We’re looking into maximizing Elasticsearch’s performance by leveraging memory usage to its fullest potential. We hypothesize that reducing disk I/O by running Elasticsearch entirely in memory could lead to more efficient query execution, requiring less CPU resources. Are there methods or configurations available to achieve this?
Here are a few specific questions we have:
-
Running Elasticsearch in Memory: Is it possible to run Elasticsearch entirely in memory(both for write and read operations), minimizing disk I/O and potentially reducing CPU usage and need?
-
Configuration for In-Memory Usage: What configuration settings should we consider to optimize Elasticsearch for in-memory usage? Are there specific parameters or settings we should tweak to achieve this goal?
-
Estimating Memory Size based on Index Size: How can we estimate the required memory size based on the size of our indexes/shards? Are there any formulas or best practices for determining the amount of memory needed relative to the size of our data?
We have already gone through the official indexing optimizations list. We need something more unusual to get better results with our existing hardware.
And also we aware of disk usage options which elasticsearch provide. mmapfs probably would perform better for the READ operations. But writing to the disk remains even in this option.
Any insights, experiences, or best practices you can share regarding optimizing Elasticsearch for in-memory usage would be greatly appreciated. Thank you!