Hardware requirements and recommendations
Elasticsearch is designed to handle large amounts of log data. The more data that you choose to retain, the more query demands, the more resources it requires. Prototyping the cluster and applications before full production deployment is a good way to measure the impact of log data on your system. For detailed capacity planning information, see your product logging and metrics capacity planning.
Note: The default number of pods and memory allocation for the managed ELK stack is not intended for production use. Actual production usage might be much higher. The default values provide a starting point for prototyping and other demonstration efforts.
The minimum required disk size generally correlates to the amount of raw log data generated for a full log retention period. It is also a good practice to account for unexpected bursts of log traffic. As such, consider allocating an extra 25-50%
of storage. If you do not know how much log data is generated for managed logging instances, a good starting point is to allocate
100Gi of storage for each management node.
Avoid NAS storage as you might experience issues around stability and latency. Sometimes, the logging system does not start. This situation can introduce a single point of failure. For more information, see Disks .
You can modify the default storage size by adding the following block to the
The number of pods and the amount of memory that is required by each pod differs depending on the query demand and volume of logs to be retained. Proper capacity planning is an iterative process that involves estimation and testing. You can start with the following guidelines:
- Allocate 16, 32 or even 64 GBs of memory for each data pod.
Insufficient memory can lead to excess garbage collection, which can add significant CPU consumption by the Elasticsearch process.
The default memory allocation settings for the managed ELK stack can be modified by adding and customizing the following lines in
config.yaml. In general,
heapSize value equals approximately half of the overall pod
- The Elastic stack works better by scaling out (adding more identical pod instances) than scaling up (adding more memory to fixed number of pods).
- The heap size is specified by using JDK units: g|G, m|M, k|K.
- The pod memory limit is specified in Kubernetes units: G|Gi, M|Mi, K|Ki.
- The Kibana Node.js maximum old space size is specified in MB without units.
logging: logstash: heapSize: "512m" memoryLimit: "1024Mi" elasticsearch: data: heapSize: "4096m" memoryLimit: "7000Mi" kibana maxOldSpaceSize: "1536" memoryLimit: "2048Mi"
CPU usage can fluctuate depending on various factors. Long or complex queries tend to require the most CPU. Plan ahead to ensure that you have the capacity that is needed to handle all of the queries that your organization needs.