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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. 
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. 
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.