High Scalability

Jaeger backend is designed to have no single points of failure and to scale with the business needs. For example, any given Jaeger installation at Uber is typically processing several billion spans per day.

Native support for OpenTracing and OpenTelemetry

Jaeger backend and Web UI have been designed from ground up to support the OpenTracing standard.

• Represent traces as directed acyclic graphs (not just trees) via span references 
• Support strongly typed span tags and structured logs

Since v1.35, the Jaeger backend can receive trace data from the OpenTelemetry SDKs in their native OpenTelemetry Protocol (OTLP) . However, the internal data representation and the UI still follow the OpenTracing specification’s model.

Multiple storage backends

Jaeger can be used with a growing number of storage backends:

• It natively supports popular open source NoSQL databases as trace storage backends: Cassandra 4.0+, Elasticsearch 7.x/8.x, and OpenSearch 1.0+.
• It integrates via a gRPC API with other well known databases that have been certified to be Jaeger compliant: ClickHouse .
• There is embedded database support using Badger  and simple in-memory storage for testing setups.
• There are ongoing community experiments using other databases; you can find more in this issue .

Sampling

To control the overhead on the applications and the storage costs, Jaeger supports multiple forms of sampling: head-based with centralized remote configuration (static or adaptive) and tail-based sampling. For more information, please refer to the Sampling page.

Modern Web UI

Jaeger Web UI is implemented in Javascript as a React application. Several performance improvements have been released in v1.0 to allow the UI to efficiently deal with large volumes of data and display traces with tens of thousands of spans (e.g. we tried a trace with 80,000 spans).

Cloud Native Deployment

Jaeger backend is distributed as a collection of Docker images. The binaries support various configuration methods, including command line options, environment variables, and configuration files in multiple formats (yaml, toml, etc.). Deployment to Kubernetes clusters is assisted by a Kubernetes operator  and a Helm chart .

Observability

All Jaeger backend components expose Prometheus  metrics by default. Logs are written to stdout using the structured logging library zap .

Backwards compatibility with Zipkin

Although we recommend instrumenting applications with OpenTelemetry, if your organization has already invested in the instrumentation using Zipkin libraries, you do not have to rewrite all that code. Jaeger provides backwards compatibility with Zipkin by accepting spans in Zipkin formats (Thrift, JSON v1/v2 and Protobuf) over HTTP. Switching from a Zipkin backend is just a matter of routing the traffic from Zipkin libraries to the Jaeger backend.

Topology Graphs

Jaeger UI supports two types of service graphs: System Architecture and Deep Dependency Graph.

System Architecture

The “classic” service dependency graph for all services observed in the architecture. The graph represents only one-hop dependencies between services, similar to what one could get from telemetry produced by service meshes. For example, a graph A - B - C means that there are some traces that contain network calls between A and B, and some traces with calls between B and C. However, it does not mean there are any traces that contain the full chain A - B - C, i.e. we cannot say that A depends on C.

The node granularity of this graph is services only, not service endpoints.

The System Architecture graph can be built on the fly from in-memory storage, or by using Spark or Flink jobs when using distributed storage.

Deep Dependency Graph

Also known as “Transitive Dependency Graph”, where a chain A -> B -> C means that A has a transitive dependency on C. A single graph requires a “focal” service (shown in pink) and only displays the paths passing through that service. Typically, this type of graph does not represent the full architecture of the system, unless there is a service that is connected to everything, e.g. an API gateway, and it is selected as a focal service.

The node granularity of this graph can be changed between services and service endpoints. In the latter mode, different endpoints in the same service will be displayed as separate nodes, e.g. A::op1 and A::op2.

At this time the transitive graph can only be constructed from traces in the search results. In the future there will be a Flink job that will compute the graphs by aggregating all traces.

Service Performance Monitoring (SPM)

Surfaced in Jaeger UI as the “Monitor” tab, the motivation for this feature is to help identify interesting traces (e.g. high QPS, slow or erroneous requests) without needing to know the service or operation names up-front.

It is essentially achieved through aggregating span data to produce RED (Request, Error, Duration) metrics.

Potential use cases include:

• Post deployment sanity checks across the org, or on known dependent services in the request chain.
• Monitoring and root-causing when alerted of an issue.
• Better onboarding experience for new users of Jaeger UI.
• Long-term trend analysis of QPS, errors and latencies.
• Capacity planning.

UI Feature Overview

The “Monitor” tab provides a service-level aggregation, as well as an operation-level aggregation within the service, of Request rates, Error rates and Durations (P95, P75 and P50), also known as RED metrics.

Within the operation-level aggregations, an “Impact” metric, computed as the product of latency and request rate, is another signal that can be used to rule-out operations that may naturally have a high latency profile such as daily batch jobs, or conversely highlight operations that are lower in the latency rankings but with a high RPS (request per second).

From these aggregations, Jaeger UI is able to pre-populate a Trace search with the relevant service, operation and lookback period, narrowing down the search space for these more interesting traces.

Getting Started

A locally runnable setup is available in the Jaeger repository  along with instructions on how to run it.

The feature can be accessed from the “Monitor” tab along the top menu.

This demo includes Microsim ; a microservices simulator to generate trace data.

If generating traces manually is preferred, the Sample App: HotROD can be started via docker. Be sure to include --net monitor_backend in the docker run command.

Architecture

The RED metrics queried by Jaeger for the Monitor tab are the result of span data collected by the OpenTelemetry Collector  which is then aggregated by the SpanMetrics Connector  component configured within its pipeline.

These metrics are finally exported by the OpenTelemetry Collector (via prometheus exporters) to a Prometheus-compatible metrics store.

It is important emphasize that this is a “read-only” feature and, as such, is only relevant to the Jaeger Query component (and All In One).

Derived Time Series

Though more in scope of the OpenTelemetry Collector , it is worth understanding the additional metrics and time series that the SpanMetrics Connector  will generate in metrics storage to help with capacity planning when deploying SPM.

Please refer to Prometheus documentation  covering the concepts of metric names, types, labels and time series; terms that will be used in the remainder of this section.

Two metric names will be created:

• calls_total
  • Type: counter
  • Description: counts the total number of spans, including error spans. Call counts are differentiated from errors via the status_code label. Errors are identified as any time series with the label status_code = "STATUS_CODE_ERROR".
• [namespace_]duration_[units]
  • Type: histogram
  • Description: a histogram of span durations/latencies. Under the hood, Prometheus histograms will create a number of time series. For illustrative purposes, assume no namespace is configured and the units are milliseconds:
    • duration_milliseconds_count: The total number of data points across all buckets in the histogram.
    • duration_milliseconds_sum: The sum of all data point values.
    • duration_milliseconds_bucket: A collection of n time series (where n is the number of duration buckets) for each duration bucket identified by an le (less than or equal to) label. The duration_milliseconds_bucket counter with lowest le and le >= span duration will be incremented for each span.

The following formula aims to provide some guidance on the number of new time series created:

num_status_codes * num_span_kinds * (1 + num_latency_buckets) * num_operations

Where:
  num_status_codes = 3 max (typically 2: ok/error)
  num_span_kinds = 6 max (typically 2: client/server)
  num_latency_buckets = 17 default

Plugging those numbers in, assuming default configuration:

max = 324 * num_operations
typical = 72 * num_operations

Note:

• Custom duration buckets  or dimensions  configured in the spanmetrics connector will alter the calculation above.
• Querying custom dimensions are not supported by SPM and will be aggregated over.

Configuration

Enabling SPM

The following configuration is required to enable the SPM feature:

• Jaeger UI
• Jaeger Query
  • Set the METRICS_STORAGE_TYPE environment variable to prometheus.
  • Optional: Set --prometheus.server-url (or PROMETHEUS_SERVER_URL environment variable) to the URL of the prometheus server. Default: http://localhost:9090.
  • Optional: Set --prometheus.query.support-spanmetrics-connector=true to explicitly enable the SpanMetrics Connector  if you intend to use it. This will become the default behavior in the future.

API

gRPC/Protobuf

The recommended way to programmatically retrieve RED metrics is via jaeger.api_v2.metrics.MetricsQueryService gRPC endpoint defined in the metricsquery.proto  IDL file.

HTTP JSON

Used internally by the Monitor tab of Jaeger UI to populate the metrics for its visualizations.

Refer to this README file  for a detailed specification of the HTTP API.

Troubleshooting

Check the /metrics endpoint

The /metrics endpoint can be used to check if spans for specific services were received. The /metrics endpoint is served from the admin port. Assuming that Jaeger all-in-one and query are available under hosts named all-in-one and jaeger-query respectively, here are sample curl calls to obtain the metrics:

$ curl http://all-in-one:14269/metrics

$ curl http://jaeger-query:16687/metrics

The following metrics are of most interest:

# all-in-one
jaeger_requests_total
jaeger_latency_bucket

# jaeger-query
jaeger_query_requests_total
jaeger_query_latency_bucket

Each of these metrics will have a label for each of the following operations:

get_call_rates
get_error_rates
get_latencies
get_min_step_duration

If things are working as expected, the metrics with label result="ok" should be incrementing, and result="err" being static. For example:

jaeger_query_requests_total{operation="get_call_rates",result="ok"} 18
jaeger_query_requests_total{operation="get_error_rates",result="ok"} 18
jaeger_query_requests_total{operation="get_latencies",result="ok"} 36

jaeger_query_latency_bucket{operation="get_call_rates",result="ok",le="0.005"} 5
jaeger_query_latency_bucket{operation="get_call_rates",result="ok",le="0.01"} 13
jaeger_query_latency_bucket{operation="get_call_rates",result="ok",le="0.025"} 18

jaeger_query_latency_bucket{operation="get_error_rates",result="ok",le="0.005"} 7
jaeger_query_latency_bucket{operation="get_error_rates",result="ok",le="0.01"} 13
jaeger_query_latency_bucket{operation="get_error_rates",result="ok",le="0.025"} 18

jaeger_query_latency_bucket{operation="get_latencies",result="ok",le="0.005"} 7
jaeger_query_latency_bucket{operation="get_latencies",result="ok",le="0.01"} 25
jaeger_query_latency_bucket{operation="get_latencies",result="ok",le="0.025"} 36

If there are issues reading metrics from Prometheus such as a failure to reach the Prometheus server, then the result="err" metrics will be incremented. For example:

jaeger_query_requests_total{operation="get_call_rates",result="err"} 4
jaeger_query_requests_total{operation="get_error_rates",result="err"} 4
jaeger_query_requests_total{operation="get_latencies",result="err"} 8

At this point, checking the logs will provide more insight towards root causing the problem.

Query Prometheus

Graphs may still appear empty even when the above Jaeger metrics indicate successful reads from Prometheus. In this case, query Prometheus directly on any of these metrics:

• duration_bucket
• duration_milliseconds_bucket
• duration_seconds_bucket
• calls
• calls_total

You should expect to see these counters increasing as spans are being emitted by services to the OpenTelemetry Collector.

Viewing Logs

If the above metrics are present in Prometheus, but not appearing in the Monitor tab, it means there is a discrepancy between what metrics Jaeger expects to see in Prometheus and what metrics are actually available.

This can be confirmed by increasing the log level by setting the following environment variable:

LOG_LEVEL=debug

Outputting logs that resemble the following:

{
    "level": "debug",
    "ts": 1688042343.4464543,
    "caller": "metricsstore/reader.go:245",
    "msg": "Prometheus query results",
    "results": "",
    "query": "sum(rate(calls{service_name =~ \"driver\", span_kind =~ \"SPAN_KIND_SERVER\"}[10m])) by (service_name,span_name)",
    "range":
    {
        "Start": "2023-06-29T12:34:03.081Z",
        "End": "2023-06-29T12:39:03.081Z",
        "Step": 60000000000
    }
}

In this instance, let’s say OpenTelemetry Collector’s prometheusexporter introduced a breaking change that appends a _total suffix to counter metrics and the duration units within histogram metrics (e.g. duration_milliseconds_bucket). As we discovered, Jaeger is looking for the calls (and duration_bucket) metric names, while the OpenTelemetry Collector is writing calls_total (and duration_milliseconds_bucket).

The resolution, in this specific case, is to set environment variables telling Jaeger to normalize the metric names such that it knows to search for calls_total and duration_milliseconds_bucket instead, like so:

PROMETHEUS_QUERY_NORMALIZE_CALLS=true
PROMETHEUS_QUERY_NORMALIZE_DURATION=true

Checking OpenTelemetry Collector Config

If there are error spans appearing in Jaeger, but no corresponding error metrics:

• Check that raw metrics in Prometheus generated by the spanmetrics connector (as listed above: calls, calls_total, duration_bucket, etc.) contain the status.code label in the metric that the span should belong to.
• If there are no status.code labels, check the OpenTelemetry Collector configuration file, particularly for the presence of the following configuration:

  exclude_dimensions: ['status.code']
  

  This label is used by Jaeger to determine if a request is erroneous.

Inspect the OpenTelemetry Collector

If the above latency_bucket and calls_total metrics are empty, then it could be misconfiguration in the OpenTelemetry Collector or anything upstream from it.

Some questions to ask while troubleshooting are:

• Is the OpenTelemetry Collector configured correctly?
  • See: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/connector/spanmetricsconnector 
• Is the Prometheus server reachable by the OpenTelemetry Collector?
• Are the services sending spans to the OpenTelemetry Collector?
  • See: https://github.com/open-telemetry/opentelemetry-collector/blob/main/docs/troubleshooting.md 

Service/Operation missing in Monitor Tab

If the service/operation is missing in the Monitor Tab, but visible in the Jaeger Trace search service and operation drop-downs menus, a common cause of this is the default server span kind used in metrics queries.

The service/operations you are not seeing could be from spans that are non-server span kinds such as client or worse, unspecified. Hence, this is an instrumentation data quality issue, and the instrumentation should set the span kind.

The reason for defaulting to server span kinds is to avoid double-counting both ingress and egress spans in the server and client span kinds, respectively.

403 when executing metrics query

If logs contain the error resembling: failed executing metrics query: client_error: client error: 403, it is possible that the Prometheus server is expecting a bearer token.

Jaeger Query (and all-in-one) can be configured to pass the bearer token in metrics queries via the --prometheus.token-file command-line parameter (or the PROMETHEUS_TOKEN_FILE environment variable), with its value set to the path of the file containing the bearer token.