Twitter Boosts Performance and Cost Efficiency

Twitter increases Hadoop performance and cost efficiency with caching, fast SSDs and more compute.

Executive Overview
Storage I/O can be a significant performance bottleneck for Hadoop* clusters, especially in hyperscale deployments like those at Twitter, where a single cluster can have up to 10,000 nodes and nearly 100 PB of logical storage. The typical Hadoop cluster at Twitter contains over 100,000 hard disk drives (HDDs)—but this configuration was reaching an I/O performance limit because while HDD capacity has increased over time, HDD performance has not significantly changed.2 Therefore, simply adding more, bigger HDDs wasn't going to solve Twitter's scaling challenges—in fact, it would make things worse as the I/O per GB decreases. Adding more spindles per node was not feasible due to space and power limitations.

Working in collaboration with an Intel engineering team, Twitter engineers conducted a series of experiments that revealed that storing temporary files managed by YARN* (Yet Another Resource Negotiator*) on a fast SSD enabled significant performance improvements on existing hardware (up to a 50 percent reduction in runtime).3 The team also discovered that removing a storage I/O bottleneck enabled them to use larger hard drives while simultaneously increasing processor utilization, which in turn resulted in the ability to use higher-core-count processors. This positively affected storage performance, and contributed to higher data center density by reducing the number of required HDDs.

Higher density leads to total cost of ownership (TCO) savings through energy efficiency, fewer racks, and a smaller data center footprint. Overall, Twitter expects that caching temporary data and increasing core counts will result in approximately 30 percent lower TCO and over 50 percent faster runtimes, compared to their legacy production cluster configuration.1

Read the white paper - Boosting Hadoop* Performance and Cost Efficiency with Caching, Fast SSDs, and More Compute

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Intel® 技術的功能與優勢取決於系統配置,而且可能需要支援的硬體、軟體或服務啟動才能使用。實際效能會依系統組態而異。沒有電腦系統能提供絕對的安全性。詳情請洽詢購入系統的製造商或零售商,或是上網參閱 效能測試中使用的軟體與工作負載,可能只有針對 Intel® 微處理器進行效能最佳化。包括 SYSmark* 與 MobileMark* 在內的效能測試是使用特定電腦系統、零組件、軟體、作業與功能進行測。這些因素若有任何異動,均可能導致測得結果產生變化。考慮購買時,為了協助您充分評估,您應該參考其他資訊及效能測試,包括該產品結合其它產品使用時的效能表現。如需更完整的資訊,請造訪 效能結果係根據截至組態中所示日期的測試,可能無法反映所有公開提供的安全性更新。請查看組態公開資料以獲得詳細資訊。沒有產品或元件能提供絕對的安全性。// 所述之成本降低情境,用意是要提供範例,指出搭載特定 Intel® 處理器的產品,在特定情況與配置,可能會如何影響未來各項成本以及提供成本節省。實際情況可能有所差異。對於各項成本,或是成本降低幅度,Intel 不提供任何保證。// Intel 並不控制或稽核本文件提及的第三方效能標竿資料或網站。您應造訪該網站並確認本文件提及的資料是否正確。// 部分測試案例結果係採用 Intel 內部分析或架構模擬或模型進行預估或模擬,僅供參考之用。系統硬體、軟體或配置如有任何差異,都可能會影響實際的效能表現。



基準:單插槽 Intel® Xeon® E3-1230 處理器 v6(4 核心);32 至 64 GB RAM;1x 1 TB 或 2 TB HDD;Intel S4500 240 GB 開機磁碟;1 GbE 至 10 GbE 乙太網路;無快取。測試:單插槽 Intel® Xeon® Gold 6262 處理器(24 核心);192 GB RAM;Intel S4500 240 GB 開機磁碟;8x 6 TB HDD;1x Intel® SSD DC P4610 6.4TB;25 GbE 乙太網路;使用 Intel® Cache Acceleration Software (Intel® CAS) 進行快取。作業系統:Twitter CentOS* 6 Derivative,核心版本 2.6.74-t1.el6.x86_64(根據上游 4.14.12 核心),BIOS 版本:D3WWM11,Microcode 版本:0xb000021。


Backblaze,2018 年 9 月,“Hard Disk Drive (HDD) vs Solid State Drive (SSD): What’s the Diff?”


基準:雙插槽 Intel® Xeon® E5-2630 處理器 v4 @ 2.2 GHz(每插槽 10 核心/20 執行緒);128 GB RAM;12x 6 TB 7200 RPM SATA HDD;1x SATA SSD 開機磁碟;25 GbE 乙太網路;102 個節點分佈在 6 個機架上 。工作負載:Gridmix* 與 Terasort* Gridmix 得分:3309 秒;Terasort 得分:5504 秒測試:雙插槽 Intel® Xeon® E5-2630 處理器 v4 @ 2.2 GHz(每插槽 10 核心/20 執行緒);128 GB RAM;12x 6 TB 7200 RPM SATA HDD;1x SATA SSD 開機磁碟;1x 750 GB Intel® Optane™ DC P4800X NVMe* 型 SSD;25 GbE 乙太網路;102 個 節點分佈在 6 個機架上 。工作負載:Gridmix 與 Terasort。Gridmix 得分:2396 秒;Terasort 得分:2640 秒 作業系統:Twitter CentOS* 6 Derivative,核心。