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Documentation Index

Fetch the complete documentation index at: https://docs.tilebox.com/llms.txt

Use this file to discover all available pages before exploring further.

Tilebox stores logs and spans for each workflow job. Use the jobs client to query that telemetry from notebooks, scripts, or automated diagnostics.

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Query job logs

query_logs() returns a LogRecords list. Pagination is handled automatically.
Python
from tilebox.workflows import Client

client = Client()
job = client.jobs().find("019e07b1-916b-0630-f3ba-f1c33235d174")

logs = client.jobs().query_logs(job)

for record in logs:
    print(record.time, record.severity_text, record.body)
    print(record.attributes)
Each log record includes:
  • time
  • severity_number and severity_text
  • body
  • trace_id and span_id
  • attributes
  • runner_attributes

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As pandas DataFrame

Use to_pandas() to convert log records to a pandas DataFrame.
Python
logs_df = client.jobs().query_logs(job).to_pandas()
logs_df[["time", "severity_text", "body"]]
Job Logs as Pandas DataFrame

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Query job spans

query_spans() returns a Spans list. Pagination is handled automatically.
Python
spans = client.jobs().query_spans(job.id)

for span in spans:
    print(span.name, span.status_code, span.duration)
    print(span.attributes)
Each span includes:
  • start_time and end_time
  • duration
  • trace_id, span_id, and parent_span_id
  • name
  • status_code and status_message
  • attributes
  • runner_attributes
  • events

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As pandas DataFrame

Use to_pandas() to convert spans to a pandas DataFrame.
Python
spans_df = client.jobs().query_spans(job).to_pandas()

slow_spans = spans_df.sort_values("duration", ascending=False).head(10)
slow_spans[["name", "duration", "start_time"]]
Job Traces as Pandas DataFrame
Nested attributes, runner_attributes, and events stay as Python objects in DataFrame columns. Span DataFrames include a computed duration column.