RisingWave Storage Full Issue: ObjectStore Failure

Understanding the Bug: RisingWave's Unwanted Writes

Hey guys, let's talk about a tricky issue with RisingWave, the cloud-native, distributed SQL database for stream processing. Specifically, we're looking at a bug that can lead to an ObjectStore failure, causing RisingWave to fill up your storage, behaving like a 'Rabbit virus'. It's super frustrating when you expect your system to behave a certain way, and then suddenly your storage is maxed out. This typically happens when RisingWave keeps writing to the state store even when no real data processing is going on. This behavior continues until all available storage on the secondary storage device is completely exhausted. Imagine having a massive storage drive and then finding it filled with unwanted data. This is what we're trying to solve here. Let's break down the problem, the error messages, and, of course, how to potentially fix it. It is very important to use the best practice in order to get the best result.

The Core Issue and Root Cause Analysis

The central problem lies in RisingWave's interaction with its state store, specifically, the Hummock storage engine. When certain conditions are met, RisingWave seems to initiate writes to the state store, even without any active data processing tasks. The main reason this happens is the system's attempts to synchronize the data to keep it consistent, leading to a constant stream of writes, thereby consuming valuable storage space. The root cause appears to be linked to how RisingWave handles schema changes and the dropping and recreating of sources and materialized views. This kind of action triggers unnecessary writes, which in turn exacerbate the issue. We're talking about a kind of 'background process' that never stops. The issue often surfaces when users periodically drop and recreate sources, materialized views, especially when accompanied by schema modifications. This is not the expected behavior, which is why we must address it.

Why This Matters for Developers and Users

For developers and users, this bug is more than just an inconvenience; it can be a showstopper. First off, It can quickly lead to storage exhaustion, which means your application grinds to a halt. When your storage is full, RisingWave can't perform basic operations. That means no data ingestion, no queries, and effectively, your stream processing system becomes unusable. Secondly, storage costs become a major concern, as you're paying for storage that's filled with unnecessary data. Also, It creates operational headaches, because you have to constantly monitor storage usage and manually intervene to prevent failures. The users will see the same. It can result in a lot of downtime, leading to frustrated users and, potentially, the loss of important data.

Decoding the Error Messages

Dissecting the Error Log

Let's analyze the error message. The error message provides important insights into the failure. The primary one is "ObjectStore failed with IO error: Unexpected (persistent) at Writer::close, context: timeout 5 => io operation timeout reached." This means that the system is unable to write data to the object store because of an I/O timeout. This timeout suggests that the system is attempting to write more data than the storage system can handle within the allocated time. This is also linked to the hummock state store, revealing that the Hummock is having trouble syncing data. This is the main reason for the issue. The error message also shows that the system is retrying the operations, trying to fix the issue. This is how the system attempts to fix the issues, but if the underlying issue continues, this will not resolve anything.

Key Components of the Error

The error message tells us the exact context where the error happens. The context includes the following: first, is a gRPC request to stream service failure. This shows there are internal issues inside the internal communication between different services. Secondly, the error message indicates a failure to complete an epoch, specifically indicating a range of epochs that the system is trying to handle. These epochs are essentially snapshots of the data. Another important thing is the Hummock error, which is crucial because it relates directly to the state store. It shows that Hummock has problems to sync the data due to I/O failures. Finally, it also mentions a timeout, which means the process has taken too long and is unable to complete within the allowed time. This provides crucial information.

What the Error Message Reveals About the Failure

In essence, the error message reveals a breakdown in RisingWave's ability to write data persistently. This failure is closely linked to I/O operations and the Hummock state store. The timeout error suggests the object store, which is used for storing the data. This problem creates a situation where data cannot be saved, resulting in the system getting stuck, which leads to the filling up of your storage device.

Steps to Reproduce the Issue: A Practical Guide

Setting Up the Environment

To effectively reproduce the bug, you'll need to set up an environment that mimics the scenario where the problem arises. Here's how to do it: First, you will need to create a source to ingest data, this can be from any data source, such as a file, a database, or even a streaming platform. You should create a materialized view to transform and aggregate the data. This view will perform calculations or transformations on the incoming data. Another important step is to create a table, a persistent store for the data. This will save the aggregated data. The last step is to create an external sink that can be anything to push your data to another place, for example, clickhouse.

Actions to Replicate the Bug

To trigger the bug, you must drop and recreate sources and materialized views. This is the main cause of the issue, and you need to perform it periodically. This can be scheduled or manual. During this period, make sure to change the schema to ensure that the writes are executed. When this process is done, give some time to your system, and it will fill up the storage. This is how the bug manifests itself. Make sure that the device is on standby. After a few hours, the storage will get full.

Docker Compose Configuration for Testing

To replicate the issue in your local environment, you can use Docker Compose. The docker-compose.yml file is set up to deploy a RisingWave cluster with all necessary components like meta service, compute nodes, frontend, and a storage backend. The configuration involves configuring the services and volumes to ensure that RisingWave components can communicate and persist data correctly. The most important thing is to make sure your Docker Compose is configured as it is in the bug report to correctly reproduce the issue.

Expected Behavior vs. Actual Outcomes

What Should Happen

Ideally, when you deploy RisingWave, you'd expect a stable and predictable system. This means that storage usage should be efficient, with data only being written when necessary. There should be a stable ingestion process, and data transformations should be performed without unexpected resource consumption. The dropping and recreating of sources and materialized views, with schema changes, should not lead to any significant storage increases.

The Reality of the Bug

However, the reality is that the bug leads to storage filling up unexpectedly. Instead of efficient storage, you'll see a steady increase in storage consumption, even when the system is idle. The error messages will show a series of I/O timeouts, which lead to slow performance and potential system crashes. The consequence of the issue is a slow and unusable system, which can cause you to lose data and can make your users unhappy.

The Discrepancy

The most important discrepancy is the difference between what's expected and what happens in practice. Instead of the system working correctly, you will experience a continuous write to storage, eventually causing the system to run out of disk space. This is a severe deviation from what's intended. This difference shows how important it is to fix the bug.

Troubleshooting and Potential Solutions

Analyzing the Problem Further

Start by checking RisingWave's logs for clues. Check the compute nodes, the meta service, and the storage component logs. Look for patterns in the errors, and try to correlate the error messages with specific actions, such as schema changes or view recreations. Analyze the logs to understand which component is failing and why. Check the storage backend (in the given case, this is MinIO) to make sure there are no issues. Check the storage performance metrics. This can highlight if the issue is with the storage itself. It is also important to verify the network connection between different components to see whether any network failures affect the behavior.

Possible Workarounds and Solutions

• Optimize Schema Changes: Minimize the frequency of dropping and recreating sources and materialized views. If possible, modify existing views instead of recreating them, in order to avoid the issue. The goal is to reduce the amount of writing to the storage. Check to make sure that the writes are executed when needed and not excessively. This can greatly minimize the storage consumption. This helps reduce the writing activity. Make sure there are no schema changes. This will also help to reduce unnecessary writes.
• Tune Hummock Configuration: Fine-tune the Hummock configuration parameters, such as the write buffer size and the compaction settings, to optimize write performance. Monitor the performance and adjust to your workload. Adjust the settings to match your current workload and needs. Increase the object store timeout to allow more time for write operations. This may prevent timeouts.
• Monitor Storage Usage: Use monitoring tools to keep track of storage usage and set up alerts for when the storage is close to being full. Keep an eye on the storage use of your system and set alarms. This can help you to react before the system crashes. Use Prometheus to monitor key metrics, such as storage usage, write throughput, and error rates. Use Grafana to visualize the metrics and configure alerts. This will help you to anticipate issues. This will give you time to address them before they turn into major problems. This is important to monitor the situation.
• Update RisingWave: Make sure you're running the latest stable version of RisingWave. There might be some fixes that can solve the issue. Check the release notes and update accordingly. Test the latest version and make sure the issue is fixed.

Docker Compose Configuration for Local Testing

Setting up the Environment

To effectively test the fix in a local environment, you need to set up RisingWave using Docker Compose. The provided docker-compose.yml file defines the services. You'll need to create a risingwave.toml file to configure RisingWave. You must configure all the parameters for your instance. This setup will include meta, compute, frontend, and compactor services. Along with these, you must also include a database (PostgreSQL), an object store (MinIO), and a monitoring stack (Prometheus and Grafana).

Configuring the Services

Configure the services by modifying the docker-compose.yml file. Customize the settings according to your needs. This involves setting resource limits, ports, and environment variables. The risingwave.toml file defines the operational parameters. Here, you'll specify backend options, compute settings, and frontend configurations. In the container section, you will be able to customize the resource allocation for the pods. Configure the health checks to ensure the services are running. The health checks monitor the system.

Running the Test

Run docker-compose up to start all the services. Verify that all components are running as expected. You can check the logs to confirm there are no errors. To reproduce the bug, you must create a source, materialized view, table, and external sink. Then, you can drop and recreate the sources and materialized views. You must monitor storage usage during these operations. Repeat these steps until the issue is triggered. You must monitor everything. This will help you to troubleshoot your issues.

Conclusion: Addressing the ObjectStore Failure

In conclusion, the ObjectStore failure in RisingWave is a significant issue. It can lead to storage exhaustion and operational disruptions. However, by understanding the root causes, analyzing error messages, and implementing effective troubleshooting strategies, you can mitigate these issues. By carefully reviewing the logs and configurations, you can identify and address the issue. You should apply workarounds to optimize schema changes and fine-tune Hummock settings. Proactive monitoring of storage usage and staying up-to-date with RisingWave releases are essential. These actions help ensure stability. By adopting these best practices, you can ensure a more stable and efficient stream processing environment.