Introduction
In the world of Solr, efficient memory management is crucial for ensuring optimal performance and system stability. One common misconception among users is that allocating a large heap size will automatically lead to improved performance. However, while a larger heap can initially seem beneficial, it can also introduce significant risks, including increased chances of system downtime and performance degradation.
This article explores the implications of large heap allocations in Solr, addressing how excessive memory settings can adversely affect your Solr nodes.
Understanding the risk of over-allocation
When the heap is very large, the garbage collector may take longer to clean up unused objects. This can cause long pauses, sometimes even leading to OutOfMemoryError if the heap becomes too full to manage efficiently. This is because Java will use as much heap as it’s allowed before cleaning up.
The larger the garbage that needs to be cleaned up, the longer it will take. A too-large heap could lead to very long GC pauses, causing Solr to slow down, go into recovery, or simply crash.
Case study: the impact of over-allocating heap
Following is an example of such a scenario, where a client allocated an excessive 50 GB heap size to their Solr instance, even though the average heap usage was only around 20-25 GB.
When indexing operations started, Solr began consuming the available memory, leading to very long Full GC pauses, as shown in the attached screenshot. These prolonged pauses ultimately caused the Solr node to crash.
As seen in the GC logs below, the maximum pause time recorded was 53 seconds, which is substantial. Similar pauses, lasting around 50 seconds, occurred frequently, severely impacting system performance.
However, after reducing the heap size to 25 GB, we observed a significant improvement in overall throughput and a substantial reduction in GC pause latency. The throughput increased by 99%, and no Full GC pauses were recorded, as seen in the below screenshots.
Conclusion:
In summary, while larger heap sizes may seem beneficial for Solr, they can actually lead to longer GC pauses, performance degradation, and system crashes. Optimizing the heap size based on actual usage, rather than over-allocating, improves system stability and throughput. Proper memory management is essential to ensure that Solr performs efficiently, even under heavy workloads.