The Service Restart That Enabled Parallel Writes: A Study in Operational Precision

Introduction

In the life of any distributed system, the most consequential actions often appear the most mundane. Message 2833 of this coding session captures one such moment: a simple service restart command executed across two QA nodes. The message reads:

Now restart the services:

``bash ssh 10.1.232.83 "sudo systemctl restart kuri-kuri_01" && ssh 10.1.232.84 "sudo systemctl restart kuri-kuri_02" && echo "Both services restarted" ``

Both services restarted

On its surface, this is nothing more than a two-line shell command followed by a success confirmation. Yet this message represents the culmination of a carefully orchestrated configuration change that fundamentally alters how the Filecoin Gateway (FGW) handles write operations. It is the moment when a theoretical feature—parallel write support—becomes operational reality in the QA environment. Understanding why this message was written, what decisions led to it, and what assumptions underpinned it requires tracing a chain of reasoning that spans architecture, configuration management, and operational deployment.

The Context: Why This Message Was Written

The immediate trigger for message 2833 was the user's instruction in message 2821: "Enable the parallel write support in qa with 2 sectors per node." This directive arrived after a lengthy debugging session in which the assistant had been investigating why cache statistics showed zero activity despite active S3 reads via rclone. The assistant had correctly identified that the cache was only used for offloaded (Filecoin-network-retrieved) data, not for local reads—a distinction that clarified the system's architecture but did not directly address the user's request.

The parallel write feature had been implemented earlier in the codebase but remained disabled by default (RIBS_ENABLE_PARALLEL_WRITES defaulted to false). The configuration struct, defined in configuration/config.go, controlled two key parameters: whether parallel writes were enabled at all, and how many parallel groups could receive writes concurrently. The user's request to use "2 sectors per node" translated to setting RIBS_MAX_PARALLEL_GROUPS=2, allowing up to two groups to be written to simultaneously rather than serializing all writes through a single group.

This feature was not merely a performance tweak. The original single-writer mode meant that all incoming write operations had to wait for a single group to complete before the next could begin—a bottleneck that became increasingly painful as data volumes grew. Parallel writes represented a fundamental architectural improvement, one that the roadmap had identified as critical for achieving the throughput targets required for production operation. Enabling it in QA was the first step toward validating the feature under real workload conditions before promoting it to production.

The Decision Chain: How the Configuration Was Applied

The assistant's response to the user's request reveals a multi-step decision process that balances immediacy against durability. Rather than simply editing a configuration file and restarting, the assistant undertook three distinct operations, each reflecting a different concern.

First, the assistant located and examined the configuration. Through a series of grep and read operations (messages 2822–2828), the assistant traced the parallel write settings from the Go configuration struct through the Ansible inventory and into the Jinja2 template used for deployment. This was not a casual lookup—it was a systematic audit of the configuration pipeline. The assistant needed to understand not just what the settings were, but how they flowed from code to deployment to runtime.

Second, the assistant updated the Ansible artifacts. The group variables file (ansible/inventory/qa/group_vars/all.yml) received the new settings, and the Jinja2 template (ansible/roles/kuri/templates/settings.env.j2) was modified to include the parallel write environment variables. This was the durable, infrastructure-as-code approach: future deployments via Ansible would automatically include the parallel write configuration. The assistant was thinking beyond the immediate restart, ensuring that the change would survive node reprovisioning or environment rebuilds.

Third, the assistant applied the changes directly to running nodes. Recognizing that running the full Ansible playbook would be slower and potentially disruptive, the assistant appended the configuration directly to /opt/fgw/config/settings.env on both kuri1 (10.1.232.83) and kuri2 (10.1.232.84) using tee -a. This approach—update the template for posterity, apply directly for immediacy—reflects a pragmatic understanding of operational tempo. The assistant was willing to bypass the formal deployment pipeline to deliver the change faster, but preserved the pipeline for future consistency.

Finally, message 2833: the restart. With the configuration files updated, the assistant issued the restart command. The choice of && chaining is deliberate: if the first restart fails, the second is never attempted, and the echo only fires if both succeed. This is a simple but effective error-handling pattern. The response "Both services restarted" confirmed success.

Assumptions Embedded in the Action

Every operational action carries assumptions, and this message is no exception. The assistant assumed that:

Environment variable changes are picked up on restart. This is true for the kuri service as configured—the systemd unit sources the settings.env file before launching the process. But it assumes that no caching layer or in-memory configuration persists across restarts.
The parallel write feature is safe to enable. The assistant did not validate that the feature would work correctly with the existing data. Groups 1 and 35 already contained 43 GB of data written under the single-writer regime. Enabling parallel writes changes the concurrency model for new writes, but the assistant assumed this would not corrupt existing data or create race conditions with ongoing operations.
Both nodes are identical in configuration needs. The same settings were applied to both kuri1 and kuri2. The assistant assumed that the QA environment was homogeneous and that no per-node tuning was required.
The service restart would be fast and non-disruptive. The command used a simple && chain with no timeout, no health check, and no verification beyond the echo. The assistant assumed that both services would restart within a reasonable window and that no dependent services (like the S3 frontend proxy) would be affected.
The configuration values were correct. The assistant set RIBS_ENABLE_PARALLEL_WRITES=true and RIBS_MAX_PARALLEL_GROUPS=2 without validating that these values were appropriate for the QA workload. The "2 sectors per node" directive from the user was accepted without questioning whether the node's hardware (CPU cores, memory, disk I/O) could support two concurrent write streams.

Potential Mistakes and Incorrect Assumptions

While the message itself is straightforward, several aspects warrant critical examination.

The most significant risk is the lack of a health check after restart. The assistant confirmed that the services restarted (the systemd process exited and re-entered the running state), but did not verify that the kuri process was actually accepting connections, that the parallel write feature was active, or that existing data was still accessible. A more thorough approach would have included a curl against the RPC endpoint to confirm the service was operational, or a log tail to check for startup errors.

The configuration was applied without a dry run or validation. The assistant appended lines to settings.env without checking whether those variables were already present (they were not, as confirmed by the grep), but also without validating that the environment file syntax was correct. A missing newline or a quoting error could silently break the configuration.

The Ansible template update was not tested. The assistant modified the Jinja2 template but did not render it to verify the output. A future Ansible run could fail if the template syntax was incorrect, though the changes were simple enough that this risk was low.

There was no rollback plan. If the parallel write feature caused instability, the assistant would need to either remove the environment variables and restart again, or revert the Ansible changes. No backup of the original settings.env was created before modification.

Input Knowledge Required

To understand message 2833, a reader must grasp several layers of context:

The parallel write feature itself: What it does, why it exists, and how it changes write concurrency.
The QA deployment architecture: Two kuri nodes behind S3 frontend proxies, with configuration managed via Ansible and environment files.
The configuration pipeline: How Go struct definitions map to environment variables, how those variables are templated into settings.env.j2, and how the systemd service sources them at startup.
The operational workflow: The distinction between updating Ansible artifacts (durable, repeatable) and directly editing files on running nodes (immediate, but ephemeral).
The systemd service management: The systemctl restart command, its behavior, and its limitations as a health indicator.

Output Knowledge Created

Message 2833 produced several tangible outcomes:

Both kuri services were restarted and entered the running state.
Parallel write support was activated on both QA nodes, with a maximum of two concurrent write groups.
The configuration change was captured in Ansible, ensuring that future deployments would preserve the setting.
A precedent was established for applying configuration changes: update the template, apply directly, restart.

The Thinking Process Visible in the Reasoning

The assistant's reasoning, visible across the preceding messages, follows a clear pattern: locate, understand, modify, apply, verify. The assistant did not blindly append configuration lines. Instead, it traced the configuration from the Go source through the Ansible inventory into the template, ensuring that the change was consistent across all layers. The decision to update both the template and the live files reflects a dual concern for immediacy and durability—a hallmark of operational maturity.

The choice to use && chaining rather than a script or a loop reveals a preference for simplicity and atomicity. Each command depends on the previous one succeeding, which means that a failure on kuri1 would prevent any attempt on kuri2. This is conservative by design: better to fail early than to have one node updated and the other not, creating an inconsistent state.

The absence of a post-restart health check is the most notable gap in the reasoning. The assistant had demonstrated thorough verification habits earlier in the session (checking RPC endpoints, examining Prometheus metrics, tracing data paths), but in this message, the verification was limited to the echo statement. This may reflect confidence in the simplicity of the change, or it may be an oversight born of operational fatigue.

Conclusion

Message 2833 is a study in how the most routine operations carry the most weight. A two-line shell command, executed in seconds, transformed the write architecture of a distributed storage system. The assistant's approach—systematic location of configuration, dual-path application (Ansible + direct edit), and atomic restart—demonstrates a thoughtful balance between speed and safety. Yet the absence of a health check and the reliance on unvalidated assumptions remind us that even the simplest operations deserve scrutiny. In distributed systems, the restart is never just a restart. It is a moment of transition, a boundary between old behavior and new, and a test of whether the configuration we intended is the configuration we achieved.