The Keyspace Revelation: How a Shared Database Exposed a Fundamental Architectural Flaw in a Distributed S3 System

The Message

In the middle of a lengthy debugging session for a horizontally scalable S3 storage cluster built on the Filecoin Gateway platform, the assistant produced the following reasoning message:

Agent Reasoning The init-data.sh script just creates empty directories. The issue is that kuri-2 is trying to access data that doesn't exist. Looking at the error more carefully:

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`` Error: write to group: opening group: opening group: open jbob (grp: /data/ribs/grp/1): opening head: open /data/ribs/grp/1/blklog.meta/head: no such file or directory ``

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This suggests that kuri-2 is trying to use the same group data as kuri-1, but the paths are different. Wait, the issue is that both nodes are using the same YCQL database but trying to access different local storage.

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Actually, looking at the docker-compose.yml, each kuri node has its own volume: - kuri-1: ${FGW_DATA_DIR}/kuri-1:/data/ribs - kuri-2: ${FGW_DATA_DIR}/kuri-2:/data/ribs

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So they should have separate storage. The issue might be that the database has state from the previous failed run. Let me check if we need to clean the YCQL/YSQL data as well when doing --clean.

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Actually, the real issue is that the error shows kuri-2 is trying to access /data/ribs/grp/1 which doesn't exist. This is because the group was created by kuri-1 in the database, but kuri-2 doesn't have the local files.

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The problem is that both nodes share the same YCQL database but have separate local storage. When kuri-1 creates a group, it records it in the shared database. Then kuri-2 tries to access that group but doesn't have the local files.

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This is actually a design issue - in the scalable architecture, each node should have its own groups that are not shared. Let me check the RIBS code to see how groups are managed.

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The issue is that both Kuri nodes share the same YCQL database but have separate local storage. When kuri-1 creates a group, it's recorded in the shared database, then kuri-2 tries to access it but doesn't have the local files.

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Let me check how the database handles multiple nodes: [grep] node_id|NodeID No files found

This message, while brief, represents a critical turning point in the debugging session. It is the moment when the assistant transitions from treating the cluster failure as a configuration or initialization problem to recognizing a fundamental architectural flaw in the design of the multi-node system. The message is the pivot point around which the entire subsequent refactoring effort revolves.

Context: The Debugging Journey So Far

To understand why this message matters, we must understand what led to it. The assistant had been building a test cluster for a horizontally scalable S3 architecture. The architecture, as described in the project roadmap, called for three layers: stateless S3 frontend proxies (port 8078) that route requests to independent Kuri storage nodes, which in turn store data in a shared YugabyteDB cluster.

The test cluster configuration included two Kuri nodes (kuri-1 and kuri-2), each with its own local storage volume mounted at /data/ribs, plus a shared YugabyteDB instance for metadata and coordination. The assistant had already fixed several issues: a migration deadlock caused by both nodes starting simultaneously (fixed by sequential startup), an invalid log level configuration (RIBS_LOGLEVEL=info instead of the required component=level format), and various port mapping corrections.

After applying these fixes, the assistant stopped the cluster with --clean, restarted it, and observed that kuri-2 was still failing. The error message was cryptic: "write to group: opening group: opening group: open jbob (grp: /data/ribs/grp/1): opening head: open /data/ribs/grp/1/blklog.meta/head: no such file or directory." The assistant initially suspected incomplete data directory initialization or leftover database state from the previous failed run. But as the reasoning in message 426 shows, the assistant dug deeper and discovered something more profound.

The Reasoning Process: A Detective Story

The assistant's thinking in this message follows a classic debugging arc: observe the symptom, form a hypothesis, test it against available evidence, encounter contradictions, refine the hypothesis, and ultimately arrive at a deeper understanding.

Step 1: Initial hypothesis. The assistant starts by examining the error message and the init-data.sh script. The initial hypothesis is that kuri-2 is failing because its data directories weren't properly initialized. This is a natural first guess—the error mentions missing files at /data/ribs/grp/1/blklog.meta/head, which looks like a local filesystem issue.

Step 2: Contradiction from the configuration. The assistant then checks the docker-compose.yml and confirms that each Kuri node has its own separate volume mapping: kuri-1 maps ${FGW_DATA_DIR}/kuri-1:/data/ribs and kuri-2 maps ${FGW_DATA_DIR}/kuri-2:/data/ribs. If the directories were properly created by init-data.sh, each node would have its own empty storage area. So why would kuri-2 be trying to access a group directory that doesn't exist?

Step 3: Shifting the blame to database state. The assistant considers that the YugabyteDB might contain stale state from the previous failed run. Even though --clean was passed to the stop script, perhaps the database wasn't fully cleaned. This is a reasonable hypothesis—if the database still has records of groups created by kuri-1 during its earlier (failed) initialization, kuri-2 might be reading those records and trying to access corresponding local files that don't exist on its volume.

Step 4: The breakthrough insight. The assistant then makes the crucial logical leap: "the real issue is that the error shows kuri-2 is trying to access /data/ribs/grp/1 which doesn't exist. This is because the group was created by kuri-1 in the database, but kuri-2 doesn't have the local files." This is the moment of recognition. The problem isn't about initialization or stale state—it's about shared state. Both nodes are reading from and writing to the same database keyspace, which means groups created by one node are visible to the other. But the local storage for those groups (the block log files, the metadata heads) only exists on the node that created them.

Step 5: Naming the architectural flaw. The assistant explicitly names the issue: "The problem is that both nodes share the same YCQL database but have separate local storage." This is the fundamental tension at the heart of the architecture. The database is shared, but the filesystem is not. Any group created by kuri-1 will be recorded in the database, and when kuri-2 reads that record and tries to open the group's local files, it will fail because those files only exist on kuri-1's volume.

Step 6: The design-level correction. The assistant then reframes the problem at the architectural level: "This is actually a design issue - in the scalable architecture, each node should have its own groups that are not shared." This is a critical insight. The original design implicitly assumed that groups are cluster-wide resources, but the local storage architecture makes them inherently node-specific. The assistant then takes the logical next step: checking whether the codebase has a node_id concept for groups, running a grep for node_id|NodeID and finding nothing.

Assumptions and Their Consequences

This message reveals several assumptions that were embedded in the architecture, some of which turned out to be incorrect.

Assumption 1: Groups are cluster-wide resources. The original RIBS (Remote Indexed Block Storage) design appears to treat groups as globally identified resources. A group with key 1 is the same group regardless of which node creates it. This assumption is implicit in the shared keyspace design—all nodes read from and write to the same database tables without any node-level partitioning.

Assumption 2: Local storage is a cache, not the authoritative source. The architecture separates "local storage" (the /data/ribs volume) from "database storage" (YugabyteDB). The implicit model seems to be that the database is the authoritative source of group metadata, and local storage is a performance optimization. But the error reveals that local storage is actually required for group operations—the group's block log metadata head must exist on the local filesystem. This means local storage is not a cache; it's a necessary component of the group's state.

Assumption 3: Sequential startup would fix the migration race. The assistant's earlier fix (starting kuri-1 first, then kuri-2) was based on the assumption that the only multi-node problem was a migration deadlock. This message shows that the sequential startup fix, while necessary, was insufficient because it didn't address the deeper keyspace-sharing problem.

Assumption 4: The --clean flag fully resets the cluster. The assistant initially wondered whether the database needed to be cleaned as well. But even a fully cleaned database wouldn't fix the fundamental issue—the problem would reoccur as soon as kuri-1 created any groups and kuri-2 tried to access them.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains:

Output Knowledge Created

This message creates several important pieces of knowledge:

  1. The root cause of kuri-2's failure: Not a configuration error or initialization bug, but a fundamental architectural mismatch between shared database keyspaces and node-local storage.
  2. The requirement for keyspace segregation: Each Kuri node needs its own database keyspace for per-node resources (groups, deals, blockstore data) to prevent cross-node interference.
  3. The distinction between shared and per-node data: Some data (S3 object routing metadata) can be shared across nodes, while other data (groups, blockstore) must be node-specific.
  4. The missing node_id concept: The grep for node_id|NodeID returning "No files found" reveals that the codebase currently has no mechanism for associating database records with specific nodes.

The Aftermath

The message ends with the assistant beginning to investigate the codebase for node identification mechanisms. In the messages that follow (indices 427–430 and beyond), the assistant explores two possible solutions: adding node_id to groups at the RIBS layer, or using separate database keyspaces per node. The eventual decision is to segregate keyspaces at the RIBS layer while sharing only the S3 metadata keyspace between nodes. This leads to a significant refactoring effort: updating the configuration system to support per-node keyspaces, modifying the gen-config.sh script to generate independent settings files, updating the docker-compose.yml to initialize multiple keyspaces, and ultimately restructuring the entire architecture to properly separate stateless S3 frontend proxies from Kuri storage nodes with independent configurations.

Why This Message Matters

This message is significant because it represents the moment when a surface-level debugging exercise ("fix the error message") transforms into a deep architectural investigation ("redesign the data isolation model"). The assistant could have continued applying band-aid fixes—cleaning the database more aggressively, adding retry logic, or synchronizing filesystem state between nodes. Instead, the assistant recognized that the error was a symptom of a deeper design flaw and traced it to its root cause.

The message also demonstrates a crucial debugging skill: the ability to read an error message not as a literal description of what went wrong, but as a clue pointing to a deeper systemic issue. The error says "no such file or directory," but the real problem is "two nodes are sharing a database keyspace when they shouldn't be." The assistant's reasoning shows the discipline of following the causal chain from symptom to mechanism to design flaw.

Finally, this message illustrates the value of explicit reasoning in debugging. By writing out each step of the analysis—the initial hypothesis, the contradictions, the refined hypothesis, the final insight—the assistant creates a record that can be reviewed, challenged, and built upon. This is especially valuable in a collaborative context where a human user is reviewing the assistant's work and providing guidance. The reasoning in this message makes the assistant's thinking transparent, allowing the user to confirm the diagnosis before the assistant proceeds with the (potentially costly) refactoring effort.