Tracing | Haystack Documentation (original) (raw)

This page explains how to use tracing in Haystack. It describes how to set up a tracing backend with OpenTelemetry, Datadog, or your own solution. This can help you monitor your app's performance and optimize it.

Traces document the flow of requests through your application and are vital for monitoring applications in production. This helps to understand the execution order of your pipeline components and analyze where your pipeline spends the most time.

Configuring a Tracing Backend​

Instrumented applications typically send traces to a trace collector or a tracing backend. Haystack provides out-of-the-box support for OpenTelemetry and Datadog. You can also quickly implement support for additional providers of your choosing.

OpenTelemetry​

To use OpenTelemetry as your tracing backend, follow these steps:

1. Install the OpenTelemetry SDK:
   shell

pip install opentelemetry-sdk  
pip install opentelemetry-exporter-otlp  

1. To add traces to even deeper levels of your pipelines, we recommend you check out OpenTelemetry integrations, such as:
   • urllib3 instrumentation for tracing HTTP requests in your pipeline,
   • OpenAI instrumentation for tracing OpenAI requests.
2. There are two options for how to hook Haystack to the OpenTelemetry SDK.
   • Run your Haystack applications using OpenTelemetry’s automated instrumentation. Haystack will automatically detect the configured tracing backend and use it to send traces.
     First, install the OpenTelemetry CLI:
     shell

   pip install opentelemetry-distro  

   Then, run your Haystack application using the OpenTelemetry SDK:
   shell

   opentelemetry-instrument \  
       --traces_exporter console \  
       --metrics_exporter console \  
       --logs_exporter console \  
       --service_name my-haystack-app \  
       <command to run your Haystack pipeline>  

— or —

• Configure the tracing backend in your Python code:
  python

from haystack import tracing  
from opentelemetry import trace  
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  
from opentelemetry.sdk.trace import TracerProvider  
from opentelemetry.sdk.trace.export import BatchSpanProcessor  
from opentelemetry.sdk.resources import Resource  
from opentelemetry.semconv.resource import ResourceAttributes  
# Service name is required for most backends  
resource = Resource(attributes={  
    ResourceAttributes.SERVICE_NAME: "haystack"  # Correct constant  
})  
tracer_provider = TracerProvider(resource=resource)  
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="http://localhost:4318/v1/traces"))  
tracer_provider.add_span_processor(processor)  
trace.set_tracer_provider(tracer_provider)  
# Tell Haystack to auto-detect the configured tracer  
import haystack.tracing  
haystack.tracing.auto_enable_tracing()  
# Explicitly tell Haystack to use your tracer  
from haystack.tracing import OpenTelemetryTracer  
tracer = tracer_provider.get_tracer("my_application")  
tracing.enable_tracing(OpenTelemetryTracer(tracer))  

Datadog​

To use Datadog as your tracing backend, follow these steps:

1. Install Datadog’s tracing library ddtrace.
2. There are two options for how to hook Haystack to ddtrace.
   • Run your Haystack application using the ddtrace:
     shell

   ddtrace <command to run your Haystack pipeline  

— or —

• Configure the Datadog tracing backend in your Python code:
  python

from haystack.tracing import DatadogTracer  
from haystack import tracing  
import ddtrace  
tracer = ddtrace.tracer  
tracing.enable_tracing(DatadogTracer(tracer))  

Langfuse​

LangfuseConnector component allows you to easily trace your Haystack pipelines with the Langfuse UI.

Simply install the component with pip install langfuse-haystack, then add it to your pipeline.

Weights & Biases Weave​

The WeaveConnector component allows you to trace and visualize your pipeline execution in Weights & Biases framework.

You will first need to create a free account on Weights & Biases website and get your API key, as well as install the integration with pip install weights_biases-haystack.

Custom Tracing Backend​

To use your custom tracing backend with Haystack, follow these steps:

1. Implement the Tracer interface. The following code snippet provides an example using the OpenTelemetry package:
   python

import contextlib  
from typing import Optional, Dict, Any, Iterator  
from opentelemetry import trace  
from opentelemetry.trace import NonRecordingSpan  
from haystack.tracing import Tracer, Span  
from haystack.tracing import utils as tracing_utils  
import opentelemetry.trace  
class OpenTelemetrySpan(Span):  
   def __init__(self, span: opentelemetry.trace.Span) -> None:  
       self._span = span  
   def set_tag(self, key: str, value: Any) -> None:  
             # Tracing backends usually don't support any tag value  
             # `coerce_tag_value` forces the value to either be a Python  
             # primitive (int, float, boolean, str) or tries to dump it as string.  
       coerced_value = tracing_utils.coerce_tag_value(value)  
       self._span.set_attribute(key, coerced_value)  
class OpenTelemetryTracer(Tracer):  
   def __init__(self, tracer: opentelemetry.trace.Tracer) -> None:  
       self._tracer = tracer  
   @contextlib.contextmanager  
   def trace(self, operation_name: str, tags: Optional[Dict[str, Any]] = None) -> Iterator[Span]:  
       with self._tracer.start_as_current_span(operation_name) as span:  
           span = OpenTelemetrySpan(span)  
           if tags:  
               span.set_tags(tags)  
           yield span  
   def current_span(self) -> Optional[Span]:  
       current_span = trace.get_current_span()  
       if isinstance(current_span, NonRecordingSpan):  
           return None  
       return OpenTelemetrySpan(current_span)  

1. Tell Haystack to use your custom tracer:
   python

from haystack import tracing  
haystack_tracer = OpenTelemetryTracer(tracer)  
tracing.enable_tracing(haystack_tracer)  

Disabling Auto Tracing​

Haystack automatically detects and enables tracing under the following circumstances:

• If opentelemetry-sdk is installed and configured for OpenTelemetry.
• If ddtrace is installed for Datadog.

To disable this behavior, there are two options:

• Set the environment variable HAYSTACK_AUTO_TRACE_ENABLED to false when running your Haystack application

— or —

• Disable tracing in Python:
  python

from haystack.tracing import disable_tracing  
disable_tracing()  

Content Tracing​

Haystack also allows you to trace your pipeline components' input and output values. This is useful for investigating your pipeline execution step by step.

By default, this behavior is disabled to prevent sensitive user information from being sent to your tracing backend.

To enable content tracing, there are two options:

• Set the environment variable HAYSTACK_CONTENT_TRACING_ENABLED to true when running your Haystack application

— or —

• Explicitly enable content tracing in Python:
  python

from haystack import tracing  
tracing.tracer.is_content_tracing_enabled = True  

Visualizing Traces During Development​

Use Jaeger as a lightweight tracing backend for local pipeline development. This allows you to experiment with tracing without the need for a complex tracing backend.

1. Run the Jaeger container. This creates a tracing backend as well as a UI to visualize the traces:
   shell

docker run --rm -d --name jaeger \  
  -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \  
  -p 6831:6831/udp \  
  -p 6832:6832/udp \  
  -p 5778:5778 \  
  -p 16686:16686 \  
  -p 4317:4317 \  
  -p 4318:4318 \  
  -p 14250:14250 \  
  -p 14268:14268 \  
  -p 14269:14269 \  
  -p 9411:9411 \  
  jaegertracing/all-in-one:latest  

1. Install the OpenTelemetry SDK:
   shell

pip install opentelemetry-sdk  
pip install opentelemetry-exporter-otlp  

1. Configure OpenTelemetry to use the Jaeger backend:
   python

from opentelemetry.sdk.resources import Resource  
from opentelemetry.semconv.resource import ResourceAttributes  
from opentelemetry import trace  
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  
from opentelemetry.sdk.trace import TracerProvider  
from opentelemetry.sdk.trace.export import BatchSpanProcessor  
# Service name is required for most backends  
resource = Resource(attributes={  
    ResourceAttributes.SERVICE_NAME: "haystack"  
})  
tracer_provider = TracerProvider(resource=resource)  
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="http://localhost:4318/v1/traces"))  
tracer_provider.add_span_processor(processor)  
trace.set_tracer_provider(tracer_provider)  

1. Tell Haystack to use OpenTelemetry for tracing:
   python

import haystack.tracing  
haystack.tracing.auto_enable_tracing()  

1. Run your pipeline:
2. Inspect the traces in the UI provided by Jaeger at http://localhost:16686.

Real-Time Pipeline Logging​

Use Haystack's LoggingTracer logs to inspect the data that's flowing through your pipeline in real-time.

This feature is particularly helpful during experimentation and prototyping, as you don’t need to set up any tracing backend beforehand.

Here’s how you can enable this tracer. In this example, we are adding color tags (this is optional) to highlight the components' names and inputs:

python

import logging
from haystack import tracing
from haystack.tracing.logging_tracer import LoggingTracer

logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.DEBUG)

tracing.tracer.is_content_tracing_enabled = True # to enable tracing/logging content (inputs/outputs)
tracing.enable_tracing(LoggingTracer(tags_color_strings={"haystack.component.input": "\x1b[1;31m", "haystack.component.name": "\x1b[1;34m"}))

Here’s what the resulting log would look like when a pipeline is run: