable analytical insights using Ollama for local LLM inference.

The Computational Challenge

Social media data analysis presents several unique computational challenges that make it an ideal candidate for advanced pipeline architecture:

1. Scale and velocity: Reddit generates millions of interactions daily
2. Semi-structured data: Posts, comments, and metadata require normalization
3. Natural language complexity: Requires sophisticated ML/AI approaches
4. Computational efficiency: Processing must be optimized for research workflows

Architecture Overview

The pipeline implements a modular, two-stage computational workflow designed for reproducibility and extensibility:

The computational pipeline is structured as two distinct directed acyclic graphs (DAGs), each handling a specific stage of the analytical process:

Stage 1: Data Acquisition and Storage DAG

• Reddit API integration via PRAW with configurable rate limiting
• Stateful processing with checkpointing every 10 posts to ensure fault tolerance
• Parallel comment tree traversal with bounded depth exploration
• Vectorized data transformation for optimal memory utilization
• Intermediate CSV storage with batch processing capabilities
• Analytical storage in DuckDB with optimized schema design

The acquisition DAG implements a sophisticated state machine that handles API rate limits, connection failures, and partial data recovery:

Stage 2: Computational Analysis DAG

• DuckDB integration for analytical queries with columnar compression
• LLM-based semantic analysis using context-aware prompting
• Parallel inference optimization with batched processing
• Vectorized feature engineering for sentiment dimensions
• Joint topic-sentiment modeling for nuanced analysis
• Multi-dimensional insight extraction with temporal correlation

This architecture follows computational principles established in distributed systems research, particularly the separation of concerns between data ingestion and analytical processing.

Technology Stack Selection Rationale

My technological choices were guided by specific computational requirements:

• Apache Airflow: For reproducible, observable workflow orchestration
• DuckDB: A column-oriented analytical database optimized for vectorized operations
• PRAW: Provides a pythonic interface to Reddit's API with robust error handling
• Ollama + Airflow AI SDK: Enables local LLM inference with computational efficiency
• Pandas: Facilitates vectorized data operations for optimal performance

Implementation: Scraping Reddit With Airflow

The first DAG (scrape_reddit_and_load.py) implements an efficient data collection protocol as shown in Figure 2. The workflow orchestrates a complex batch processing system that gracefully handles API limitations while ensuring data integrity:

# Implementing a bounded scraping algorithm with checkpointing
for post in subreddit.new(limit=POSTS_LIMIT):
    post_data = {
        'id': post.id,
        'title': post.title,
        'author': str(post.author),
        'created_utc': post.created_utc,
        'score': post.score,
        'upvote_ratio': post.upvote_ratio,
        'num_comments': post.num_comments,
        'selftext': post.selftext
    }
    posts_data.append(post_data)
    
    # Process comment tree with depth-first traversal
    if post.num_comments > 0:
        post.comments.replace_more(limit=5)
        for comment in post.comments.list():
            comment_data = {
                'id': comment.id,
                'post_id': post.id,
                'author': str(comment.author),
                'created_utc': comment.created_utc,
                'score': comment.score,
                'body': comment.body
            }
            comments_data.append(comment_data)
    
    # Implement checkpointing for fault tolerance
    if len(posts_data) % 10 == 0:
        save_checkpoint(posts_data, comments_data)

This implementation employs several computational optimizations:

1. Bounded iteration: Controls resource utilization with configurable post limits
2. Incremental checkpointing: Ensures fault tolerance by saving state every 10 posts
3. Structured data modeling: Facilitates downstream processing with normalized schemas
4. Efficient tree traversal: Optimizes comment extraction with depth-limited exploration
5. Batched write operations: Minimizes I/O overhead by grouping data persistence tasks

The DAG includes sophisticated error handling to manage Reddit API rate limits, network failures, and data consistency issues. The checkpoint mechanism allows the pipeline to resume from the last successful state in case of interruption, making the system robust for long-running collection tasks.

Implementation: Computational Analysis With LLMs

The second DAG (load_and_analyze.py) implements sophisticated analytical processing as depicted in Figure 1. This workflow handles the transformation of raw text data into structured insights through a series of computational stages:

@task.llm(
    model=model,
    result_type=SentimentAnalysis,
    system_prompt="""You are a computational linguist specializing in sentiment analysis.
    Analyze the following conversation for emotional valence, intensity, and confidence."""
)
def analyze_sentiment(conversation=None):
    """Perform computational sentiment analysis using large language models."""
    return f"""
    Analyze the sentiment of this conversation:
    {conversation}
    
    Return only a JSON object with sentiment (positive/negative/neutral),
    confidence (0-1), and key emotional indicators.
    """

@task.llm(
    model=model,
    result_type=TopicAnalysis,
    system_prompt="""You are a computational topic modeling expert.
    Extract the primary topics and keyphrases from the following conversation."""
)
def analyze_topics(conversation=None):
    """Extract topic vectors from conversation text."""
    return f"""
    Analyze the main topics in this conversation:
    {conversation}
    
    Return only a JSON object with primary_topic, subtopics array, 
    and keyphrases array.
    """

This implementation leverages:

1. Declarative task definition: Simplifies complex LLM interactions
2. Structured type definitions: Ensures consistent output schemas
3. Domain-specific prompting: Optimizes LLM performance
4. JSON-structured responses: Facilitates computational analysis

The DAG employs a sophisticated pipeline that:

• Extracts the top Reddit posts and comments as conversational units
• Processes these through parallel LLM inference pathways
• Aggregates results into a unified data model
• Performs temporal correlation to identify trend patterns
• Generates community-level insights based on aggregated results

The final stage generates comprehensive analytical reports that capture community sentiment trends, emerging topics, and potential correlations between discussion themes and emotional responses. This approach extends beyond simple classification to provide a nuanced understanding of community dynamics.

The Computational Advantages of DuckDB

DuckDB represents an optimal choice for analytical workflows due to its computational characteristics:

1. Vectorized execution engine: Leverages modern CPU architectures with SIMD instructions
2. Column-oriented storage: Optimizes analytical query patterns with efficient data compression
3. Zero-dependency integration: Simplifies deployment environments in research contexts
4. Pandas compatibility: Streamlines data science workflows with seamless DataFrame conversion

In benchmark tests against traditional SQLite for this Reddit dataset, DuckDB demonstrated significant performance advantages:

DuckDB's performance advantages derive from its architectural design, which includes:

1. Query compilation: Just-in-time compilation of queries to optimized machine code
2. Vectorized processing: Operating on batches of values rather than scalar operations
3. Parallel execution: Automatic parallelization of query execution
4. Adaptive compression: Intelligent selection of compression schemes based on data patterns

These characteristics make DuckDB particularly well-suited for the computational demands of social media analysis, where researchers often need to perform complex analytical queries over large text datasets with minimal computational overhead.

Operational Implementation

The pipeline can be executed in a computational research environment using Astronomer, an enterprise-grade Airflow platform that significantly accelerates development:

1. Environment Setup With Astronomer

# Install Astronomer CLI
pip install astro-cli

# Initialize Airflow project with Astronomer
astro dev init

# Install required dependencies in requirements.txt
pip install -r requirements.txt

# Start Airflow with Astronomer
astro dev start

Astronomer provides several key advantages for computational research workflows:

• Accelerated development: Pre-configured environment with all dependencies
• Local testing: Run the full pipeline locally before deployment
• Simplified DAG authoring: Focus on the computational logic instead of infrastructure
• Integrated observability: Monitor execution with minimal configuration

2. API Configuration

Configure Reddit API credentials via environment variables or .env file:

REDDIT_CLIENT_ID=your_client_id
REDDIT_CLIENT_SECRET=your_client_secret
REDDIT_USER_AGENT=your_user_agent

3. DAG Execution

1. Execute reddit_scraper_dag to collect and structure data.
2. Execute reddit_analyzer_dag to perform computational analysis.

The runtime environment leverages containerization to ensure computational reproducibility, with all dependencies explicitly versioned and isolated from the host system. This approach aligns with best practices in computational research, where environment consistency is critical for reproducible results.

Research Applications and Future Extensions

This computational pipeline enables several advanced research applications:

1. Sentiment Dynamics Analysis

Track emotional patterns in community discussions over time, with potential applications in:

• Detecting community response to policy changes
• Identifying emotional contagion patterns in social networks
• Quantifying the impact of external events on community sentiment

2. Topic Evolution Modeling

Identify how conversations evolve over time using:

• Dynamic topic modeling techniques
• Temporal sequence analysis of discussion threads
• Markov process modeling of conversation flow

3. Community Interaction Network Analysis

Map social dynamics through comment relationships:

• Graph-theoretic modeling of interaction patterns
• Centrality measures to identify influential community members
• Cohesion analysis to detect sub-communities and interest groups

4. Temporal Trend Detection

Identify emerging topics and concerns with:

• Time series analysis of topic frequencies
• Change point detection in sentiment patterns
• Anomaly detection in community engagement metrics

Future work could extend this pipeline to incorporate more sophisticated computational approaches:

1. Representation learning: Implement embedding-based approaches to capture semantic relationships:

# Example of extending the pipeline with text embeddings
@task
def generate_embeddings(text_data):
    embeddings = []
    for text in text_data:
        embedding = embedding_model.encode(text)
        embeddings.append(embedding)
    return np.array(embeddings)

2. Graph neural networks: Model interaction patterns using GNN architectures:

# Example of constructing a comment interaction graph
@task
def build_interaction_graph(comments_data):
    G = nx.DiGraph()
    for comment in comments_data:
        if comment['parent_id'].startswith('t1_'):  # Comment reply
            G.add_edge(comment['parent_id'][3:], comment['id'])
    return G

3. Multi-modal analysis: Extend the pipeline to process image and video content:

# Example of integrating image analysis for Reddit posts with images<br>@task<br>def analyze_image_content(posts_with_images):<br>    image_features = []<br>    for post in posts_with_images:<br>        if post['url'].endswith(('.jpg', '.png', '.gif')):<br>            features = vision_model.extract_features(post['url'])<br>            image_features.append((post['id'], features))<br>    return image_features

These extensions would enable more sophisticated computational research on social media dynamics while maintaining the same architectural principles of modularity, reproducibility, and scalability.

Conclusion

The integration of Apache Airflow, DuckDB, and LLMs creates a computational framework that transforms unstructured social media data into structured insights. This approach demonstrates how modern computational techniques can be applied to extract meaningful patterns from human social interactions at scale.

For computer scientists and researchers, this pipeline offers several key advantages:

1. Reproducibility: The DAG-based workflow ensures consistent results across different environments
2. Modularity: Components can be extended or replaced without disrupting the overall architecture
3. Scalability: The architecture supports increasing data volumes with minimal modifications
4. Computational efficiency: Optimized data structures and processing patterns minimize resource usage

This implementation aligns with current research directions in computational social science, where the integration of traditional data engineering approaches with advanced AI capabilities creates new opportunities for understanding complex social phenomena.

As social media continues to generate vast quantities of unstructured data, computational frameworks like this one will become increasingly valuable for researchers seeking to understand human behavior, communication patterns, and community dynamics at scale.

Future research directions could explore:

1. Transfer learning approaches: Using pre-trained models fine-tuned on domain-specific data
2. Hierarchical topic modeling: Capturing nested relationships between discussion themes
3. Causal inference: Moving beyond correlation to identify causal relationships in conversation patterns
4. Cross-platform integration: Extending the pipeline to incorporate data from multiple social networks

By combining systems engineering principles with AI capabilities, we can develop increasingly sophisticated computational tools for understanding the digital traces of human interaction in the age of social media.

You can find the complete source here.