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Cookbook Deploy Static Compute
Static MDS plus a compute tier - a projector that derives computed and interpolated content from what the other projectors serve, rather than sourcing an external feed. Add it when you want the platform to produce derived content: computed columns, curve interpolation, and functions from its computation libraries.
Audience: Architect, Operator. Start from Static MDS; this page adds one image.
The compute tier is the projector tier used as a compute node, described in Architecture: Advanced.
This is the Static MDS deployment with one image added, mf-projector-compute. Everything from Static MDS applies unchanged, including how its MDS projectors find their ETA server; below is the delta.
| Method | You launch it with | Ideal for |
|---|---|---|
| Conventional | the host ./run launcher |
the standard install - this is the primary path |
| Docker | docker compose up |
a demo or lab on a single machine |
| Kubernetes | standard Deployment / Service manifests |
a cluster-managed fixed install |
| Image | Role | Release |
|---|---|---|
mf-projector-compute |
Derives computed and interpolated content from other projectors' output | deploy-mf-projector-compute |
It joins the Static MDS images: gateway, admin, core services, and one or more MDS projectors.
The compute projector hosts computation libraries. intrinsic (the SQL arithmetic and comparison operators) is always present; basic, math, and interpolation are available, with more to come. See the Image Catalog for what each library provides.
Install exactly as Static MDS, then add one more package:
- Download and unpack the
mf-projector-computerelease above. - Edit its
deployment-config/for your environment. Point it at the pub/sub service so it can source the content it computes from -METAFLUENT_PUBSUB_ADAPTER_CONNECTION=mf-session:8900, or the equivalent indeployment-config/. - Set
JAVA_HOMEand start it with./run.
Run more than one compute projector the same way when you want more computation throughput or resilience.
Take the Static MDS Compose file and add this service:
mf-projector-compute:
image: ghcr.io/metafluent/mf-projector-compute-cfg-stable:milestone
container_name: mf-projector-compute
network_mode: host
environment:
METAFLUENT_HOST_UID: "${METAFLUENT_HOST_UID}"
METAFLUENT_PUBSUB_ADAPTER_CONNECTION: "mf-session:8900" # where it sources content to compute from
volumes:
- ./logs:/app/logs
- ./data:/app/dataBring it up with the rest: METAFLUENT_HOST_UID=$(id -u) docker compose up -d.
An example. Add this to the Static MDS manifests:
apiVersion: apps/v1
kind: Deployment
metadata: { name: mf-projector-compute }
spec:
replicas: 1
selector: { matchLabels: { app: mf-projector-compute } }
template:
metadata: { labels: { app: mf-projector-compute } }
spec:
containers:
- name: mf-projector-compute
image: ghcr.io/metafluent/mf-projector-compute-cfg-stable:milestone
env:
- { name: METAFLUENT_PUBSUB_ADAPTER_CONNECTION, value: "mf-session:8900" }Raise replicas, or add another Deployment, for more computation throughput or resilience.
The same as Static MDS - add the mf-eta-simulator image to feed the MDS projectors, and the compute tier derives its content from what they serve.
- Static MDS - the base deployment this extends.
- Fine-Grained - run each service separately.
- Scalable MDS - let the projector and compute tiers grow and shrink with load automatically.
- Serving real, entitled Refinitiv data? See Configure DACS to turn on authentication and entitlement checks.
- Confirm it's running - check every component's health once the deployment is up.
- Architecture: Advanced - the projector tier as a compute node.
- Image Catalog - the compute image and its libraries.
- Deployment: Without Docker - the conventional install mechanics in full.
- Configuration: Basics - what the configuration you supply contains.
Elastic MDS documentation - (c) MetaFluent LLC - Confidential. Tracked in IssueTracking#586.
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