Jaeger analytics Java

Experimental repository with data analytics models, pipelines for Jaeger tracing data.

Repository contains:

Graph trace DSL based on Apache Gremlin. It helps to write graph "queries" against a trace
Spark streaming integration with Kafka for Jaeger topics
Loading trace from Jaeger query service
Jupyter notebooks to run examples with data analytic models
Data analytics models, metrics based on tracing data
Grafana dashboards

Blog posts, demos and conference talks:

Metrics

The library calculates various metrics from traces. The metrics are currently exposed in Prometheus format.

Currently these metrics are calculated:

Trace height - trace tree height. Maximum number of spans from root to leaf
Service depth - number of service hops from a service to the root service
Service height - number of service hops from a service to the leaf service
Service's direct downstream dependencies - number services a service directly calls
Service's direct upstream parents - number of services directly calling a service
Number of errors - number of errors per service
Network latency - latency between client and server spans split by service names

network_latency_seconds_bucket{client="frontend",server="driver",le="0.005",} 32.0
network_latency_seconds_bucket{client="frontend",server="driver",le="0.01",} 32.0
network_latency_seconds_bucket{client="frontend",server="driver",le="0.025",} 32.0
service_height_total{quantile="0.7",} 2.0

Trace quality metrics

Trace quality metrics measure the quality of tracing data reported by services. These metrics can indicate that further instrumentation is needed or the instrumentation quality is not high enough.

These metrics are ported from jaeger-analytics-flink/tracequality. The original design stores results in separate storage table (Cassandra). The intention here is to export results as metrics and link relevant traces as exemplars (once OSS metrics APIs support that).

Minimum Jaeger client version - minimum Jaeger client version
Has client and server tags - span contains client or server tags
Unique span IDs - trace contains spans with unique span IDs

trace_quality_server_tag_total{pass="false",service="mysql",} 32.0
trace_quality_server_tag_total{pass="true",service="customer",} 26.0
trace_quality_minimum_client_version_total{pass="false",service="route",version="Go-2.21.1",} 320.0

Example Prometheus queries:

(trace_quality_server_tag_total{pass="true",service="customer",} / trace_quality_server_tag_total{service="customer",}) * 100
trace_quality_server_tag_total{pass="true",service="customer",} / ignoring (pass,fail) sum without(pass, fail) (trace_quality_server_tag_total)
// if values are missing
(trace_quality_server_tag_total{pass="true",service="mysql",}  / trace_quality_server_tag_total{service="mysql",} ) * 100 or vector(0)

Development

Add annotation processor is needed for IDE configuration. It is used to generate trace DSL.

org.apache.tinkerpop.gremlin.process.traversal.dsl.GremlinDslProcessor

Build and run

mvn clean compile exec:java

Configuration

Configuration properties for SparkRunner.

SPARK_MASTER: Spark master to submit the job to; Defaults to `local[*]
SPARK_STREAMING_BATCH_DURATION: interval defines the size of the batch in milliseconds; Defaults to `5000
KAFKA_JAEGER_TOPIC: Kafka topic with Jaeger spans; Defaults to jaeger-spans
KAFKA_BOOTSTRAP_SERVER: Kafka bootstrap servers; Defaults to localhost:9092
KAFKA_START_FROM_BEGINNING: Read kafka topic from the beginning; Default to true
PROMETHEUS_PORT: Prometheus exporter port; Defaults to 9111
TRACE_QUALITY_{language}_VERSION: Minimum Jaeger client version for trace quality metric; Supported languages java, node, python, go; Defaults to latest client versions

Gremlin documentation

http://kelvinlawrence.net/book/Gremlin-Graph-Guide.html

Spark Kafka documentation

Deploy Kafka, Elasticsearch and Jaeger on Kubernetes using operators

The following command creates Jaeger CR which triggers deployment of Jaeger, Kafka and Elasticsearch. This works only on OpenShift 4.x and prior deploying make sure Jaeger, Strimzi(Kafka) and Elasticsearch(from OpenShift cluster logging) operators are running.

oc create -f manifests/jaeger-auto-provisioned.yaml

If you are running on vanilla Kubernetes you can deploy jaeger-external-kafka-es.yaml CR and configure connection strings to Kafka and Elasticsearch.

Expose Kafka outside of cluster and get host:port

Expose Kafka IP address outside of the cluster:

listeners:
  # ...
  external:
    type: loadbalancer
    tls: false

Get external broker address:

oc get kafka simple-streaming -o jsonpath="{.status.listeners[*].addresses}"

Expose Jaeger collector outside of the cluster

oc create route edge --service=simple-streaming-collector --port c-binary-trft --insecure-policy=Allow

Deploy Hotrod example application

oc get routes # get jaeger collector route
docker run --rm -it -e "JAEGER_ENDPOINT=http://host:80/api/traces" -p 8080:8080 jaegertracing/example-hotrod:latest

Get exposed metrics

The streaming job exposes metrics on http://localhost:9001.

Run Jupyter as docker

The docker image should be published on Docker Hub. If you are modifying the source code of the library then inject it as volume -v ${PWD}:/home/jovyan/work or rebuild the image too see the latest changes.

make jupyter-docker
make jupyter-run

Open browser on http://localhost:8888/lab and copy token from the command line. Then navigate to ./work/jupyter/ directory and open notebook.

Run on Mybinder

Using Jaeger in JUnit with Testcontainers

Artifact io.jaegertracing:jaeger-testcontainers contains an implementation for using Jaeger all-in-one docker container in JUnit tests:

JaegerAllInOne jaeger = new JaegerAllInOne("jaegertracing/all-in-one:latest");
jaeger.start();
io.opentracing.Tracer tracer = jaeger.createTracer("my-service");

grimmmyshini/jaeger-analytics-java