If youre an architectural or engineering firm, youve undoubtedly confronted the difficulty of managing and collaborating on 3D assets like CAD drawings. ?The act of sharing massive files is hard but feasible, but it is significantly compli
If you’re an architectural or engineering firm, you’ve undoubtedly confronted the difficulty of managing and collaborating on 3D assets like CAD drawings. ?The act of sharing massive files is hard but feasible, but it is significantly complicated by the inability to determine that you’re using the latest version. ?For the CAD-inclined, there’s hope.?Jozef Dobos, a doctoral student at University College London (UCL), has applied the geospatial indexing capabilities of MongoDB a version control system for 3D assets called?3D Repo.??Sponsored by?Arup Foresight, the built environment innovation division of Arup Group Limited, a global design and business consulting firm with offices in over 30 countries, 3D Repo leverages the flexibility of MongoDB’s data model, not to mention its geospatial capabilities, to make collaboration on 3D assets easy.
The Problem
Whether an architectural firm or a product design company, collaboration on large (500GB+) 3D assets is a critical but difficult task, one not solved simply by sending links around to these assets. The maintenance of assets in a large 3D visualisation can involve authors that are numerous, geographically dispersed, and diverse in their skillsets and tools.
This presents problems including, but not limited to, maintaining consistency of the models and dealing with concurrent edits in the same part of a 3D scene. For example, in the industry today, each user loads a particular 3D scene into a modeling tool, modifies it, and then re-saves the entire file again, making any version tracking and sharing unnecessarily unclear. By the time a simulation is completed, the design might progress so rapidly that the results are often inapplicable.?
An SVN for 3D Assets
Looking for a better way to collaborate on 3D files, Dobos developed a unified and integrated framework, called?3D Repo,?that supports collaborative editing and distribution of 3D assets. ?Think of it like Subversion (SVN), but for 3D assets, not code. ?
The 3D Repo framework tracks multiple revisions of 3D assets so that they can be integrated later on. It thus provides similar functionality to file-based revision control systems like SVN, but is built around MongoDB, thereby avoiding the constraints of a file-based system. The framework also supports distributed editing over the Internet and additional lightweight clients in web-browsers and mobile devices. Such an approach is expected to improve the engineering as well as public engagement and considerably reduce the costs of future industrial development.
Why MongoDB?
While there are other proprietary and open-source databases that support spatial data directly - e.g. Oracle Spatial and PostGIS for PostgreSQL - these alternatives focus on 2D geometry, not 3D models and their properties. They also do not support the type of revision histories that is commonly required in 3D asset management.
MongoDB, on the other hand, avoids rigid data models and is optimized for large read-write operations. It is therefore possible to store 3D models in a database and, due to MongoDB’s schema flexibility, also track other associated data such as semantic relationships and even individual revisions, as is the case in 3D Repo.?
As Dobos explains:
Basically, relational databases can in no way cut it. Rigid table structures are not suitable for highly diverse and large 3D data. In our case, a single 3D model or, better said, a “3D scene” is represented as a scene graph, where each node can be, and most of the time is, something different. It can be a node for a mesh, hence a large binary array, or a transformation, where transformation matrix is simply 16 numbers, or animation, bone, material, texture etc. Each of these is represented and stored differently. So key-value pairs are a match made in heaven for this type of data.
In MongoDB, 3D Repo assigns two collections (tables) per 3D scene, one for all the scene graph constituents and one for all the documents that belong to a revision history. Hence, each of these collections stores a directed acyclic graph, making the access implementation reusable. Once the data is in a database, access is implicitly supported in a distributed manner via a dedicated query language.?
In order to offer a scalable visualization platform, 3D Repo streams decomposed scene graph components from MongoDB onto client devices, reconstructs their 3D representation and displays them for viewing.
This explains why MongoDB is superior to an RDBMS like Oracle for this kind of application, but it doesn’t address the possibility that Dobos could have turned to another NoSQL database, and particularly a graph database, given that a scene graph is a directed acyclic graph. ?Surely this should be a fit for a graph database?
Not really, as Dobos articulates:?
Mostly simply stated, we don’t need any complicated graph traversal operations. We opted for MongoDB because of its efficiency with binary storage, BSON rather than ASCII (hence a clear winner over CouchDB, too), and due to its flexibility, not to mention its web-ready approach. MongoDB is proven to scale well, has sharding built-in and there is a thriving community to support it. Most of the design inspiration in our system actually comes from webinars by Wordnik and their experience. ?We are really grateful for those!
In sum, MongoDB delivers the flexibility and 3D geospatial capabilities necessary to making 3D Repo a reality.
Results
The 3D Repo approach offers significant benefits over alternative techniques like geographic information systems (GIS), given the direct MongoDB database connection, the scalability of MongoDB, and its built-in support for geospatial indexing. In addition, MongoDB enables version control for 3D assets in a way impossible to proprietary or open-source RDBMS. ?Geographic information systems such as Oracle Spatial or PostGIS may be acceptable for 2D assets such as lines, but they cannot match MongoDB for representing 3D data efficiently and carrying metadata on top.?Importantly, 3D Repo’s MongoDB-based solution can be extended to support various data types including building information modelling (BIM) or other representations.?
For more information on the research Dobos is doing on 3D Repo, please see “Revision Control Framework for 3D Assets” (PDF) or review his?related research.?
Note: MongoDB does not currently support 3D geospatial indexing.?
原文地址:3D Repo Runs MongoDB, 感谢原作者分享。

MySQL是一種開源的關係型數據庫管理系統,主要用於快速、可靠地存儲和檢索數據。其工作原理包括客戶端請求、查詢解析、執行查詢和返回結果。使用示例包括創建表、插入和查詢數據,以及高級功能如JOIN操作。常見錯誤涉及SQL語法、數據類型和權限問題,優化建議包括使用索引、優化查詢和分錶分區。

MySQL是一個開源的關係型數據庫管理系統,適用於數據存儲、管理、查詢和安全。 1.它支持多種操作系統,廣泛應用於Web應用等領域。 2.通過客戶端-服務器架構和不同存儲引擎,MySQL高效處理數據。 3.基本用法包括創建數據庫和表,插入、查詢和更新數據。 4.高級用法涉及復雜查詢和存儲過程。 5.常見錯誤可通過EXPLAIN語句調試。 6.性能優化包括合理使用索引和優化查詢語句。

選擇MySQL的原因是其性能、可靠性、易用性和社區支持。 1.MySQL提供高效的數據存儲和檢索功能,支持多種數據類型和高級查詢操作。 2.採用客戶端-服務器架構和多種存儲引擎,支持事務和查詢優化。 3.易於使用,支持多種操作系統和編程語言。 4.擁有強大的社區支持,提供豐富的資源和解決方案。

InnoDB的鎖機制包括共享鎖、排他鎖、意向鎖、記錄鎖、間隙鎖和下一個鍵鎖。 1.共享鎖允許事務讀取數據而不阻止其他事務讀取。 2.排他鎖阻止其他事務讀取和修改數據。 3.意向鎖優化鎖效率。 4.記錄鎖鎖定索引記錄。 5.間隙鎖鎖定索引記錄間隙。 6.下一個鍵鎖是記錄鎖和間隙鎖的組合,確保數據一致性。

MySQL查询性能不佳的原因主要包括没有使用索引、查询优化器选择错误的执行计划、表设计不合理、数据量过大和锁竞争。1.没有索引导致查询缓慢,添加索引后可显著提升性能。2.使用EXPLAIN命令可以分析查询计划,找出优化器错误。3.重构表结构和优化JOIN条件可改善表设计问题。4.数据量大时,采用分区和分表策略。5.高并发环境下,优化事务和锁策略可减少锁竞争。

在數據庫優化中,應根據查詢需求選擇索引策略:1.當查詢涉及多個列且條件順序固定時,使用複合索引;2.當查詢涉及多個列但條件順序不固定時,使用多個單列索引。複合索引適用於優化多列查詢,單列索引則適合單列查詢。

要優化MySQL慢查詢,需使用slowquerylog和performance_schema:1.啟用slowquerylog並設置閾值,記錄慢查詢;2.利用performance_schema分析查詢執行細節,找出性能瓶頸並優化。

MySQL和SQL是開發者必備技能。 1.MySQL是開源的關係型數據庫管理系統,SQL是用於管理和操作數據庫的標準語言。 2.MySQL通過高效的數據存儲和檢索功能支持多種存儲引擎,SQL通過簡單語句完成複雜數據操作。 3.使用示例包括基本查詢和高級查詢,如按條件過濾和排序。 4.常見錯誤包括語法錯誤和性能問題,可通過檢查SQL語句和使用EXPLAIN命令優化。 5.性能優化技巧包括使用索引、避免全表掃描、優化JOIN操作和提升代碼可讀性。


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