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URL: https://www.coursera.org/learn/helixdb

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HelixDB From Zero

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HelixDB From Zero

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 hour to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 hour to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply the three-element HelixQL schema (N::, V::, E::) and the four-keyword QUERY shape compiled into typed Rust handlers.

  • Build a hybrid-RAG pipeline with SearchV and ::In<EmbeddingOf>, shipped as a typed Rust client.

  • Compose graph traversal and vector search in one HelixQL query language and one engine.

Details to know

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Recently updated!

May 2026

Assessments

5 assignments

Taught in English

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This course is part of the Rust for Data Engineering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course

Stop stitching three databases together. HelixDB is a Rust-native graph plus vector engine that holds your nodes, your embeddings, your typed edges, and your key-value documents in one process — no separate Postgres, no separate Qdrant, no separate Neo4j. This course walks a Rust-fluent engineer from helix init through a typed HelixQL schema, the helix check and helix compile pre-deploy gates, side-by-side graph traversal and vector search in the same query language, and a typed Rust client that calls a live HelixDB instance with four runtime contracts. Every primitive you meet is wired into a working .hx schema and a running engine you can install in one command. Module 4 puts graph traversal verbs (Out, In, WHERE, ORDER, RANGE) and vector top-k search side by side in the same query language, and adds ShortestPathDijkstras with composable weight expressions for cost-aware routing. You finish with the canonical hybrid-RAG pattern — SearchV returns top-k embeddings, then a typed edge climbs back to the source documents — shipped as a typed Rust client (helix-rs plus serde) with four runtime assertion contracts. The course closes with an honest read on where HelixDB is the wrong choice: columnar OLAP, Spark execution semantics, multi-tenant analytical warehouses.

The unified-engine pitch — why one process holding graph + vector + KV + document beats stitching Postgres, Qdrant, and Neo4j at the application layer. You will see the four-layer Helix stack and the honest boundary where HelixDB is the wrong choice.

What's included

3 videos3 readings1 assignment

3 videos•Total 5 minutes
  • What HelixDB Is•2 minutes
  • The Helix Stack•1 minute
  • HelixDB vs the Postgres + Qdrant Stack•2 minutes
3 readings•Total 3 minutes
  • About This Course•1 minute
  • Key Terms: What HelixDB Is•1 minute
  • Reflection: Why HelixDB•1 minute
1 assignment•Total 1 minute
  • Ungraded Quiz: Why HelixDB•1 minute

The three-element schema in HelixQL and the four-keyword QUERY shape. Walks N::, V::, E:: declarations, INDEX + UNIQUE INDEX, field types, default values, and a queries.hx file end to end.

What's included

2 videos2 readings1 assignment

2 videos•Total 3 minutes
  • N::, V::, E:: — The Three-Element Schema•1 minute
  • INDEX, Field Types, and Default Values•2 minutes
2 readings•Total 2 minutes
  • Key Terms: HelixQL Schema and Your First Queries•1 minute
  • Reflection: HelixQL Schema and Your First Queries•1 minute
1 assignment•Total 10 minutes
  • Ungraded Quiz: HelixQL Schema and Your First Queries•10 minutes

The helix-cli pipeline walked verb by verb. helix init scaffolds the project; helix check is the lint pass; helix compile is the type-safe lower-to-Rust artifact; helix push dev deploys to a local instance on port 6969 where every QUERY becomes an HTTP endpoint.

What's included

1 video2 readings1 assignment

1 video•Total 2 minutes
  • helix check and helix compile•2 minutes
2 readings•Total 2 minutes
  • Key Terms: helix-cli from Init to Push•1 minute
  • Reflection: helix-cli from Init to Push•1 minute
1 assignment•Total 10 minutes
  • Ungraded Quiz: helix-cli from Init to Push•10 minutes

Graph traversal (Out, In, WHERE, ORDER<Asc>, RANGE) and vector search (SearchV) in the same query language. ShortestPathDijkstras with composable weight expressions (ADD, MUL, POW, SQRT) for cost-aware routing.

What's included

3 videos2 readings1 assignment

3 videos•Total 4 minutes
  • Traversal — Out, In, WHERE, ORDER, RANGE•1 minute
  • SearchV — Vector Similarity•1 minute
  • ShortestPathDijkstras•1 minute
2 readings•Total 2 minutes
  • Key Terms: Graph Traversal and Vector Search Side by Side•1 minute
  • Reflection: Graph Traversal and Vector Search Side by Side•1 minute
1 assignment•Total 10 minutes
  • Ungraded Quiz: Graph Traversal and Vector Search Side by Side•10 minutes

The canonical hybrid-RAG schema (Doc → Embedding → EmbeddingOf) and the hit-to-context traversal. BM25 plus vector reciprocal-rank fusion via the built-in reranker. Closes with a typed Rust client that validates four runtime contracts against a live HelixDB instance.

What's included

3 videos3 readings

3 videos•Total 7 minutes
  • Doc → Embedding → Edge Traversal•2 minutes
  • BM25 + Vector Fusion via Reranker•2 minutes
  • Typed Rust Client Demo•3 minutes
3 readings•Total 3 minutes
  • Key Terms: Hybrid RAG and a Typed Rust Client•1 minute
  • Reflection: Hybrid RAG and a Typed Rust Client•1 minute
  • Ungraded Check: Hybrid RAG and a Typed Rust Client•1 minute

Synthesize what you learned across the course. A short reading prepares you for the graded quiz, followed by a 5-question scenario-based assessment that connects schema design, helix-cli, traversal, vector search, and the hybrid-RAG client. Closes with next steps and references.

What's included

2 readings1 assignment

2 readings•Total 2 minutes
  • Before You Go•1 minute
  • Next Steps: Where to Go from Here•1 minute
1 assignment•Total 5 minutes
  • Graded Quiz: HelixDB From Zero•5 minutes

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Instructor

Pragmatic AI Labs
61 Courses•5,916 learners

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Frequently asked questions

No. The course names the three-service stack as the problem HelixDB solves, but you do not need prior experience with any one of them. You DO need to be comfortable in Rust — at the level of having written a small service with serde, tokio, and an HTTP client.

Partly. HelixDB carries vectors as a first-class primitive via V::Embedding and SearchV, so the vector half of RAG is identical. The difference is the graph half — ::In<EmbeddingOf> traversals climb from vector hits back to source documents inside the same query, with no second connection pool, no second schema, and no second transaction.

Honest answer: anything that wants columnar OLAP execution semantics — Spark, DuckDB, or a multi-tenant analytical warehouse. HelixDB is built for graph-shaped and vector-shaped workloads in one process. Module 1 names the boundary explicitly so you can rule it in or out for your stack before you commit.

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