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張 旭

[Elasticsearch] 分散式特性 & 分散式搜尋的機制 | 小信豬的原始部落 - 0 views

godleon.github.io/...icsearch-distributed-mechanism

elasticsearch database

shared by 張 旭 on 17 Apr 21 - No Cached
  • 水平擴展儲存空間
  • Data HA：若有 node 掛掉，資料不會遺失
  • 若是要查詢 cluster 中的 node 狀態，可以使用 GET /_cat/nodes API
  • ...39 more annotations...
  • 決定每個 shard 要被分配到哪個 data node 上
  • 為 cluster 設置多個 master node
  • 一旦發現被選中的 master node 出現問題，就會選出新的 master node
  • 每個 node 啟動時就預設是一個 master eligible node，可以透過設定 node.master: false 取消此預設設定
  • 處理 request 的 node 稱為 Coordinating Node，其功能是將 request 轉發到合適的 node 上
  • 所有的 node 都預設是 Coordinating Node
  • coordinating node 可以直接接收 search request 並處理，不需要透過 master node 轉過來
  • 可以保存資料的 node，每個 node 啟動後都會預設是 data node，可以透過設定 node.data: false 停用 data node 功能
  • 由 master node 決定如何把分片分發到不同的 data node 上
  • 每個 node 上都保存了 cluster state
  • 只有 master 才可以修改 cluster state 並負責同步給其他 node
  • 每個 node 都會詳細紀錄本身的狀態資訊
  • shard 是 Elasticsearch 分散式儲存的基礎，包含 primary shard & replica shard
  • 每一個 shard 就是一個 Lucene instance
  • primary shard 功能是將一份被索引後的資料，分散到多個 data node 上存放，實現儲存方面的水平擴展
  • primary shard 的數量在建立 index 時就會指定，後續是無法修改的，若要修改就必須要進行 reindex
  • 當 primary shard 遺失時，replica shard 就可以被 promote 成 primary shard 來保持資料完整性
  • replica shard 數量可以動態調整，讓每個 data node 上都有完整的資料
  • ES 7.0 開始，primary shard 預設為 1，replica shard 預設為 0
  • replica shard 若設定過多，會降低 cluster 整體的寫入效能
  • replica shard 必須和 primary shard 被分配在不同的 data node 上
  • 所有的 primary shard 可以在同一個 data node 上
  • 透過 GET _cluster/health/<target> 可以取得目前 cluster 的健康狀態
  • Yellow：表示 primary shard 可以正常分配，但 replica shard 分配有問題
  • 透過 GET /_cat/shards/<target> 可以取得目前的 shard 狀態
  • replica shard 無法被分配，因此 cluster 健康狀態為黃色
  • 若是擔心 reboot 機器造成 failover 動作開始執行，可以設定將 replication 延遲一段時間後再執行(透過調整 settings 中的 index.unassigned.node_left.delayed_timeout 參數)，避免無謂的 data copy 動作 (此功能稱為 delay allocation)
  • 集群變紅，代表有 primary shard 丟失，這個時候會影響讀寫。
  • 如果 node 重新回來，會從 translog 中恢復沒有寫入的資料
  • 設定 index settings 之後，primary shard 數量無法隨意變更
  • 不建議直接發送請求到master節點，雖然也會工作，但是大量請求發送到 master，會有潛在的性能問題
  • shard 是 ES 中最小的工作單元
  • shard 是一個 Lucene 的 index
  • 將 Index Buffer 中的內容寫入 Segment，而這寫入的過程就稱為 Refresh
  • 當 document 被 refresh 進入到 segment 之後，就可以被搜尋到了
  • 在進行 refresh 時先將 segment 寫入 cache 以開放查詢
  • 將 document 進行索引時，同時也會寫入 transaction log，且預設都會寫入磁碟中
  • 每個 shard 都會有對應的 transaction log
  • 由於 transaction log 都會寫入磁碟中，因此當 node 從故障中恢復時，就會優先讀取 transaction log 來恢復資料
張 旭

Logstash Alternatives: Pros & Cons of 5 Log Shippers [2019] - Sematext - 0 views

sematext.com/...logstash-alternatives

log monitor system logstash fluentd

shared by 張 旭 on 05 Nov 19 - No Cached
  • In this case, Elasticsearch. And because Elasticsearch can be down or struggling, or the network can be down, the shipper would ideally be able to buffer and retry
  • Logstash is typically used for collecting, parsing, and storing logs for future use as part of log management.
  • Logstash’s biggest con or “Achille’s heel” has always been performance and resource consumption (the default heap size is 1GB).
  • ...37 more annotations...
  • This can be a problem for high traffic deployments, when Logstash servers would need to be comparable with the Elasticsearch ones.
  • Filebeat was made to be that lightweight log shipper that pushes to Logstash or Elasticsearch.
  • differences between Logstash and Filebeat are that Logstash has more functionality, while Filebeat takes less resources.
  • Filebeat is just a tiny binary with no dependencies.
  • For example, how aggressive it should be in searching for new files to tail and when to close file handles when a file didn’t get changes for a while.
  • For example, the apache module will point Filebeat to default access.log and error.log paths
  • Filebeat’s scope is very limited,
  • Initially it could only send logs to Logstash and Elasticsearch, but now it can send to Kafka and Redis, and in 5.x it also gains filtering capabilities.
  • Filebeat can parse JSON
  • you can push directly from Filebeat to Elasticsearch, and have Elasticsearch do both parsing and storing.
  • You shouldn’t need a buffer when tailing files because, just as Logstash, Filebeat remembers where it left off
  • For larger deployments, you’d typically use Kafka as a queue instead, because Filebeat can talk to Kafka as well
  • The default syslog daemon on most Linux distros, rsyslog can do so much more than just picking logs from the syslog socket and writing to /var/log/messages.
  • It can tail files, parse them, buffer (on disk and in memory) and ship to a number of destinations, including Elasticsearch.
  • rsyslog is the fastest shipper
  • Its grammar-based parsing module (mmnormalize) works at constant speed no matter the number of rules (we tested this claim).
  • use it as a simple router/shipper, any decent machine will be limited by network bandwidth
  • It’s also one of the lightest parsers you can find, depending on the configured memory buffers.
  • rsyslog requires more work to get the configuration right
  • the main difference between Logstash and rsyslog is that Logstash is easier to use while rsyslog lighter.
  • rsyslog fits well in scenarios where you either need something very light yet capable (an appliance, a small VM, collecting syslog from within a Docker container).
  • rsyslog also works well when you need that ultimate performance.
  • syslog-ng as an alternative to rsyslog (though historically it was actually the other way around).
  • a modular syslog daemon, that can do much more than just syslog
  • Unlike rsyslog, it features a clear, consistent configuration format and has nice documentation.
  • Similarly to rsyslog, you’d probably want to deploy syslog-ng on boxes where resources are tight, yet you do want to perform potentially complex processing.
  • syslog-ng has an easier, more polished feel than rsyslog, but likely not that ultimate performance
  • Fluentd was built on the idea of logging in JSON wherever possible (which is a practice we totally agree with) so that log shippers down the line don’t have to guess which substring is which field of which type.
  • Fluentd plugins are in Ruby and very easy to write.
  • structured data through Fluentd, it’s not made to have the flexibility of other shippers on this list (Filebeat excluded).
  • Fluent Bit, which is to Fluentd similar to how Filebeat is for Logstash.
  • Fluentd is a good fit when you have diverse or exotic sources and destinations for your logs, because of the number of plugins.
  • Splunk isn’t a log shipper, it’s a commercial logging solution
  • Graylog is another complete logging solution, an open-source alternative to Splunk.
  • everything goes through graylog-server, from authentication to queries.
  • Graylog is nice because you have a complete logging solution, but it’s going to be harder to customize than an ELK stack.
  • it depends
張 旭

Ruby on Rails 實戰聖經 | 網站效能 - 0 views

ihower.tw/performance.html

rails programming mysql performance

shared by 張 旭 on 28 Nov 18 - No Cached
  • 依照慣例是_count結尾，型別是integer，有預設值0。
  • lol_dba提供了Rake任務可以幫忙找忘記加的索引。
  • Bullet是一個外掛可以在開發時偵測N+1 queries問題。
  • ...19 more annotations...
  • 存取資料庫是一種相對很慢的I/O的操作：每一條SQL query都得耗上時間、執行回傳的結果也會被轉成ActiveRecord物件全部放進記憶體
  • 如果需要撈出全部的資料做處理，強烈建議最好不要用all方法，因為這樣會把全部的資料一次放進記憶體中，如果資料有成千上萬筆的話，效能就墜毀了。
  • .find_each( :batch_size => 100 )
  • .find_in_batches( :batch_size => 100 )
  • 在Transaction交易範圍內的SQL效能會加快，因為最後只需要COMMIT一次即可
  • Elasticsearch全文搜尋引擎和elasticsearch-rails gem
  • QueryReviewer這個套件透過SQL EXPLAIN分析SQL query的效率
  • 必要時可以採用逆正規化的設計。犧牲空間，增加修改的麻煩，但是讓讀取這事件變得更快更簡單。
  • 將成本轉嫁到寫入，而最佳化了讀取時間
  • 在效能還沒有造成問題前，就為了優化效能而修改程式和架構，只會讓程式更混亂不好維護
  • 當效能還不會造成問題時，程式的維護性比考慮效能重要
  • 會拖慢整體效能的程式，只佔全部程式的一小部分而已，所以我們只最佳化會造成問題的程式。
  • 善用分析工具找效能瓶頸，最佳化前需要測量，最佳化後也要測量比較。
  • rack-mini-profiler在頁面的左上角顯示花了多少時間，並且提供報表，推薦安裝
  • 如果是不需要權限控管的靜態檔案，可以直接放在public目錄下讓使用者下載。
  • Web伺服器得先安裝好x_sendfile功能
  • 如果要讓你的Assets例如CSS, JavaScript, Images也讓使用者透過CDN下載，只要修改config/environments/production.rb的config.action_controller.asset_host為CDN網址即可。
  • 有時候「執行速度較快」的程式碼不代表好維護、好除錯的程式碼
  • Ruby不是萬能，有時候直接呼叫外部程式是最快的作法
張 旭

Best practices for building Kubernetes Operators and stateful apps | Google Cloud Blog - 0 views

cloud.google.com/...es-operators-and-stateful-apps

kubernetes system operator

shared by 張 旭 on 17 May 21 - No Cached
  • use the StatefulSet workload controller to maintain identity for each of the pods, and to use Persistent Volumes to persist data so it can survive a service restart.
  • a way to extend Kubernetes functionality with application specific logic using custom resources and custom controllers.
  • An Operator can automate various features of an application, but it should be specific to a single application
  • ...12 more annotations...
  • Kubebuilder is a comprehensive development kit for building and publishing Kubernetes APIs and Controllers using CRDs
  • Design declarative APIs for operators, not imperative APIs. This aligns well with Kubernetes APIs that are declarative in nature.
  • With declarative APIs, users only need to express their desired cluster state, while letting the operator perform all necessary steps to achieve it.
  • scaling, backup, restore, and monitoring. An operator should be made up of multiple controllers that specifically handle each of the those features.
  • the operator can have a main controller to spawn and manage application instances, a backup controller to handle backup operations, and a restore controller to handle restore operations.
  • each controller should correspond to a specific CRD so that the domain of each controller's responsibility is clear.
  • If you keep a log for every container, you will likely end up with unmanageable amount of logs.
  • integrate application-specific details to the log messages such as adding a prefix for the application name.
  • you may have to use external logging tools such as Google Stackdriver, Elasticsearch, Fluentd, or Kibana to perform the aggregations.
  • adding labels to metrics to facilitate aggregation and analysis by monitoring systems.
  • a more viable option is for application pods to expose a metrics HTTP endpoint for monitoring tools to scrape.
  • A good way to achieve this is to use open-source application-specific exporters for exposing Prometheus-style metrics.
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