Elastic released version 7.12, delivering many new features in its Elastic Enterprise Search, Observability, and Security solutions that are part of Elastic Stack providing distributed search and analysis capabilities.

Perhaps the most important of these is new schema flexibility that allows users to have both performance and adaptability in Elasticsearch. Users can also unlock new value by making object stores fully searchable and automatically scale deployments to the Elastic Cloud hosted service, according to the company.

The flexibility of the schema comes from a feature known as runtime fields, which allows users to create a schema on the fly at request time – known as a read schema – in addition to using the conventional pattern in writing.

Schema-on-Write helps make Elasticsearch fast, but requires planning and testing beforehand of an organized structure to know how the data will be represented. This can lead to difficulties if some time later the user has to ingest new data or adopt a new data use case within a short period of time.

According to Elastic, runtime fields have been implemented in such a way that users don’t have to choose between schema speed and scale in writing or schema flexibility in read. Both can be used at the same time, on the same Elastic Stack and on the same data. The feature is believed to dramatically reduce the time to value customer data by swapping out certain search performance.

The frozen level feature is currently in technical preview, but adds the ability to directly search object stores such as Amazon’s S3, Google Cloud Storage, and Microsoft Azure Storage. The benefit of this is the cost, allowing users to search for data while reducing the amount of dedicated resources needed for the search, but with a performance tradeoff.

Elastic claims that by retrieving only the data needed to complete a query in the object store and caching it locally, the frozen tier provides the best search experience while still allowing users to access an unlimited amount of data. This should make it cost effective to store more analytical data for marketing analysis, or keep all log and security data for security teams to analyze.

Elastic Cloud now offers autoscaling, which monitors both storage usage for Elasticsearch data nodes and available capacity for machine learning tasks and automatically adjusts resource capacity to maintain node performance.

Once autoscaling is enabled through the API, command line, or the Elastic Cloud console, the capacity of the user’s Elasticsearch data node will automatically increase as more data is stored, while the memory of the node d Machine learning and processor capacity will increase or decrease depending on the resource requirements of the machine learning jobs. Elastic indicates that users can set thresholds to prevent uncontrolled cluster growth.

Elastic 7.12 is now available on Elastic Cloud, while customers can also download Elastic Stack and cloud orchestration products, Elastic Cloud Enterprise and Elastic Cloud for Kubernetes, for a self-managed experience.

As we reported earlier this year, starting with Elasticsearch and Kibana 7.11, the source code can be used under the SSPL or Elastic license.