In a recent discussion with a major user of Spinnaker, some key insights emerged regarding the platform’s resource usage, especially in relation to Clouddriver and Orca. These takeaways shed light on how large-scale organizations manage Spinnaker’s performance and resource consumption, and they offer lessons for teams looking to optimize their own deployments.
1. Clouddriver Resource Usage: Not a Major Concern in Large Environments
Although some consumers have voiced concerns over Clouddriver’s memory use, our discussion revealed that major companies shouldn’t be concerned about this. In actuality, Clouddriver uses less resources than rival companies like Front50 or Orca. Here’s why.
- Scale: In large-scale setups, the sheer size of the infrastructure consumes Clouddriver’s resource utilization. This makes memory consumption less visible.
- Disabled Kubernetes Caching: Disabling Kubernetes caching allows enterprises to lessen the burden on Clouddriver. In environments where Kubernetes is managed separately or does not rely significantly on caching, this can result in considerable speed gains.
- On-Demand Caching: This caches only active accounts (those used in the previous 60 minutes or pipelined). This selective caching reduces memory utilization by handling only relevant accounts.
Why It's Not a Big Deal for Large Users?
The ability to scale and tweak Clouddriver settings makes it less of a concern in large environments. When cloud accounts and permissions are well-managed, and Kubernetes caching is disabled, Clouddriver doesn’t need to handle as much data, which results in lower memory consumption.
2. Orca Resource Usage: The Real Resource Hog
On the other hand, Orca—which manages Spinnaker’s pipelines and orchestrates tasks—tends to be the real resource consumer in large environments. Orca’s resource usage is directly tied to the number and size of pipelines running, which can quickly escalate when deploying thousands of pipelines across different environments.
Pipeline Load
Pipeline load drives Orca resource utilization. Orca uses more CPU and memory as pipes operate simultaneously.Clouddriver is used for cloud account management, although Orca’s resource needs increase with pipeline complexity and traffic.
This makes Orca the bottleneck for large users. Optimize pipeline layouts, split down huge pipelines, and reduce superfluous runs to reduce Orca resource pressure.
3. On-Demand Caching: A Significant Improvement
One of the most notable solutions was on-demand caching, which limits caching to active cloud accounts. For large users, selective caching has considerably reduced Clouddriver’s CPU and memory use, improving their experience.
Why On-Demand Caching Works?
By caching only accounts that have been used recently or are part of an active pipeline, this feature eliminates unnecessary cache refreshes. Clouddriver’s CPU would spike from data loading and cache management without this. Peaks are avoided using on-demand caching, improving performance and resource use.
4. Simplified Caching Logic: Reducing Complexity
On-demand caching slowed Clouddriver’s algorithm. Cloud management required cache refreshes and complicated algorithms. Everything is easier with on-demand caching.
Cleaner Cache Updates
Instead of refreshing or manually updating the cache, agents now schedule cache updates. This method decreases system complexity and stress, making Spinnaker deployments easier in big contexts.
5. Upstreaming On-Demand Caching
While this feature has proven valuable, it is currently only implemented for certain cloud environments like AWS. There’s potential for upstreaming the feature so that it becomes available for other platforms as well. However, this depends on community interest and available resources to fund further development.
For now, large users have primarily used on-demand caching for AWS and Kubernetes accounts. The possibility of upstreaming this feature could lead to broader adoption, making it a useful option for a wider range of cloud providers.
6. SQL vs. Redis for Caching
Without Redis, SQL and in-memory backends were used for on-demand caching. Redis is a common caching solution, however it may not work here.
Why Not Redis?
Many caching apps use Redis, however massive cloud account data and frequent changes may cause issues. SQL-based caching is more dependable and scalable for Spinnaker’s complex needs.
7. Clouddriver in High Availability (HA) Mode
Clouddriver behaves differently in HA mode. Clouddriver distributes caching across several instances in HA mode. This configuration manages traffic in huge setups with hundreds of cloud accounts and pipelines.
Benefits of HA Mode
In this manner, each Clouddriver instance updates and manages its cache separately. This minimizes instance strain and assures effective scaling of big Spinnaker installations.
8. Future Optimizations
These conversations also suggested further improvements. On-demand caching has simplified caching agents, but Spinnaker’s services can minimize resource utilization even further.
Optimizing Spinnaker pipelines and memory management might improve large-scale deployment efficiency.
Conclusion
Spinnaker’s resource usage in large systems with cloud accounts and pipelines was discussed. On-demand caching and fine-tuning solutions like Orca and Clouddriver reduce infrastructure burden.
Once Spinnaker evolves, these improvements will make large-scale deployments smoother, more efficient, and less resource-intensive. On-demand caching or pipeline orchestration upgrades can help Spinnaker users increase their deployments.
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