Manticore Search 27.1.5:身份验证、分片、对话式搜索及更快的向量搜索
Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

原始链接: https://manticoresearch.com/blog/manticore-search-27-1-5/

Manticore Search 27.1.5 版本带来了涵盖 25.0.1 至 27.1.5 各个发布版本的重大升级。主要亮点包括: * **安全性:** 内置的身份验证和授权功能现已支持所有协议下的用户、令牌和细粒度权限管理。 * **可扩展性:** 新增的分片表支持简化了大型、高写入量部署的管理。 * **AI 集成:** 通过 `CALL CHAT` 实现原生对话式搜索,允许用户直接在引擎内利用大语言模型(LLM)查询向量化数据。 * **向量性能:** 得益于多线程构建,HNSW 构建速度显著提升;此外还改进了 KNN 性能,并支持本地 ONNX 嵌入。 * **分析能力:** 增强了分面(faceting)和聚合功能,包括对 `date_histogram` 的改进、新增统计函数(百分位数、MAD),并支持 OpenSearch Dashboards。 * **运维改进:** 新增了配置验证(`--check`)、优化的复制管理以及对长令牌索引的支持。 **重要升级说明:** 引入身份验证更改了默认的访问假设;建议在集群拓扑中分阶段进行部署。此外,由于复制存储布局的变更,以及要求保持 Manticore Library (MCL) 与守护进程同步,用户应查阅完整的升级指南以避免兼容性问题。

抱歉。
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原文

Manticore Search 27.1.5 has been released. This release brings built-in authentication and authorization, sharded tables, conversational search, faster HNSW builds, better faceting and aggregations, and a long list of fixes across KNN, replication, protocol compatibility and other areas.

This post is a catch-up for everything shipped from 25.0.1 through 27.1.5.


Upgrade Notes

Please review these before upgrading:

  • 27.0.0 adds built-in auth/authz, and enabling it changes access assumptions. Auth is not enabled by default, but once you enable it, anonymous access no longer works. Roll it out in stages: upgrade remote agents and replication peers first, then upgrade the masters that query or manage them, and enable auth only after the whole topology is on the new version. Distributed remote-agent and replication-related operations also need matching stored auth data across the participating daemons. A successful JOIN CLUSTER replaces the joining node's local auth data with the donor cluster's auth data. (Issue #2833 , PR #3648 )
  • 26.0.0 changed replication storage layout. Incoming replicated tables now live under the normal data_dir/<table> layout instead of the cluster path. If you run replication clusters with a custom path, you may need to move or re-synchronize replicated tables after upgrade. Downgrade is only safe before the new layout is adopted. (Issue #4431 , PR #4598 )
  • If you manage MCL separately from the daemon, upgrade it together with Manticore. This release line moves through several MCL updates, from vector-performance work to multithreaded HNSW builds and later stability fixes. Mixing an older library with a newer daemon is not recommended. (25.2.0 , 25.15.0 , 26.0.3 , 26.3.2 , 27.1.0 )

Highlights

Built-in authentication and authorization

Manticore now supports users, passwords, bearer tokens, and fine-grained permissions across MySQL, HTTP/HTTPS, distributed remote agents, and replication-related operations. This makes access control a first-class part of the product instead of something that always has to be handled outside the database.

Sharded tables

Manticore can now create and manage sharded tables , distribute inserts across shards, and handle more of the surrounding lifecycle in one place. That makes larger write-heavy deployments easier to operate and reduces the amount of sharding-specific logic that has to live outside the engine.

This release adds conversational search to Manticore Search. It is exposed through CREATE CHAT MODEL and CALL CHAT , so you can ask questions over an existing vectorized table instead of building a separate retrieval layer around the same data.

Under the hood, Manticore Search runs KNN on a FLOAT_VECTOR field, builds LLM context from that field's from='...' source columns, keeps conversation history by conversation_uuid, and returns both the answer and the supporting sources. If you already keep embeddings in Manticore, this makes document Q&A and support-style assistants much easier to wire up.

Faster vector builds and KNN improvements

Vector search kept improving throughout this cycle.

Manticore improved KNN performance, added local ONNX embeddings support, sped up ONNX inference, and then made HNSW build and rebuild work much faster with multithreaded index construction.

A few important steps in that work:

  • 25.1.0 improved KNN distance calculation and AVX-512 loading.
  • 25.2.0 added local ONNX embeddings support in MCL and improved vector-search performance further.
  • 25.14.0 and 25.15.0 added multithreaded HNSW builds together with the required library support.

The biggest practical improvement here is a much faster auto-embedding and shorter build and rebuild time for large vector tables. Initial KNN builds, chunk merges, and ALTER TABLE ... REBUILD KNN are all affected.

Better faceting and aggregations

Faceting and aggregations also became more useful.

facet_filter_mode makes it easier to build e-commerce-style filters that preserve selected, available, and unavailable buckets under active filtering.

On the analytics side:

  • date_histogram() gained time_zone and offset
  • Opensearch dashboards support
  • Manticore added statistical aggregations such as percentiles, percentile_ranks, and mad

Other Notable Improvements

This release line also includes several smaller but useful additions:

  • searchd --check validates configuration before startup without side effects.
  • EXIT CLUSTER lets a node leave a replication cluster online without restarting.
  • dict=keywords_32k makes it possible to index very long machine-generated tokens such as hashes and message IDs without silent truncation.
  • The built-in Ukrainian lemmatizer expands native morphology support for Ukrainian text search.
  • Systemd Type=notify improves startup and shutdown supervision.
  • searchd process under systemd management now logs to systemd journal
  • JOIN queries now support explicit left-table column prefixes.
  • OpenSearch Dashboards support.
  • manticore-load gained multi-query support.

Bug Fixes

This release line also includes 65 changelog-listed fixes. The latest follow-up releases added a few more worth calling out:

  • 27.1.5 fixed a crash when fetching columnar float_vector attributes.
  • 27.1.4 fixed ALTER TABLE ... RECONFIGURE and SHOW CREATE TABLE for one-way upgrades from dict='keywords' to dict=keywords_32k.
  • 27.1.3 updated Buddy to 4.0.1 and tightened Queue-plugin mutation permission handling under auth.
  • KNN-by-doc_id queries now preserve offset and max_matches correctly.
  • KNN rescoring order was fixed, so explicit ORDER BY tie-breakers work again.
  • Hybrid fused queries with GROUP BY on columnar tables stopped crashing.
  • Replication and node-rejoin crash paths were cleaned up further.
  • Binary MySQL protocol behavior was fixed in 25.12.1, which matters for integrations that expect real client compatibility.
  • Fluent Bit bulk-ingest interoperability was fixed, preventing successful responses from being replayed as duplicate inserts.
  • 27.1.2 fixed sql_attr_multi handling for plain indexes built from multiple source blocks.

For the complete list, see the changelog .


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