從前世今生聊一聊，大廠爲啥親睞時序數據庫

{"type":"doc","content":[{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"摘要：本文會從時序數據庫的基本概念、應用場景、需求與能力等方面一一展開，帶你瞭解時序數據庫的前世今生。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"時序數據庫忽然火了起來。Facebook開源了beringei時序數據庫，基於PostgreSQL打造的時序數據庫TimeScaleDB也開源了。時序數據庫作爲物聯網方向一個非常重要的服務，業界的頻頻發聲，正說明各家企業已經迫不及待的擁抱物聯網時代的到來。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本文會從時序數據庫的基本概念、應用場景、需求與能力等方面一一展開，帶你瞭解時序數據庫的前世今生。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"應用場景","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/cf/cf06520b75681a2e943853f1ff3ce1d1.jpeg","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"時序數據庫是一種針對時序數據高度優化的垂直型數據庫。在製造業、銀行金融、DevOps、社交媒體、衛生保健、智慧家居、網絡等行業都有大量適合時序數據庫的應用場景：","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"製造業：比如輕量化的生產管理雲平臺，運用物聯網和大數據技術，採集、分析生產過程產生的各類時序數據，實時呈現生產現場的生產進度、目標達成狀況，以及人、機、料的利用狀況，讓生產現場完全透明，提高生產效率。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"銀行金融：傳統證券、新興的加密數字貨幣的交易系統，採集、分析交易過程中產生的時序數據，實現金融量化交易。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"DevOps：IT基礎設施和應用的運維繫統，採集、分析設備運行和應用服務運行監控指標，實時掌握設備和應用的健康狀態。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"社交媒體：社交APP大數據平臺，跟蹤用戶交互數據，分析用戶習慣、改善用戶體驗；直播系統，採集直播過程中包括主播、觀衆以及中間各環節的監控指標數據，監控直播質量。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"衛生保健：商業智能工具，採集智能手錶，智能手環中的健康數據，跟蹤關鍵指標和業務的總體健康情況","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"智慧家居：居家物聯網平臺，採集家居智能設備數據，實現遠程監控。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"網絡：網絡監控系統，實時呈現網絡延時、帶寬使用情況。","attrs":{}}]}],"attrs":{}}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"時序數據的需求","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在上述場景中，特別在IoT物聯網以及OPS運維監控領域，存在海量的監控數據需要存儲管理。以華爲雲Cloud Eye Service(CES)服務爲例，單個Region需要監控7000多萬個監控指標，每秒需要處理90萬個上報的監控指標項，假設每個指標50個字節，一年的監控數據有1PB；自動駕駛的車輛一天各種傳感器監測數據就80G。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"傳統關係型數據庫很難支撐這麼大的數據量以及這麼大的寫入壓力，Hadoop大數據解決方案以及現有的時序數據庫也會面臨非常大的挑戰。大規模IoT物聯網，以及公有云規模的運維監控場景，對時序數據庫的需求主要包括：","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"持續高性能寫入：監控指標往往以固定的頻率採集，部分工業物聯網場景傳感器的採集頻率非常高，有的已經達到100ns，公有云運維監控場景基本也是秒級採集。時序數據庫需要支持7*24小時不中斷的持續高壓力寫入。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"高性能查詢：時序數據庫的價值在於數據分析，而且有較高的實時性要求，典型分析任務如異常檢測及預測性維護，這類時序分析任務需要頻繁的從數據庫中獲取大量時序數據，爲了保證分析的實時性，時序數據庫需要能快速響應海量數據查詢請求。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"低存儲成本：IoT物聯網及運維監控場景的數據量曾現指數級增長，數據量是典型的OLTP數據庫場景的千倍以上，並且對成本非常敏感，需要提供低成本的存儲方案。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"支持海量時間線：在大規模IoT物聯網及公有云規模的運維場景，需要監控的指標通常在千萬級甚至億級，時序數據庫要能支持億級時間線的管理能力。","attrs":{}}]}],"attrs":{}},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"彈性：監控場景也存在業務突發增長的場景，例如：華爲Welink服務的運維監控數據在疫情期間暴增100倍，時序數據庫需要提供足夠靈敏的彈性伸縮能力，能夠快速擴容以應對突發的業務增長。","attrs":{}}]}],"attrs":{}}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"開源時序數據庫能力","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/62/625c76161802667bf1706ecc9a1dff0c.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"過去10年，隨着移動互聯網、大數據、人工智能、物聯網、機器學習等相關技術的快速應用和發展，湧現出許多時序數據庫，因爲不同數據庫採用的技術和設計初衷不一樣，所以在解決上述時序數據需求上，他們之間也表現出現較大的差異，本文在下面內容將選擇使用最多的幾種開源時序數據庫爲分析對象進行討論。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"OpenTSDB","attrs":{}}]}],"attrs":{}}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/c6/c69414951e1d8b6b161d2baa2e5015a3.jpeg","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"OpenTSDB基於Hbase數據庫作爲底層存儲，向上封裝自己的邏輯層和對外接口層。這種架構可以充分利用Hbase的特性實現了數據的高可用和較好的寫入性能。但相比Influxdb，OpenTSDB數據棧較長，在讀寫性能和數據壓縮方面都還有進一步優化的空間。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"InfluxDB","attrs":{}}]}],"attrs":{}}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/inf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Influx）汲取了開源各家之長，設計了雲原生架構的時序數據庫。架構如下圖所示。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/53/53c9e5881920fec3e1acdda2e9e0c83a.jpeg","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"相比現有的開源時序數據庫，架構設計上有以下兩方面的考慮：","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"存儲與計算分離","attrs":{}}]}],"attrs":{}}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"存儲計算分離，一方面利用成熟的分佈式存儲系統提高系統的可靠性。監控數據一直持續高性能寫入，同時還有大量的查詢業務，任何系統故障導致業務中斷甚至數據丟失都會造成嚴重的業務影響，而利用經過驗證的成熟的分佈式存儲系統，能夠顯著的提升系統可靠性，降低數據丟失風險，並明顯縮短構建本系統的時間。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"另一方面，解除在傳統Share Nothing架構下，數據和節點物理綁定的約束，數據只是邏輯上歸宿於某個計算節點，使得計算節點無狀態化。這樣在擴容計算節點時，可以避免在計算節點間遷移大量的數據，只需要邏輯上將部分數據從一個節點移交給另外一個節點即可，可以將集羣擴容的耗時從以天爲單位縮短爲分鐘級別。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"再一方面，通過將多副本複製從計算節點卸載到分佈式存儲節點，可以避免用戶以Cloud Hosting形態在雲上自建數據庫時，分佈式數據庫和分佈式存儲分別做3副本複製導致總共9副本冗餘的問題，能夠顯著降低存儲成本。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Kernel 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