即時清除：所有CloudFlare客戶現在都可以清除任何內容類型

Cloudflare的傳統是在4月1日推出真實產品，而不是今天在線流傳的通常的笑話產品。

There's a tradition at Cloudflare of launching real products on April 1, instead of the usual joke product announcements circulating online today. In previous years, we've introduced impactful products like 1.1.1.1 and 1.1.1.1 for Families. Today, we're excited to continue this tradition by making every purge method available to all customers, regardless of plan type.

Cloudflare的傳統是在4月1日推出真實產品，而不是今天在線流傳的通常的笑話產品。在過去的幾年中，我們推出了有影響力的產品，例如家庭的1.1.1和1.1.1.1。今天，無論計劃類型如何，我們都能通過使所有客戶都可以使用每種清除方法來繼續這一傳統。

During Birthday Week 2024, we announced our intention to bring the full suite of purge methods - including purge by URL, purge by hostname, purge by tag, purge by prefix, and purge everything - to all Cloudflare plans. Historically, methods other than "purge by URL" and "purge everything" were exclusive to Enterprise customers. However, we've been openly rebuilding our purge pipeline over the past few years (hopefully you've read some of our blog series), and we're thrilled to share the results more broadly. We've spent recent months ensuring the new Instant Purge pipeline performs consistently under 150 ms, even during increased load scenarios, making it ready for every customer.

在2024年生日週期間，我們宣布打算將完整的清除方法（包括URL清除，主機名清除，Tag清除，tag tag by Tag，Porge by Porge by Purge）都帶入所有Cloudflare計劃。從歷史上看，除了“ url”和“清除一切”以外的方法是企業客戶獨有的。但是，在過去的幾年中，我們一直在公開重建清除管道（希望您已經閱讀了一些博客系列），我們很高興能更廣泛地分享結果。近幾個月來，我們已經確保了新的即時清除管道在150毫秒以下始終如一，即使在增加負載方案中，也可以為每個客戶做好準備。

But that's not all - we're also significantly raising the default purge rate limits for Enterprise customers, allowing even greater purge throughput thanks to the efficiency of our newly developed Instant Purge system.

但這還不是全部 - 我們也大大提高了企業客戶的默認清除率限制，這得益於我們新開發的即時清除系統的效率，可以更大的清除吞吐量。

Building a better purge: a two-year journey

建立更好的清除：兩年的旅程

Stepping back, today's announcement represents roughly two years of focused engineering. Near the end of 2022, our team went heads down rebuilding Cloudflare's purge pipeline with a clear yet challenging goal: dramatically increase our throughput while maintaining near-instant invalidation across our global network.

退後一步，今天的公告代表了大約兩年的重點工程。在2022年底，我們的團隊以一個明確但充滿挑戰的目標降低了Cloudflare的清除管道的重建：大大增加了我們的吞吐量，同時使我們的全球網絡保持近乎固有的無效。

Cloudflare operates data centers in over 335 cities worldwide. Popular cached assets can reside across all of our data centers, meaning each purge request must quickly propagate to every location caching that content. Upon receiving a purge command, each data center must efficiently locate and invalidate cached content, preventing stale responses from being served. The amount of content that must be invalidated can vary drastically, from a single file, to all cached assets associated with a particular hostname. After the content has been purged, any subsequent requests will trigger retrieval of a fresh copy from the origin server, which will be stored in Cloudflare's cache during the response.

CloudFlare在全球335多個城市中運營數據中心。流行的緩存資產可以駐留在我們所有的數據中心中，這意味著每個清除請求都必須迅速傳播到該內容的每個位置。收到清除命令後，每個數據中心必須有效地定位並使緩存的內容無效，從而阻止了陳舊的響應。必須無效的內容量可能會大大變化，從單個文件到與特定主機名相關的所有緩存資產。清除內容後，任何後續請求都會觸發從Origin服務器中觸發新副本的檢索，該副本將在響應期間存儲在Cloudflare的緩存中。

Ensuring consistent, rapid propagation of purge requests across a vast network introduces substantial technical challenges, especially when accounting for occasional data center outages, maintenance, or network interruptions. Maintaining consistency under these conditions requires robust distributed systems engineering.

確保在龐大的網絡中確保清除請求的一致，快速傳播會引入實質性的技術挑戰，尤其是在考慮偶爾數據中心的中斷，維護或網絡中斷時。在這些條件下保持一致性需要強大的分佈式系統工程。

How did we scale purge?

我們是如何擴展清除的？

We've previously discussed how our new Instant Purge system was architected to achieve sub-150 ms purge times. It's worth noting that the performance improvements were only part of what our new architecture achieved, as it also helped us solve significant scaling challenges around storage and throughput that allowed us to bring Instant Purge to all users.

我們之前已經討論瞭如何建立新的即時清除系統以達到150毫秒的清除時間。值得注意的是，績效的改進只是我們新建築所取得的成就的一部分，因為它也幫助我們解決了圍繞存儲和吞吐量的大量擴展挑戰，這使我們能夠即時清除所有用戶。

Initially, our purge system scaled well, but with rapid customer growth, the storage consumption from millions of daily purge keys that needed to be stored reduced available caching space. Early attempts to manage this storage and throughput demand involved queues and batching for smoothing traffic spikes, but this introduced latency and underscored the tight coupling between increased usage and rising storage costs.

最初，我們的清除系統縮放得很好，但是隨著客戶增長的迅速增長，需要存儲的數百萬輛清除鍵的存儲消耗可存儲，從而減少了可用的緩存空間。早期管理此存儲和吞吐量需求的嘗試涉及排隊和批處理以使流量平滑尖峰，但這引入了延遲，並強調了使用量增加和存儲成本上升之間的緊密耦合。

We needed to revisit our thinking on how to better store purge keys and when to remove purged content so we could reclaim space. Historically, when a customer would purge by tag, prefix or hostname, Cloudflare would mark the content as expired and allow it to be evicted later. This is known as lazy-purge because nothing is actively removed from disk. Lazy-purge is fast, but not necessarily efficient, because it consumes storage for expired but not-yet-evicted content. After examining global or data center-level indexing for purge keys, we decided that wasn't viable due to increases in system complexity and the latency those indices could bring due to our network size. So instead, we opted for per-machine indexing, integrating indices directly alongside our cache proxies. This minimized network complexity, simplified reliability, and provided predictable scaling.

我們需要重新審視如何更好地存儲清除鍵以及何時刪除清除內容，以便我們可以收回空間。從歷史上看，當客戶通過TAG，前綴或主機名清除時，CloudFlare將標記內容為過期，並允許以後將其驅逐。這被稱為懶惰，因為沒有任何積極從磁盤中刪除的東西。懶惰 - 普爾奇（Lazy-Purge）很快，但不一定是有效的，因為它消耗了過期但尚未引發的內容的存儲空間。在檢查了清除鍵的全球或數據中心級索引之後，我們認為由於系統複雜性的增加以及由於我們的網絡大小而帶來的延遲可能會帶來的延遲。因此，我們選擇了每機索引，將索引直接集成到我們的高速緩存代理。這種最小化的網絡複雜性，簡化的可靠性，並提供了可預測的縮放。

After careful analysis and benchmarking, we selected RocksDB, an embedded key-value store that we could optimize for our needs, which formed the basis of CacheDB, our Rust-based service running alongside each cache proxy. CacheDB manages indexing and immediate purge execution (active purge), significantly reducing storage needs and freeing space for caching.

經過仔細的分析和基準測試後，我們選擇了RocksDB，RocksDB是一個嵌入式的鍵值商店，我們可以為我們的需求進行優化，這形成了CachedB的基礎，CachedB是我們基於Rust的服務與每個緩存代理一起運行的。 CachedB管理索引和立即清除執行（主動清除），大大減少了存儲需求以及緩存空間的空間。

Local queues within CacheDB buffer purge operations to ensure consistent throughput without latency spikes, while the cache proxies consult CacheDB to guarantee rapid, active purges. Our updated distribution pipeline broadcasts purges directly to CacheDB instances across machines, dramatically improving throughput and purge speed.

緩存緩衝區清洗操作中的本地隊列，以確保無潛伏期尖峰的一致吞吐量，而高速緩存代理則諮詢緩存B，以確保快速，主動的清除。我們更新的分銷管道將廣播直接清除到跨機器的CachedB實例上，從而大大提高了吞吐量和清除速度。

Using CacheDB, we've reduced storage requirements 10x by eliminating lazy purge storage accumulation, instantly freeing valuable disk space. The freed storage enhances cache retention, boosting cache HIT ratios and minimizing origin egress. These savings in storage and increased throughput allowed us to scale to the point where we can offer Instant Purge to more customers.

使用cachedb，我們通過消除懶惰的清除存儲積累，立即釋放有價值的磁盤空間來減少存儲需求10倍。釋放存儲可增強緩存的保留，提高緩存命中率並最大程度地減少出口。這些節省的存儲空間和增加的吞吐量使我們可以擴展到我們可以立即向更多客戶提供清除的地步。

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