The Purity Test has historically served as a segue from O-week to true college life at Rice.
It's a voluntary opportunity for O-week groups to bond, and for students to track the maturation
of their experiences throughout college.
Caution: This is not a bucket list. Completion of all items on this test will likely result in death.
If you are looking to integrate this into a specific project, let me know: (nodes/edges) you are planning to store?
Kùzu (often spelled Kuzu) is an built for query speed and scalability. Unlike traditional database servers like Neo4j or PostgreSQL, Kuzu is designed to run "in-process." This means it operates inside your application (Python, Node.js, Rust, Java, Go, etc.) without needing a separate server process, no Docker containers, and no complex configuration. You can integrate it as easily as you would a standard code library.
Leverages modern CPU features for high-speed data processing. Major Updates in Kuzu v0.13.6 (Full Release) kuzu v0 136 full
For those new to Kuzu, it's an open-source graph database designed to efficiently store and query large-scale graph data. Built from the ground up with a focus on performance, scalability, and ease of use, Kuzu has been gaining popularity among developers and data scientists working with complex, interconnected data.
import kuzu # 1. Initialize Database and Connection db = kuzu.Database('./my_graph_db') conn = kuzu.Connection(db) # 2. Create Schema conn.execute('CREATE NODE TABLE Person (ID INT64, name STRING, PRIMARY KEY (ID));') conn.execute('CREATE REL TABLE Knows (FROM Person TO Person, since INT64);') # 3. Insert Data conn.execute('CREATE (:Person ID: 0, name: "Alice");') conn.execute('CREATE (:Person ID: 1, name: "Bob");') conn.execute('CREATE (:Person ID: 2, name: "Charlie");') conn.execute('CREATE (:Knows FROM: 0, TO: 1, since: 2020);') conn.execute('CREATE (:Knows FROM: 1, TO: 2, since: 2021);') # 4. Perform a 2-hop query (utilizing the new optimizer optimizations) results = conn.execute( 'MATCH (a:Person)-[:Knows]->(b:Person)-[:Knows]->(c:Person) ' 'RETURN a.name, c.name;' ) while results.has_next(): print(results.get_next()) Use code with caution. Conclusion If you are looking to integrate this into
npm install @kuzu/embedded@0.136.0
: Graph analytical paths often demand multiple many-to-many joins. Kùzu implements advanced, novel join algorithms that minimize data duplication and prevent the exponential structural blow-ups common to SQL or basic NoSQL architectures during deep multi-hop graph lookups. High-Performance Feature Set in v0.13.6 You can integrate it as easily as you
: The easiest way to get started is by installing the Kuzu Python client, which will be familiar to most data scientists and developers. Simply run:
For a full list of commits, visit the Kuzu GitHub Repository .
This is where Kùzu comes in. It's a paradigm shift in data management, offering the power of graph analytics right inside your application.