# Neo4jの機能と実践的な使い方 🔒 インポート前に一意制約を1本張る——たったそれだけで、MERGEの重複防止と高速ルックアップを同時に手に入れられます。公式もインポートの前提手順として明記しています。 🏷️ タイトル: Uniqueness / Property existence / Node key constraints 🔗 URL: 📘 概要 制約は、ノードやリレーションシップが満たすべきルールをDBレイヤで強制する仕組みです。キーの一意性や必須プロパティを保証し、複数パイプラインからの書き込みでもエンティティの整合性を守ります。一意制約は裏で range インデックスを生成し、ルックアップも速くします。 ⚙️ 機能の説明 ・プロパティ一意制約: ラベル/型ごとにプロパティ値(または組み合わせ)の一意を保証します。Community Editionでも利用可能で、裏側の range インデックスがMERGEやインポートを最適化します。 ・プロパティ存在制約: 指定プロパティが必ず存在することを保証します(Enterprise限定)。 ・プロパティ型制約: プロパティが指定の型であることを保証し、スキーマのドリフトを防ぎます(Enterprise限定)。 ・キー制約(Node key / Relationship key): 一意性と存在を兼ね備え、複合主キーに相当します(Enterprise限定)。 🛠️ 実践的な使い方 インポート前に必ず作成する一意制約です。 ```cypher CREATE CONSTRAINT person_id IF NOT EXISTS FOR (p:Person) REQUIRE IS UNIQUE; ``` ノードキー(複合キー)と存在制約の例です。 ```cypher CREATE CONSTRAINT order_key IF NOT EXISTS FOR (o:Order) REQUIRE (o.tenantId, o.orderId) IS NODE KEY; CREATE CONSTRAINT user_email_exists IF NOT EXISTS FOR (u:User) REQUIRE IS NOT NULL; ``` 確認・削除は次の通りです。 ```cypher SHOW CONSTRAINTS; DROP CONSTRAINT person_id IF EXISTS; ``` 💡 ユースケース ・大量インポート前に一意制約を張り、MERGEの重複防止と高速化を同時に得る。 ・マスタ系ノードのキー整合性をDBで強制し、複数パイプライン書き込みでも二重化を防ぐ。 ⚠️ 注意点 ・一意制約以外(存在・型・キー)はEnterprise Edition限定です。Communityでは一意制約のみ使えます。 ・既にデータに違反がある状態で制約を作成すると失敗します。先にデータをクレンジングします。 ・バルクインポートは有効な制約に対して検証され、違反があると失敗し得ます。 ・より高度な制約や一元管理が必要なら、graph type によるスキーマ定義が推奨されています。 #Neo4j# #Cypher#

「無登録で金融商品取引業を行う者の名称等」を更新しました。 証券取引等監視委員会が東京地方裁判所に対し、金融商品取引法違反行為の禁止及び停止を命ずるよう申立てを行ったことにあわせ掲載しております。 #GOO_PROPERTY# #ITA# #インベスターズトラスト#

# Learning Palantir Foundry 🚀 Action Types are what decisively separate Foundry from read-only BI. Approvals and assignments become safe, validated, structured writes. 📌 Title and Feature URL Title: アクションタイプ URL: 📝 Overview An Action Type defines a set of changes a user can apply to ontology objects, properties, and links in a single transaction. It encapsulates both the data modifications and any side effects triggered on submission, letting users think in terms of overall goals rather than individual property edits. 🔧 How It Works - Write-back to the ontology: when an action runs, all changes are committed to the ontology and reflected across every app. The latest object data, including user edits, is captured in the object type's write-back dataset. - Parameters and defaults: parameters standardize input, supporting default values, filtered dropdown results, and overrides. - Rules: define when and how an action executes, including object relationships and property constraints. - Submission criteria and validation: validation rules control execution eligibility and error handling before changes persist. - Action logs: a full audit trail of every executed action supports accountability and compliance. 🛠 Practical Usage - Run an "Assign Employee" style action that changes a role property, auto-creates a manager-employee link, and notifies stakeholders in one transaction. - Embed submission criteria like "only a director may submit amounts over 1M yen" as validation, replacing Excel-plus-email approvals with structured operations. - Reuse the same validation logic and workflow consistently across every user-facing app. 🎯 Use Cases - Standardize status changes, approvals, and assignments as permissioned, criteria-bound operations. - Let non-technical users safely execute multi-step changes spanning several objects. - Use the action log of every operation as an audit trail for internal controls. ⚠️ Caveats - Actions execute only after passing their validation rules, so submission-criteria design drives the quality of your controls. - Changes propagate immediately across the ontology and all apps, so do not leave rule and parameter design ambiguous. #PalantirFoundry# #Ontology#

# Learning Palantir Foundry 🚀 The first move that turns a dataset into a business object. How you design Object Types largely decides downstream app performance and UX. 📌 Title and Feature URL Title: オブジェクトタイプ URL: 📝 Overview An Object Type defines the schema for a real-world entity or event. A single occurrence is an object instance (e.g., employee "Melissa Chang"), while a group is an object set (e.g., all tenured employees). This mirrors how datasets handle rows and filtered row collections. 🔧 How It Works - Primary keys and identity: objects need a primary key to uniquely identify instances. Mapping a data source to the object type lets you create and display objects in applications. - Properties: define an object's characteristics, with options such as edit-only properties, required properties, and shared properties reused across multiple object types. - Property types: support time series data, geospatial information, and struct types (nested, complex properties). - Display and search: title/display settings and search indexing improve discoverability inside apps. - Value Types: custom value types with versions, permissions, and constraints standardize representation across the ontology. 🛠 Practical Usage - Connect an employee directory or enterprise data to an Employee object type, converting raw datasets into actionable ontology instances. - Nail down primary key design first and index for search to secure downstream app performance and UX. - Use struct properties to auto-map hierarchical data, combined with shared properties for reuse. 🎯 Use Cases - Turn a customer master into a Customer object so the whole company shares one identity. - Model sensor-equipped assets with time series properties to retain operating history. - Model sites and stores with geospatial properties for map-based search and aggregation. ⚠️ Caveats - Primary key design, property types, and search indexing largely determine later app performance and UX, so treat them as your most important modeling decisions. - You must correctly map a data source to the object type before objects can be created or displayed. #PalantirFoundry# #Ontology#