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feat: Add remote materialization support via feature server #6590
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Allow store.materialize() and materialize_incremental() to delegate execution to the feature server instead of running the batch engine locally. This eliminates the need for engine-specific dependencies (PySpark, kubernetes client, etc.) and RBAC on the client pod. Components: - New shared poll_materialization_status() function - Client-side _remote_materialize() with registry-based FV state polling - POST /materialize-async and /materialize-incremental-async endpoints (202) - materialize_mode, url, timeout, poll_interval config fields - 19 unit tests + 2 E2E integration tests Addresses #4526 (remote materialization). Signed-off-by: Aniket Paluskar <apaluska@redhat.com>
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I think we dont need to provide the materialize_mode config. We should strictly feature server take care of materialization delegation to compute engine. Leaving no choice to do materialization on client side.
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A cleaner approach might be: a RemoteComputeEngine (like RemoteOnlineStore/RemoteOfflineStore) that POSTs to the existing /materialize endpoint, returning a job handle with a dedicated /materialize-status/{job_id} endpoint - keeping the materialization delegation at the engine layer rather than the FeatureStore layer.
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if you mean something like
I thought about this before choosing current approach. I see two points which made me not take this decision.
My proposed approach is
This approach has
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@aniketpalu Ok, so, I think this PR is mixing three orthogonal concerns: async execution, remote delegation, and job tracking.
Remote delegation already works today - POST /materialize runs materialization server-side. A client with
online_store: remotecan call this endpoint directly. As @jyejare mentioned, the feature server should strictly handle materialization - no per-call remote=True flag needed; the deployment topology already decides this.Async execution - The real gap is that long-running engines (SparkApplication) block the HTTP caller. The existing /materialize endpoint just needs an
asyncmode (?async=true → 202 with job_id) rather than entirely new/materialize-asyncendpoints. This serves both UI on-demand requests and SDK wait=False use cases, while CronJob continues using the existing sync path unchanged.Job tracking - Polling FeatureViewState from the registry is fragile (race conditions, no job_id, ambiguous failure, breaks FeatureView.eq). If we need job tracking, it should be a dedicated MaterializationJob entity in the registry with proper RPCs - not piggybacking on FV metadata. For SparkApplication, the SparkApp CR status on K8s is already the source of truth.
May be we can scope this PR to just remote delegation and async execution ?
?async=trueon the existing/materializeendpoint (fire-and-forget, returns 202. Completion is already observable via existing mechanisms like materialization_intervals on the FV, SparkApp CR status) + SDK support for async with remote delegation.Job tracking with dedicated status endpoints / registry entities can come as a separate effort.
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agree on both point
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@ntkathole @jyejare
To keep the changes and commit history clean, I have raised separate PR. Would love your feedback on that. #6649