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1. Benefits of Using Multiple Tasks in Databricks Jobs

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What are Multi-Task Jobs?

Jobs in Databricks can consist of multiple tasks that run in a specified order, with dependencies between them. This creates a workflow pipeline.

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Key Benefits

1. Modularity

   • Break complex workflows into smaller, manageable tasks (e.g., ingest → transform → analyze).
   • Easier debugging and maintenance.

2. Parallel Execution

   • Independent tasks can run in parallel (e.g., processing different datasets simultaneously).

3. Conditional Execution

   • Tasks can depend on the success/failure of previous tasks.

4. Reusability

   • The same task can be reused across multiple jobs.

5. Resource Optimization

   • Assign different clusters to different tasks based on workload needs.

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Example Workflow

Task 1 (Ingest) → Task 2 (Clean) → Task 3 (Aggregate)

• If Task 1 fails, downstream tasks (Task 2, Task 3) are skipped.

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2. Setting Up a Predecessor Task in Jobs

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What is a Predecessor Task?

A task that must complete before another task (successor) can run.

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How to Set Up

1. In Databricks Jobs UI:

   • Create a new job with multiple tasks.
   • In the task settings, select “Depends on” and choose the predecessor task.

2. Using Jobs API:

   {
     "task_key": "transform_data",
     "depends_on": [{"task_key": "ingest_data"}]
   }
   

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Example Scenario

• Task 1 (ingest_data): Loads raw data.
• Task 2 (transform_data): Cleans and processes data (depends on ingest_data).

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3. When to Use Predecessor Tasks

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Common Scenarios

1. Data Dependency

   • A task requires output from a previous task (e.g., raw data must be ingested before transformation).

2. Error Handling

   • If an early task fails, downstream tasks should not execute (e.g., avoid processing incomplete data).

3. Cost Optimization

   • Skip expensive computations if upstream validation fails.

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Example

validate_input → (if valid) → process_data → generate_report

• If validate_input fails, the pipeline stops early.

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4. Reviewing a Task’s Execution History

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Why Review Execution History?

• Debug failures.
• Monitor performance (duration, resource usage).
• Audit job runs.

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How to Access

1. Databricks UI:

   • Navigate to “Jobs” → Select job → “Runs” tab.
   • Click on a run to see task history.

2. Key Details Available:

   • Start/end time.
   • Status (Success, Failed, Skipped).
   • Logs (stdout, stderr).
   • Cluster metrics (CPU, memory).

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Example Debugging Flow

1. Find failed run → Check logs.
2. Identify error (e.g., FileNotFound).
3. Fix issue (e.g., correct input path).

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5. CRON Scheduling for Jobs

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What is CRON?

A time-based job scheduler in Unix systems. Databricks supports CRON expressions for scheduling jobs.

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Syntax

┌───────────── minute (0 - 59)
│ ┌───────────── hour (0 - 23)
│ │ ┌───────────── day of month (1 - 31)
│ │ │ ┌───────────── month (1 - 12)
│ │ │ │ ┌───────────── day of week (0 - 6, Sun-Sat)
│ │ │ │ │
* * * * *

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Examples

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How to Set Up

1. In Job settings → “Schedule” → “Cron Schedule”.
2. Enter expression (e.g., 0 0 * * * for daily midnight runs).

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6. Debugging a Failed Task

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Steps to Debug

1. Check Run Logs:

   • Navigate to the failed run → “Logs” tab.
   • Look for errors (e.g., Exception: File not found).

2. Reproduce Locally:

   • Run the notebook interactively with the same inputs.

3. Common Issues:

   • Missing data/files.
   • Permission errors.
   • Syntax errors in code.

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Example Fix

• Error: AnalysisException: Table not found.
• Solution: Correct table name or ensure table exists.

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7. Setting Up a Retry Policy

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Why Retry?

• Handle transient failures (e.g., network issues).
• Avoid manual intervention.

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Configuration Options

1. Number of Retries: Max attempts (default: 0).
2. Retry Delay: Wait time between retries (e.g., 5 mins).

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How to Set Up

1. UI:

   • In task settings → “Retry Policy” → Set max retries and delay.

2. API:

   {
     "retry_on_timeout": true,
     "max_retries": 3,
     "min_retry_interval_millis": 300000
   }
   

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Example

• Task fails due to temporary API outage → Retries 3x with 5-minute gaps.

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8. Creating Alerts for Failed Tasks

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Why Alert?

• Get notified immediately when a job fails.
• Reduce downtime.

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Alert Options

1. Email Notifications:
   • Send alerts to individuals or groups.
2. Webhooks:
   • Integrate with Slack, PagerDuty, etc.

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How to Set Up

1. UI:

   • Navigate to Jobs → Select job → “Alerts” tab.
   • Add email/webhook.

2. API:

   {
     "email_notifications": {
       "on_failure": ["user@example.com"]
     }
   }
   

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Example

• Job fails → Email sent to team@company.com.

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9. Email Alerts for Failed Tasks

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How It Works

• Databricks sends an email to specified addresses when:
  • A task fails.
  • The entire job fails.

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Configuration

1. UI:

   • Job settings → “Notifications” → Add email.

2. Limitations:

   • Only supports email (for advanced integrations, use webhooks).

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Example Email Content

Subject: Job Failed - "daily_etl" (Run ID: 123)
Details: Task "transform_data" failed at 2023-10-01 02:00.
Error: FileNotFoundError: No such file: /data/input.csv

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Summary Table: Key Concepts