Wednesday, October 28, 2026

Narayana LRA 2.0.0.Final: propagating JWT identity through a saga

When a single business operation spans several microservices, the caller's identity must travel with it. Without it, the downstream services cannot authorize the work, and the audit trail of who initiated the operation is lost. This blog walks through a Quarkus quickstart that carries a JSON Web Token (JWT) through a MicroProfile LRA saga using the @PropagateToken annotation, and then examines where that propagation ends and explicit configuration takes over.

You can find the runnable code in the quickstart module rts/lra-examples/lra-jwt/quarkus/token-propagation.

The two specifications

MicroProfile LRA coordinates distributed transactions with the saga pattern. Instead of holding database locks across services, each participant does its work immediately and registers a compensation to undo it. A coordinator drives the participants to either @Complete (on success) or @Compensate (on failure). This works across plain HTTP, so it suits microservices far better than classic two-phase commit.

MicroProfile JWT turns a signed bearer token into an authenticated identity. Inject JsonWebToken and you obtain the caller's subject and issuer. The signature makes the token tamper-proof; the runtime verifies it against the issuer's public key (a JWKS).

Combining them raises a practical question: how does the token reach the inventory service, the payment service, and the coordinator?

Environment setup

Clone and build lra:

git clone https://github.com/jbosstm/lra.git
cd lra
mvn clean install -DskipTests
cd ..

Clone and build the quickstart:

git clone https://github.com/marcosgopen/quickstart -b lra-jwt
cd quickstart
mvn clean install -DskipTests
cd rts/lra-examples/lra-jwt/quarkus/token-propagation

The scenario

                       ┌────────────────┐  reserve / release
Client ──Bearer JWT──► │  OrderService    │ ─────────────────► InventoryService
                       │  @LRA(REQUIRED)  │  process / refund
                       │  @PropagateToken │ ─────────────────► PaymentService
                       └─────────────────┘

OrderService opens an LRA and calls two participants. If the payment is declined, the whole order is rolled back and the inventory reservation is compensated.

The forward path: @PropagateToken

The orchestrator method is concise:

@POST
@Path("/create")
@Authenticated
@LRA(LRA.Type.REQUIRED)
@PropagateToken
public Response createOrder(OrderRequest order,
                            @HeaderParam(LRA_HTTP_CONTEXT_HEADER) URI lraId,
                            @HeaderParam(HttpHeaders.AUTHORIZATION) String authHeader,
                            @Context UriInfo uriInfo) {
    // ... calls inventory then payment, forwarding authHeader on each hop
}

@PropagateToken — introduced with the JWT integration in LRA 2.0.0.Final — propagates the caller's identity on outbound calls. It works as follows:

  1. The LRA server filter (ServerLRAFilter) reads the inbound Authorization: Bearer <jwt> header, confirms it is a well-formed JWT, and stores it in a thread-local for the request.
  2. Every LRA-protocol call the service makes to the coordinator — starting the LRA, enlisting a participant, closing or cancelling it — passes through the LRA client request filter, which reads that thread-local (or a CDI-injected JsonWebToken, or a configured client property) and re-attaches Authorization: Bearer <jwt>.
  3. The service-to-service calls forward the same header explicitly, so each participant receives the token, verifies it, and repeats the process when it enlists.

The result is visible in the logs: the inventory and payment endpoints inject the token and record the authenticated caller:

Reserving 2 x ITEM-123 for order ORDER-001 ... (caller=demo-user, authHeader=present)
Processing payment of 99.99 for order ORDER-001 ... (caller=demo-user, authHeader=present)

Identity on the callback path

The callback path works differently from the forward path. The coordinator does not persist the end user's token and replay it on the @Complete/@Compensate callbacks. It attaches a token only when it has been configured to, and the token it then sends is one of:

  • a request-scoped token, when a close/cancel is driven by an inbound request that the coordinator itself authenticates, or
  • a pre-provisioned service token, used by recovery — the background threads that finish interrupted sagas, where no inbound user request exists at all.

That second case is the real reason a stored user token wouldn't work anyway: a long-running action might not complete until minutes, hours, or days later, by which point the user's short-lived access token has long expired. Recovery needs its own durable credential.

In this quickstart the standalone coordinator is not configured with outbound auth providers or a service token, so the callbacks arrive with no Authorization header, as the logs confirm:

Complete callback for LRA ... (caller=null, authHeader=absent): committed ITEM-123

This is the observable default, and it marks the boundary between the two halves of the story: forward propagation requires no configuration, whereas authenticated callbacks require coordinator configuration and a shared identity provider. The next section configures them — see Authenticating the callbacks.

Watching a compensation

Send an order with a negative amount and the payment declines (HTTP 402). OrderService turns that into a failure, the LRA is cancelled, and the reservation that already joined is released:

Reserving 1 x ITEM-123 for order ORDER-002 ... (caller=demo-user, authHeader=present)
Payment declined for order ORDER-002 (negative amount)
Compensate callback for LRA ... (caller=null, authHeader=absent): released ITEM-123

The forward hops are authenticated; the compensation callback is not, following the same rule as above.

Authenticating the callbacks

The section above shows the default: an unconfigured coordinator, with callbacks that carry no token. To complete the picture, use a coordinator configured for authentication. The lra-coordinator-quarkus application provides this — opt-in inbound JWT protection of its own endpoints, an outbound callback filter, and a pluggable service-token provider — and is published as quay.io/jbosstm/lra-coordinator:latest.

Two pieces have to line up:

  1. The participant enforces authentication on its callback endpoints. This module leaves that disabled by default (so the walkthrough above works without a configured coordinator); enable it by starting the participant with -Dparticipant.callback.auth.policy=authenticated. The /inventory/compensate, /payment/complete, and related paths then reject any request without a valid token.
  2. The coordinator presents a token on every callback. Run the image with the callback filter enabled and a service token mounted as a secret volume — the classic Kubernetes pattern. The coordinator re-reads the file every refresh-seconds, so a rotated secret never goes stale:
    # the coordinator's own identity; in production this comes from your IdP, not the demo endpoint
    curl -s "http://localhost:8081/auth/token?subject=lra-coordinator" | sed 's/^Bearer //' > ~/lra-secrets/service-token.jwt
    
    podman run -d --network host \
      -v ~/lra-secrets:/var/run/secrets/lra:ro,Z \
      -e LRA_AUTH_POLICY=authenticated \
      -e MP_JWT_VERIFY_PUBLICKEY_LOCATION=http://localhost:8081/auth/jwks \
      -e MP_JWT_VERIFY_ISSUER=https://demo.narayana.io \
      -e LRA_HTTP_CLIENT_PROVIDERS=io.narayana.lra.coordinator.security.JwtTokenCallbackRequestFilter \
      -e LRA_SECURITY_SERVICE_TOKEN_LOCATION=/var/run/secrets/lra/service-token.jwt \
      quay.io/jbosstm/lra-coordinator:latest

With both sides configured, the callback log shows an authenticated caller, and the token it uses depends on how the callback was driven:

Complete callback for LRA ... (caller=demo-user, authHeader=present): committed ITEM-123
Compensate callback for LRA ... (caller=lra-coordinator, authHeader=present): released ITEM-123

The first ran inline with a close that the coordinator handled on a request thread, so it reused the caller's propagated token. The second was driven by a timeout or recovery on a background thread with no inbound request — the recovery case described in Identity on the callback path — so it fell back to the coordinator's service token (subject=lra-coordinator). This is the purpose of a dedicated service credential: it is available precisely when the user's token is not.

Requiring an authenticated caller

Every forward endpoint in this example — createOrder and the reserve/process participant operations — is annotated @Authenticated, so a request without a valid token is rejected with 401 before any work runs. (@LRA(MANDATORY) only requires an LRA context, not an identity, so the participant methods need @Authenticated as well.) No custom security code is required; authentication is enforced by MicroProfile JWT.

Generating keys

The demo mints its own tokens, so no external identity provider is required to run it. The RSA key pair is generated in memory at start-up — nothing is written to disk or committed to source control — and the public half is published at /auth/jwks, the location that mp.jwt.verify.publickey.location points to. The application therefore verifies the tokens it issues, and the unit tests assert the key properties: a token verifies only against the published key, and the JWKS never exposes private material.

In production, remove JwtKeyGenerator and point mp.jwt.verify.publickey.location at a real provider's JWKS (Keycloak, Entra ID, Auth0, and similar).

Taking it to production

  • Real IdP. Replace the in-process key/token generator with an external identity provider.
  • HTTPS everywhere. The coordinator URL and every inter-service hop should be TLS.
  • Separate credentials for separate jobs. Use short-lived access tokens for the forward path, and give the coordinator a dedicated service token (lra.security.service-token.location) so recovery-driven callbacks can authenticate long after the user's token has expired. Mount it as a secret volume into quay.io/jbosstm/lra-coordinator (as shown above) and rotate it with your secrets tooling — the coordinator re-reads it on an interval.
  • Secure the coordinator's own endpoints too. That is a distinct concern from propagation. The lra-coordinator-quarkus image does it with lra.auth.policy=authenticated plus mp.jwt.verify.* (shown above); the sibling wildfly example shows the WildFly equivalent with endpoint-to-role mappings.

Code: https://github.com/jbosstm/quickstart · LRA: https://github.com/eclipse/microprofile-lra · Narayana LRA: https://github.com/jbosstm/lra · JWT: https://github.com/eclipse/microprofile-jwt-auth · Narayana: https://narayana.io/

Wednesday, July 8, 2026

From Stuck to Fixed: Debugging an LRA Heuristic with an AI Assistant

 

Distributed sagas are hard to debug. When a participant service fails mid-transaction, the

coordinator enters a *heuristic* state — a stuck, partially-compensated mess that requires

manual operator action. Finding out *which* participant failed, *why*, and *what to do about

it* normally means grepping logs across services and reading coordinator state dumps.


This post walks through a live example using the Narayana LRA coordinator and the LRA AI

dashboard — a Quarkus application that lets you chat with the coordinator state in plain English.

We will deliberately break a saga, watch it enter a heuristic state, and then fix it through

a conversation with the AI assistant.


---


## What Is a Heuristic Outcome?


In the LRA (Long Running Actions) model, when you cancel a transaction the coordinator asks

every enrolled participant to *compensate* — roll back its local changes. If a participant

service is down or returns an error, the coordinator retries periodically. When it gives up

after exhausting retries, the LRA enters **FailedToCancel** and the participant is marked

**FailedToCompensate**. At that point automatic recovery is no longer possible and an operator

must intervene.


This is the heuristic outcome: the saga is partially compensated, state is inconsistent, and

a human needs to decide what to do.


---


## Setup


You need three things running before starting:


| Component | Port |

|-----------|------|

| LRA coordinator | 8080 |

| LRA AI assistant | 8082 |

| Mock participant (bad) | 9999 |


**1 — LRA coordinator**


```bash

podman run --network host quay.io/jbosstm/lra-coordinator:latest

```


`--network host` is required so the coordinator can call back to the mock participant running on the host (`localhost:9999`).


**2 — AI assistant**


```bash

git clone https://github.com/marcosgopen/lra-ai-dashboard.git

cd lra-ai-dashboard

mvn quarkus:dev -Dquarkus.http.port=8082

```


Open **http://localhost:8082** — you should see the chat UI.

 

Note: the quarkus application should start a container with the llama3.1 model, if you want to install ollama by yourself you can see the guide here https://github.com/marcosgopen/lra-ai-dashboard#install-and-start-ollama

 

**3 — A deliberately broken participant service**


This one-liner starts an HTTP server on port 9999 that returns `500 FailedToCompensate`.

The body is critical: a bare 500 with no body is treated as a transient error and retried

every 120 seconds indefinitely — the LRA would stay stuck in `Cancelling` forever. The

coordinator only moves the LRA to `FailedToCancel` when the response body is a valid

`ParticipantStatus` enum value (`FailedToCompensate` or `FailedToComplete`).


```bash

python3 -c "

import http.server


class AlwaysFail(http.server.BaseHTTPRequestHandler):

def do_PUT(self):

body = b'FailedToCompensate'

self.send_response(500)

self.send_header('Content-Type', 'text/plain')

self.send_header('Content-Length', str(len(body)))

self.end_headers()

self.wfile.write(body)

print(f' [{self.command}] {self.path} -> 500 FailedToCompensate')

def log_message(self, *a): pass


print('Bad participant listening on :9999')

http.server.HTTPServer(('', 9999), AlwaysFail).serve_forever()

"

```


---


## Creating the LRA and Enrolling the Bad Participant


Open a new terminal. The steps below use `curl` to drive the coordinator REST API directly.


**Start a new LRA:**


```bash

LRA_ID=$(curl -s -X POST \

"http://localhost:8080/lra-coordinator/start?ClientID=payment-saga&TimeLimit=0" \

-H "Accept: text/plain")


echo "LRA ID: $LRA_ID"

# → http://localhost:8080/lra-coordinator/0_ffff7f000001_b07b_68401234_0

```


**Enroll the bad participant:**


The coordinator records participant callback URLs from the `Link` header. It will call

`compensate` on cancel and `complete` on close.


```bash

curl -s -X PUT "$LRA_ID" \

-H "Link: <http://localhost:9999/compensate>;rel=compensate, <http://localhost:9999/complete>;rel=complete, <http://localhost:9999/forget>;rel=forget" \

-H "Long-Running-Action: $LRA_ID" \

-H "Accept: text/plain"

```


A successful enrolment returns a recovery URL — save it, you may need it later:


```

http://localhost:8080/lra-coordinator/recovery/0_ffff7f000001_b07b_68401234_0/1

```


At this point the LRA is `Active` with one enrolled participant. Now switch entirely to the

AI assistant — all remaining steps happen through the chat UI.


---


## Step 1 — AI Lists the Active Transactions


Open the chat UI at **http://localhost:8082** and ask:


> **You:** Show me all active transactions.


The assistant calls `listAllLRAs()` and responds with something like:


> **Assistant:** There is one active LRA:

>

> — **LRA** `http://localhost:8080/lra-coordinator/0_ffff7f000001_b07b_68401234_0`

> — **Status:** Active

> — **ClientID:** payment-saga

>

> This transaction is in progress and has not been closed or cancelled yet.


The AI can see the LRA because it is still in the active store. The coordinator API returns

LRA-level metadata (ID, clientId, status, timestamps) but does not expose participant details

such as participant URLs or per-participant status.


---


## Step 2 — AI Cancels the Saga


Ask the assistant to cancel the transaction:


> **You:** Cancel the payment-saga LRA.


The assistant calls `cancelLRA()` with the LRA ID. The coordinator processes the cancel

synchronously: it calls `PUT http://localhost:9999/compensate`, gets back

`500 FailedToCompensate`, immediately transitions the LRA to **FailedToCancel**, and moves it

to the failed store — all before returning the response. The AI sees the result and explains:


> **Assistant:** I cancelled LRA

> `http://localhost:8080/lra-coordinator/0_ffff7f000001_b07b_68401234_0`.

>

> The cancel completed, but the result is **FailedToCancel** — at least one participant

> returned a permanent failure (`FailedToCompensate`) during compensation. The coordinator

> has given up and moved this LRA to the failed store. There is no automatic retry.

>

> To resolve this you will need to:

> 1. Check the coordinator logs to identify which participant failed and why.

> 2. Fix the failing service.

> 3. Call the participant's compensate endpoint directly — the coordinator will not retry.

> 4. Once done, tell me and I will remove the failed record from the coordinator.


Watch the bad participant terminal — you will see the single compensate call:


```

Bad participant listening on :9999

[PUT] /compensate -> 500 FailedToCompensate

```


---


## Step 3 — Fixing the Participant


In this demo you already know the failing participant is `localhost:9999` because you enrolled

it yourself. In a production system you would identify it from the coordinator logs as the

assistant suggested.


**Restore the participant service**


Stop the broken server (Ctrl-C in its terminal) and start a fixed version that returns `200`:


```bash

python3 -c "

import http.server


class AlwaysOK(http.server.BaseHTTPRequestHandler):

def do_PUT(self):

body = b'Compensated'

self.send_response(200)

self.send_header('Content-Type', 'text/plain')

self.send_header('Content-Length', str(len(body)))

self.end_headers()

self.wfile.write(body)

print(f' [{self.command}] {self.path} -> 200 Compensated')

def log_message(self, *a): pass


print('Fixed participant listening on :9999')

http.server.HTTPServer(('', 9999), AlwaysOK).serve_forever()

"

```


**Manually drive the compensation**


The coordinator will not call the participant again — you must do it directly:


```bash

curl -s -X PUT http://localhost:9999/compensate

# → 200 Compensated

```


This is the actual business compensation: the participant rolls back its local state. In a real

system this is the step where you restore a database row, reverse a payment, release a booking,

and so on.


---


## Step 4 — AI Cleans Up the Failed Record


With the participant compensated, tell the assistant and ask it to close the books:


> **You:** I have manually compensated the participant. Please clean up the failed LRA record.


The assistant calls `listFailedLRAs()` to confirm the record is still there, then calls

`deleteFailedLRA()` to remove it:


> **Assistant:**

> I've deleted the failed LRA record. The participant has been successfully compensated and the transaction can now be considered complete.

>

> **Result:** HTTP 204. The failed LRA record has been removed from the coordinator's

> ObjectStore. The saga is fully resolved — all business-level compensation has been confirmed

> and the coordinator no longer tracks this transaction.


---


## Summary


| Step | What happened |

|------|---------------|

| Create LRA | Coordinator created a new saga in `Active` state |

| Enroll bad participant | Coordinator recorded callback URLs for `localhost:9999` |

| AI: list LRAs | Assistant found the Active LRA via `listAllLRAs()` |

| AI: cancel | Assistant called `cancelLRA()` → coordinator called `/compensate` → got `500 FailedToCompensate` → `FailedToCancel` |

| AI: diagnosis | Assistant explained the failure state and recommended checking coordinator logs for the failing participant |

| Fix participant | Operator restored service and called compensate endpoint directly |

| AI: cleanup | Assistant called `deleteFailedLRA` → coordinator record removed → saga resolved |


The AI assistant drove the entire workflow — monitoring, action, diagnosis, and cleanup — through

a natural-language conversation. The operator made the decisions; the assistant handled the

coordinator API and explained what was happening at each step.


---


## Going Further


- **Real participants:** Replace the Python mock with a Quarkus microservice using the

`@Compensate` and `@Complete` annotations from the MicroProfile LRA API.

- **Nested LRAs:** A parent LRA cancellation propagates to its children — ask the assistant

to trace the compensation cascade down the hierarchy.

- **Proactive alerting:** Add a Quarkus `@Scheduled` job that calls `listFailedLRAs()`

periodically and raises an alert when the count exceeds a threshold.


Thursday, October 2, 2025

Narayana LRA participation in Hacktoberfest 2025

 

Hi,

Hacktoberfest 2025 has just started and the Narayana LRA project is participating. Any contribution to the project is welcome!

Here are the GitHub repository, Contributing Guide, and Hacktoberfest Issues link. Please notice that now Narayana LRA project uses GitHub issues to track bugs, tasks and features! 

Reach out to us on our zulip hacktoberfest dedicated channel for any questions or help. 

Please register to the Hacktoberfest website in order to track all your contributions and discover the rewards for 2025.

For more information and to discover other interesting projects participating to the event I suggest you have a look at the nice WildFly blog post.

Good month of collaborations! 

Marco 

Wednesday, June 18, 2025

Narayana is now in Commonhaus Foundation!

Hi,

Further to the earlier post about Narayana in a foundation (https://jbossts.blogspot.com/2025/03/request-for-input-narayana-in-foundation.html) I am pleased to share here that the Narayana project has successfully transferred to Commonhaus Foundation (as part of a move with WildFly - please see the WildFly announcement here https://www.wildfly.org/news/2025/04/30/WildFly-Commonhaus/).

You can find us now listed at: https://www.commonhaus.org/.

Please feel free to reach out on Zulip to chat about this: https://narayana.zulipchat.com/ 

Congratulations to the project on this new home!

Tom 

Monday, March 31, 2025

Request for input: Narayana in a foundation?

The Narayana project has been very successful for many, many years and we are very grateful for the contributions that our upstream community give to us through discussing, reporting issues, and providing code contributions to help the project thrive.

I would like to share an update with our community that we are considering to move the Narayana project to a vendor-neutral software foundation. By doing this we can hopefully further expand our community and continue to improve openness and transparency in the project.

Important Considerations

Moving to a foundation is not a trivial task, so it’s critical that the choice we make is a net benefit to our community. To help ensure this, there are a number of key factors we’re looking at when evaluating what foundation would be the best fit:

  1. Flexibility to continue shipping third-party components using a wide array of Open Source Initiative (OSI)-approved Open Source licences.
  2. Maintain as much as possible our current release processes.
  3. Retain independence in decision making, particularly on technical matters.
  4. Ensure Narayana remains visible and recognizable within a foundation’s potentially larger portfolio of projects.
  5. Ensure Narayana can make decisions based on technical merits, not foundation-imposed options.
  6. Provide flexibility in using Open Source Initiative (OSI)-approved Open Source licences for Narayana.

Support and Alignment with Red Hat Values

Red Hat business leaders are fully behind this move. Red Hat is dedicated to participating in and supporting vendor-neutral collaboration projects, such as the Linux kernel, Kubernetes, and OpenJDK. We seek the same for Narayana.

Please let us know what you think

We invite members of our Narayana community, inside and outside of Red Hat to join the discussion in the community either on the blog, in our users forum (https://groups.google.com/g/narayana-users) or over on Zulip (https://narayana.zulipchat.com/).


Monday, March 17, 2025

Narayana and its relationship to Red Hat middleware strategy



Hi everyone,


You might have already seen that Red Hat announced significant changes to its middleware strategy last month (if not, please do check out the relevant “Red Hat Blog” article: Evolving our middleware strategy [1]) and so I want to speak a little to the change and its relevance to our Narayana project.


As you may know, Narayana is a part of a number of Red Hat products, in particular Red Hat’s JBoss Enterprise Application Platform product and so this makes the strategic decision relevant to the Narayana project. That said, a key point in that article from the “Red Hat Blog” with regards to our Narayana project is that all transitioning Red Hat technology will remain open source and continue to follow an upstream-first development model. So as well as the technology still relying on being able to upstream-first (in projects like Narayana), it’s also that this upstream should remain open source (you can find what open source means at Red Hat over here [2]). Not only is the Narayana source code open source, but moreover its project operates in an open source manner, exhibiting the principles of open source and gratefully benefits from a healthy community of users and contributors. This will help us to keep innovating in the area of transactions as we move forwards.


I will also take this opportunity to add a “Thank you” for being part of our Narayana community - I am excited to see the results of what we achieve together next!



Tom Jenkinson


[1] https://www.redhat.com/en/blog/evolving-our-middleware-strategy

[2] https://www.redhat.com/en/about/open-source

Monday, January 6, 2025

Managing the availability of LRA participants

This post is a continuation of a series of jbosts blogs that discuss the MicroProfile LRA specification.

Services manage their workloads by providing endpoints to an LRA coordinator which in turn uses those endpoints to drive the LRA protocol forward thereby enabling the construction of reliable services. These endpoints may need to be modified over the long run so it ought to be possible to replace them with different ones in response to changes to the environment in which the service executes. Although the specification does not discuss how the endpoints can be replaced, the Narayana LRA REST API for the coordinator includes Microprofile OpenAPI documentation for replacing endpoints.

There are various administrative and management reasons for why the capability can be useful, such as controlling where termination handling is to take place, or to facilitate service replacement, etc. It may also be desirable for work completion, compensation, status reporting and clean up activities to be handled on different endpoints and at different times and this goal is facilitated via annotations including @Compensate, @Complete, @Status, @Forget and @AfterLRA.

When a participant does work in the context of a long running action, a “recovery URL” is created which services may use to associate their work with various management actions such as changing the participant endpoints as the action proceeds, after all a long running action can be of arbitrary duration and the needs of a service may change as the action evolves. The example I created for this post halts the JVM during “complete”, asks the user to send a curl request to the LRA coordinator to provide it with a new participant completion endpoint, restarts the participant on the new endpoint and waits for recovery to resend the completion callback to the new endpoint.

By leveraging the feature admins may proactively react to changing conditions (connectivity, throughput, functionality updates, etc) and be able to tune and or reconfigure the environment accordingly, perhaps bringing up a more reliability aware service that more intelligently operates within the more limited environment.

Build and start a coordinator on port 8080

Use the quarkus-maven-plugin to create a project for the coordinator, adding a dependency on maven artifact org.jboss.narayana.lra:lra-coordinator-jar:0.0.10.Final to the resulting pom. Also specify that the build should produce an uber jar so that the coordinator can run standalone:

    mvn io.quarkus:quarkus-maven-plugin:3.3.1:create -DprojectGroupId=org.acme -DprojectArtifactId=narayana-lra-coordinator -Dextensions="rest-jackson,rest-client"
    cd narayana-lra-coordinator
    rm -rf src/test src/main/java # the sources created by the example aren't required
    echo "quarkus.package.jar.type=uber-jar" > src/main/resources/application.properties
    # don't forget to add a dependency on maven artifact: org.jboss.narayana.lra:lra-coordinator-jar:0.0.10.Final
    ./mvnw clean package

and then start it on port 8080 by running the resulting jar

   java -jar target/narayana-lra-coordinator-1.0.0-SNAPSHOT-runner.jar &

Build and start a participant on port 8081 and run an LRA but halt the JVM before closing it

The service will be quite basic:

@Path("/halt")
public class MigratableResource {
    private static final AtomicBoolean halt = new AtomicBoolean(false);

    @LRA(value = LRA.Type.REQUIRED)
    @PUT
    public void doInTransaction() {
        halt.set(true); // halt when compensate or complete are called
        // when the business method finishes the LRA is closed and the complete endpoint will be called
    }

    @PUT
    @Path("/compensate")
    @Compensate
    public Response compensate() {
        return Response.ok().build();
    }

    @PUT
    @Path("/complete")
    @Complete
    public Response complete(@HeaderParam(LRA_HTTP_RECOVERY_HEADER) String recoveryUrl) {
        if (halt.get()) {
            int port = 8082;
            String completionUrl = String.format("http://localhost:%d/halt/complete", port);

            System.out.printf("Ask the coordinator to send the completion notification on a new endpoint using:%n");
            System.out.printf("curl -X PUT %s -d '<%s>; rel=complete'%n", recoveryUrl, completionUrl);
            Runtime.getRuntime().halt(1);
        }
        System.out.printf("completed%n");
        return Response.ok().build();
    }
}

The interesting part happens during completion where the JVM is halted. Notice that the curl command for migrating the completion endpoint is printed prior to halting.

Now build and run the participant on port 8081 - the maven project is available from the narayana artifacts maven repository.

cd <participant directory>
mvn clean package
java -Dquarkus.http.port=8081 -jar target/quarkus-app/quarkus-run.jar &

and then call the service method using the curl utility, or otherwise:

curl -X PUT -I http://localhost:8081/halt

The service method is annotated with just @LRA(value = LRA.Type.REQUIRED) so when it finishes the completion callback will be invoked by the coordinator. Make a note of the curl request printed by the completion callback just before it halts the JVM. An example is (the Uids will change on each run):

curl -X PUT http://localhost:8080/lra-coordinator/recovery/0_ffffc0a801c7_9d57_677ad0a4_2/0_ffffc0a801c7_9d57_677ad0a4_5 \
  -d '<http://localhost:8082/halt/complete>; rel=complete'

Notice that the payload of the HTTP PUT request includes the specification of the new completion callback, namely <http://localhost:8082/halt/complete>; rel=complete.

The new endpoint will be used on the next recovery pass which is every two minutes by default.

Finally restart the service on the new endpoint (port 8082):

java -Dquarkus.http.port=8082 -jar target/quarkus-app/quarkus-run.jar &

When the coordinator next runs a recovery scan it should use the new endpoint and the service will report that it has completed its' service work by printing the text “completed” when the completion endpoint is by the coordinator.