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What you’ll learn
- How to identify and troubleshoot disk issues caused by indexes in RavenDB.
- Practical tools and techniques for diagnosing disk usage issues.
- Using RavenDB tools to monitor index performance.
- Where to look for information to resolve disk problems related to indexing.
Introduction
Disk usage issues often stem from indexes consuming more resources than expected. Identifying and resolving these problems is crucial for maintaining optimal database performance. This guide will help you understand how to find indexes that are eating up your precious disk resources and where to look for help to alleviate related concerns.
Indexing Performance View
Let’s start by going through a tool that is available to every RavenDB user, which is the Indexing Performance view in Studio.
The Indexing Performance View in RavenDB Studio is a powerful tool that provides insights into how your indexes are performing. Understanding these metrics can help you identify and troubleshoot issues that may be consuming excessive disk resources.
Purpose of the Indexing Performance View
The Indexing Performance View allows you to monitor and analyze the behavior of your indexes. By providing real-time data on various performance metrics, this tool helps you pinpoint problematic indexes and optimize them for better performance and resource efficiency.
Key metrics
Indexing Time: This metric shows the total time taken by the index to process documents. Long indexing times can indicate complex computations or large data sets.
- Example: If an index is taking unusually long to process, it may be due to large fields or complex logic within the index definition.
Reduce Time: For map-reduce indexes, this metric shows the time taken for the reduce phase.
- Example: If the reduce time is high, it may indicate that the reduction logic is too complex or the data set is too large.
Indexing Throughput: This measures the number of documents indexed per second.
- Example: Low throughput could point to inefficiencies in the indexing process, potentially due to resource contention or suboptimal index definitions.
Interpreting metrics
To effectively use the Indexing Performance View, it’s crucial to understand how to interpret these metrics:
- High Indexing Time: Indicates potential inefficiencies in index processing. Look for ways to simplify the index logic or reduce the amount of data being indexed.
- Long Reduce Time: May indicate complex reduction logic or large data sets. Simplify the reduction process where possible.
- Low Indexing Throughput: Could be a sign of resource contention or inefficiencies. Investigate server performance and optimize index definitions.
Example: A common mistake is to index large text fields or frequently changing fields which can lead to high resource consumption. By analyzing these metrics, you can identify such issues and take corrective actions.
You can also take this approach further by using the built-in stack trace viewer for the relevant indexing thread. That can be surprisingly useful in pointing out exactly where most of the indexing time is spent. We’ll see some of those capabilities later in this article.
You can find more information about Indexing performance view in documentation.
Common Indexing Definition Issues
To optimize your indexes and reduce disk usage, it’s essential to understand common indexing definition issues, including the use of References (using LoadDocument()):
Over-Indexing
- Issue: Indexing too many fields or large fields unnecessarily.
- Solution: Review and simplify your index definitions to include only essential fields.
Frequent updates
- Issue: Indexes that require frequent updates can consume a lot of resources.
- Solution: Use static fields or less dynamic data where possible to reduce update frequency.
LoadDocument Misuse
- Issue: Using LoadDocument to fetch related documents can lead to significant performance issues if overused.
- Solution: Minimize the use of LoadDocument in your index definitions. Ensure that it is used only when absolutely necessary. If possible, denormalize data to reduce dependencies on LoadDocument.
Complex index definitions
- Issue: Indexes with complex calculations or transformations can be resource-intensive.
- Solution: Simplify index logic and reduce unnecessary computations. For example, you may want to move some computation (especially those involving machine learning) to a subscription, rather than as part of the indexing process.
High cardinality fields
- Issue: Indexing fields with many unique values can increase index size.
- Solution: Avoid indexing high cardinality fields unless absolutely necessary. For example, when indexing dates, consider if you need sub-millisecond precision, or if you only deal with dates (without time) or times at a minute granularity.
Indexing large collections
- Issue: Indexing large arrays or collections can significantly increase resource usage.
- Solution: Limit indexing to the most critical elements of large collections.
For more information about these problems and how to fix them, see the documentation.
Finding which index ate my disk using iotop
When your disk resources start to dwindle due to heavy indexing activity in RavenDB, identifying the specific index responsible is crucial. One effective tool for diagnosing disk usage issues on Linux is iotop, a disk I/O monitor. For Windows users, a similar tool is the Resource Monitor, which provides detailed information about disk usage by processes.
When running on the cloud, it is common to have to deal with disks that are slow or being limited by the number of IOPS that are available to them. This is particularly the case if your index supports bursting. You may get some duration of reasonable speed from the disk, and then a massive slowdown. Using iotop to figure out where all the IOPS are going can help you troubleshoot these sorts of issues.
Here’s a step-by-step guide to find which index is consuming the most resources:
- Install iotop
If iotop is not already installed on your Linux machine, and you’re for example using Debian/Ubuntu you can use:
sudo apt-get install iotop- Run iotop
Launch iotop with root privileges and -a option to show accumulated I/O instead of bandwidth. In this mode, iotop shows the amount of I/O processes that have been done since iotop started:
sudo iotop -aYou’ll see something like this:
There’s a lot of information! But we’re mostly interested in columns:
- TID (Thread ID),
- DISK READ,
- DISK WRITE,
- COMMAND.
- Identify resource-intensive thread
In iotop you can sort values by each of the visible columns using arrows on the keyboard.
For example I will sort by Disk Write:
Now we can see in the first row the most write I/O intensive thread.
Raven.Server -c /ravendb/con~ttings.json [Idx NorthwindNZ]That “Idx NorthwindNZ” at the end is a clear indicator that we’re dealing with an index thread.
NorthwindNZD (full name won’t show up in iotop if it’s too long) is the name of an origin database.
Let’s note its thread Id: 27349
- Go to RavenDB Studio
Open RavenDB Studio in your browser and navigate to Manage Server and Advanced in the Debug section.
On this view you can filter threads by id which is perfect for us.
When I enter thread id from previous step I get exactly what I want:
Orders/ByShipment/Location which is a name of an index that is causing the most write I/O operations on my server.
- Analyze and optimize the index
Once we’ve identified the problematic index, we can take steps to optimize it:
- Review index definitions: Start by carefully reviewing your index definitions. Look for fields that can be removed or simplified.
- Store frequently accessed fields: Utilize the storing data in indexes feature for fields that are frequently queried but infrequently changed. Read more about here
- Use Static Indexes: Where possible, use static indexes that provide more control over indexing behavior and can be optimized for specific use cases.
- Throttling: Consider throttling the indexing process. This can help balance the load and prevent the index from overwhelming the system resources. Read more about here
If you are running a RavenDB server on a Windows machine you should be able to access a tool like Resource Monitor. It is quite a handy tool. Using it you can cut straight to the chase since you can see the file path and finding the most I/O intensive index is just a matter of ordering by Read, Write or Total (B/sec):
We can see that index Orders/ByShipment/Location is doing a lot of writing operations.
Conclusions
We explored how to identify and troubleshoot disk usage issues caused by indexes in RavenDB, using tools like iotop for Linux and RavenDB Studio’s Indexing Performance View. We also reviewed common indexing definition issues.
Best Practices
- Simplify Index Definitions: Regularly review and optimize your indexes.
- Store Data in Indexes: Use this feature for frequently queried but infrequently updated fields.
- Throttling: Implement throttling to balance the indexing load.
- Monitor Performance: Continuously monitor index performance using RavenDB tools.
Monitor and Analyze Regularly
Set up a monitoring system to keep track of key performance metrics. Regular analysis helps prevent unexpected spikes in resource usage and ensures efficient database performance.
Further Resources
