tracking: Test demos on nightly versions for 26.3.0

@adwk67

This is testing that the nightly release of the operators and products do not negatively impact
the products.

Note

Record the results (issues, anomalies, or success) during the process in a comment on this issue.
Eg:

:green_circle: **airflow-scheduled-job**

The CRD had been updated and I needed to change the following in the manifest:
...

The following emojis can be used to indicate the status:

hourglass: In progress / waiting
red_circle: In progress with errors
orange_circle: Completed with minor issues or anomalies
green_circle: Completed without issues or errors

List of demos

Replace the items in the lists below with the applicable Pull Requests (if any). Also put your
GitHub handle next to the demo to indicate a particular demo was picked up for testing.

List of stacks

Similar to the demos above we need to test all stacks that don't have a demo.
Replace the items in the lists below with the applicable Pull Requests (if any). Also put your
GitHub handle next to the stack to indicate a particular stack was picked up for testing.

Tip

Some of the stacks have a tutorial to follow.

~~airflow-trino-dbt~~ (this was merged into the airflow demo and was only removed from the stacks recently: it has also been removed from the template: @adwk67 )
monitoring @sbernauer - All metrics of the trino-taxi-data demo showed up
observability @NickLarsenNZ -> fix: Observability stack now works #398
openldap (comes up)
tutorial-openldap @Techassi

Nightly from Scratch Testing Instructions

These instructions are for deploying and completing the nightly demo from scratch.

Tip

Be sure to select the nightly docs version on https://docs.stackable.tech/home/nightly/demos/.

# For demo testing, install demo (stable operators) for the nightly release.
stackablectl demo install <DEMO_NAME> --release dev

# For stack testing, install stack via
stackablectl stack install <STACK_NAME> --release dev

# --- IMPORTANT ---
# Run through the (still) nightly demo/stack instructions (refer to the list above).

🟢 airflow-scheduled-job

OK with the changes in the PR below
fix: Airflow demo fixes pre-26.3 #380

🟢 hbase-hdfs-load-cycling-data

🟢 jupyterhub-keycloak

Worked OK with the change below
fix: JupyterHub/Keycloak demo fixes pre-26.3 #381

🟢 logging

Worked without any issues. The log view image is updated in #379.

🟢 trino-iceberg worked fine

🟢 signal-processing

The Jupyter notebook failed on the first try. This seems to be due to slow data ingestion. A second try worked without any issues.

Previous error

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In [4], line 30
     18 od = SpectralResidual(
     19     threshold=1.,
     20     window_amp=20,
   (...)
     26     n_grad_points=5
     27 )
     29 for col in cols:
---> 30     result = od.predict(
     31         df[col].to_numpy(),
     32         t=None,
     33         return_instance_score=True
     34     )
     35     df_scores[col+'_score'] = result['data']['instance_score'].tolist()
     37 df_scores.to_sql('scores_sr', engine, index=False, if_exists='append')

File /opt/conda/lib/python3.9/site-packages/alibi_detect/od/sr.py:398, in SpectralResidual.predict(self, X, t, return_instance_score)
    395     t = np.arange(X.shape[0])
    397 # compute outlier scores
--> 398 iscore = self.score(X, t)
    400 # values above threshold are outliers
    401 outlier_pred = (iscore > self.threshold).astype(int)

File /opt/conda/lib/python3.9/site-packages/alibi_detect/od/sr.py:346, in SpectralResidual.score(self, X, t)
    342     if X.shape[0] != n_samples:
    343         raise ValueError("Only uni-variate time series allowed for SR method. Received a time "
    344                          f"series with {n_dim} features.")
--> 346 X_pad = self.add_est_points(X, t)  # add padding
    347 sr = self.saliency_map(X_pad)      # compute saliency map
    348 sr = sr[:-self.n_est_points]       # remove padding again

File /opt/conda/lib/python3.9/site-packages/alibi_detect/od/sr.py:314, in SpectralResidual.add_est_points(self, X, t)
    297 def add_est_points(self, X: np.ndarray, t: np.ndarray) -> np.ndarray:
    298     """
    299     Pad the time series with additional points since the method works better if the anomaly point
    300     is towards the center of the sliding window.
   (...)
    312     Padded version of X.
    313     """
--> 314     grads = self.compute_grads(X, t)
    315     X_add = X[-self.n_grad_points] + grads
    316     X_pad = np.concatenate([X, np.tile(X_add, self.n_est_points)])

File /opt/conda/lib/python3.9/site-packages/alibi_detect/od/sr.py:292, in SpectralResidual.compute_grads(self, X, t)
    275 def compute_grads(self, X: np.ndarray, t: np.ndarray) -> np.ndarray:
    276     """
    277     Slope of the straight line between different points of the time series
    278     multiplied by the average time step size.
   (...)
    290     Array with slope values.
    291     """
--> 292     dX = X[-1] - X[-self.n_grad_points - 1:-1]
    293     dt = t[-1] - t[-self.n_grad_points - 1:-1]
    294     mean_grads = np.mean(dX / dt) * np.mean(dt)

IndexError: index -1 is out of bounds for axis 0 with size 0

🟢 nifi-kafka-druid-water-level-data

Docs PR: #385

🟢 tutorial-openldap (stack)

🟢 data-lakehouse-iceberg-trino-spark (needed #390 and #391)

This is just open for me to check the observability stack. It doesn't block the release.

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tracking: Test demos on nightly versions for 26.3.0 #372

List of demos

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Nightly from Scratch Testing Instructions

🟢 airflow-scheduled-job

🟢 hbase-hdfs-load-cycling-data

🟢 jupyterhub-keycloak

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tracking: Test demos on nightly versions for 26.3.0 #372

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List of demos

List of stacks

Nightly from Scratch Testing Instructions

Activity

adwk67 commented on Mar 12, 2026

🟢 airflow-scheduled-job

adwk67 commented on Mar 12, 2026

🟢 hbase-hdfs-load-cycling-data

adwk67 commented on Mar 12, 2026

🟢 jupyterhub-keycloak

Techassi commented on Mar 12, 2026

sbernauer commented on Mar 12, 2026

Techassi commented on Mar 12, 2026

6 remaining items

dervoeti commented on Mar 13, 2026

Techassi commented on Mar 13, 2026

sbernauer commented on Mar 16, 2026

NickLarsenNZ commented on Mar 23, 2026

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