Elastic Cloud subscription and billing enhancements come to AWS Marketplace
We are excited to bring you a number of updates for using Elastic Cloud (Elasticsearch managed service) in the AWS Marketplace.
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Autoscale your Elastic Cloud data and machine learning nodes
We're excited to announce that autoscaling is now available on Elastic Cloud. In our initial release, autoscaling monitors the storage utilization of your Elasticsearch data nodes and the available memory capacity for your machine learning jobs.
Ruby and Python clients for Elastic Enterprise Search now generally available
Back in our 7.10 release of the Elastic Stack, we announced the beta of our Ruby and Python clients for Elastic Enterprise Search. Now, with 7.11, both the Ruby and Python clients are generally available. We’ve also begun work on a PHP client. All client source code for both enterprise-search-ruby and enterprise-search-python is available on GitHub. Documentation on how to get started with each client is available on elastic.co.
Testing the new Elasticsearch cold tier of searchable snapshots at scale
The cold tier of searchable snapshots, previously beta in Elasticsearch 7.10, is now generally available in Elasticsearch 7.11. This new data tier reduces your cluster storage by up to 50% over the warm tier while maintaining the same level of reliability and redundancy as your hot and warm tiers.
Creating a Day of Week Runtime Field and Using It in Kibana
The video contains a demonstration of the creation of a runtime field in which the day of the week is calculated from a timestamp field that contains the date. A visualization is then created in Kibana Lens using an indexed field and the newly created runtime field. Runtime field is the name given to the implementation of schema on read in Elasticsearch.
Shadow an Indexed Field With a Runtime Field to Fix Errors
The video contains a demonstration of using a runtime field to fix errors in the indexed data. We intentionally index documents with some errors, and then use a runtime field to shadow the indexed field. The demonstration shows how a user querying the data or creating a visualization in Kibana Lens will see the correct information, which is calculated in the runtime field. This scenario allows for immediate fixing of errors in the indexed data by shadowing them with runtime fields (instead of reindexing). Runtime field is the name given to the implementation of schema on read in Elasticsearch.
Dynamically Created Runtime Fields
The video contains a demonstration of the creation of an index template that defines that unknown fields will be created as runtime fields. Documents are then indexed into an index that inherits from that template, and because these documents contain fields that are not defined in the template, the fields are automatically created as runtime fields (i.e. these fields are usable for search and aggregation, but are not indexed). Runtime field is the name given to the implementation of schema on read in Elasticsearch.
Getting started with runtime fields, Elastic's implementation of schema on read
Historically, Elasticsearch has relied on a schema on write approach to make searching data fast. We are now adding schema on read capabilities to Elasticsearch so that users have the flexibility to alter a document's schema after ingest and also generate fields that exist only as part of the search query. Together, schema on read and schema on write provides users with the choice to balance performance and flexibility based on their needs.
Runtime fields: Schema on read for Elastic
In 7.11, we’re excited to announce support for schema on read in the Elastic Stack. We now offer the best of both worlds on a single platform — the performance and scale of the existing schema on write mechanism that our users love and depend on, coupled with a new level of flexibility for defining and executing queries with schema on read. We call our implementation of schema on read runtime fields.
What is Elasticsearch and how are enterprises using it?
What does Netflix, eBay and Walmart have in common? They all use Elasticsearch. Elasticsearch is a real-time open-source distributed search and analytics engine built on top of Apache Lucene™, a fulltext search-engine library and developed in Java. Elasticsearch started as a scalable version of the Lucene open-source search framework that uses a structure based on documents instead of tables and schemas and comes with extensive REST APIs for storing and searching the data.