of Perl code (excluding tests) ◦ 270000 lines of tests ◦ About 70000 lines of HTML template ◦ (at November 2016) • And many git submodules... Hatena-Bookmark 9 モノリシックなPerlアプリケーションとして構築 されていた
Web framework • The models no longer reflects the real • Fat model / Fat controller • Too slow tests • Complicated release processes • Difficult to setup develop environment Hatena-Bookmark was “legacy” project 10 ソースコードやDBの肥大化・老朽化によりソ フトウェアの最適化や変更が難しく
in 2015 !!! Rewrite Hatena-Bookmark using Scala 11 2015年にはてなブックマークのリライトを決め る。Scalaを採用。 Source: Hatena Bookmark in Scala https://www.slideshare.net/oarat/2015-0801-scala (Scala Kansai Summit 2015) • Reduce the costs of maintaining. • Optimize the application.
2 - 4 members ◦ Develop the new system, and take care of the original system. • Infrastructure engineer ◦ 1 member Team members (engineers) 12 はてなブックマーク(Web)の開発チーム構成
new application ◦ (Not sharing the existing database with old application) ◦ Requires data migration Brand New Database 13 新システムでは新しいDBを利用し、旧DBの 再利用はしない Original App New App Original DB New DB
Server にScala、ユーザーからの リクエストを処理する部分にPerl Core App Server (Scala) BFF (Perl) Microservices (Go/Python/Perl) Reverse proxy CDN Split to • Backend For Frontend(Perl) • Core App Server (Scala) • (and some microservices)
the features ◦ Now, we release the software to production almost everyday. • Save the substantial amount of computation resource for running an application Benefits of Rewrite 19 サービスへの変更が非常に容易に 計算リソースの大幅な節約
rewrite for revitalising the project? • Rewrite is not the only option to revitalize the project. ◦ Refactoring ◦ Re-architecting ◦ Full Rewrite Was rewrite the best option? 20 ソフトウェアのフルスクラッチが唯一の選択肢 ではない
even years. ◦ Risk of the regressions. • Overhead ◦ We may have to freeze the development on the original software while rewriting. Rewrite is basically undesirable... 21 リライトには数年かかることも、既存プロジェ クトの開発を止めることにも
• They had been useful for rapid development, but… ◦ No longer maintained. ◦ People started to deviate the “convention”... • Tight coupled with the system. ◦ Hinder the large scale refactoring and optimization. (Homegrown) ORM, Web App Framework 25 もうメンテされてない内製フレームワークへの 依存。
(entry) may have the multiple URLs. The difference between model and reality (example) 26 現実世界ではひとつのコンテンツが複数の URLを持ちうる http://example.com/ https://example.com/ https://foo.bar/ Entry 301 redirect / canonical Bookmark Bookmark
been modeled to have each different entry. The difference between model and reality (example) 27 旧システムでは各URLはそれぞれ異なるエン トリを指し示す。 http://example.com/ https://example.com/ https://foo.bar/ Entry Bookmark Bookmark Same contents!
has more logics than its own behavior. ◦ $ wc -l lib/Hatena/Bookmark/MoCo/Entry.pm ▪ 4611 lib/Hatena/Bookmark/MoCo/Entry.pm • Fat controller ◦ The controllers sometimes have the logics that represents model’s behavior. Fat model / Fat controller 28 モデルの振る舞い以上のロジックまで持った モデルが出現
software keep to thrive... • Revise DB schema / model • Remove the dependency on the homegrown ORM and framework. Fundamental changes 31 ソフトウェアに対する根本的な変更が必要だ ということがわかっていた
refactoring ended in failure. • Tried to replace the framework and gave up. • Tried to refactor around the database architecture / connection and failed. Past failures on refactoring 32 過去に大規模なリファクタリングを試みようと して失敗
◦ Expressive type system ◦ Scalability ◦ Type safe • Concise syntax • Already adopted Scala in other projects Why Scala for Core App Server ? 35 社内での利用実績、複雑なドメインを簡潔に 表現できる。
App Server (Scala) BFF (Perl) Microservices (Go/Python/Perl) Reverse proxy CDN Split to • Backend For Frontend(Perl) • Core App Server (Scala) • (and some microservices)
the barrier to onboard the project, • Prepare learning materials • Try to avoid using “difficult” libraries ◦ Monocle / cats / scalaz … ◦ Though they are quite useful, they make it more difficult for non-scala engineer to onboard. Learning curve for Scala 38 Scala学習教材の用意、「難しい」ライブラリは できる限り避け参入障壁を下げる
old system, design the architecture based on Domain Driven Design. • Problems in the old system ◦ The gap between models and real world. ◦ Fat model / Fat controller. ◦ Inconsistent wording. Domain Driven Design 40 旧システムでの課題を解決するためドメイン 駆動設計の徹底
and all members who are related to the project. • Domain model name after the ubiquitous languages. Discuss and re-define the ubiquitous languages, share those languages. Ubiquitous Languages 41 ユビキタス言語の再定義 ✅ inconsistent wording
and resolving the relationships with other models (in the old system) ◦ Fat Model • Define it as a extension method in domain service (domain relation) (in the new system). Relations between entities 45 エンティティ間の関係の解決
domain.relation trait BookmarkLocationComponent { self: repository.LocationComponent => implicit class BookmarkSeqLocationsRelation( bookmarks: Seq[BookmarkEntity] ) { // In the real system, the return value is something like // Bookmark with { def location: Location } def withLocations: Stream[(BookmarkEntity, Location)] = … } }
◦ Expressive type system ◦ Scalability ◦ Type safe • Concise syntax • Already adopted Scala in other projects Why Scala for Core App Server ? 47 社内での利用実績、複雑なドメインを簡潔に 表現できる。
of phases. • Aug 2017: Replace comment list page • Nov 2017: Replace user page • Mar 2018: Replace top page • Mar 2018: Replace search feature • ... Incremental Rewrite 52 一度に全てを置き換えず、何度かに分けて 徐々にリライト
clarifies the progress and business value. ◦ Safer than a big-bang rewrite. • Cons ◦ We have to run both the new and original system until the rewrite complete. Incremental Rewrite 53 利点: 各フェーズ毎に進捗と成果を可視化 欠点: 新旧両システムを稼働させる必要
THE RESOURCES EACH FEATURE DEPENDS (BY READING SOURCE CODE) • Choose which features to re-implement or not. • Prioritize based on the dependencies and business impact. • Group them into the components. ◦ Rewrite each group one by one. Thorough investigation on the old system 54 既存システムの全ての機能と依存するリソー スの洗い出し
◦ The list will help clarifying the progress. • Encounter an unexpected features / dependencies while the rewrite project… ◦ There’s no way to avoid it other than listing all features and dependencies thoroughly before rewrite... Thorough investigation on the old system 55 プロジェクトの進捗を明らかに 想定外の仕様が後で発覚するのを防ぐ
brand new DB structure, it was required to migrate all the data in old database to new one. Data migration 58 新アプリケーションのために新しくDBを作っ たのでデータ移行が必要 Original App New App Original DB New DB
for each data migration. • Maintenance time might continue several hours. ◦ Large scale ◦ Complexed ETL process Downtime for maintenance vs zero-downtime 59 メンテナンスを挟むデータ移行と、ゼロダウン タイムでのデータ移行 Zero-downtime • No downtime • Require real-time data replication. • Replication delay.
Replicate the writes on the old system to new system. • 2. Batch data migration ◦ Copy all existing data into the new database. • 3. Data verification • 4. Replace Real-time and batch data migration 61 リアルタイムデータ移行とバッチデータ移行で ゼロダウンタイムを実現
• Nov 2017: Replace user page • Mar 2018: Replace top page • … • May 2019: Stop the old system Finally, released all the replaces!! 62 2019年5月に全てのデータ移行と置き換え作 業が完了し旧システム停止
Faster response time ▪ Improved algorithms • Over the estimated development cost ◦ It is hard to estimate the exact cost for the rewrite. ◦ Rewriting the big legacy software always takes years. • We didn’t have any big re-work ◦ Thanks to the thorough investigation and. Review 63 見積もりより時間がかかってしまった しかし大きな手戻りなく進められた
/ Refactoring first ▪ Rewrite is really powerful but tough • Solved problems in the old system thanks to Scala! ◦ Thank you!!! • Consider incremental rewrite for big rewrite ◦ Clarify the progress / safer / cost • Thorough research on the original system ◦ Prevent big-rework / listing all tasks Summary 64 まとめ
system to the new app, from the original app. Real-time data migration (From App) 68 旧システムに対する書き込みを旧アプリから 新アプリに対して同期する Original App Original DB New App New DB write enqueue write
transform data so that it fits to the new DB structure. • Cons ◦ Necessary to add code to the original app to send updates to the queue. ◦ Need to grasp all the sources of the updates (otherwise, some updates will lost). Real-time data migration (From App) 69 旧システムにおける書き込みの口を全て把握 する必要がある。
updates to the queue. Real-time data migration (From DB) 70 旧DBにtriggerを定義してそこからキューに書 き込む方法 Original App Original DB New App New DB write enqueue write
on old application • Comprehensive (No worry about missing updates) • Cons ◦ Need to maintain complexed triggers and UDFs that write the updates to the queue. ◦ The migration logic will be regulated by SQL’s expressibility. Real-time data migration (From DB) 71 各テーブルへの書き込みの移行漏れの心配 がないが、複雑なトリガの運用が必須
on the original system ◦ Can build the migration system independently. • Cons ◦ Delayed replication. Real-time data migration (Poll) 73 旧システムと独立して移行システムを構築で きる。同期に大きな遅延が起こる。
to synchronize the data between original and new DB with small delays. • Complexed data transformation process. Our choice 74 アプリケーションからのpushを採用、遅延の 少なさやデータ構造の変換のため
updates to the original system, batch data migration aims to copy all the existing data in the original system. Batch data migration 75 バッチデータ移行では既存の全てのデータを 新システムに移行する
hard to migrate all the data to the new system only with a single trial. ◦ We’ll need to re-run our migration again to complete the job. ◦ Idempotency will help the cycle of trial and error. Tips for writing a batch data migration script 76 移行スクリプトを冪等にすることで再実行を容 易にできるよにしておく。
batch script ◦ Try to estimate how much time our script to run. ◦ If it is too long, consider to ▪ Running the script on a dedicated server. ▪ Scale up original or new database server. ▪ Performance optimization on the script. Tips for writing a batch data migration script 77 実行にかかる時間を計算。長すぎる場合は 高速化のための対応を検討。
stop in the middle of the migration because of an unexpected error. ◦ It will save your time to design the script so that it can re-run from the specific point of migration. Tips for writing a batch data migration script 78 スクリプトを任意の点から再開できるようにし ておくと再実行の時間を節約可 Re-run from here Already migrated Not yet migrated
data migration 3. Replace the application If the step1 and 2 reverse, some data won’t be migrated. Steps of data migration - Otherwise... 80 リアルタイムとバッチデータ移行の順序 Run batch data migration Batch data migration Real-time migration Start real-time data migration Data in this period will lost
for update intensive data. Suppose we are trying to migrate data “X” from original DB to the new DB. Data collision between real-time and batch 81 更新頻度の高いデータではバッチとリアルタ イム移行間でデータ競合のリスク Original DB New DB X = 1
X from original DB. Data collision between real-time and batch 82 まず最初にバッチデータ移行スクリプトが データを旧DBから読み込む Original DB New DB X = 1 Batch data migration script X = 1
updated to 2, and synchronized to the new DB, before the batch script write the data to the new DB. Data collision between real-time and batch 83 次にバッチスクリプトが新DBにデータを書く 前にリアルタイム移行が起きたとき Original DB New DB X = 2 Update X = 2 Batch data migration script X = 1 X = 2 Real-time data migration
value X in the new DB with X = 1. Data collision between real-time and batch 84 最後にバッチ移行スクリプトが新DBに書き込 みを行うと不整合が起きる。 Original DB New DB X = 2 Batch data migration script X = 1 X = 1 The value X should be equal to the X in the original DB... Update X = 2 Update on the original DB lost
new DB, and adopt the newer value as the resulting data. To avoid the Lost Update 85 データの更新時刻を比較して新しい方を採用 することで不整合を防ぐ。 Original DB New DB Batch data migration script X = 2 updated_at = 1970-01-01 12:00:01 X = 1 updated_at = 1970-01-01 12:00:00 X = 2 updated_at = 1970-01-01 12:00:01 Do not update because the existing data is newer.
on the update intensive data, in the most cases, the probability of data collision might be ignorable and it is sufficient to validate and re-run the data migration (only if the migration went wrong). Should we always implement it? 86 更新頻度の低いデータでは起こりにくいので 多少無視できる
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