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Add capacity planning document (#1714)
* 📝 add capacity plannnig document Signed-off-by: vankichi <[email protected]> * Apply suggestions from code review Co-authored-by: Kevin Diu <[email protected]> * 📝 fix RAM calculation Signed-off-by: vankichi <[email protected]> * Apply suggestions from code review * Update docs/user-guides/capacity-plannig.md Co-authored-by: Yusuke Kato <[email protected]> * 📝 fix capacity planning doc Signed-off-by: vankichi <[email protected]> * 📝 add affinity section Signed-off-by: vankichi <[email protected]> * 📝 add warning about affinity setting Signed-off-by: vankichi <[email protected]> * ♻️ update .prh.yaml Signed-off-by: vankichi <[email protected]> Co-authored-by: Kevin Diu <[email protected]> Co-authored-by: Yusuke Kato <[email protected]>
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# Capacity Planning | ||
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## What is capacity planning for the Vald cluster? | ||
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Capacity planning is essential before deploying the Vald cluster to the cloud service. | ||
There are three viewpoints: Vald cluster view, Kubernetes view, and Component view. | ||
Let's see each view. | ||
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## Vald cluster view | ||
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The essential point at the Vald cluster view is the hardware specification, especially RAM. | ||
The Vald cluster, especially Vald Agent components, requires much RAM capacity because the vector index is stored in memory. | ||
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It is easy to figure out the minimum required RAM capacity by the following formula. | ||
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```bash | ||
( { the dimension vector } × { bit number of vector } + { the bit of vectors ID string } ) × { the maximum number of the vector } × { the index replica } | ||
``` | ||
For example, if you want to insert 1 million vectors with 900 dimensions and the object type is 32-bit with 32 byte (256 bit) ID, and the index replica is 3, the minimum required RAM capacity is: | ||
```bash | ||
(900 × 32 + 256 ) × 1,000,000 × 3 = 8,7168,000,000 (bit) = 10.896 (GB) | ||
``` | ||
It is just the minimum required RAM for indexing. | ||
Considering the margin of RAM capacity, the minimum RAM capacity should be less than 60% of the actual RAM capacity. | ||
Therefore, the actual minimum RAM capacity will be: | ||
```bash | ||
8,7168,000,000 (bit) / 0.6 = 145,280,000,000 (bit) = 18.16 (GB) | ||
``` | ||
<div class="warn"> | ||
In the production usage, memory usage may be not enough in the minimum required RAM.<BR> | ||
E.g., there are a noisy problem, high memory usage for createIndex (indexing on memory), high traffic needs more memory, etc. | ||
</div> | ||
## Kubernetes cluster view | ||
### Pod priority & QoS | ||
When the Node capacity (e.g., RAM, CPU) reaches the limit, Kubernetes will decide to kill some Pods according to QoS and Pod priority. | ||
Kubernetes performs pod scheduling with pods Priority Class as the priority and QoS as the second priority. | ||
**Pod priority** | ||
Pod priority has the integer value, and the higher value, the higher priority. | ||
Each Vald component has the default priority value: | ||
- Agent: 1000000000 | ||
- Discoverer: 1000000 | ||
- Filter Gateway: 1000000 | ||
- LB Gateway: 1000000 | ||
- Index Manager: 1000000 | ||
Therefore, the order of priority is as follows: | ||
```bash | ||
Agent > Discoverer = Filter Gateway = LB Gateway = Index Manger | ||
``` | ||
Those values will be helpful when the Pods other than the Vald component are in the same Node. | ||
It is easy to change by editing your `values.yaml`. | ||
```yaml | ||
# e.g. LB Gateway podPriority settings. | ||
... | ||
gateway: | ||
lb: | ||
... | ||
podPriority: | ||
enabled: true | ||
value: {new values} | ||
... | ||
``` | ||
**QoS** | ||
QoS value can be either Guaranteed, Burstable, or BestEffort. | ||
And, QoS priority is higher in the order of Guaranteed, Burstable, BestEffort, and Kubernetes will kill Pods in ascending order of importance. | ||
Resource request and limit determine QoS. | ||
The below table shows the condition for each QoS. | ||
| QoS | request CPU | request Memory | limit CPU | request Memory | Sup. | | ||
| :--------: | :-------------: | :------------: | :-------------: | :-------------: | :---------------------------------- | | ||
| Guaranteed | :o: | :o: | :o: | :o: | All settings are required. | | ||
| Burstable | :o: (:warning:) | :o: (:warning) | :o: (:warning:) | :o: (:warning:) | One to three settings are required. | | ||
| BestEffort | :x: | :x: | :x: | :x: | No setting is required. | | ||
Vald requires many RAM resources because of on-memory indexing, so we highly recommend that you do not specify a limit, especially for the Vald Agent. | ||
In this case, QoS will be Burstable. | ||
**Throttling** | ||
The CPU throttling affects the pod performance. | ||
If it occurs, the Vald cluster operator must consider each component's CPU resource request and limit. | ||
It is easy to change by editing the `values.yaml` file and applying it. | ||
```yaml | ||
# e.g. LB Gateway resources settings. | ||
... | ||
gateway: | ||
lb: | ||
... | ||
resources: | ||
requests: | ||
cpu: 200m | ||
memory: 150Mi | ||
limits: | ||
cpu: 2000m | ||
memory: 700Mi | ||
... | ||
``` | ||
<div class="warning"> | ||
Please take care of pod priority and QoS. | ||
</div> | ||
### Node & Pod affinity | ||
Kubernetes scheduler often applies Pods on Node based on resource availability. | ||
In production usage, other components sometimes work on the Kubernetes cluster where the Vald cluster runs. | ||
Depending on the situation, you may want to deploy to a different Node: e.g., when running a machine learning component that requires high memory on an independent Node. | ||
In this situation, we recommend you to set the affinity/anti-affinity configuration for each Vald component. | ||
It is easy to change by editing each component setting on your `values.yaml`. | ||
<div class="warn"> | ||
The affinity setting for Vald Agent is the significant for the Vald cluster.<BR> | ||
Please DO NOT remove the default settings. | ||
</div> | ||
```yaml | ||
# e.g. Agent's affinity settings | ||
... | ||
agent: | ||
... | ||
affinity: | ||
nodeAffinity: | ||
preferredDuringSchedulingIgnoredDuringExecution: [] | ||
requiredDuringSchedulingIgnoredDuringExecution: | ||
nodeSelectorTerms: [] | ||
podAffinity: | ||
preferredDuringSchedulingIgnoredDuringExecution: [] | ||
requiredDuringSchedulingIgnoredDuringExecution: [] | ||
podAntiAffinity: | ||
preferredDuringSchedulingIgnoredDuringExecution: | ||
- weight: 100 | ||
podAffinityTerm: | ||
topologyKey: kubernetes.io/hostname | ||
labelSelector: | ||
matchExpressions: | ||
- key: app | ||
operator: In | ||
values: | ||
- vald-agent-ngt | ||
requiredDuringSchedulingIgnoredDuringExecution: [] | ||
... | ||
``` | ||
For more information about Kubernetes affinity, please refer to [here](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity) | ||
## Component view | ||
Depending on the customization of each component for each user, there are some points to be aware of. | ||
**Index Manager** | ||
If the `saveIndex` is executed frequently, the backup data per unit time will increase, which consumes bandwidth. | ||
Similarly, as the saveIndex concurrency increases, the backup data per unit time increases. |
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