Consolidating Local DB Event & Log Fetching: A Guide

Let's dive into how we can streamline the process of fetching events and store logs from our local database. This article will cover the challenges, the proposed solutions, and the steps to make our system more efficient and maintainable. So, buckle up, guys, we're going on a code consolidation adventure!

Background

Currently, we have two functions, fetch_events_with_config and fetch_store_set_events, that are doing pretty much the same thing. They both handle chunking block ranges, making concurrent RPC calls, retrying if things go wrong, sorting the results, and adding those all-important timestamps. The only real difference? The topics and addresses they're using for the fetching. Think of it like two chefs using the same recipe but with slightly different ingredients – it works, but it's not ideal.

Problem

The big issue here is code duplication. When logic is duplicated, it becomes a headache to maintain consistency. Imagine a bug fix – you'd have to apply it in multiple places, and if you forget one, things can get messy. Plus, over time, the two implementations can drift apart, leading to different behaviors for orderbook events and interpreter store events. This divergence can cause unexpected issues and make debugging a nightmare. It’s like having two slightly different versions of the same software – eventually, they'll start acting differently, and you'll be scratching your head trying to figure out why. This duplication not only increases the workload but also introduces a higher risk of errors. Keeping code DRY (Don't Repeat Yourself) is crucial for maintainability and reducing potential bugs. Therefore, consolidating these processes is a key step toward ensuring a robust and reliable system.

Proposed Approach

To tackle this, we're going to consolidate these similar processes into a single, reusable function. Here’s the plan:

1. Introduce a Private collect_logs Routine

We'll create a new, private function called collect_logs inside crates/common/src/raindex_client/local_db/fetch.rs. This function will be the workhorse, taking in the target addresses, topics, block range, and a FetchConfig. We'll reuse the existing retry_with_attempts backoff mechanism to handle failures, respect the chunk_size to manage the amount of data processed at once, and limit concurrency using max_concurrent_requests. This way, both orderbook and store callers will be using the same, well-defined pipeline. Think of it as building a universal tool that can handle both tasks efficiently. This approach ensures consistency and reduces the risk of divergent behavior. The goal is to have a single, reliable function that handles all the heavy lifting for fetching logs. By centralizing this logic, we make it easier to maintain and update the system in the future.

2. Generalize the Job Builder

Right now, the chunking logic lives in both fetch_events_with_config and build_store_jobs. We're going to extract this logic into a single helper function. This helper will be responsible for producing log fetch jobs for either a single orderbook contract or the deduped store addresses. We'll also preserve the early-return guards that drop empty address lists or invalid block ranges to avoid unnecessary processing. It's like creating a master builder that can assemble jobs for different scenarios, ensuring that everything is set up correctly before execution. This step is crucial for streamlining the process and reducing redundancy. Having a single job builder simplifies the overall architecture and makes it easier to manage the workflow. It also allows for better error handling and optimization.

3. Centralize Post-Processing

Sorting and timestamp hydration are currently done separately in both code paths. We'll move these tasks into the shared helper function. We'll use the sort_events_by_block_and_log semantics to order the results by block number and numeric log index before calling backfill_missing_timestamps. This ensures that both code paths order the results consistently and fill in any missing timestamps in a uniform manner. This centralization is key to maintaining data integrity and consistency across the board. It's like having a dedicated post-processing unit that ensures all data is properly formatted and complete before being used. This step not only simplifies the code but also ensures that the results are consistent and reliable. By centralizing the post-processing, we reduce the chances of errors and inconsistencies.

4. Refactor Existing Functions

We'll refactor fetch_events_with_config to assemble its orderbook topics (AddOrderV3, TakeOrderV3, WithdrawV2, DepositV2, RemoveOrderV3, ClearV3, AfterClearV2, MetaV1_2) and delegate to the new helper with the single contract address. Similarly, we'll refactor fetch_store_set_events to dedupe addresses, supply the store-set topic (Set::SIGNATURE_HASH), and call the same helper. This step is about reorganizing the existing code to leverage the new, centralized functionality. It's like re-plumbing the system to connect to the new, more efficient pipeline. The goal is to make both functions leaner and more focused on their specific tasks, while relying on the shared helper for the heavy lifting. This refactoring will make the code easier to understand and maintain.

5. Add Test Coverage

To ensure that everything is working correctly, we'll add coverage in crates/common (unit or async integration tests) that exercises both callers through the new helper. We'll specifically test chunk sizing, retry attempts, address dedupe, and timestamp backfilling to make sure these critical functions remain intact. Think of it as putting the new system through a series of rigorous tests to ensure it meets all the requirements and performs as expected. Thorough testing is crucial for identifying and fixing any potential issues before they make it into production. By adding comprehensive test coverage, we can be confident that the consolidated system is robust and reliable.

By implementing these steps, we'll have a more maintainable, efficient, and consistent system for fetching events and store logs from our local database. This consolidation will reduce code duplication, simplify maintenance, and improve the overall reliability of our system. So, let's get to work and make our codebase cleaner and more efficient! This approach will undoubtedly make our lives easier in the long run.