Sentry Metrics

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Overview

SentryMetrics extends FrameProcessorMetrics to provide performance monitoring integration with Sentry. It tracks Time to First Byte (TTFB) and processing duration metrics for frame processors.

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Installation

To use Sentry metrics, install the Sentry SDK:

pip install --upgrade 'sentry-sdk[flask]'

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Configuration

Sentry must be initialized in your application before metrics will be collected:

import sentry_sdk

sentry_sdk.init(
    dsn="your-sentry-dsn",
    traces_sample_rate=1.0,
)

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Metrics Collection

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TTFB Metrics

Time to First Byte metrics track how long it takes to receive the first response:

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Processing Metrics

Processing metrics track the total duration of frame processing:

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Usage Example

from pipecat.processors.metrics.sentry_metrics import SentryMetrics

class MyProcessor(BaseProcessor):
    def __init__(self):
        super().__init__()
        self._metrics = SentryMetrics()

    async def process_frame(self, frame: Frame):
        await self._metrics.start_processing_metrics()
        # Process frame
        await self._metrics.stop_processing_metrics()

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Span Information

Each span includes:

• Operation type (ttfb or processing)
• Description with processor name
• Start timestamp
• End timestamp
• Unique span ID

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Fallback Behavior

If Sentry is not available (not installed or not initialized):

• Warning logs are generated
• Metric methods execute without error
• No data is sent to Sentry

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Notes

• Requires Sentry SDK to be installed and initialized
• Thread-safe metric collection
• Automatic span management
• Supports selective TTFB reporting
• Integrates with Sentry’s performance monitoring
• Provides detailed timing information
• Maintains timing data even when Sentry is unavailable