This track has a variety of challenges which stress disk, memory, and CPU differently, so it makes a good benchmarking candidate. We are going to use Rally, the official benchmarking tool for Elasticsearch, and we'll be using the geonames track. Let's look at these in concrete terms with some benchmarks. So, is there an actual number we could recommend as the heap size to use? Online forums are full with contradicting advice that is unfortunately too visible, more than the official docs. It also recommends not to set it to more than 26-30GB due to pointer compaction. The official documentation specifies that 50% of available system memory should be set as the heap size for Elasticsearch (also known as the ES_HEAP_SIZE environment variable). On the other hand, a larger heap means that each garbage collection takes longer, and these longer pauses can also lead to reduced performance. If responding to a single query requires Elasticsearch to run the garbage collector multiple times, it can severely degrade the performance of the cluster. On one hand, a smaller heap results in more frequent garbage collections. Garbage collection frequency is more nuanced. Correctly sized caches can have a huge impact on the overall query performance. For caches, Elasticsearch dynamically sizes its internal caches based on the amount of heap size available. Using a larger heap size has two advantages: caches can be larger, and garbage collection can be done less often. This makes application programming much simpler, but it requires a separate step to happen later on to "collect the garbage", which finds the discarded data and makes that space available for the program to use again.Įlasticsearch, like all Java applications, allows us to specify how much memory will be dedicated to the heap. In Java and other garbage-collected languages, the application can freely request memory from the heap, and simply forget about it when it's no longer necessary. And as we usually do in our work with customers, we will use tooling and scientific methods and not just guesswork.įirst a quick background: What is heap used for? In all computer programs, "the heap" is the part of memory where all data that lives longer than a single function call is stored. In follow-up posts we'll examine levers more applicable in specific Elasticsearch use cases, as well as tuning the performance of an entire cluster. We helped many projects succeed and while every engagement required attention to different details, eventually the basics are all the same and we always start at the same point. This series is being written after many years of consulting many customers world-wide on a variety of use-cases, cluster sizes and hardware specs - ever since Elasticsearch 0.11 was released 11 years ago. Those are also the ones with most confusion and even disinformation spread on online forums. This is the first in a series of articles about tuning your Elasticsearch cluster. In this article, we start discussing one of the two most common levers available for tuning a single node in a cluster: heap size and garbage collection algorithm. To help reduce the operating cost, Elasticsearch provides you with many different levers to tune the performance for each cluster. Elasticsearch is a complex distributed system, and as your dataset and query volume grow, the cost of operating a cluster grows as well.
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