AutoMQ: Multi-Ceph Cluster Implementation Strategy

Hey guys! Today, we're diving deep into a fascinating discussion about implementing AutoMQ across multiple Ceph clusters. This is super important because, as you know, Ceph clusters have limitations when it comes to spanning multiple data centers (IDCs). To tackle this, we've been brainstorming a robust solution for replicating data across several Ceph clusters. Let's break it down!

The Challenge: Ceph Cluster Limitations

When dealing with distributed systems, especially in cloud-native environments, the ability to span multiple data centers is crucial for high availability and disaster recovery. However, traditional Ceph clusters often hit a roadblock here. They're not designed to stretch across multiple IDCs seamlessly. This limitation can be a significant headache for organizations needing geo-redundancy and resilience.

Our main focus is on how to extend AutoMQ's capabilities to work effectively with multiple Ceph clusters. This involves replicating data across these clusters to ensure data durability and availability even if one cluster goes down. We need a solution that not only works but also maintains performance and adds minimal overhead.

Multi-Ceph Cluster Approach

To overcome this limitation, we are exploring a multi-Ceph-cluster approach. The core idea is to replicate data across multiple independent Ceph clusters. This way, if one cluster experiences an outage, the data is still accessible from another cluster. This approach requires careful planning and implementation to ensure consistency and performance.

The Proposed Solution: MultiClusterObjectStorage

After a detailed discussion with expert Zhou Xinyu, we've come up with a plan that we believe is both elegant and efficient. The key is to encapsulate the complexity within a new class, MultiClusterObjectStorage. This class will handle the intricacies of writing data to multiple Ceph clusters and reading data from them.

Conceptual Overview

The diagram below illustrates the plan discussed with expert Zhou Xinyu for replicating data across multiple Ceph clusters:

Implementing MultiClusterObjectStorage

So, how will this MultiClusterObjectStorage class actually work? Let's dive into the nitty-gritty details. After studying the relevant source code of AutoMQ’s ObjectStorage layer, I’m going to implement multi-cluster writes as follows:

1. Create a new class MultiClusterObjectStorage that implements AutoMQ’s ObjectStorage interface. This new class will serve as the entry point for all multi-cluster operations.
2. Internally, it will hold two AwsObjectStorage (or CephObjectStorage) instances, each pointing to a different Ceph endpoint. Think of these as our primary and secondary storage endpoints. This dual-instance setup is crucial for the dual-write strategy we're adopting.
3. For every write operation (whether it's message data, Write-Ahead Log (WAL), or operational data), the class will delegate the write request to both inner clients concurrently. It will then wait for acknowledgments (ACKs) from both clients before considering the write successful. This ensures that the data is safely written to both clusters before proceeding.
4. For read operations, the class will first attempt to read from the first inner client. If it encounters a 404 error or a NoSuchKey exception, it will then fall back to the second client. This fallback mechanism ensures that we can still retrieve the data even if it's not immediately available in the first cluster.
5. Expose the same metrics and retry policies that the original AwsObjectStorage already provides. This is super important because it means that the upper-layer code doesn't need to change at all. We're keeping the interface consistent, which simplifies integration and reduces the risk of introducing bugs.

Dual-Write Strategy

The cornerstone of our approach is the dual-write strategy. When data needs to be written, we write it to both Ceph clusters simultaneously. This ensures that we have two copies of the data, providing redundancy. The class waits for acknowledgments from both writes before considering the operation successful.

Here’s a diagram illustrating the multi-cluster writes:

Read Operations and Fallback Mechanism

For read operations, we employ a primary-secondary read strategy. The system first attempts to read from the primary Ceph cluster. If the data is not found (e.g., due to a temporary unavailability or replication delay), it falls back to the secondary cluster. This ensures that reads are as fast as possible while still providing resilience.

Here’s a diagram illustrating the read fallback mechanism:

Key Considerations and Potential Pitfalls

While this plan looks solid, it's crucial to consider potential pitfalls and areas that might need extra attention. Here are a few aspects we need to watch out for:

1. Consistency: Ensuring data consistency across multiple clusters is paramount. The dual-write strategy helps, but we need to handle edge cases like network partitions or temporary unavailability of one cluster during writes. We might need to implement mechanisms to reconcile data discrepancies if they occur.
2. Latency: Writing to two clusters will inherently introduce more latency compared to writing to a single cluster. We need to minimize this overhead as much as possible. This might involve optimizing the write paths, using asynchronous writes where appropriate, and carefully tuning the Ceph clusters.
3. Network Bandwidth: Dual writes will consume more network bandwidth. We need to ensure that our network infrastructure can handle the increased traffic. Monitoring network usage and potentially provisioning additional bandwidth might be necessary.
4. Error Handling and Retries: We need robust error handling and retry mechanisms. If a write fails to one cluster, we need to retry the operation intelligently, possibly with exponential backoff. We also need to monitor these errors and alert the operations team if there are persistent issues.
5. Monitoring and Metrics: Exposing the same metrics as the original AwsObjectStorage is a great start, but we might need additional metrics specific to the multi-cluster setup. For example, we might want to track the latency of writes to each cluster, the number of fallback reads, and any data discrepancies detected.
6. Cost: Running multiple Ceph clusters will likely increase costs. We need to factor in the cost of additional hardware, networking, and power. It's essential to balance the benefits of increased availability and durability with the added expenses.

Potential Flaws and Areas to Watch Out For

One potential flaw might be the increased latency due to dual writes. While the concurrent writes should mitigate this, network conditions and cluster load could still cause delays. We need to thoroughly test the performance under various conditions.

Another area to watch out for is the complexity of managing two Ceph clusters. Operational overhead, such as upgrades, maintenance, and troubleshooting, will be higher. We need to streamline these processes as much as possible and consider automation.

Next Steps

So, what's next? The plan is to proceed with implementing the MultiClusterObjectStorage class and conduct thorough testing. We'll start with a proof-of-concept implementation and gradually add more features and optimizations. Performance testing, fault injection, and chaos engineering will be crucial to ensure the solution is robust and reliable.

We'll also need to define clear operational procedures for managing the multi-cluster setup. This includes monitoring, alerting, and disaster recovery procedures. Collaboration with the operations team will be essential to ensure a smooth transition to production.

Conclusion

Implementing AutoMQ across multiple Ceph clusters is a challenging but achievable goal. The MultiClusterObjectStorage approach, with its dual-write strategy and read fallback mechanism, seems promising. However, careful consideration of consistency, latency, network bandwidth, and error handling is crucial.

By addressing these challenges and rigorously testing our implementation, we can build a robust and highly available AutoMQ system that spans multiple data centers. This will significantly enhance the resilience and scalability of our infrastructure. Let's keep the discussion going and refine this plan further! Your feedback and insights are invaluable.