Jaeger backend is itself a distributed system, composed of different components, potentially running on many hosts. It might be the case that one of these moving parts is not working properly, causing spans to not be processed or stored. When something goes wrong, make sure to check the items listed here.

Verify the sampling strategy

Before everything else, make sure to confirm what sampling strategy is being used. For development purposes or for low-traffic scenarios, it is useful to sample every trace. In production, you may want to use lower rates. When diagnosing why spans are not being received by the backend, make sure to configure the SDK to sample every trace. Typically, the sampling strategy can be set via environment variables.

OpenTelemetry SDKs

If you are using OpenTelemetry SDKs, they should default to parentbased_always_on sampler, which is effectively sampling at 100%. It can be changed via OTEL_TRACES_SAMPLER environment variable (see documentation ).

Using stdout Exporter

OpenTelemetry SDKs can be configured with an exporter that prints recorded spans to stdout. Enabling it allows you to verify if the spans are actually being recorded.

Jaeger SDKs (deprecated)

If you are using one of the Jaeger SDKs, they default to a probabilistic sampling strategy with 1-in-1000 chance that the trace will be recorded. The strategy can be changed by setting these environment variables:

JAEGER_SAMPLER_TYPE=const
JAEGER_SAMPLER_PARAM=1

For example, when using the Jaeger SDK for Java, the strategy is usually printed out via the logging facility provided by the instrumented application when creating the tracer:

2018-12-10 16:41:25 INFO  Configuration:236 - Initialized  tracer=JaegerTracer(..., sampler=ConstSampler(decision=true,  tags={sampler.type=const, sampler.param=true}), ...)

Use the logging reporter

Most Jaeger SDKs are able to log the spans that are being reported to the logging facility provided by the instrumented application. Typically, this can be done by setting the environment variable JAEGER_REPORTER_LOG_SPANS to true, but refer to the Jaeger SDK’s documentation for the language you are using. In some languages, specifically in Go and Node.js, there are no de-facto standard logging facilities, so you need to explicitly pass a logger to the SDK that implements a very narrow Logger interface defined by the Jaeger SDKs. When using the Jaeger SDK for Java, spans are reported like the following:

2018-12-10 17:20:54 INFO  LoggingReporter:43 - Span reported:  e66dc77b8a1e813b:6b39b9c18f8ef082:a56f41e38ca449a4:1 -  getAccountFromCache

The log entry above contains three IDs: the trace ID e66dc77b8a1e813b, the span ID 6b39b9c18f8ef082 and the span’s parent ID a56f41e38ca449a4. When the backend components have the log level set to debug, the span and trace IDs should be visible on their standard output (see Increase the logging in the backend components below).

The logging reporter follows the sampling decision made by the sampler, meaning that if the span is logged, it should also reach the backend.

Remote Sampling

The Jaeger backend supports Remote Sampling, i.e., configuring sampling strategies centrally and making them available to the SDKs. All Jaeger SDKs support it by setting JAEGER_SAMPLER_TYPE=remote. Some, but not all, OpenTelemetry SDKs also support remote sampling, often via extensions (refer to Migration to OpenTelemetry for details).

If you suspect the remote sampling is not working correctly, try these steps:

1. Make sure that the SDK is actually configured to use remote sampling, points to the correct sampling service address (see APIs), and that address is reachable from your application’s networking namespace.
2. Look at the root span of the traces that are captured in Jaeger. If you are using Jaeger SDKs, the root span will contain the tags sampler.type and sampler.param, which indicate which strategy was used. (TBD - do OpenTelemetry SDKs record that?)
3. Verify that the server is returning the appropriate sampling strategy for your service:

    $ curl "jaeger-collector:14268/api/sampling?service=foobar"
    {"strategyType":"PROBABILISTIC","probabilisticSampling":{"samplingRate":0.001}}

Bypass the Jaeger Agent

By default, the Jaeger SDK is configured to send spans via UDP to a jaeger-agent running on localhost. As some networking setups might drop or block UDP packets, or impose size limits, the Jaeger SDK can be configured to bypass jaeger-agent, sending spans directly to jaeger-collector. Some SDKs, such as the Jaeger SDK for Java, support the environment variable JAEGER_ENDPOINT which can be used to specify jaeger-collector’s location, such as http://jaeger-collector:14268/api/traces. Refer to the Jaeger SDK documentation for the language you are using. For example, when you have configured the JAEGER_ENDPOINT property in the Jaeger SDK for Java, it logs the following when the tracer is created (notice sender=HttpSender):

2018-12-10 17:06:30 INFO  Configuration:236 - Initialized  tracer=JaegerTracer(...,  reporter=CompositeReporter(reporters=[RemoteReporter(sender=HttpSender(),  ...), ...]), ...)

Note: the Jaeger SDK for Java will not fail when a connection to jaeger-collector cannot be established. Spans will be collected and placed in an internal buffer. They might eventually reach jaeger-collector once a connection is established, or get dropped in case the buffer reaches its maximum size.

Networking Namespace

If your Jaeger backend is still not able to receive spans (see the following sections on how to check logs and metrics for that), then the issue is most likely with your networking namespace configuration. When running the Jaeger backend components as Docker containers, the typical mistakes are:

• Not exposing the appropriate ports outside of the container. For example, the collector may be listening on :14268 inside the container network namespace, but the port is not reachable from the outside.
• Not making jaeger-agent’s or jaeger-collector’s host name visible from the application’s network namespace. For example, if you run both your application and Jaeger backend in separate containers in Docker, they either need to be in the same namespace, or the application’s container needs to be given access to Jaeger backend using the --link option of the docker command.

Increase the logging in the backend components

jaeger-agent and jaeger-collector provide useful debugging information when the log level is set to debug. Every UDP packet that is received by jaeger-agent is logged, as well as every batch that is sent by jaeger-agent to jaeger-collector. jaeger-collector also logs every batch it receives and logs every span that is stored in the permanent storage.

Here’s what to expect when jaeger-agent is started with the --log-level=debug flag:

{"level":"debug","ts":1544458854.5367086,"caller":"processors/thrift_processor.go:113","msg":"Span(s) received by the agent","bytes-received":359}
{"level":"debug","ts":1544458854.5408711,"caller":"tchannel/reporter.go:133","msg":"Span batch submitted by the agent","span-count":3}

On the jaeger-collector side, these are the expected log entries when the flag --log-level=debug is specified:

{"level":"debug","ts":1544458854.5406284,"caller":"app/span_handler.go:90","msg":"Span batch processed by the collector.","ok":true}
{"level":"debug","ts":1544458854.5406587,"caller":"app/span_processor.go:105","msg":"Span written to the storage by the collector","trace-id":"e66dc77b8a1e813b","span-id":"6b39b9c18f8ef082"}
{"level":"debug","ts":1544458854.54068,"caller":"app/span_processor.go:105","msg":"Span written to the storage by the collector","trace-id":"e66dc77b8a1e813b","span-id":"d92976b6055e6779"}
{"level":"debug","ts":1544458854.5406942,"caller":"app/span_processor.go:105","msg":"Span written to the storage by the collector","trace-id":"e66dc77b8a1e813b","span-id":"a56f41e38ca449a4"}

Check the /metrics endpoint

For the cases where it’s not possible or desirable to increase the logging on the jaeger-collector side, the /metrics endpoint can be used to check if spans for specific services are being received. The /metrics endpoint is served from the admin port, which is different for each binary (see Deployment). Assuming that jaeger-collector is available under a host named jaeger-collector, here’s a sample curl call to obtain the metrics:

curl http://jaeger-collector:14269/metrics

The following metrics are of special interest:

jaeger_collector_spans_received
jaeger_collector_spans_saved_by_svc
jaeger_collector_traces_received
jaeger_collector_traces_saved_by_svc

The first two metrics should have similar values for the same service. Similarly, the two traces metrics should also have similar values. For instance, this is an example of a setup that is working as expected:

jaeger_collector_spans_received{debug="false",format="jaeger",svc="order"} 8
jaeger_collector_spans_saved_by_svc{debug="false",result="ok",svc="order"} 8
jaeger_collector_traces_received{debug="false",format="jaeger",svc="order"} 1
jaeger_collector_traces_saved_by_svc{debug="false",result="ok",svc="order"} 1

Istio: missing spans

When deploying your application as part of a service mesh like Istio, the number of moving parts increases significantly and might affect how (and which) spans are reported. If you expect to see spans generated by Istio but they aren’t being visible in the Jaeger UI, check the troubleshooting guide on Istio’s website .

Run debug images of the backend components

We provide debug images for each Jaeger component. These images have delve  and respective Jaeger component compiled with optimizations disabled. When you run these images, delve triggers the execution of the Jaeger component as its child process and immediately attaches to it to begin a new debug session and start listening on TCP port 12345 for remote connections. You can then use your IDEs like Visual Studio Code  or GoLand  to connect to this port and attach with it remotely and perform debugging  by adding breakpoints.

For Visual Studio Code, you need to have the following configuration at the root of your local clone of the Jaeger source code:

$ cat .vscode/launch.json
{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Launch remote",
            "type": "go",
            "request": "attach",
            "mode": "remote",
            "remotePath": "",
            "port": 12345,
            "host": "127.0.0.1",
            "cwd": "${workspaceRoot}",
        }
    ]
}