Fusion 5.9.12

• Access to latest features: Stay current with the latest features and functionality to ensure compatibility and optimal performance.
• Simplified process: Fusion 5.9.5 and later use an in-place upgrade strategy, making upgrades easier than ever.
• Extended support: Upgrading keeps you up-to-date with the latest supported Kubernetes versions, as outlined in the Lucidworks Fusion Product Lifecycle policy.

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Reinstall 5.9.12 for self-hosted Fusion

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Instructions

1. Verify the job-launcher pod is using the correct image by comparing its sha256 digest to the expected value of fusion/lucidworks/job-launcher@sha256:02624cfbc3eb536e4a29353b7bfe0846380a09f98ae3c78da859793686782092.
   1. Run the following command:
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      kubectl get pod job-launcher-pod -n ns-name -o jsonpath='{.status.containerStatuses[0].imageID}'
      

   2. If the digest output is different in any way, the image is not from the re-release.
2. Check the imagePullPolicy of your Deployments:
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   kubectl get deployment -n ns-name -o=jsonpath='{range .items[*]}{.metadata.name}{"\t"}{range .spec.template.spec.containers[*]}{.name}={.imagePullPolicy}{" "}{end}{"\n"}{end}'
   

   1. If the policy is set to IfNotPresent, patch it to Always:
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      kubectl patch deployments -n ns-name --type=json -p='[{"op":"replace","path":"/spec/template/spec/containers/0/imagePullPolicy","value":"Always"}]'
      

   2. Restart the Deployments and StatefulSets to apply the changes:
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      kubectl rollout restart deployment,statefulset -n ns-name
      

   3. (Optional) Check the imagePullPolicy for StatefulSets specifically to ensure they are set correctly:
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      kubectl get sts -n ns-name -o=jsonpath='{range .items[*]}{.metadata.name}{"\t"}{range .spec.template.spec.containers[*]}{.name}={.imagePullPolicy}{" "}{end}{"\n"}{end}'
      

3. After the rollout, confirm again that the job-launcher pod is using the correct image:
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   kubectl get pod job-launcher-pod -n ns-name -o jsonpath='{.status.containerStatuses[0].imageID}'
   

   1. If the result matches the expected digest of sha256:02624cfbc3eb536e4a29353b7bfe0846380a09f98ae3c78da859793686782092 then the pod is running the latest re-release version.

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Key highlights

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Improved relevance in large documents with chunking

• Search relevance: Users get results that point to the most relevant sections within large documents, not just documents that match overall.
• Neural search precision: Vector chunks improve hybrid scoring by aligning semantic relevance with specific lexical content.
• Scalability and maintainability: Updates or deletions are applied at the chunk level, ensuring consistency and avoiding stale or orphaned content.
• Faceted search and UX: Results can be grouped and ranked more accurately, especially in use cases where dense documents contain multiple topics.

• A new LWAI Chunker index pipeline stage uses one of the available chunking strategies (chunkers) for the specified LW AI model to provide optimized storage and retrieval. The chunkers asynchronously split the provided text in various ways such as by sentence, new line, semantics, and regular expression (regex) syntax.
• A new Chunking Neural Hybrid Query pipeline stage now detects chunked documents and retrieves the most relevant lexical and vector segments for hybrid search.
• Updates and deletions now ensure consistent chunk synchronization to prevent orphaned data.
• This feature includes a new Lucidworks AI Async Chunking API.

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Model hosting with Ray

• The Seldon vectorize stages have been renamed to Ray/Seldon Vectorize Field and Ray/Seldon Vectorize Query.
• There are two new jobs for deploying models with Ray:
  • Create Ray Deployment.
  • Delete Ray Deployment.

If you previously deployed a model with Seldon, you can deploy the same model with Ray.Just follow the instructions in Develop and deploy a machine learning model with Ray, and deploy the model with a different name to avoid conflicts.

Develop and deploy a machine learning model with Ray

This tutorial walks you through deploying your own model to Fusion with Ray.

This feature is only available in Fusion 5.9.x for versions 5.9.12 and later.

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Prerequisites

• A Fusion instance with an app and indexed data.
• An understanding of Python and the ability to write Python code.
• Docker installed locally, plus a private or public Docker repository.
• Ray installed locally: pip install ray[serve] using the version of ray[serve] found in the release notes for your version of Fusion.
• Code editor; you can use any editor, but Visual Studio Code is used in this example.
• Model: intfloat/e5-small-v2
• Docker image: e5-small-v2-ray

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Tips

• Always test your Python code locally before uploading to Docker and then Fusion. This simplifies troubleshooting significantly.
• Once you’ve created your Docker you can also test locally by doing docker run with a specified port, like 9000, which you can then curl to confirm functionality in Fusion. See the testing example below.
• If you previously deployed a model with Seldon, you can deploy the same model with Ray after making a few changes to your Docker image as explained in this topic. To avoid conflicts, deploy the model with a different name. When you have verified that the Ray model is working after deployment with Ray, you can delete the Seldon model using the Delete Seldon Core Model Deployment job.
• If you run into an issue with the model not deploying and you’re using the ‘real’ example, there is a very good chance you haven’t allocated enough memory or CPU in your job spec or in the Ray-Argo config. It’s easy to increase the resources. To edit the ConfigMap, run kubectl edit configmap argo-deploy-ray-model-workflow -n <namespace> and then find the ray-head container in the artisanal escaped YAML and change the memory limit. Exercise caution when editing because it can break the YAML. Just delete and replace a single character at a time without changing any formatting.
  • For additional guidance, see the testing locally e5-model example.

LucidAcademyLucidworks offers free training to help you get started.The Course for Intro to Machine Learning in Fusion focuses on using machine learning to infer the goals of customers and users in order to deliver a more sophisticated search experience:

Visit the LucidAcademy to see the full training catalog.

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Local testing example

1. Docker command:
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   docker run -p 127.0.0.1:9000:9000 DOCKER_IMAGE
   

2. Curl to hit Docker:
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   curl -i -X POST http://127.0.0.1:8000 -H 'Content-Type: application/json' -d '{"text": "The quick brown fox jumps over the lazy dog."}'
   

3. Curl model in Fusion:
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   curl -u $FUSION_USER:$FUSION_PASSWORD -X POST -H 'Content-Type: application/json' -d '{"text": "i love fusion"}' https://FUSION_HOST.com:6764/api/ai/ml-models/MODEL_NAME/prediction
   

4. See all your deployed models:
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   curl -u USERNAME:PASSWORD http://FUSION_HOST:FUSION_PORT/api/ai/ml-models
   

5. Check the Ray UI to see Replica State, Resources, and Logs.
   If you are getting an internal model error, the best way to see what is going on is to query via port-forwarding the model.
   The MODEL_DEPLOYMENT in the command below can be found with kubectl get svc -n NAMESPACE. It will have the same name as set in the model name in the Create Ray Model Deployment job.
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   kubectl -n NAMESPACE port-forward svc/MODEL_DEPLOYMENT-head-svc 8000:8000
   

   Once port-forwarding is successful, you can use the below cURL command to see the issue. At that point your worker logs should show helpful error messages.
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   curl --location 'http://127.0.0.1:8000/' \
   --header 'charset: utf-8' \
   --header 'Content-Type: application/json' \
   --data '{"text": "i love fusion"}'
   

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Download the model

This tutorial uses the e5-small-v2 model from Hugging Face, but any pre-trained model from https://huggingface.co will work with this tutorial.If you want to use your own model instead, you can do so, but your model must have been trained and then saved though a function similar to the PyTorch’s torch.save(model, PATH) function. See Saving and Loading Models in the PyTorch documentation.

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Format a Python class

The next step is to format a Python class which will be invoked by Fusion to get the results from your model. The skeleton below represents the format that you should follow. See also Getting Started in the Ray Serve documentation.

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from ray import serve
from starlette.requests import Request

# These defaults are for the ray serve deployment
# when running simply from docker. The 'Create Ray Model Deployment'
# job can override these replicas and resources if needed.
@serve.deployment(num_replicas=1, ray_actor_options={"num_cpus": 1})
class Deployment(object):
    def __init__(self):
        """
        Add any initialization parameters. Generally this is where you would load
        your model. This method will be called once when the deployment is created.
        """
        print("Initializing")
        self.model = load_model() #faux code

    # This can be named as any method which takes a dictionary as input and returns a dictionary
    # as output. In this example, we are using the encode method to encode the
    # input text into a vector.
    def encode(self, input_dict: Dict[str, Any]) -> Dict[str, Any]:
        """
        This method will be called when the deployment is queried. It will receive
        the input data and should return the output data.
        """
        text = input_dict["text"]
        embeddings = self.model.encode #faux code
        return { "vector": embeddings } # To use the 'Ray / Seldon Vectorize Field' stage, the output key should be `vector`, if using the 'Machine Learning' stage you must ensure the output key matches the output key in the 'Machine Learning' stage

    async def __call__(self, http_request: Request) -> Dict[str, Any]:
        input_dict: Dict[str, Any] = await http_request.json()
        return self.encode(input_dict=input_dict) # This will be the function you defined above, in this case encode


app = Deployment.bind()

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A real instance of this class with the e5-small-v2 model is as follows:

This code pulls from Hugging Face. To have the model load in the image without pulling from Hugging Face or other external sources, download the model weights into a folder name and change the model name to the folder name preceded by ./.

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import json
import sys
from time import time
from typing import Any, Dict

import torch
import torch.nn.functional as F
from ray import serve
from starlette.requests import Request
from starlette.responses import JSONResponse
from torch import Tensor
from transformers import AutoModel, AutoTokenizer

HUB_MODEL_NAME = "intfloat/e5-small-v2"


@serve.deployment(num_replicas=1, ray_actor_options={"num_cpus": 1})
class Deployment(object):
    def __init__(self):
        from loguru import logger

        self.logger = logger
        # Initializing logger
        self.logger.remove()
        self.logger.add(sys.stdout, level="INFO", serialize=False, colorize=True)

        # Initializing model
        self.logger.info("Loading model...")
        self.tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL_NAME)
        self.model = AutoModel.from_pretrained(HUB_MODEL_NAME)
        self.model.eval()
        self.logger.info("Model initialization finished!")

    def encode(self, input_dict: Dict[str, Any]) -> Dict[str, Any]:
        _start_time = time()

        # Extracting text from input
        text = input_dict["text"]

        # Tokenization
        tokenized_texts = self.tokenizer(
            text,
            max_length=512,
            padding=True,
            truncation=True,
            return_tensors="pt",
        )

        # Encoding
        with torch.inference_mode():
            # Forward pass of the model
            outputs = self.model(**tokenized_texts)

            # Average pooling the last hidden states
            embeddings = self.average_pool(
                outputs.last_hidden_state, tokenized_texts["attention_mask"]
            )

            # Normalizing embeddings
            embeddings = F.normalize(embeddings, p=2, dim=1)

            # Converting into output format
            output_dict = {"vector": embeddings.squeeze().tolist()}

        prediction_time = (time() - _start_time) * 1000
        self.logger.info(f"Time taken to make a prediction: {prediction_time:.0f}ms")
        return output_dict

    async def __call__(self, http_request: Request) -> Dict[str, Any]:
        try:
            input_dict: Dict[str, Any] = await http_request.json()
        except UnicodeDecodeError:
            body_bytes = await http_request.body()
            try:
                decoded = body_bytes.decode("utf-8", errors="replace")
                input_dict = json.loads(decoded)
            except json.JSONDecodeError:
                return JSONResponse({"error": "Invalid JSON"}, status_code=400)
        return self.encode(input_dict=input_dict)

    @staticmethod
    def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
        last_hidden = last_hidden_states.masked_fill(
            ~attention_mask[..., None].bool(), 0.0
        )
        return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


app = Deployment.bind()

See all 89 lines

In the preceding code, logging has been added for debugging purposes.The preceding code example contains the following functions:

• __call__: This function is non-negotiable.
• init: The init function is where models, tokenizers, vectorizers, and the like should be set to self for invoking.
  It is recommended that you include your model’s trained parameters directly into the Docker container rather than reaching out to external storage inside init.
• encode: The encode function is where the field or query that is passed to the model from Fusion is processed.
  Alternatively, you can process it all in the __call__ function, but it is cleaner not to.
  The encode function can handle any text processing needed for the model to accept input invoked in its model.predict() or equivalent function which gets the expected model result.

If the output needs additional manipulation, that should be done before the result is returned. For embedding models, the return value must have the shape of (1, DIM), where DIM (vector dimension) is a consistent integer, to enable Fusion to handle the vector encoding into Ray.

The class name must be Deployment() and the name of this file must be deployment.py.

In the preceding example, the Python file is named deployment.py and the class name is Deployment().

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Create a Dockerfile

The next step is to create a Dockerfile. The Dockerfile should follow this general outline; read the comments for additional details:

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#It is important that python version is 3.x-slim
FROM python:3.10-slim

# Install dependencies
RUN apt-get update && apt-get install -y wget

# Create working app directory
RUN mkdir -p /app
WORKDIR /app

# Copy the requirements file and install the dependencies
COPY requirements.txt /app
RUN pip install -r requirements.txt --no-cache-dir

# Copy source code
COPY deployment.py /app

# Expose serving port for HTTP communication with Fusion
EXPOSE 8000

# The end of the command follows module:application and the below value should be set in the RAY DEPLOYMENT IMPORT PATH field in 'Create Ray Model Deployment' job
CMD exec serve run deployment:app

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Create a requirements file

The requirements.txt file is a list of installs for the Dockerfile to run to ensure the Docker container has the right resources to run the model. For the e5-small-v2 model, the requirements are as follows:

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torch -f https://download.pytorch.org/whl/torch_stable.html # Make sure that we download CPU version of PyTorch
transformers
loguru
ray[serve]==2.42.1

Any recent ray[serve] version should work, but the tested value and known supported version is 2.42.1. In general, if an item was used in an import statement in your Python file, it should be included in the requirements file.To populate the requirements, use the following command in the terminal, inside the directory that contains your code:

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pip freeze > requirements.txt

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Build and push the Docker image

After creating the MODEL_NAME.py, Dockerfile, and requirements.txt files, you need to run a few Docker commands. Run the following commands in order:

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DOCKER_DEFAULT_PLATFORM=linux/amd64 docker build . -t [DOCKERHUB-USERNAME]/[REPOSITORY]:[VERSION-TAG]

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docker push [DOCKERHUB USERNAME]/[REPOSITORY]:[VERSION-TAG]

Using the example model, the terminal commands would be as follows:

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DOCKER_DEFAULT_PLATFORM=linux/amd64 docker build . -t jstrmec/e5-small-v2-ray:0.1

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docker push jstrmec/e5-small-v2-ray:0.1

This repository is public and you can visit it here: e5-small-v2-ray

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Deploy the model in Fusion

Now you can go to Fusion to deploy your model.

1. In Fusion, navigate to Collections > Jobs.
2. Add a job by clicking the Add+ Button and selecting Create Ray Model Deployment.
3. Fill in each of the text fields:

   Parameter	Description
   Job ID	A string used by the Fusion API to reference the job after its creation.
   Model name	A name for the deployed model. This is used to generate the deployment name in Ray. It is also the name that you reference as a model-id when making predictions with the ML Service.
   Model min replicas	The minimum number of load-balanced replicas of the model to deploy.
   Model max replicas	The maximum number of load-balanced replicas of the model to deploy. Specify multiple replicas for a higher-volume intake.
   Model CPU limit	The number of CPUs to allocate to a single model replica.
   Model memory limit	The maximum amount of memory to allocate to a single model replica.
   Ray Deployment Import Path	The path to your top-level Ray Serve deployment (or the same path passed to serve run). For example, deployment:app
   Docker Repository	The public or private repository where the Docker image is located. If you’re using Docker Hub, fill in the Docker Hub username here.
   Image name	The name of the image. For example, e5-small-v2-ray:0.1.
   Kubernetes secret	If you’re using a private repository, supply the name of the Kubernetes secret used for access.

4. Click Advanced to view and configure advanced details:

   Parameter	Description
   Additional parameters.	This section lets you enter parameter name:parameter value options to be injected into the training JSON map at runtime. The values are inserted as they are entered, so you must surround string values with ". This is the sparkConfig field in the configuration file.
   Write Options.	This section lets you enter parameter name:parameter value options to use when writing output to Solr or other sources. This is the writeOptions field in the configuration file.
   Read Options.	This section lets you enter parameter name:parameter value options to use when reading input from Solr or other sources. This is the readOptions field in the configuration file.

5. Click Save, then Run and Start. When the job finishes successfully, you can proceed to the next section.

Now that the model is in Fusion, you can use it in the Machine Learning or Ray / Seldon Vectorize index and query stages.

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Configure the Fusion pipelines

Your real-world pipeline configuration depends on your use case and model, but for our example we will configure the index pipeline and then the query pipeline.Configure the index pipeline

1. Create a new index pipeline or load an existing one for editing.
2. Click Add a Stage and then Machine Learning.
3. In the new stage, fill in these fields:
   • The model ID
   • The model input
   • The model output
4. Save the stage in the pipeline and index your data with it.

Configure the query pipeline

1. Create a new query pipeline or load an existing one for editing.
2. Click Add a Stage and then Machine Learning
3. In the new stage, fill in these fields:
   • The model ID
   • The model input
   • The model output
4. Save the stage and then run a query by typing a search term.
5. To verify the Ray results are correct, use the Compare+ button to see another pipeline without the model implementation and compare the number of results.

You have now successfully uploaded a Ray model to Fusion and deployed it.

When you have verified that the Ray model is working after deployment with Ray, you can delete the Seldon model using the Delete Seldon Core Model Deployment job.

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AI and machine learning features

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Improved prefiltering support for Neural Hybrid Search (NHS)

• Chunking Neural Hybrid Query
• Neural Hybrid Query

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Bug fixes

• Fixed a Helm chart rendering issue that blocked ArgoCD deployments with TLS flags enabled.
  In previous releases, using TLS flags during deployment caused Helm chart rendering to fail in ArgoCD due to the use of the unsupported lookup function. Fusion 5.9.12 resolves this issue by modifying the chart templates for compatibility with ArgoCD, restoring support for TLS-enabled deployments in GitOps workflows.
• Fixed incorrect image repository for Solr in Helm charts.
  In Fusion 5.9.11, the Helm chart specified an internal Lucidworks Artifactory repository for the Solr image.
  This has been corrected in 5.9.12 so the Solr image repository is either empty or points to lucidworks/fusion-solr, aligning with other components and simplifying deployment for external environments.
• Added support for configuring the Spark version used by Fusion.
  Fusion 5.9.12 now lets you switch from the default Spark 3.4.1 version(introduced in Fusion 5.9.10) to the earlier Spark 3.2.2 version used in Fusion 5.9.9.
  This flexibility helps maintain compatibility with legacy Python (3.7.3) and Scala environments, especially for apps that depend on specific Spark runtime behaviors. When Spark 3.4.1 is enabled, custom Python jobs require Python 3.10.
  To enable Spark 3.2.2 instead, change the fusion-spark image in the configmap of the job-launcher, like this:
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      driver:
        container:
          image: example.com/dir/fusion-spark-3.2.2
      executor:
        container:
          image: example.com/dir/fusion-spark-3.2.2
  

  The Spark 3.4.1 runtime is incompatible with Azure Blob Storage when accessed via the deprecated wasbs:// protocol due to a Jetty version conflict; run Spark 3.2.2 instead if your jobs rely on wasbs://.
  In the long term, we recommend migrating to the abfs:// protocol for Azure Blob Storage access, which is fully supported in Spark 3.4.1.
• Fixed incorrect started-by values for datasource jobs in the job history.
  In previous versions, datasource jobs started from the Fusion UI were incorrectly shown as started by default-subject instead of the actual user.
  Fusion now correctly records and displays the initiating user in the job history, restoring accurate audit information for datasource operations.
• Fixed a schema loading issue that prevented older apps from working with the Schema API.
  Fusion now correctly handles both managed-schema and managed-schema.xml files when reading Solr config sets, ensuring backward compatibility with apps created before the move to template-based config sets.
  This prevents Schema API failures caused by unhandled exceptions during schema file lookup.
• Scheduled jobs now correctly trigger dependent jobs.
  In Fusion 5.9.12, we fixed an issue that prevented scheduled jobs from triggering other jobs based on their success or failure status.
  This includes jobs configured to run “on_success_or_failure” or using the “Start + Interval” option.
  With this fix, dependent jobs now execute as expected, restoring reliable job chaining and scheduling workflows.
• Fixed an issue that prevented updates to existing scheduled job triggers in the Schedulers view.
  This bug was caused by inconsistencies in how the API returned UTC timestamps, particularly for times after 12:00 UTC. The Admin UI now correctly detects changes and allows updates to trigger times without requiring the entry to be deleted and recreated.
• Fixed multiple scheduling issues in the job-config service to improve reliability and user experience.
  This release includes several important fixes to ensure that scheduled jobs function correctly across upgrades and user actions:
  • Improved leader election recovery: Ensures the scheduler service recovers if all job-config pods lose connection to ZooKeeper.
  • Corrected permission checks: Prevents scheduling failures caused by mismatches between user and service account permissions.
  • Fixed job history display for system jobs: Resolved an issue where delete-old-system-logs and delete-old-job-history jobs did not appear in the UI after upgrade, despite existing in the backend.
  • Restored schedule creation in certain apps: Fixed a bug where clicking Save when adding a schedule in the Run dialog appeared to succeed but failed to persist the schedule in specific app states.
• Fixed a simulation failure in the Index Workbench when configuring new datasources.
  Fusion 5.9.12 resolves an issue where Index Workbench failed to simulate results after configuring a new datasource, displaying the error “Failed to simulate results from a working pipeline.” This fix restores full functionality to the Index Workbench, allowing you to preview and configure indexing workflows in one place without switching between multiple views.
• Fixed a bug that caused aborted jobs to appear twice in the job history.
  Previously, when you manually aborted a job, it was recorded twice in the job history.
  This duplication has been resolved, and each aborted job now appears only once in the history log.
• Fixed an issue that prevented segment-based rule filtering from working correctly in Commerce Studio. Fusion now honors the lw.rules.target_segment parameter, ensuring only matching rules are triggered and improving rule targeting and safety.
• This release eliminates extra warning messages in the API Gateway related to undetermined service ports. Previously, the gateway logged repeated warnings about missing primary-port-name labels, even though this did not impact functionality. This fix reduces unnecessary log noise and improves the clarity of your logs.

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Known issues

• Job-config service may appear “down” in UI even when running correctly.
  In Fusion versions 5.9.12 through 5.9.13, the job-config service may falsely report as “down” in the Fusion UI, particularly during startup or in TLS-enabled deployments. This issue is fixed in Fusion 5.9.14.
• Jobs and V2 datasources may fail when Fusion collections are remapped to different Solr collections.
  In Fusion versions 5.9.12 through 5.9.13, strict validation in the job-config service causes “Collection not found” errors when jobs or V2 datasources target Fusion collections that point to differently named Solr collections.
  This issue is fixed in Fusion 5.9.14.
  As a workaround, use V1 datasources or avoid using REST call jobs on remapped collections.
• Saving large query pipelines may cause OOM failures under high load.
  In Fusion versions 5.9.12 through 5.9.13, saving a large query pipeline during high query volume can result in thread lock, excessive memory use, and eventual OOM errors in the Query service.
  This issue is fixed in Fusion 5.9.14.
• Some S3 and Web datasource jobs cannot be stopped in Fusion 5.9.12.
  Clicking the Stop button has no effect in some cases where the backend job is no longer being tracked. This causes the job-config service to ignore the job and prevents the system from updating the job status. A workaround is to issue a POST {"action": "start"} to the appropriate job actions endpoint, which aborts the stuck job.
  This issue is fixed in Fusion 5.9.14.
• Spark jobs may disappear from the job list after pod deletion
  In Fusion 5.9.12 and 5.9.13, Spark jobs may vanish from the job list if a Spark driver pod is deleted. This behavior can cause confusion and require a job-launcher restart to restore job visibility.
  This issue is fixed in Fusion 5.9.12.
• Jobs for Web V2 connectors may fail to start after an earlier failure.
  A bug in the connectors-backend service can prevent jobs from running if a previous crawl attempt was interrupted—for example, if the connector pod was scaled down mid-job.
  A subsequent crawl attempt may fail with the error The state should never be null, even after clearing the datasource configuration.
  This issue is fixed in Fusion 5.9.13.
• The fusion-spark-3.2.2 image included in Fusion 5.9.12 contains a Fabric8 token refresh bug.
  This issue affects Spark jobs running in Kubernetes environments that rely on token-based authentication.
  Due to an outdated Fabric8 client library, the image fails to refresh Kubernetes tokens correctly, which can cause authentication errors in long-running or distributed Spark jobs.
  This issue is fixed in Fusion 5.9.13.
• The job-config service may report a DOWN status at /actuator/health even when it is fully operational.
  This issue can occur after a prolonged ZooKeeper outage when TLS is enabled.
  Despite the service being healthy and showing UP status on readiness and liveness checks, the /job-config/actuator/health endpoint may still report DOWN, potentially triggering false alarms or unnecessary restarts.
  This issue is fixed in Fusion 5.9.13.
• When upgrading to Fusion 5.9.12, add the following to your values.yaml file to avoid a known issue that prevents the kuberay-operator pod from launching successfully:

kuberay-operator:
  crd:
    create: true

• Web connector may fail to index due to corrupted job state
  Fusion 5.9.12 may fail to index with the Webv2 connector (v2.0.1) due to a corrupted job state in the connectors-backend service.
  Affected jobs log the error The state should never be null, and common remediation steps like deleting the datasource or reinstalling the connector plugin may not resolve the issue.
  The issue is fixed in Fusion 5.9.13.
• Saving new datasource schedules may fail silently.
  In some Fusion 5.9.12 environments, clicking Save when adding a schedule from the datasource “Run” dialog does not persist the schedule or show an error message, particularly in apps created before the upgrade.
  As a workaround, use a new app or manually verify that the job configuration was saved.
  This issue is fixed in Fusion 5.9.13.

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Removals

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Bitnami removal

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Tika Server Parser removal

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MLeap removal

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Platform support and component versions

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Kubernetes platform support

• Google Kubernetes Engine (GKE): 1.29, 1.30, 1.31
• Microsoft Azure Kubernetes Service (AKS): 1.29, 1.30, 1.31
• Amazon Elastic Kubernetes Service (EKS): 1.29, 1.30, 1.31

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Component versions