Storage Metrics

本文介绍如何通过SharePoint的StorageMetrics功能来管理站点内容占用的空间。管理员可以利用此功能了解哪些文件或文件夹占用了大量空间,特别是长时间未修改的大文件,如视频等。

SharePoint站点在使用过程中,内容会越来越过,这会导致占用内容数据库,也会增加管理的复杂度,甚至会降低系统的响应时间。可以通过设定site quota来设定警告,这样管理员就会知道一些站点的内容过大了。

 

下一步就是要查找,站点里哪里占用了空间。显然通过手动浏览文档库和文件夹的方式是不现实的。而SharePoint提供的Storage Metrics 功能对管理员就很有帮助了。

 

进入到Site Settings, 在Site Collection Settings中找到Storage Metrics:



 

进入之后,SharePoint就会把整个站点的存储分布和Last Modified显示出来:


 

对于占用空间很大,并且Last Modified是很久以前的,需要优先处理。比如是否有大的视频文件,等等。

### Metrics Store Implementation and Concepts In the context of IT systems, a metrics store serves as a specialized database designed to collect, store, and provide access to performance-related data or operational statistics from various components within an environment. This type of storage solution is optimized specifically for handling time-series data that can be used for monitoring system health, diagnosing issues, capacity planning, and more. A key characteristic of a metrics store lies in its ability to efficiently manage large volumes of metric points over extended periods while supporting fast read/write operations required by real-time analytics applications[^1]. To achieve this efficiency, several design principles are commonly adopted: #### Data Model Design The schema typically includes dimensions such as timestamp, entity identifier (e.g., host ID), measurement name (CPU usage), along with associated values. Such structures facilitate querying based on specific criteria like time ranges or entities involved. #### Storage Optimization Techniques - **Downsampling**: Reduces granularity at older timestamps. - **Compaction**: Combines multiple small writes into larger ones to improve I/O throughput. - **Compression**: Minimizes disk space consumption without compromising query speed significantly. #### Query Language Support Supporting SQL-like languages allows users to perform complex queries easily against stored datasets. For instance, one might want to aggregate CPU utilization across all servers during peak hours using simple commands similar to those found in traditional relational databases. ```sql SELECT AVG(cpu_usage) FROM metrics WHERE entity_id IN ('server_1', 'server_2') AND timestamp BETWEEN 'start_time' AND 'end_time'; ``` #### Scalability Considerations Given the potentially massive scale of incoming telemetry information, horizontal scaling through sharding techniques becomes essential. By distributing partitions among different nodes according to certain rules—such as hash-based allocation—the overall load gets balanced effectively, ensuring consistent performance even under heavy workloads. --related questions-- 1. What are some popular open-source tools available today for implementing a metrics store? 2. How does downsampling impact long-term trend analysis when working with historical data inside a metrics store? 3. Can you explain how compaction works internally within common implementations of metrics stores? 4. In what scenarios would compression negatively affect the functionality of a metrics store application?
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