Meet Assistant X - an AI-powered debugging companion specifically designed for users working on the Yeedu. Assistant X transforms the traditional debugging experience into an intuitive, conversational process, bringing new capabilities in Spark job debugging and intelligent analysis.
The Spark Debugging Challenge
Spark developer's generic challenges:
Complex Error Traces: Stack traces that span hundreds of lines with obscure JVM errors
Performance Mysteries: Jobs that suddenly slow down during Spark job execution
Time-Consuming Investigation: Hours spent correlating logs, metrics, and configuration files
Traditional tools provide raw data but lack the intelligence to connect the dots and provide actionable insights for debugging Spark code.
Enter Assistant X: Your Intelligent Debugging Partner
Assistant X doesn’t just surface information - it understands context, analyses patterns, and provides human-readable explanations for complex Spark issues. It acts as a AI Spark debugging assistant, designed to accelerate both investigation and Spark job optimization.
Exhibit A: Yeedu AI Architecture
Conceptual Architecture Flow
User Query → Claude/ChatGPT AI → MCP Tools → Yeedu APIs → Response Processing → User
Core Capabilities
1. Conversational Error Analysis
Instead of parsing raw logs, simply ask:
“Why did my job fail?”
“What caused the out of memory error in run 12345?”
“My job is running slow - what’s wrong?”
Assistant X automatically:
Retrieves job configuration and runtime details
Performs deep log analysis on STDOUT/STDERR
Examines cluster resource allocation
Identifies root causes and provides specific solutions, dramatically improving Spark job debugging workflows.
2. Contextual Recommendations
Rather than generic advice, Assistant X provides workspace-specific recommendations:
Optimal memory configurations for your cluster size
Suggested parallelism levels based on data volume
Package dependencies that might resolve compatibility issues
Code-level improvements that supports Spark job optimizations
3. Natural Language Interaction
No need to remember complex query syntax or parameter names:
“Show me all failed jobs from last week”
“Which workspace has the most expensive compute usage?”
“Find jobs that failed with memory errors”
Assistant X translates natural language into actionable platform operations, offering a smoother Spark job execution view across clusters.
4. Auto completion of code
It suggests/auto complete the code inside a notebook
It diagnoses the error and gives required solution
Real-World Spark Debugging Scenarios
Scenario 1: The Mysterious Memory Error
Developer Query: “My job 15847 keeps failing with OutOfMemoryError”
Suggests code-level and configuration-level fixes for Spark job optimization
Exhibit C: Scenario 2 analysis
Exploring and Understanding Your Yeedu Environment
Assistant X helps you make sense of your Yeedu world: tenants, workspaces, files, jobs, notebooks, and more with added context-awareness of an AI Spark debugging assistant.
1. "What workspaces do I have?"
Don't remember workspace IDs or names? Ask:
"Show me my workspaces."
"Which workspaces do I have access to?"
"What's going on in workspace 42?"
Assistant X can:
List your workspaces
Help you search for a workspace by name or keyword
Summarize a workspace with things like:
How many jobs and notebooks it has
Recent activity
High-level job statistics
2. Browsing workspace files
Inside a workspace, you can ask:
"What files are in this workspace?"
"Show me the notebooks under /analytics/monthly."
"Open the contents of etl/users_daily.sql."
Assistant X can:
Browse directories
Show you which files/notebooks exist and where
Fetch the contents of a file and explain what it does in plain language
This is especially helpful when you're new to a workspace or taking over someone else's project.
3. "What can I do in this workspace?"
Permissions can be confusing. Assistant X can check your workspace permissions and explain them simply:
"Do I have permission to edit jobs in this workspace?"
"Why can't I run this job?"
"What role do I have here?"
It maps technical permission names into simple, human-readable explanations like:
"You can run jobs but you can't edit their configuration."
Building and Managing Spark Jobs with Assistant X
Assistant X can be your assistant for the entire job lifecycle: create → edit → run → monitor → debug.
1. Discover your jobs
Ask:
"Show me the jobs in this workspace."
"Find jobs related to 'daily billing'."
"What does the customer_retention_job do?"
Assistant X can:
List jobs in a workspace
Search by keywords in job names or descriptions
Open a job's configuration and explain it in simple terms:
Type of job (notebook, Spark, SQL, etc.)
Inputs/outputs (tables, files, etc.)
Key parameters
2. Guided job creation
Not sure how to define a job spec in one go? You can say:
"I want a job that loads CSVs from S3 and writes them into a Delta table."
Assistant X can:
Ask you a few interactive questions (cluster, input paths, output tables, etc.)
Build a job configuration based on your answers
Show you the final spec in a human-friendly way before creating it
You don't have to memorize every field in a job config page – the assistant walks you through it.
3. Editing jobs safely
When you want to change something:
"Switch this job to use the specific cluster."
"Optimize my job code"
Assistant X can:
Read the current job configuration
Propose a clean change (and explain what's changing)
Apply the update after you confirm
Warn you if a change might impact existing runs (for example, changing schedule on a heavily used job)
4. Triggering jobs on demand
Need to run a job right now?
"Run this job once with runtime config(“spark.sql.shuffle.partitions=10”)"
"Trigger the monthly billing job for a backfill."
Assistant X can:
Start the job run
Tell you when it has started and what run ID to look for
Guide you to the run page to monitor progress
Each of these steps leverages knowledge from Spark log analysis and past runs to enhance Spark job execution reliability.
The "Diagnose" Button
A key strength of Assistant X is its advanced Spark job debugging engine built into the Diagnose button: When you click it:
Assistant X reads the run's logs and system errors
It looks for root causes (missing tables, permission issues, cluster problems, bad configs, etc.)
It explains what likely went wrong
As an AI Spark debugging assistant, it suggests specific fixes, such as:
“Create this missing table or change the table name."
"Increase cluster memory; your job ran out of memory."
"You don't have permission to write to this path; ask an admin or use another location."
For clusters in error state also get a Diagnose feature in the UI. When you click Diagnose on a cluster:
Assistant X inspects cluster logs and system errors
It summarizes what went wrong
It suggests next actions such as:
Changing instance type
Adjusting configuration
Checking permissions or network settings
Assistant X in Notebook:
Inside notebooks, Assistant X becomes your Notebook Companion. It has deep understanding of:
Your current cell
The rest of the notebook code
Recent queries or commands you ran
The operation you're performing (e.g., diagnose-error, generate-code, etc.)
Here's what it can help you with.
Auto-complete while you type
As you write code in a cell, Assistant X can suggest:
Next lines of Python, SQL, or Spark code
Helper functions
You stay in flow instead of hunting for examples.
Explaining and fixing errors
When a cell fails, you can:
Click on Diagnose (or)
Ask: "Why did this cell fail?" / "Explain this error."
AssistantX will:
Look at the error message
Read the current cell and related cells
Tell you what the error means
Suggest corrected code
Understanding Billing and Usage
Assistant X also helps you understand your costs.
You can ask things like:
"Show me my usage for the last 30 days."
"Which clusters are costing me the most?"
"Why was my bill higher this month than last month?"
These often tie back to poor or missed Spark job optimization opportunities.
Assistant X can:
Pull billing usage data for a date range
Break down usage by time period or resource
Highlight patterns (e.g., "Costs increased mainly due to cluster X running longer jobs.")
This awareness makes Spark job debugging instantaneous.
Screen-Aware Help: Assistant X Knows Where You Are
A powerful new capability is screen context.
Assistant X is aware of which page you're on in the Yeedu UI:
Cluster page
Workspace page
Job configuration page
Job run page
Notebook page, etc.
This means you can just say:
"What's wrong with this job?" from the job run page
"Can I run jobs in this workspace?" from a workspace page
"Is this cluster healthy?" from the cluster view
You don't have to provide IDs or URLs – Assistant X already knows what "this" refers to.
Technical Architecture
Deep Integration with Yeedu Platform
Assistant X seamlessly integrates with Yeedu’s ecosystem:
Real-time monitoring of job runs and cluster health
Historical Spark log analysis of performance trends
Cost optimization recommendations in YCU (Yeedu Compute Units)
Multi-workspace debugging across development environments
Before Assistant X:
Job fails with cryptic error
Download and parse lengthy log files
Cross-reference with Spark documentation
Trial-and-error configuration changes
Repeat cycle until resolution Time to resolution: Hours to days
With Assistant X:
Job fails
Ask: “What went wrong with job run 12345?” (or) Click on Diagnose
Receive detailed analysis with specific solutions
Apply recommended fixes
Job runs successfully Time to resolution: Minutes
Spark Performance Optimization
Identifies inefficient transformations before they impact production
Suggests optimal cluster configurations for specific workloads
Recommends code refactoring for better resource utilization
Security & Trust: AI + Yeedu Integration
Core Security Principles
No Data Storage: The AI doesn’t retain or store any code, data, or configurations after conversations end
Session-Based Access: All API calls are made in real-time during active sessions - no persistent connections or cached data
Read-Only Analysis: The system analyses existing data but cannot modify, delete, or export without explicit user commands
Scoped Permissions: Access is limited to specifically requested items - workspace files, job logs, cluster stats, etc.
Built-in Safeguards
Authentication Required: All Yeedu API access requires pre-configured user authentication tokens
Request-Response Model: Fresh API calls are made for each query - no background data collection occurs
Visible Actions: Every tool execution is displayed in real-time with full transparency
Limited Scope: Access is restricted to Yeedu platform data the user has permissions
System Limitations
Cannot store user code, credentials, or data beyond current conversations
Cannot share information with other users or external systems • Cannot modify production systems without explicit user commands
Cannot access data outside authorized Yeedu workspaces
User Control Mechanisms
Selective Sharing: Users choose exactly which workspaces, jobs, or clusters to analyze
Command Approval: The system requests confirmation before potentially impactful actions
Key Guarantees
No Persistent Storage: User data exists only during active conversations
No Cross-Contamination: Workspace data never mixes between different users
No Backdoors: Only official Yeedu APIs with user permissions are utilized
No Hidden Actions: All operations are visible and explainable
Supported AI Models Spark Debugging
Below are the currently supported Claude and ChatGPT model variants available for use.
1) Claude
Sonnet-4.5
Sonnet-4
3-7-Sonnet
3-5-Sonnet
3-5-Haiku
3-Opus
3-Haiku
2)ChatGPT
4o
4o-mini
4.1
4.1-mini
4.1-nano
4-turbo
Conclusion
Assistant X represents a paradigm shift in Spark development from reactive debugging to proactive Spark job optimization, from cryptic error messages to clear, actionable insights. By combining deep technical knowledge with intuitive interaction patterns, it empowers developers to focus on building great data applications rather than wrestling with infrastructure complexities.
Ready to transform your Spark debugging experience? Assistant X is available now on the Yeedu your new AI Spark debugging assistant for faster, smarter, and more reliable bold spark job execution.
