![]() ![]() ![]() Sizing by data volumeįor metrics and logging use cases, we typically manage a huge amount of data, so it makes sense to use the data volume to initially size our Elasticsearch cluster. The network performance - both bandwidth and latency - can have an impact on the inter-node communication and inter-cluster features like cross-cluster search and cross-cluster replication. The quantity and performance of CPU cores governs the average speed and peak throughput of data operations in Elasticsearch. OS Cache:Elasticsearch will use the remainder of available memory to cache data, improving performance dramatically by avoiding disk reads during full-text search, aggregations on doc values, and sorts.Įlasticsearch nodes have thread pools and thread queues that use the available compute resources.This is ideally set to 50% of available RAM. JVM Heap:Stores metadata about the cluster, indices, shards, segments, and fielddata.Elasticsearch does not need redundant storage (RAID 1/5/10 is not necessary), logging and metrics use cases typically have at least one replica shard, which is the minimum to ensure fault tolerance while minimizing the number of writes.When operating on bare metal, local disk is king!.Due to the higher cost of SSD storage, a hot-warm architecture is recommended to reduce expenses. SSDs are recommended whenever possible, in particular for nodes running search and index operations.For each search or indexing operation the following resources are involved: Storage: Where data persists Let's review some fundamentals around computing resources. Performance is contingent on how you're using Elasticsearch, as well as what you're running it on. Feel free to spin up a free trial cluster as you follow along. We also provide a predefined observability template along with a checkbox to enable the shipment of logs and metrics to a dedicated monitoring cluster. Note that with the Elasticsearch Service on Elastic Cloud, we take care of a lot of the maintenance and data tiering that we describe below. In addition, the architecture can be influenced by the constraints that we may have, like the hardware available, the global strategy of our company and many other constraints that are important to consider in our sizing exercise. When we define the architecture of any system, we need to have a clear vision about the use case and the features that we offer, which is why it’s important to think as a service provider - where the quality of our service is the main concern. As sizing exercises are specific to each use case, we will focus on logging and metrics in this post. We’ll go beyond “it depends” to equip you with a set of methods and recommendations to help you size your Elasticsearch cluster and benchmark your environment. In this post, we'll tackle performance Elasticsearch benchmarking and sizing questions like the above. If these sound familiar, great! These are questions all of us should be thinking about as we deploy new products into our ecosystems. Do I have enough available resources in my cluster?.What is the throughput capacity of my cluster?.How confident am I that this will work in production?.But then I remembered I needed to slow down (we all need that reminder sometimes!) and answer a few questions before I got ahead of myself. And while I was pleasantly surprised at how quickly I was able to deploy it, my mind was already racing towards next steps. When I built my first Elasticsearch cluster, it was ready for indexing and search within a matter of minutes. With Elasticsearch, it's easy to hit the ground running. ![]()
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