Aggregate Logging Sizing Guidelines

The Elasticsearch, Fluentd, and Kibana (EFK) stack aggregates logs from nodes and applications running inside your OpenShift Container Platform installation. Once deployed it uses Fluentd to aggregate logs from all nodes, and pods into Elasticsearch (ES). It also provides a centralized Kibana web UI where users and administrators can create rich visualizations and dashboards with the aggregated data.

The general procedure for installing an aggregate logging stack in OpenShift Container Platform is described in Aggregating Container Logs. There are some important things to keep in mind while going through the installation guide:

In order for the logging pods to spread evenly across your cluster, an empty node selector should be used when creating the project.

In conjunction with node labeling, which is done later, this controls pod placement across the logging project.

Elasticsearch (ES) should be deployed with a cluster size of at least three for resiliency to node failures. This is specified by setting the openshift_logging_es_cluster_size parameter in the inventory host file.

Refer to Ansible Variables for a full list of parameters.

Kibana requires a hostname that can be resolved from wherever the browser will be used to access it. For example, you might need to add a DNS alias for Kibana to your corporate name service in order to access Kibana from the web browser running on your laptop. Logging deployment creates a Route to Kibana on one of your "infra" nodes or wherever the OpenShift router is running. The Kibana hostname alias should point to this machine. This hostname is specified as the Ansible openshift_logging_kibana_hostname variable.

Installation can take some time depending on whether the images were already retrieved from the registry or not, and on the size of your cluster.

Inside the openshift-logging project, you can check your deployment with oc get all.

You should end up with a similar setup to the following.

By default the amount of RAM allocated to each ES instance is 16GB. openshift_logging_es_memory_limit is the parameter used in the openshift-ansible host inventory file. Keep in mind that half of this value will be passed to the individual elasticsearch pods java processes heap size.

Learn more about installing EFK.

In Red Hat Enterprise Linux (RHEL) 7 the systemd-journald.socket unit creates /dev/log during the boot process, and then passes input to systemd-journald.service. Every syslog() call goes to the journal.

The default rate limiting for systemd-journald causes some system logs to be dropped before Fluentd can read them. To prevent this add the following to the /etc/systemd/journald.conf file:

Then restart the services.

These settings account for the bursty nature of uploading in bulk.

After removing the rate limit, you may see increased CPU utilization on the system logging daemons as it processes any messages that would have previously been throttled.

If you do not indicate the desired scale at first deployment, the least disruptive way of adjusting your cluster is by re-running the Ansible logging playbook after updating the inventory file with an updated openshift_logging_es_cluster_size value. parameter. Refer to the Performing Administrative Elasticsearch Operations section for more in-depth information.

An Elasticsearch index is a collection of shards and their corresponding replicas. This is how ES implements high availability internally, so there is little need to use hardware based mirroring RAID variants. RAID 0 can still be used to increase overall disk performance.

A persistent volume is added to each Elasticsearch deployment configuration. On OpenShift Container Platform this is usually achieved through Persistent Volume Claims.

The PVCs is named based on the openshift_logging_es_pvc_prefix setting. Refer to Persistent Elasticsearch Storage for more details.

Below are capacity planning guidelines for OpenShift Container Platform aggregate logging. Example scenario

Assumptions:

Baseline (256 characters per second → 15KB/min)

Calculating total logging throughput and disk space required for your logging environment requires knowledge of your applications. For example, if one of your applications on average logs 10 lines-per-second, each 256 bytes-per-line, calculate per-application throughput and disk space as follows:

Fluentd ships any logs from systemd journal and /var/lib/docker/containers/*.log to Elasticsearch. Learn more.

Local SSD drives are recommended in order to achieve the best performance. In Red Hat Enterprise Linux (RHEL) 7, the deadline IO scheduler is the default for all block devices except SATA disks. For SATA disks, the default IO scheduler is cfq.

Sizing storage for ES is greatly dependent on how you optimize your indices. Therefore, consider how much data you need in advance and that you are aggregating application log data. Some Elasticsearch users have found that it is necessary to keep absolute storage consumption around 50% and below 70% at all times. This helps to avoid Elasticsearch becoming unresponsive during large merge operations.