diff --git a/pages/data-modeling.mdx b/pages/data-modeling.mdx index bc76b9f50..0f94833cb 100644 --- a/pages/data-modeling.mdx +++ b/pages/data-modeling.mdx @@ -56,6 +56,9 @@ data in Memgraph. structuring it into an efficient graph model. It’s perfect for those working with raw tabular data and looking to translate it into a connected, queryable format. +- [Versioning](/data-modeling/versioning): Learn how to model historical states, + time-based validity windows, ordered revisions, and timeline-oriented history + without copying the whole graph. - [Best practices](/data-modeling/best-practices): It provides practical guidance for designing efficient and maintainable graph data models in Memgraph. It outlines common pitfalls, such as overcomplicating models, duplicating data, and neglecting indexing, and diff --git a/pages/data-modeling/_meta.ts b/pages/data-modeling/_meta.ts index 6aa93bb72..da927f1b2 100644 --- a/pages/data-modeling/_meta.ts +++ b/pages/data-modeling/_meta.ts @@ -1,5 +1,6 @@ export default { "graph-data-model": "Graph data model", "modeling-guides": "Modeling guides", + "versioning": "Versioning", "best-practices": "Best practices" } \ No newline at end of file diff --git a/pages/data-modeling/versioning.mdx b/pages/data-modeling/versioning.mdx new file mode 100644 index 000000000..0f861c156 --- /dev/null +++ b/pages/data-modeling/versioning.mdx @@ -0,0 +1,422 @@ +--- +title: Versioning +description: Learn how to model entity history, validity windows, and ordered revisions in Memgraph. +--- + +import { Callout } from 'nextra/components' +import {CommunityLinks} from '/components/social-card/CommunityLinks' + +# Versioning graph data + +Every time you update operational data or refactor part of your model, you +create a new version of something: a project plan, a task state, or a +stream-derived reading. In Memgraph, you rarely need to version the entire +graph. Most of the time, you only version the nodes and relationships whose +history matters to your queries. + +The examples on this page reuse domains that already appear elsewhere in the +Memgraph docs: + +- [project management knowledge graph](/data-modeling/modeling-guides/model-a-knowledge-graph) +- [temporal data types](/fundamentals/data-types) +- [nested delivery properties and indices](/fundamentals/indexes) +- [streaming sensor data](/data-streams/kafka) + +## How to choose a strategy + +- Use **entity versioning** when you mostly care about the current state, but + still need to retrieve explicit older versions. +- Use **time-based versioning** when your main question is "What was true at a + specific point in time?" +- Use **linked list versioning** when the exact order of revisions matters. +- Use a **timeline tree** when you browse or aggregate historical data by day, + month, or year. + + + +Only version the part of the graph that actually changes and needs an audit +trail. Since Memgraph is an in-memory database, duplicating the whole graph by +default increases both memory usage and write cost. + + + +## Entity versioning + +Entity versioning keeps one stable identity node and stores each mutable state +in a separate version node. This is a good fit when you need both a fast path to +the current state and an easy way to inspect older states. + +### How it works + +This example builds on the `Project` domain from the knowledge graph guide and +reuses the nested `delivery.status.due_date` structure shown in the indexes +documentation. The `Project` node stays stable, while `ProjectSnapshot` nodes +store the mutable planning data. + +```cypher +CREATE (p:Project {name: "Data Preprocessing"}) + +CREATE (v1:ProjectSnapshot { + budget: 85000, + delivery: {milestone: "v3.12", status: {due_date: date("2025-05-15")}}, + updated_at: localDateTime("2025-01-10T09:00:00") +}) +CREATE (v2:ProjectSnapshot { + budget: 90000, + delivery: {milestone: "v3.13", status: {due_date: date("2025-06-04")}}, + updated_at: localDateTime("2025-02-18T14:30:00") +}) +CREATE (v3:ProjectSnapshot { + budget: 95000, + delivery: {milestone: "v3.14", status: {due_date: date("2025-07-01")}}, + updated_at: localDateTime("2025-04-02T11:15:00") +}) + +CREATE (p)-[:HAS_VERSION {version: 1}]->(v1) +CREATE (p)-[:HAS_VERSION {version: 2}]->(v2) +CREATE (p)-[:HAS_VERSION {version: 3}]->(v3) +CREATE (p)-[:LATEST]->(v3); +``` + +With this model: + +- The `Project` node keeps the business identity stable. +- Each `ProjectSnapshot` node stores one version of the mutable state. +- A `HAS_VERSION` relationship stores the explicit version number. +- A dedicated `LATEST` relationship provides a fast lookup for the current + state. + +### Pros and cons + +| Pros | Cons | +|---|---| +| Simple to model and explain to users. | Every update requires a new version node and a new `LATEST` relationship. | +| Easy to retrieve either a specific version or the latest version. | Relationship count grows linearly with the number of versions. | +| Version numbers are explicit and easy to audit. | "As of date X" queries are weaker unless you also store time metadata. | + +### Query examples + +Get version 2 of the `Data Preprocessing` project: + +```cypher +MATCH (:Project {name: "Data Preprocessing"})-[:HAS_VERSION {version: 2}]->(state:ProjectSnapshot) +RETURN state.delivery.status.due_date AS due_date, + state.delivery.milestone AS milestone, + state.budget AS budget; +``` + +Get the latest version of the project: + +```cypher +MATCH (:Project {name: "Data Preprocessing"})-[:LATEST]->(state:ProjectSnapshot) +RETURN state.delivery.status.due_date AS due_date, + state.delivery.milestone AS milestone, + state.budget AS budget; +``` + +Get the full version history ordered by version number: + +```cypher +MATCH (:Project {name: "Data Preprocessing"})-[v:HAS_VERSION]->(state:ProjectSnapshot) +RETURN v.version AS version, + state.delivery.status.due_date AS due_date, + state.delivery.milestone AS milestone, + state.budget AS budget, + state.updated_at AS updated_at +ORDER BY version; +``` + + + +When inserting a new version, update `LATEST` in the same query. That keeps the +current-state pointer and the version history consistent. + + + +## Time-based versioning of entities + +Time-based versioning stores the validity window of each state. Use this when +you need graph snapshots, historical comparisons, or "show me the state as of +this date" queries. + +### How it works + +Using the same `Project` domain, this model represents planning history through +`ProjectPlan` nodes. Instead of an explicit version number, each state is valid +for a specific time interval. + +```cypher +CREATE (p:Project {name: "Data Preprocessing"}) + +CREATE (plan1:ProjectPlan { + delivery: {milestone: "v3.12", status: {due_date: date("2025-05-15")}}, + validFrom: date("2025-01-01"), + validTo: date("2025-02-17") +}) +CREATE (plan2:ProjectPlan { + delivery: {milestone: "v3.13", status: {due_date: date("2025-06-04")}}, + validFrom: date("2025-02-18"), + validTo: date("2025-04-01") +}) +CREATE (plan3:ProjectPlan { + delivery: {milestone: "v3.14", status: {due_date: date("2025-07-01")}}, + validFrom: date("2025-04-02"), + validTo: date("9999-12-31") +}) + +CREATE (p)-[:USES_PLAN]->(plan1) +CREATE (p)-[:USES_PLAN]->(plan2) +CREATE (p)-[:USES_PLAN]->(plan3); +``` + +With this model: + +- Each `ProjectPlan` node is valid only during its `validFrom`/`validTo` + interval. +- `date("9999-12-31")` acts as the open-ended interval for the current plan. +- The same pattern can be applied to relationships when the connection itself + changes over time. + +### Pros and cons + +| Pros | Cons | +|---|---| +| Excellent for "state at time X" queries. | Updates duplicate state across intervals. | +| Works well for audits and temporal comparisons. | Requires careful interval management to avoid gaps and overlaps. | +| Flexible enough for both nodes and relationships. | Slightly more complex than explicit version numbers. | + +### Query examples + +Get the current delivery plan: + +```cypher +MATCH (:Project {name: "Data Preprocessing"})-[:USES_PLAN]->(plan:ProjectPlan) +WHERE plan.validTo = date("9999-12-31") +RETURN plan.delivery.status.due_date AS due_date, + plan.delivery.milestone AS milestone; +``` + +Get the delivery plan that was valid on March 1st 2025: + +```cypher +MATCH (:Project {name: "Data Preprocessing"})-[:USES_PLAN]->(plan:ProjectPlan) +WHERE plan.validFrom <= date("2025-03-01") + AND plan.validTo >= date("2025-03-01") +RETURN plan.delivery.status.due_date AS due_date, + plan.delivery.milestone AS milestone; +``` + +Get the current delivery plans for all projects: + +```cypher +MATCH (project:Project)-[:USES_PLAN]->(plan:ProjectPlan) +WHERE plan.validTo = date("9999-12-31") +RETURN project.name, + plan.delivery.status.due_date AS due_date, + plan.delivery.milestone AS milestone; +``` + + + +Use Memgraph temporal types such as `date` and `localDateTime` instead of +strings. That keeps comparisons, filtering, and range queries simple. + + + +## Linked list versioning + +Linked-list versioning is useful when the order of changes matters as much as +the content of each change. It is a natural fit for revision history, state +machines, and undo/redo style workflows. + +### How it works + +This example uses the `Task` domain from the project management guide. Each +change to the `Data Cleaning` task is modeled as a `TaskRevision` node, and the +revisions are chained through `NEXT` relationships. + +```cypher +CREATE (t:Task {name: "Data Cleaning"}) + +CREATE (r1:TaskRevision { + status: "planned", + assigned_to: "Jane Smith", + updated_at: localDateTime("2025-01-12T08:30:00") +}) +CREATE (r2:TaskRevision { + status: "in_progress", + assigned_to: "Jane Smith", + updated_at: localDateTime("2025-01-20T13:15:00") +}) +CREATE (r3:TaskRevision { + status: "completed", + assigned_to: "Jane Smith", + updated_at: localDateTime("2025-01-27T17:45:00") +}) + +CREATE (t)-[:FIRST_REVISION]->(r1) +CREATE (t)-[:LAST_REVISION]->(r3) +CREATE (r1)-[:NEXT]->(r2) +CREATE (r2)-[:NEXT]->(r3); +``` + +With this model: + +- The `Task` node is the stable anchor. +- `FIRST_REVISION` and `LAST_REVISION` give you fast access to both ends. +- The `NEXT` chain keeps the exact order of changes in the graph itself. + +### Pros and cons + +| Pros | Cons | +|---|---| +| Efficient for stepping forward and backward through revisions. | Reaching the middle of a long chain requires traversal. | +| Makes ordered change history explicit. | On its own, it is weaker for direct "state at time X" lookups. | +| Works well for workflows and revision logs. | Insertions in the middle require relinking. | + +### Query examples + +Get the latest revision of the task: + +```cypher +MATCH (:Task {name: "Data Cleaning"})-[:LAST_REVISION]->(revision:TaskRevision) +RETURN revision.status, revision.assigned_to, revision.updated_at; +``` + +Get the revision before the latest one: + +```cypher +MATCH (:Task {name: "Data Cleaning"})-[:LAST_REVISION]->(latest:TaskRevision) +MATCH (previous:TaskRevision)-[:NEXT]->(latest) +RETURN previous.status, previous.assigned_to, previous.updated_at; +``` + +Walk the full revision history in order: + +```cypher +MATCH (:Task {name: "Data Cleaning"})-[:FIRST_REVISION]->(first:TaskRevision) +MATCH path = (first)-[:NEXT*0..]->(revision:TaskRevision) +RETURN length(path) + 1 AS revision_number, + revision.status, + revision.assigned_to, + revision.updated_at +ORDER BY revision_number; +``` + +## Timeline tree + +A timeline tree organizes historical data into a Year -> Month -> Day hierarchy. +This pattern is especially useful when your graph already stores event-like +nodes and your queries are centered around time windows. + +### How it works + +This example reuses the `SensorData` records from the Kafka guide. The readings +remain ordinary nodes, while the timeline tree provides a reusable time index +for browsing history. + +```cypher +CREATE (root:Timeline {name: "Sensor Readings"}) + +CREATE (y2025:Year {value: 2025}) +CREATE (dec:Month {value: 12}) +CREATE (d15:Day {value: 15}) +CREATE (d16:Day {value: 16}) + +CREATE (root)-[:IN_YEAR]->(y2025) +CREATE (y2025)-[:IN_MONTH]->(dec) +CREATE (dec)-[:ON_DAY]->(d15) +CREATE (dec)-[:ON_DAY]->(d16) + +CREATE (reading1:SensorData { + sensorId: "S101", + temperature: 22.5, + humidity: 45, + timestamp: localDateTime("2025-12-15T09:00:00") +}) +CREATE (reading2:SensorData { + sensorId: "S102", + temperature: 24.1, + humidity: 50, + timestamp: localDateTime("2025-12-15T09:05:00") +}) +CREATE (reading3:SensorData { + sensorId: "S103", + temperature: 23.3, + humidity: 48, + timestamp: localDateTime("2025-12-16T09:10:00") +}) + +CREATE (reading1)-[:RECORDED_ON]->(d15) +CREATE (reading2)-[:RECORDED_ON]->(d15) +CREATE (reading3)-[:RECORDED_ON]->(d16); +``` + +With this model: + +- The timeline tree gives you a stable entry point for time-based queries. +- The event nodes themselves stay simple and queryable outside the tree. +- You can attach many event types to the same calendar structure if needed. + +### Pros and cons + +| Pros | Cons | +|---|---| +| Excellent for browsing and aggregating data by day, month, or year. | Adds extra nodes and relationships to maintain. | +| Makes calendar-based traversals very clear. | Can be unnecessary for small datasets with simple timestamp filters. | +| Useful when many queries start with a time window. | Deep trees add hops to each query. | + +### Query examples + +Get all sensor readings recorded on December 15th 2025: + +```cypher +MATCH (:Timeline {name: "Sensor Readings"})-[:IN_YEAR]->(:Year {value: 2025})-[:IN_MONTH]->(:Month {value: 12})-[:ON_DAY]->(:Day {value: 15})<-[:RECORDED_ON]-(reading:SensorData) +RETURN reading.sensorId, reading.temperature, reading.humidity, reading.timestamp +ORDER BY reading.sensorId; +``` + +Get all readings from December 2025: + +```cypher +MATCH (:Timeline {name: "Sensor Readings"})-[:IN_YEAR]->(:Year {value: 2025})-[:IN_MONTH]->(:Month {value: 12})-[:ON_DAY]->(day:Day) +MATCH (reading:SensorData)-[:RECORDED_ON]->(day) +RETURN day.value AS day, + reading.sensorId, + reading.temperature, + reading.humidity, + reading.timestamp +ORDER BY day, reading.sensorId; +``` + +## Combined approach + +Real systems often need more than one versioning pattern at the same time. The +best combination depends on the questions your application needs to answer. + +For example: + +- Use **entity versioning** on `Project` nodes when you want simple access to + version numbers and the latest plan. +- Add **time-based versioning** when project plans need accurate "as of" lookups. +- Use **linked-list versioning** on `Task` revisions when the order of changes + matters. +- Add a **timeline tree** for stream data such as `SensorData` when dashboards + and historical exploration start from calendar ranges. + +When choosing a combination, focus on: + +- **Query patterns**: optimize for the questions you ask most often. +- **Write frequency**: frequent updates favor simpler models. +- **Audit depth**: stronger history requirements justify more explicit models. +- **Memory trade-offs**: storing many historical states increases memory usage. + + + +Start with the smallest model that answers your real historical queries. Add +more structure only when ordered revisions, calendar navigation, or exact +time-slice queries become first-class requirements. + + + + diff --git a/public/sitemap.xml b/public/sitemap.xml index a109c0b0c..dbe3cc86c 100644 --- a/public/sitemap.xml +++ b/public/sitemap.xml @@ -1,335 +1,351 @@ -https://memgraph.com/docs2026-03-25T11:16:02.028Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms2026-03-25T11:16:02.029Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms/available-algorithms2026-03-25T11:16:02.029Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms/available-algorithms/algo2026-03-25T11:16:02.029Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms/available-algorithms/betweenness_centrality2026-03-25T11:16:02.029Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms/available-algorithms/betweenness_centrality_online2026-03-25T11:16:02.029Zdaily0.7 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