This highly practical course focuses primarily on the node and cluster management aspects of Elasticsearch. The video contains recipes and hands-on solutions to backing up and restoring your nodes and clusters in Elasticsearch, as well as working with user interfaces.
About The Author
Alberto Paro is an engineer, project manager, and software developer. He currently works as freelance trainer/consultant on big data technologies and NoSQL solutions. He loves to study emerging solutions and applications mainly related to big data processing, NoSQL, natural language processing, and neural networks. He began programming in BASIC on a Sinclair Spectrum when he was eight years old, and to date, has collected a lot of experience using different operating systems, applications, and programming languages.
Elasticsearch provides a convenient way to manage the cluster state, which is one of the first things to check if any problems occur. If you need more details on your cluster, you need to query its state.
In production clusters, it's very important to monitor nodes via this API to detect misconfiguration and problems relating to
different plugins and modules.
When some actions are called, they create a server side task that executes the job. The task management API allows you to control these actions. Sometimes, your cluster slows down due to massive CPU usage and you need to understand why.
Sometimes, due to massive relocation, or due to nodes restarting or some other cluster issues, it's necessary to monitor or define custom shard allocation.
Monitoring the index segments means monitoring the health of an index. Cleaning the cache helps to speed up searching, such as cache results, items and filter results.
An Elasticsearch snapshot allows for the creation of snapshots of individual indices (or aliases), or an entire cluster, into a remote repository.
We can create snapshots of indices, a full backup of an index, in the exact instant that the command is called
Once you have the snapshots of your data, it can be restored.
It's not possible to restore backups of a newer Elasticsearch version in an older version. The restore is only forward-compatible. So, we need reindexing from a remote cluster.
Cerebro is a partial rewrite of the previous plugin available as a self-working application server.
Kibana is an opensource pluggable interface, free to change to be used for Elasticsearch. It provides data visualization and data discovery and with commercial products such as X-Pack, and also supports security, graph, and cluster monitoring.
The core of Kibana are the dashboards–an aggregation of widgets that are the results of queries and aggregations.
X-Pack provides cluster functionalities that allows to control and monitor your nodes and cluster. This is a very useful component of X-Pack as it is the lifesaver on large installations.
X-Pack provides cluster functionalities that allow you to control and monitor your nodes and cluster. This is a very useful component of X-Pack, as it is the lifesaver on large installations.
Kibana allows you to create reusable data representations called visualizations. They are the representations of aggregations and can be used to power up the dashboard with custom graphs.
The job of ingest nodes is to pre-process the documents before sending them to the data nodes.
The definition is stored in a cluster state via the put pipeline API. After having stored your pipeline, it is common to retrieve its content, for checking its definition. This action can be done via the get pipeline API. To clean up our Elasticsearch cluster for obsolete or unwanted pipelines, we need to call the delete pipeline API with the ID of the pipeline.
The ingest part of every architecture is very sensitive, so the Elasticsearch team has created the possibility of simulating your pipelines without the need to store them in Elasticsearch.
Elasticsearch provides, by default, a large set of ingest processors. Their number and functionalities can also change from minor versions to extended versions for new scenarios. We will cover one of the most used for log analysis: the grok processor, which is well known to Logstash users.
An ingest node can be held under very high pressure without causing problems to the rest of the Elasticsearch cluster. GeoIP allows us to map an IP address to a GeoPoint and other location data.
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