DuckDB: Fixing Secret Storage Name Collision Error

by Omar Yusuf 51 views

Hey guys,

We've got a bit of a situation in the DuckDB nightly tests, and I wanted to break it down for you all in a way that's super clear and easy to follow. We're seeing a Secret storage name collision error popping up in our release assertions tests, specifically in the v1.3-ossivalis branch. It's happening across multiple environments, which tells us it's likely a core issue rather than something specific to one setup. Let's dive into the details so we can get a handle on what's going on and how to tackle it.

Understanding the Issue: Secret Storage Name Collision

So, what's this "Secret storage name collision" all about? To put it simply, it means that we're trying to register a secret storage with the same name more than once. In this case, the culprit seems to be a storage named 'memory'. The error message Secret Storage with name 'memory' already registered! is pretty explicit. This is a problem because DuckDB's secret management system is designed to keep things organized and secure, and having duplicate names throws a wrench in the works. It’s like trying to save two files with the exact same name in the same folder – things are bound to get messy.

Think of secret storage as a vault where DuckDB keeps sensitive information, like API keys or passwords. Each vault needs a unique name so the system can find the right one when it needs it. When we try to create a new vault with a name that's already taken, DuckDB throws this error to prevent confusion and potential data mix-ups. This is crucial for maintaining the integrity and security of your data operations. We need to ensure that each storage is uniquely identified to prevent overwriting or accessing the wrong secrets. It's kind of like having two keys with the same label – you wouldn't know which lock each key opens, right?

Why is this happening?

Now, let's get to the million-dollar question: why is this happening? The error suggests that the SecretManager is loading after it has already been initialized. This implies that there might be an issue in the order of initialization or some kind of race condition where the secret storage is being set up multiple times. Digging into the code, we need to figure out why the memory secret storage is being registered more than once. Is it a test setup issue? Is there a bug in how DuckDB handles secret storage initialization? These are the questions we need to answer.

To nail down the cause, we'll need to look closely at the SecretManager class and how it interacts with the rest of DuckDB's system. We'll need to trace the execution flow to see where the LoadSecretStorageInternal function is being called and why it's being called multiple times for the same storage name. It's like detective work, but instead of a crime scene, we're investigating a code crash!

Examining the Failure Details

The error messages give us some serious clues. Let's break down those stack traces and see what they tell us. We've got failures in three different jobs:

  1. Release Assertions OSX: This one failed on macOS, which means the issue isn't limited to Linux.
  2. Release Assertions: This is a standard release assertion build, so it's a key one to fix.
  3. Release Assertions with Clang: This uses the Clang compiler, further suggesting a core issue.

The stack traces all point to the same root cause: the SecretManager is throwing an exception because it's trying to register a secret storage with a name that's already in use. Let's look at the common parts of the stack traces to understand the sequence of events leading to the crash.

The stack traces consistently show that the error originates from duckdb::SecretManager::LoadSecretStorageInternal, which is the function responsible for loading secret storage instances. The trace then goes through duckdb::SecretManager::InitializeSecrets and duckdb::SecretManager::AllSecrets, indicating that the issue arises during the initialization or retrieval of secrets. Finally, duckdb::DuckDBSecretsFunction and duckdb::PhysicalTableScan::GetData are involved, suggesting the problem surfaces when querying or accessing secrets during a table scan operation. This entire chain of calls highlights that the secret management system is central to this error.

Decoding the Stack Traces

Stack traces can look intimidating, but they're really just a step-by-step history of how the code got to the point of failure. Each line in the trace represents a function call. By reading the trace from the bottom up, we can see the order in which functions were called leading to the error. Here's a simplified breakdown of what the stack trace is telling us:

  1. The error starts with an ABORT thrown by an InternalException: This means DuckDB encountered a problem it couldn't recover from and had to stop.
  2. The exception message is Secret Storage with name 'memory' already registered!: This confirms the name collision issue.
  3. The trace leads us to duckdb::SecretManager::LoadSecretStorageInternal: This is where the secret storage is being loaded.
  4. duckdb::SecretManager::InitializeSecrets and duckdb::SecretManager::AllSecrets are also in the mix: These functions are responsible for setting up and retrieving secrets.
  5. duckdb::DuckDBSecretsFunction and duckdb::PhysicalTableScan::GetData suggest the error occurs during a query that involves secrets: This gives us a context for when the error is happening.

By dissecting these traces, we can start to form a hypothesis about what might be going wrong. The fact that the error occurs during secret initialization and retrieval suggests that the issue might be related to how DuckDB is managing the lifecycle of secret storages. Perhaps there's a case where the same storage is being initialized multiple times, or there's a race condition where multiple threads are trying to register the same storage simultaneously. Understanding these potential scenarios is the first step toward finding a solution.

Analyzing the Workflow and Job Environments

To really crack this, we need to look at the bigger picture. What's the environment like when these failures occur? Let's dive into the workflow details and job environments.

Nightly Tests Workflow

These failures are happening in the NightlyTests workflow. This workflow is designed to run a comprehensive suite of tests every night, ensuring that DuckDB is in good shape. The fact that these errors are happening in nightly tests means they are likely regressions – something that was working before is now broken. This makes it even more critical to address the issue quickly to prevent further disruptions.

Workflows like NightlyTests are essential for maintaining the quality and stability of DuckDB. They act as an early warning system, catching potential issues before they make their way into releases. By running tests in a variety of environments and configurations, we can identify problems that might not be apparent during local development. This proactive approach is key to ensuring that DuckDB remains reliable and robust.

Failed Job Environments

We have failures in Release Assertions OSX, Release Assertions, and Release Assertions with Clang. Let's look at the commonalities and differences in their job environments:

  • Branch: All failures are on the v1.3-ossivalis branch. This narrows down the scope of the issue – it's likely something specific to this branch.
  • Command: The failure occurs when running python3 scripts/run_tests_one_by_one.py build/relassert/test/unittest "*" --no-exit --timeout 1200. This script runs the DuckDB unit tests one by one, which helps isolate the failing test case. The unittest executable suggests the issue lies within the unit test suite.
  • Job Env: Here's where it gets interesting. We see differences in the operating system (OSX vs. Linux), compiler (default vs. Clang), and build configurations (with and without JEMALLOC). However, the core extensions enabled (icu;tpch;tpcds;fts;json;inet;httpfs) are consistent across failures. This suggests the issue isn't tied to a specific OS, compiler, or memory allocator, but might be related to one of the core extensions or the base DuckDB system.

Key Environment Variables

Some environment variables stand out as potentially relevant:

  • CRASH_ON_ASSERT: 1: This is good – it means DuckDB is configured to crash when an assertion fails, giving us clear error messages and stack traces.
  • RUN_SLOW_VERIFIERS: 1: This indicates that slow verifiers are enabled, which might uncover issues that faster tests miss. However, it also means the tests take longer, so we need to balance thoroughness with speed.
  • DISABLE_SANITIZER: 1: Sanitizers help detect memory errors, but they're disabled here. This is a trade-off – sanitizers can add overhead, but they can also catch tricky bugs. We might want to consider running tests with sanitizers in a separate workflow to get the best of both worlds.

By comparing the environments, we can eliminate some potential causes and focus on the most likely culprits. The fact that the issue occurs across different OSes and compilers suggests a more fundamental problem in the DuckDB codebase. The consistent failure with the unit test runner points us toward looking at the tests themselves or the core DuckDB functionality they exercise.

Formulating a Hypothesis and Action Plan

Alright, guys, let's put all this together and come up with a plan of attack. Based on the information we've gathered, here's my hypothesis:

The "Secret storage name collision" error is likely due to a race condition or a double initialization issue within the SecretManager. The memory secret storage is being registered more than once, leading to the crash. This issue is specific to the v1.3-ossivalis branch and is triggered during unit tests that interact with the secret management system.

Action Plan

To confirm this hypothesis and fix the issue, here's what I propose we do:

  1. Reproduce the Error Locally: The first step is always to reproduce the error locally. This will allow us to debug the issue more easily. We can use the same command that failed in the CI (python3 scripts/run_tests_one_by_one.py build/relassert/test/unittest "*" --no-exit --timeout 1200) and try to trigger the crash.
  2. Examine the SecretManager Code: We need to dive into the SecretManager class and related code to understand how secret storages are initialized and registered. We should pay close attention to the LoadSecretStorageInternal, InitializeSecrets, and AllSecrets functions.
  3. Identify the Double Initialization: We need to figure out why the memory secret storage is being registered more than once. Is it a test setup issue? Is there a bug in the initialization logic? We might need to add some logging or debugging statements to trace the execution flow.
  4. Look for Race Conditions: If we suspect a race condition, we can use threading tools or sanitizers to help detect it. We might also need to add synchronization mechanisms to protect the SecretManager from concurrent access.
  5. Write a Test Case: Once we've fixed the issue, we should write a new test case that specifically targets the bug. This will help prevent regressions in the future.
  6. Submit a Pull Request: Finally, we'll submit a pull request with the fix and the new test case. This will allow others to review the changes and ensure they're correct.

This is a classic debugging scenario, and by systematically investigating the issue, we should be able to track down the root cause and get DuckDB back on track. Let's roll up our sleeves and get to work!

Next Steps: Diving Deeper into the Code

Now that we have a solid hypothesis and a plan, the next step is to get our hands dirty with the code. We need to dig into the SecretManager and related classes to understand the flow of execution and pinpoint where the double initialization is occurring. Here's a breakdown of how we can approach this:

Setting Up a Local Debugging Environment

Before we can start debugging, we need to set up a local environment that mimics the conditions in which the error occurs. This typically involves:

  1. Cloning the DuckDB Repository: If you haven't already, clone the DuckDB repository to your local machine.
  2. Checking Out the Correct Branch: Make sure you're on the v1.3-ossivalis branch, since that's where the failures are happening.
  3. Building DuckDB: Build DuckDB with the relassert configuration, as that's the build type used in the failing tests. This will create a debug build with assertions enabled, which will help us catch errors.
  4. Running the Failing Test: Use the command python3 scripts/run_tests_one_by_one.py build/relassert/test/unittest "*" --no-exit --timeout 1200 to run the unit tests and try to reproduce the error.

Once you can reproduce the error locally, you can start using a debugger to step through the code and examine the state of the program.

Exploring the SecretManager Class

The SecretManager class is the heart of the secret management system in DuckDB. It's responsible for:

  • Registering Secret Storages: Adding new secret storages to the system.
  • Loading Secret Storages: Retrieving secret storages from persistent storage.
  • Managing Secret Storage Lifecycle: Ensuring that secret storages are properly initialized and destroyed.

Here are some key functions within the SecretManager that we should investigate:

  • LoadSecretStorageInternal: This function is responsible for loading secret storage instances. The stack trace clearly points to this function as the origin of the error, so we need to understand why it's being called multiple times for the same storage name.
  • InitializeSecrets: This function likely initializes the secret management system, including registering default secret storages. We need to examine how this function is called and whether it could be called more than once.
  • AllSecrets: This function retrieves all registered secret storages. We can use this function to inspect the state of the secret manager and see if the memory storage is already registered before LoadSecretStorageInternal is called.

By stepping through these functions in a debugger, we can trace the execution flow and identify where the double initialization is occurring. We can also examine the call stack to see who is calling these functions and why.

Identifying the Root Cause

To pinpoint the root cause, we can use a combination of debugging techniques:

  • Breakpoints: Set breakpoints in LoadSecretStorageInternal, InitializeSecrets, and AllSecrets to pause execution and inspect the state of the program.
  • Watch Expressions: Use watch expressions to monitor the values of key variables, such as the name of the secret storage being registered and the list of registered storages.
  • Call Stack Inspection: Examine the call stack to see the sequence of function calls that led to the current point of execution. This can help us understand the context in which the functions are being called.
  • Logging: Add logging statements to the code to record the execution flow and the values of key variables. This can be especially helpful if the error is difficult to reproduce or if it involves multithreading.

By carefully analyzing the code and using these debugging techniques, we should be able to identify the exact sequence of events that leads to the "Secret storage name collision" error. Once we understand the root cause, we can develop a fix that addresses the underlying issue and prevents it from happening again.

Implementing a Solution and Preventing Regressions

After diving deep into the code and pinpointing the source of the secret storage name collision, the real challenge begins: implementing a robust solution and ensuring that the bug doesn't creep back in the future. This involves crafting a fix that addresses the underlying issue, writing a test case to specifically target the bug, and submitting a pull request for review.

Crafting a Solution

Based on our hypothesis and investigation, the issue likely stems from a race condition or a double initialization within the SecretManager. This means we need to ensure that the registration of secret storages, particularly the memory storage, happens only once and is properly synchronized if multithreading is involved. Here are a few potential strategies:

  1. Synchronization Mechanisms: If the issue is a race condition, we can use synchronization primitives like mutexes or locks to protect the SecretManager's internal state. This will ensure that only one thread can register a secret storage at a time, preventing the collision.
  2. Initialization Flags: We can add a flag to the SecretManager to track whether the secret storages have already been initialized. Before registering any storages, we check this flag. If it's already set, we skip the initialization. This prevents double initialization scenarios.
  3. Singleton Pattern: If the SecretManager is intended to be a singleton (a class with only one instance), we can enforce this pattern to ensure that only one instance of the manager is ever created. This can prevent multiple instances from trying to register the same storage.
  4. Refactoring Initialization Logic: We might need to refactor the initialization logic to ensure that it's called in a controlled and predictable manner. This could involve moving the initialization code to a specific function or class and ensuring that it's only called once during the application lifecycle.

When choosing a solution, it's important to consider factors like performance, complexity, and maintainability. We want a fix that's not only effective but also easy to understand and doesn't introduce new problems.

Writing a Test Case

Once we've implemented a fix, we need to write a test case that specifically targets the bug. This test case should:

  1. Reproduce the Error: The test should be designed to trigger the "Secret storage name collision" error if the fix is not working correctly.
  2. Verify the Fix: The test should verify that the fix prevents the error from occurring and that the secret management system is functioning as expected.
  3. Be Robust: The test should be robust and reliable, meaning it should pass consistently if the fix is correct and fail consistently if the fix is broken.

For this specific issue, we might want to write a test case that:

  • Simulates Concurrent Access: If the issue is a race condition, the test should simulate multiple threads trying to register secret storages simultaneously.
  • Triggers Double Initialization: The test should try to trigger the double initialization scenario that we identified during our investigation.
  • Checks Storage Registration: The test should verify that the memory secret storage is registered only once and that all other secret management operations are functioning correctly.

A well-written test case serves as a safety net, preventing regressions and ensuring that the fix remains effective as the codebase evolves. It also provides a clear demonstration of the bug and how the fix addresses it.

Submitting a Pull Request

With the fix implemented and the test case written, the final step is to submit a pull request (PR). A PR is a proposal to merge your changes into the main codebase. It allows other developers to review your changes, provide feedback, and ensure that they meet the project's standards.

A good PR should include:

  1. A Clear Description: The PR should have a clear and concise description of the bug, the fix, and the test case. This helps reviewers understand the context and purpose of the changes.
  2. Well-Documented Code: The code should be well-documented, with comments explaining the logic and rationale behind the fix. This makes it easier for reviewers to understand the code and for future developers to maintain it.
  3. Passing Tests: The PR should include all the necessary tests, and all tests should pass. This demonstrates that the fix is working correctly and doesn't introduce any new issues.
  4. Clean Commits: The PR should have a clean commit history, with each commit addressing a specific aspect of the fix. This makes it easier to review the changes incrementally and to revert individual commits if necessary.

Submitting a well-crafted PR is a crucial step in the development process. It ensures that your changes are thoroughly reviewed and that they integrate smoothly into the codebase. It also fosters collaboration and knowledge sharing within the development team.

By following these steps – crafting a robust solution, writing a targeted test case, and submitting a well-prepared pull request – we can effectively address the "Secret storage name collision" error and prevent similar issues from arising in the future. This not only improves the stability and reliability of DuckDB but also strengthens our development process and our ability to tackle complex bugs.

This was the complete analysis of the error. Hope this helps.