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Job Lifecycle

Understanding how Geneva jobs work, their lifecycle states, and how to monitor and manage them.

Geneva uses background jobs to execute long-running operations like backfills and materialized view refreshes. This guide explains how jobs work, their lifecycle states, and how to monitor and manage them.

Overview

Jobs in Geneva are asynchronous operations that process data in the background. There are two primary job types:

Job Type	Purpose	Created By
Backfill	Compute column values using UDFs	table.backfill()
Materialized View Refresh	Update precomputed query results	view.refresh()

Both job types share the same lifecycle states and monitoring capabilities.

Job States

Every job progresses through a well-defined state machine:

stateDiagram-v2
    [*] --> PENDING
    PENDING --> RUNNING
    RUNNING --> DONE
    RUNNING --> FAILED
    DONE --> [*]
    FAILED --> [*]

State	Description
PENDING	Job has been created and is queued for execution
RUNNING	Job is actively processing data
DONE	Job completed successfully
FAILED	Job encountered an error during execution

Monitoring Jobs

The Geneva Console provides a web-based interface for monitoring job status, progress, and history across your database. This is the recommended way to track jobs in collaborative environments.

For programmatic access, you can query job status directly via the API:

Querying Job Status

import geneva

db = geneva.connect("/path/to/db")

# Get job state manager
jsm = db._history

# Get a specific job
job = jsm.get(job_id)[0]
print(f"Status: {job.status}")
print(f"Started: {job.launched_at}")
print(f"Completed: {job.completed_at}")

# List jobs for a table
pending_jobs = jsm.list_jobs(table_name="my_table", status="PENDING")
running_jobs = jsm.list_jobs(table_name="my_table", status="RUNNING")

Progress Metrics

Jobs report progress through metrics:

# Access job metrics
for metric in job.metrics:
    print(f"{metric['name']}: {metric['count']}/{metric['total']}")

Common metrics include:

Metric	Description
fragments	Fragments scheduled for processing
writer_fragments	Fragments written to storage
udf_values_computed	Rows processed by UDFs
rows_checkpointed	Rows saved to checkpoint store
rows_committed	Rows committed to the table
workers	Workers started for parallel execution

Job Events

Jobs log significant events during execution:

for event in job.events:
    print(f"{event['timestamp']}: {event['message']}")

Example events:

• "Job started"
• "Checkpointing complete for fragment 42"
• "Partial commit: 64 fragments"
• "Job completed successfully"

Fault Tolerance

Geneva jobs are designed to be resilient to failures:

Checkpoint-Based Recovery

Jobs save intermediate results to a checkpoint store. If a job fails:

1. Completed work is preserved - Checkpointed batches are not lost
2. Resume from checkpoint - Restarted jobs skip already-processed data
3. No duplicate processing - Each batch is processed exactly once

By default, checkpoints are stored in a _ckp/ subdirectory inside the table's storage location. At scale, you can redirect checkpoints to a separate bucket to avoid IOPS contention. See Checkpoint Storage configuration for details.

Resuming Failed Jobs

To resume a failed job, simply re-run the same backfill or refresh command. The job will automatically detect existing checkpoints, skip already-processed fragments, and continue from where it left off.

API Reference

• Table — backfill(), backfill_async(), refresh(), JobFuture
• Connection — get_job(), list_jobs()