Emily Chang has a big, four-part series on monitoring Elasticsearch performance. Part 1 is a nice introduction to Elasticsearch and important metrics out of the box:
The three most common types of nodes in Elasticsearch are:
Master-eligible nodes: By default, every node is master-eligible unless otherwise specified. Each cluster automatically elects a master node from all of the master-eligible nodes. In the event that the current master node experiences a failure (such as a power outage, hardware failure, or an out-of-memory error), master-eligible nodes elect a new master. The master node is responsible for coordinating cluster tasks like distributing shards across nodes, and creating and deleting indices. Any master-eligible node is also able to function as a data node. However, in larger clusters, users may launch dedicated master-eligible nodes that do not store any data (by adding node.data: false to the config file), in order to improve reliability. In high-usage environments, moving the master role away from data nodes helps ensure that there will always be enough resources allocated to tasks that only master-eligible nodes can handle.
Data nodes: By default, every node is a data node that stores data in the form of shards (more about that in the section below) and performs actions related to indexing, searching, and aggregating data. In larger clusters, you may choose to create dedicated data nodes by addingnode.master: false to the config file, ensuring that these nodes have enough resources to handle data-related requests without the additional workload of cluster-related administrative tasks.
Client nodes: If you set node.master and node.data to false, you will end up with a client node, which is designed to act as a load balancer that helps route indexing and search requests. Client nodes help shoulder some of the search workload so that data and master-eligible nodes can focus on their core tasks. Depending on your use case, client nodes may not be necessary because data nodes are able to handle request routing on their own. However, adding client nodes to your cluster makes sense if your search/index workload is heavy enough to benefit from having dedicated client nodes to help route requests.
Part 2 shows how to collect metrics using various APIs:
The Node Stats API is a powerful tool that provides access to nearly every metric from Part 1, with the exception of overall cluster health and pending tasks, which are only available via the Cluster Health API and the Pending Tasks API, respectively. The command to query the Node Stats API is:
curl localhost:9200/_nodes/stats
The output includes very detailed information about every node running in your cluster. You can also query a specific node by specifying the ID, address, name, or attribute of the node. In the command below, we are querying two nodes by their names, node1 and node2 (node.name in each node’s configuration file):
curl localhost:9200/_nodes/node1,node2/stats
Each node’s metrics are divided into several sections, listed here along with the metrics they contain from Part 1.
Part 3 is a brief for using Datadog for metrics collection and display:
The Datadog Agent is open source software that collects and reports metrics from each of your nodes, so you can view and monitor them in one place. Installing the Agent usually only takes a single command. View installation instructions for various platforms here. You can also install the Agent automatically with configuration management tools like Chef orPuppet.
Part 4 walks through some common Elasticsearch performance issues:
How to solve 5 Elasticsearch performance and scaling problemsseries / elasticsearch
This post is the final part of a 4-part series on monitoring Elasticsearch performance. Part 1 provides an overview of Elasticsearch and its key performance metrics, Part 2 explains how to collect these metrics, and Part 3 describes how to monitor Elasticsearch with Datadog.
Like a car, Elasticsearch was designed to allow its users to get up and running quickly, without having to understand all of its inner workings. However, it’s only a matter of time before you run into engine trouble here or there. This article will walk through five common Elasticsearch challenges, and how to deal with them.
Problem #1: My cluster status is red or yellow. What should I do?
If you recall from Part 1, cluster status is reported as red if one or more primary shards (and its replicas) is missing, and yellow if one or more replica shards is missing. Normally, this happens when a node drops off the cluster for whatever reason (hardware failure, long garbage collection time, etc.). Once the node recovers, its shards will remain in an initializing state before they transition back to active status.
The number of initializing shards typically peaks when a node rejoins the cluster, and then drops back down as the shards transition into an active state, as shown in the graph below.
During this initialization period, your cluster state may transition from green to yellow or red until the shards on the recovering node regain active status. In many cases, a brief status change to yellow or red may not require any action on your part.
However, if you notice that your cluster status is lingering in red or yellow state for an extended period of time, verify that the cluster is recognizing the correct number of Elasticsearch nodes, either by consulting Datadog’s dashboard or by querying the Cluster Health API detailed in Part 2.
If the number of active nodes is lower than expected, it means that at least one of your nodes lost its connection and hasn’t been able to rejoin the cluster. To find out which node(s) left the cluster, check the logs (located by default in the logs folder of your Elasticsearch home directory) for a line similar to the following:
[TIMESTAMP] ... Cluster health status changed from [GREEN] to RED
Reasons for node failure can vary, ranging from hardware or hypervisor failures, to out-of-memory errors. Check any of the monitoring tools outlined here for unusual changes in performance metrics that may have occurred around the same time the node failed, such as a sudden spike in the current rate of search or indexing requests. Once you have an idea of what may have happened, if it is a temporary failure, you can try to get the disconnected node(s) to recover and rejoin the cluster. If it is a permanent failure, and you are not able to recover the node, you can add new nodes and let Elasticsearch take care of recovering from any available replica shards; replica shards can be promoted to primary shards and redistributed on the new nodes you just added.
However, if you lost both the primary and replica copy of a shard, you can try to recover as much of the missing data as possible by using Elasticsearch’s snapshot and restore module. If you’re not already familiar with this module, it can be used to store snapshots of indices over time in a remote repository for backup purposes.
Problem #2: Help! Data nodes are running out of disk space
If all of your data nodes are running low on disk space, you will need to add more data nodes to your cluster. You will also need to make sure that your indices have enough primary shards to be able to balance their data across all those nodes.
However, if only certain nodes are running out of disk space, this is usually a sign that you initialized an index with too few shards. If an index is composed of a few very large shards, it’s hard for Elasticsearch to distribute these shards across nodes in a balanced manner.
This is the most thorough look at Elasticsearch internals that I’ve seen (although admittedly that’s not something I’m usually on the lookout for).