Analyze Logfire Data

Query the Logfire database and generate interactive dashboards from telemetry data.

Prerequisites

•LOGFIRE_READ_TOKEN environment variable must be set
•Run queries using: uv run --with logfire python logfire_query.py

Workflow

1. Understand the Schema

First, use the mcp__logfire__schema_reference tool to understand the database schema. Key tables and columns:

•records table contains spans and logs
•Important columns: message, span_name, trace_id, exception_type, exception_message, start_timestamp, service_name, attributes
•Use -> and ->> operators for JSON fields in attributes

2. Query the Data

Use logfire_query.py from this skill directory to execute queries:

python

from logfire_query import query_sync, load_results

# Execute query and save results
query_sync(
    '''
    SELECT span_name, count(*) as count
    FROM records
    WHERE start_timestamp > now() - INTERVAL '1 hour'
    GROUP BY span_name
    ORDER BY count DESC
    LIMIT 20
    ''',
    'results.json'
)

# Load results for analysis
rows = load_results('results.json')

Alternatively, use the mcp__logfire__arbitrary_query tool directly for simpler queries.

3. Generate Dashboards

Write custom Plotly code for rich, interactive dashboards. Do NOT use predefined chart utilities - write the visualization code directly.

Dashboard Guidelines

Structure:

•Use plotly.subplots.make_subplots() for multi-panel layouts
•Include multiple visualization types: bar, line, scatter, heatmap, pie
•Save as HTML for full interactivity

Styling:

•Consistent color scheme (use plotly.express.colors palettes)
•Clear, descriptive titles for each subplot
•Proper axis labels with units
•Legend placement that doesn't obscure data

Interactivity:

•Enable hover tooltips with relevant data
•Support zoom/pan for time series
•Add range sliders for date filtering where appropriate

Example Dashboard Code

python

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px

# Create multi-panel dashboard
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Requests by Endpoint', 'Error Rate Over Time',
                    'Response Time Distribution', 'Top Error Types'),
    specs=[[{'type': 'bar'}, {'type': 'scatter'}],
           [{'type': 'histogram'}, {'type': 'pie'}]]
)

# Add traces to each subplot
fig.add_trace(
    go.Bar(x=endpoints, y=counts, marker_color=px.colors.qualitative.Set2),
    row=1, col=1
)

# ... add more traces ...

fig.update_layout(
    height=800,
    showlegend=True,
    title_text='Logfire Telemetry Dashboard',
    title_x=0.5
)

fig.write_html('dashboard.html')
print('Dashboard saved to dashboard.html')

Common Query Patterns

Find Exceptions

sql

SELECT exception_type, exception_message, count(*) as count
FROM records
WHERE exception_type IS NOT NULL
  AND start_timestamp > now() - INTERVAL '24 hours'
GROUP BY exception_type, exception_message
ORDER BY count DESC
LIMIT 20

Trace Latency Analysis

sql

SELECT span_name,
       avg(duration) as avg_duration,
       percentile_cont(0.95) WITHIN GROUP (ORDER BY duration) as p95
FROM records
WHERE start_timestamp > now() - INTERVAL '1 hour'
GROUP BY span_name
ORDER BY avg_duration DESC

Service Dependencies

sql

SELECT service_name, span_name, count(*) as calls
FROM records
WHERE start_timestamp > now() - INTERVAL '1 hour'
GROUP BY service_name, span_name
ORDER BY calls DESC

Output

•Save dashboards as .html files for interactivity
•Include the file path in your response so the user can open it
•For quick insights, print summary statistics to stdout