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Setup a distributed tracing infrastructure with Zipkin, Kafka and Cassandra

For a master table-of-contents for blog posts on microservice topics, please refer —

This blog will demonstrate how you can set up a scalable distributed tracing infrastructure on Oracle Cloud

One of my earlier blogs showcased a simple example of distributed tracing for microservices (based on Spring Boot) using an example revolving around Spring Cloud Sleuth and Zipkin

There are couple of key points which need to be highlighted with regards to the default behavior of the system

  • Sending data to Zipkin — Spring Cloud Sleuth sends spans/trace data to Zipkin over HTTP
  • Persisting the trace data — Zipkin stores span data in-memory

This is obviously not a bullet-proof solution

  • Single point of failure — Since Zipkin is core component of our distributed tracing solution, it also has the potential of becoming a bottleneck both from an availability and performance standpoint
  • Ephemeral storage — since Zipkin stores span data in-memory, it will be lost if the Zipkin instance restarts or crashes

Although nothing is perfect, this situation can be improved. Here is an overview of what this blog will demonstrate in terms of the solution

  • Zipkin runtime — we will continue to use Oracle Application Container Cloud as the runtime for the Zipkin server
  • Cassandra for persistent storageOracle Data Hub Cloud is a managed Cassandra offering which we will use as the NoSQL persistent store for Zipkin
  • Kafka as the message busOracle Event Hub Cloud is a managed Kafka service which will be leveraged in order to decouple Spring Cloud Sleuth from Zipkin

Time to dive deeper… !

Use case overview

The use case is still the same as the earlier blog i.e. distributed tracing. But this particular blog is focused on the infrastructure side of things, hence the sample itself is very simple — so all we have is a single Spring Boot app (named inventory) to test things out (as opposed to a couple of microservices which were used as an example to illustrate the concepts in the earlier blog post)

Here is the high level flow

  • the inventory app sends span data to Kafka..
  • … which is (asynchronously) consumed by Zipkin and persisted to Cassandra

The key focus is on

  • revamping the Zipin server to communicate to Kafka and Cassandra on Oracle Cloud
  • updating the inventory service to talk to Kafka rather than interface directly with Zipkin

Architecture components

Here is a diagram to give you a high level idea of what the architecture is..

High level arch diagram

Kafka (on Oracle Event Hub Cloud)

As mentioned above, since Kafka sits between your application (which is using Spring Cloud Sleuth) and Zipkin, trace data will be sent to Kafka which Zipkin can consume asynchronously and persist to Cassandra

Although it is another piece of infrastructure to deal with, the obvious benefits are

  • It acts as buffer and helps manage the back pressure — think multiple microservices pumping trace data
  • It also helps with availability — if the Zipkin cluster is unavailable, applications still continue enqueue data into Kafka which can be picked up by Zipkin when its back up

Both Zipkin server as well as the individual applications talk to Kafka

The Zipkin server component…

  • … uses @EnableZipkinStreamServer (thanks to the spring-cloud-sleuth-zipkin-stream dependency) annotation to signal that it will be receiving span/trace messages from Kafka
  • the Spring Cloud Stream Kafka binder is pulled in via spring-cloud-starter-stream-kafka and this takes care of the Kafka consumer part
  • the use and to specify the co-ordinates of Kafka and Zookeeper respectively

Our Sleuth enabled Spring Boot microservice ..

  • .. also pushes trace/span data to Kafka spring-cloud-sleuth-stream and (the usual) spring-cloud-starter-stream-kafka modules
  • it also uses the same as the Zipkin server to specify Kafka and Zookeeper co-ordinates

Cassandra (on Oracle Data Hub Cloud)

Only the Zipkin server (not the applications) interacts with Cassandra to persist the trace data/messages it receives from Kafka. It uses a Java driver for Cassandra and zipkin-autoconfigure-storage-cassandra3 module

The key highlights are the configuration parameters in

  • — value used is cassandra3
  • — where is the Cassandra cluster
  • and — quite obvious
  • — which keyspace should Zipkin create objects in
  • — if set to true, Zipkin automatically seeds the schema with the required objects (UDTs, tables etc.).. isn’t that sweet !

More details on the Cassandra objects in the next section

Service Bindings in Oracle Application Container Cloud

Its important to reiterate as to how important Service Bindings are and they how they make things much simpler in terms of secure and hassle-free communication between applications (Zipkin and other microservices) and downstream infrastructure components like Kafka (Eventh Hub) and Cassandra (Data Hub)

All you really need to do is declare a dependency (binding) and you’ll get a internal communication channel set up for free without punching holes in the firewalls/access rules of the respective services

This concept will be highlighted in the upcoming sections and you can always refer to the official documentation for more info

Let’s quickly cover the setup for our infrastructure components

  • Cassandra, and
  • Kafka

Infrastructure setup

Oracle Data Hub Cloud

Provision Cassandra cluster

Start by bootstrapping a new cluster — detailed documentation here

Cluster create options

It is also possible to do this using a CLI

Below snippet shows a basic single node cluster running Cassandra 3.10.0

Oracle Datahub cloud Cassandra Cluster

Check Zipkin related schema objects

As mentioned above, Zipkin related Cassandra schema objects are auto created — these include user defined types and tables

To check these,

  • SSH into the Cassandra cluster node — documentation available here
  • Fire up cqlsh — execute sudo su oracle (relevant information here) and then cqlsh -u admin `hostname`
Logged into cqlsh
  • get the details of your zipkin specific keyspace (which you configured in — e.g. desc zipkin .. if don’t remember it, just use desc keyspaces and you should see yours there

Here is a screenshot of some of the objects

Zipkin schema objects in Cassandra

Oracle Event Hub Cloud (Kafka broker)

The Kafka cluster topology used in this case is relatively simple i.e. a single broker with co-located with Zookeeper). You can opt for a topology specific to your needs e.g. HA deployment with 5-node Kafka cluster and 3 Zookeeper nodes

Please refer to the documentation for further details on topology and the detailed installation process (hint: its straightforward!)

Kafka broker on Event Hub Cloud

Creating custom access rule

You would need to create a custom Access Rule to open port 2181 on the Kafka Server VM on Oracle Event Hub Cloud — details here

Oracle Application Container Cloud does not need port 6667 (Kafka broker) to be opened since the secure connectivity is taken care of by the service binding

Build, configure & deploy

Start by fetching the project from Github — git clone


Zipkin server

  • cd zipkin
  • mvn clean install

The build process will create in the target directory

Inventory service

  • cd inventory
  • mvn clean install

The build process will create in the target directory


Before you launch these into the cloud, please tweak the configuration parameters as per your environment

deployment.json and manifest.json for Zipkin server

deployment.json and manifest.json for inventory service

Now that you have configured your apps, it’s time to deploy them

Deployment a.k.a push to cloud

With Oracle Application Container Cloud, you have multiple options in terms of deploying your applications. This blog will leverage PSM CLI which is a powerful command line interface for managing Oracle Cloud services

other deployment options include REST API, Oracle Developer Cloud and of course the console/UI

You can download and setup PSM CLI on your machine (using psm setup) — details here

Here are the CLI commands to deploy the apps

Zipkin psm accs push -n zipkin -r java -s hourly -m manifest.json -d deployment.json -p target/

Inventory psm accs push -n inventory -r java -s hourly -m manifest.json -d deployment.json -p target/

Sanity checks

  • check service bindings, and,
  • access the Zipkin server to confirm its functional

After the Zipkin server has been successfully deployed, you can check Service Binding its details by navigating to the details screen -> Deployments section — you will see both Data Hub (Cassandra) and Event Hub (Kafka) bindings along with their respective environment variables (cropped image)

Zipkin bindings to Kafka, Cassandra

Same applies for the inventory service (it binds only to Kafka)

Inventory service binding to Kafka

Finally, access the Zipkin server — note the app URL e.g. https://zipkin-<mydomain>

Zipkin on Oracle Application Container Cloud

Test drive…

Alright, everything is setup for us to test things out

Invoke inventory service

To start with, check the URL of the inventory app and invoke it a few times

Access Zipkin dashboard to see the span data

Zipkin dashboard with span/trace data

If you dig down further into the span, you’ll see more details

Span details

Above is the span from our invocation of iphoneX (one of the three invocations listed above). This is relatively simple since you just have single service, but the same would apply if you had a chain of invocations with different (micro) services involved

If you dig in even further, there is more to unearth — focus on the highlighted info

More details about a specific trace

Span data in Cassandra

you can double check Cassandra as well. Using cqlsh, you execute select * from zipkin.traces; (assuming zipkin is the keyspace name)

You can also query other related tables

What about Kafka ?

As you might understood, the span data is sent by Sleuth to a Kafka topic (unsurprisingly named sleuth) which is then consumed by Zipkin and persisted to Cassandra.. try this out as well

Use the kafka CLI to set up a consumer — kafka-console-consumer.bat --bootstrap-server <event_hub_kakfa_IP>:6667 --topic sleuth

Invoke the inventory app again — curl https://inventory-<mydomain>

You will see a rather cryptic message whose contents are pasted below for your easy reference — recall that you saw the same trace data in Zipkin before

Monitor the sleuth topic in Kafka for trace data

♣♂contentType ♀”text/plain”‼originalContentType “application/json;charset=UTF-8”♠spanId ↕”e7bd0aabba1fa09c”♂spanTraceId ↕”e7bd0aabba1fa09c”♂spanSampled ♥”0"{“host”:{“serviceName”:”inventory”,”address”:”",”port”:8080},”spans”:[{“begin”:1516776180246,”end”:1516776180258,”name”:”http:/inventory/AppleWatch”,”traceId”:8394174692565835560,”spanId”:8394174692565835560,”exportable”:true,”tags”:{“mvc.controller.class”:”InventoryApplication”,”http.status_code”:”200",”mvc.controller.method”:”getInventory”,”spring.instance_id”:”",”http.path”:”/inventory/AppleWatch”,”http.url”:”http://inventory-<mydomain>","http.method":"GET","":"inventory-<mydomain>"},"logs":[{"timestamp":1516776180246,"event":"sr"},{"timestamp":1516776180258,"event":"ss"}],"durationMicros":12550}]}


Well, that’s all there is to this blog post which was the second (and final) installment in the distributed monitoring series..

A quick recap never harms!

  • we covered the drawbacks of the default monitoring setup and introduced Kafka and Cassandra as core infrastructure components to overcome some of the issues
  • went through the details of the architecture
  • provisioned the infrastructure on Oracle Cloud as ready-to-use managed services — Data Hub Cloud and Event Hub Cloud
  • built and deployed our revamped apps to Oracle Application Container Cloud
  • and finally, tested everything end-to-end !

Don’t forget to…

  • go through the Oracle Data Hub Cloud documentation for a deep dive
  • check out the tutorials for Oracle Application Container Cloud — there is something for every runtime!
  • other blogs on Application Container Cloud


The views expressed in this post are my own and do not necessarily reflect the views of Oracle.



Aggregation of articles from Oracle engineers, Groundbreaker Ambassadors, Oracle ACEs, and Java Champions on all things Oracle technology. The views expressed are those of the authors and not necessarily of Oracle.

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Abhishek Gupta

Principal Developer Advocate at AWS | I ❤️ Databases, Go, Kubernetes