7.29.0

Features

• A new search grader plugin that allows grading of search results and supports evaluating grade changes based on the search relevance strategy changes

Fixes

• Always fetch the alias/index map from Elasticsearch prior to re-indexing to avoid issues caused by an outdated cache

7.28.4_beta.1

feat: add feature exception for search grader (#130)

* feat: add searchgrader category

* fix: export port

* feat: add feature exception for search grader

7.28.4_beta

feat: add feature exception for search grader (#130)

* feat: add searchgrader category

* fix: export port

* feat: add feature exception for search grader

7.28.4

Bug Fixes

• Fix the grade evaluation process - noss
• Add new endpoint to get the graded documents - noss

7.28.3

New Addition

• Adds support for the updated Arc Basic plan, now priced at $29/mo instead of $39/mo

Fixes

• Updates cache when a user or permission is updated or deleted so that invalid credentials can't be used oss
• Give priority to query_string over search_operators if both are set simultaneously in the ReactiveSearch API noss

7.28.2

Fixes

• Handles _bulk request for .logs indices by fixing the mappings
• Returns empty response for _reindex api as {} instead of nil to be consistent with other API endpoints

7.28.1

Fixes

• handles an edge-case with replace search query rule where scores were skewed due to addition of default fields key. We have removed passing this key entirely
• handles indices endpoint to return hidden indices for all versions >= 7.7.0

7.28.0

Reliability and Performance

This release includes major reliability ⎍ and performance enhancements 🚀 and as a result of the adoption of a rigorous benchmarking framework based on esrally by our team. 🤓

You should absolutely consider updating to this release! Read more on the details of the improvements below:

• Latency improvements: Arc is now very close to being optimal as a reverse proxy to an Elasticsearch upstream. This release includes performance enhancements based on profiling Arc's CPU and memory usage and should show a 50ms to 1s latency improvement [1] per request.
• Memory usage improvements: We've been able to get peak memory usage [2] shrink from 1.2GB+ to ~300MB (over 4x improvement). A major chunk of this improvement comes from a decision to have Arc save logs to the filesystem instead of sending to the Elasticsearch upstream. A fluentbit process now separately logs these to the Elasticsearch upstream, and controls the logging throughput using a combination of throttling, windowing and sending logs in bulk to have minimal impact on the actual throughput of the search applications.
• Reliability improvements: Earlier versions of Arc couldn't complete the benchmarking tests without crashing. Arc now reliably completes benchmarking without adding a significant throughput or latency overhead over directly using an Elasticsearch upstream.

See the detailed benchmark results report - Baseline uses an Elasticsearch upstream and the contender is Arc v7.28.0 release.

Features

• An enhancement in how Arc creates an index's search template covers some important gaps for a general-purpose search strategy and at the same time, improves the index's storage efficiency by up to 10% #123
• Introduction of a fluentbit based logging daemon to ensure that search application bursts get a higher priority than logs
• Add support for copying synonyms and search relevancy strategy across indexes
• Adds support for setting all Arc generated indices as hidden (which has been introduced starting Elasticsearch v7.7.0)

Fixes

• Search relevancy: Field weights can have floating values (previously, only integer values were supported)
• Default sniffing to false (no sniffing) instead of true as most Elasticsearch hosting services offer load balancing with an option to set sniffing (when you're hosting on your own and/or want to bypass an additional load balancer layer b/w Arc and Elasticsearch)

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[1] Latency improvements came as a result of making our usage of bcrypt more efficient (check once per unique user encountered) v/s check per request, and improving how each middleware interacts with the request and response bodies in a request's lifecycle. The most latency improvements are related to advanced (noss) features of search relevancy and query rules.

[2] Peak memory usage was measured after benchmarking with esrally on a custom track by performing a series of bulk requests, index-stats requests, node-stats requests, term queries, phrase queries, and uncached aggregations.

7.27.0_beta.5

fix: hidden index util to compare es version other than 7.7.X (#121)

7.27.0_beta.4

feat(reindex): add support to copy searchrelevancy and synonyms (#117)