Query telemetry | Tilebox Docs

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

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)

package main

import (
	"context"
	"fmt"
	"log/slog"
	"time"

	"github.com/google/uuid"
	"github.com/tilebox/tilebox-go/workflows/v1"
)

func main() {
	ctx := context.Background()
	client := workflows.NewClient()
	jobID := uuid.MustParse("019e07b1-916b-0630-f3ba-f1c33235d174")

	for record, err := range client.Jobs.QueryLogs(
		ctx,
		jobID,
		workflows.WithSortDirection(workflows.Ascending),
	) {
		if err != nil {
			slog.ErrorContext(ctx, "failed to query job logs", slog.Any("error", err))
			return
		}

		fmt.Println(record.Time.Format(time.RFC3339Nano), record.Level, record.Body)
		fmt.Println(record.Attributes)
	}
}

Each log record includes:

time
severity_number and severity_text
body
trace_id and span_id
attributes
runner_attributes

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"]]

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)

package main

import (
	"context"
	"fmt"
	"log/slog"

	"github.com/google/uuid"
	"github.com/tilebox/tilebox-go/workflows/v1"
)

func main() {
	ctx := context.Background()
	client := workflows.NewClient()
	jobID := uuid.MustParse("019e07b1-916b-0630-f3ba-f1c33235d174")

	for span, err := range client.Jobs.QuerySpans(
		ctx,
		jobID,
		workflows.WithSortDirection(workflows.Ascending),
	) {
		if err != nil {
			slog.ErrorContext(ctx, "failed to query job spans", slog.Any("error", err))
			return
		}

		fmt.Println(span.Name, span.StatusCode, span.Duration())
		fmt.Println(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

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"]]

Nested attributes, runner_attributes, and events stay as Python objects in DataFrame columns. Span DataFrames include a computed duration column.