Cypher Query Language¶

Uni supports a substantial subset of the OpenCypher query language, optimized for vectorized execution. This guide covers all supported syntax with practical examples.

Overview¶

Cypher is a declarative graph query language that uses ASCII-art patterns to describe graph structures:

// Pattern: (startNode)-[relationship]->(endNode)
MATCH (person:Person)-[:KNOWS]->(friend:Person)
WHERE person.age > 30
RETURN friend.name

Query Structure¶

A complete Cypher query follows this structure:

[MATCH clause]      -- Pattern matching
[WHERE clause]      -- Filtering
[WITH clause]       -- Intermediate processing
[RETURN clause]     -- Output projection
[ORDER BY clause]   -- Sorting
[SKIP/LIMIT clause] -- Pagination

Example Query¶

MATCH (p:Paper)-[:AUTHORED_BY]->(a:Author)
WHERE p.year >= 2020 AND a.affiliation = 'MIT'
RETURN p.title, a.name, p.year
ORDER BY p.year DESC
LIMIT 10

MATCH Clause¶

The MATCH clause specifies graph patterns to find.

Node Patterns¶

// Any node
MATCH (n)

// Node with label
MATCH (p:Paper)

// Node with multiple labels (future)
MATCH (n:Paper:Preprint)

// Node with variable binding
MATCH (paper:Paper)
RETURN paper.title

// Anonymous node (no variable)
MATCH (:Paper)-[:CITES]->(cited)
RETURN cited.title

Node Properties in Pattern¶

// Filter in pattern (equivalent to WHERE)
MATCH (p:Paper {year: 2023})
RETURN p.title

// Multiple properties
MATCH (p:Paper {year: 2023, venue: 'NeurIPS'})
RETURN p.title

Relationship Patterns¶

// Outgoing relationship
MATCH (a)-[:KNOWS]->(b)

// Incoming relationship
MATCH (a)<-[:KNOWS]-(b)

// Either direction
MATCH (a)-[:KNOWS]-(b)

// Any relationship type
MATCH (a)-[r]->(b)

// Relationship with variable
MATCH (p:Paper)-[c:CITES]->(cited:Paper)
RETURN c  // Access edge properties

Multi-Hop Patterns¶

// 2-hop path
MATCH (a:Paper)-[:CITES]->(b:Paper)-[:CITES]->(c:Paper)
RETURN a.title, b.title, c.title

// Chain multiple relationships
MATCH (p:Paper)-[:AUTHORED_BY]->(a:Author)-[:AFFILIATED_WITH]->(u:University)
RETURN p.title, a.name, u.name

Variable-Length Paths¶

Variable-length path patterns allow traversing multiple hops in a single pattern.

// 1 to 3 hops
MATCH (a:Paper)-[:CITES*1..3]->(b:Paper)
RETURN a.title, b.title

// Exactly 2 hops
MATCH (a)-[:KNOWS*2]->(b)

// Any length (use with caution on large graphs)
MATCH (a)-[:KNOWS*]->(b)

// Zero or more hops
MATCH (a)-[:KNOWS*0..]->(b)

// With path variable
MATCH path = (a:Paper)-[:CITES*1..5]->(b:Paper)
WHERE a.title = 'Attention Is All You Need'
RETURN path, length(path) AS hops

WHERE Clause¶

The WHERE clause filters matched patterns.

Comparison Operators¶

// Equality
WHERE p.year = 2023

// Inequality
WHERE p.year <> 2020
WHERE p.year != 2020

// Comparison
WHERE p.year > 2020
WHERE p.year >= 2020
WHERE p.year < 2025
WHERE p.year <= 2025

Boolean Logic¶

// AND
WHERE p.year > 2020 AND p.venue = 'NeurIPS'

// OR
WHERE p.venue = 'NeurIPS' OR p.venue = 'ICML'

// NOT
WHERE NOT p.is_retracted

// Parentheses for grouping
WHERE (p.year > 2020 AND p.venue = 'NeurIPS') OR p.citations > 1000

NULL Handling¶

// Check for NULL
WHERE p.doi IS NULL
WHERE p.doi IS NOT NULL

// COALESCE (use default if NULL)
RETURN COALESCE(p.nickname, p.name) AS display_name

String Operations¶

// Starts with
WHERE p.title STARTS WITH 'Attention'

// Ends with
WHERE p.title ENDS WITH 'Networks'

// Contains (uses JSON FTS index if available)
WHERE p.title CONTAINS 'Transformer'

// Full-text search on JSON document column (requires JSON FTS index)
WHERE a._doc CONTAINS 'graph database'

// Path-specific JSON FTS search
WHERE a._doc.title CONTAINS 'graph'

// Regular expression (future)
WHERE p.title =~ '.*Transform.*'

Note: The CONTAINS operator is automatically routed to JSON FTS indexes when the column has an FTS index, enabling BM25-based full-text search with relevance ranking.

List Operations¶

// IN list
WHERE p.venue IN ['NeurIPS', 'ICML', 'ICLR']

// NOT IN
WHERE p.venue NOT IN ['Workshop', 'Demo']

Property Existence¶

// Check if property exists
WHERE p.abstract IS NOT NULL

// In pattern
WHERE EXISTS(p.doi)

RETURN Clause¶

The RETURN clause specifies output columns.

Basic Projections¶

// Single property
RETURN p.title

// Multiple properties
RETURN p.title, p.year, p.venue

// All properties (*)
RETURN p.*

// Entire node
RETURN p

Aliases¶

// AS keyword
RETURN p.title AS paper_title, a.name AS author_name

// Expression aliases
RETURN p.year + 1 AS next_year

Expressions¶

// Arithmetic
RETURN p.citations * 2 AS double_citations

// String concatenation
RETURN p.title + ' (' + p.venue + ')' AS formatted

// Conditional (CASE)
RETURN CASE
  WHEN p.citations > 1000 THEN 'High Impact'
  WHEN p.citations > 100 THEN 'Medium Impact'
  ELSE 'Low Impact'
END AS impact_level

DISTINCT¶

// Remove duplicates
RETURN DISTINCT p.venue

// Distinct combinations
RETURN DISTINCT p.venue, p.year

Aggregations¶

Uni supports standard aggregation functions.

Aggregation Functions¶

Function	Description	Example
`COUNT(*)`	Count all rows	`RETURN COUNT(*)`
`COUNT(x)`	Count non-null values	`RETURN COUNT(p.doi)`
`COUNT(DISTINCT x)`	Count distinct values	`RETURN COUNT(DISTINCT p.venue)`
`SUM(x)`	Sum numeric values	`RETURN SUM(p.citations)`
`AVG(x)`	Average	`RETURN AVG(p.citations)`
`MIN(x)`	Minimum	`RETURN MIN(p.year)`
`MAX(x)`	Maximum	`RETURN MAX(p.year)`
`COLLECT(x)`	Collect into list	`RETURN COLLECT(p.title)`

Implicit Grouping¶

Non-aggregated columns become GROUP BY keys:

// Group by venue, count papers per venue
MATCH (p:Paper)
RETURN p.venue, COUNT(p) AS paper_count
ORDER BY paper_count DESC

Aggregation Examples¶

// Count papers per author
MATCH (p:Paper)-[:AUTHORED_BY]->(a:Author)
RETURN a.name, COUNT(p) AS papers, AVG(p.citations) AS avg_citations
ORDER BY papers DESC

// Most cited papers per venue
MATCH (p:Paper)
RETURN p.venue, MAX(p.citations) AS top_citations, COUNT(p) AS total
ORDER BY top_citations DESC

Window Functions¶

Window functions perform calculations across a set of rows related to the current row, without collapsing results like aggregations.

Basic Syntax¶

function_name(args) OVER (
  [PARTITION BY partition_expr, ...]
  [ORDER BY order_expr [ASC|DESC], ...]
)

Supported Window Functions¶

Function	Description	Example
`row_number()`	Sequential row number	`row_number() OVER (ORDER BY p.year)`
`rank()`	Rank with gaps for ties	`rank() OVER (ORDER BY p.citations DESC)`
`dense_rank()`	Rank without gaps	`dense_rank() OVER (ORDER BY p.citations DESC)`
`sum()`	Running sum	`sum(p.citations) OVER (ORDER BY p.year)`
`avg()`	Running average	`avg(p.citations) OVER (PARTITION BY p.venue)`
`count()`	Running count	`count(*) OVER (PARTITION BY p.venue)`
`min()` / `max()`	Running min/max	`max(p.citations) OVER (PARTITION BY p.author)`

Examples¶

Ranking within partitions:

MATCH (p:Paper)
RETURN p.title, p.venue, p.citations,
       rank() OVER (PARTITION BY p.venue ORDER BY p.citations DESC) AS venue_rank
ORDER BY p.venue, venue_rank

Running totals:

MATCH (p:Paper)
WHERE p.author = 'Alice'
RETURN p.title, p.year, p.citations,
       sum(p.citations) OVER (ORDER BY p.year) AS cumulative_citations
ORDER BY p.year

Top N per group:

MATCH (p:Paper)
WITH p, row_number() OVER (PARTITION BY p.venue ORDER BY p.citations DESC) AS rn
WHERE rn <= 3
RETURN p.venue, p.title, p.citations
ORDER BY p.venue, p.citations DESC

Scalar Functions¶

Uni provides a comprehensive set of scalar functions for data transformation.

String Functions¶

Function	Description	Example
`toUpper(s)` / `upper(s)`	Convert to uppercase	`RETURN toUpper('hello')` → `'HELLO'`
`toLower(s)` / `lower(s)`	Convert to lowercase	`RETURN toLower('HELLO')` → `'hello'`
`trim(s)`	Remove leading/trailing whitespace	`RETURN trim(' hi ')` → `'hi'`
`ltrim(s)`	Remove leading whitespace	`RETURN ltrim(' hi')` → `'hi'`
`rtrim(s)`	Remove trailing whitespace	`RETURN rtrim('hi ')` → `'hi'`
`substring(s, start, [len])`	Extract substring	`RETURN substring('hello', 1, 3)` → `'ell'`
`left(s, n)`	First n characters	`RETURN left('hello', 2)` → `'he'`
`right(s, n)`	Last n characters	`RETURN right('hello', 2)` → `'lo'`
`reverse(s)`	Reverse string	`RETURN reverse('hello')` → `'olleh'`
`replace(s, old, new)`	Replace occurrences	`RETURN replace('hello', 'l', 'x')` → `'hexxo'`
`split(s, delim)`	Split into list	`RETURN split('a,b,c', ',')` → `['a','b','c']`
`lpad(s, len, [pad])`	Pad left to length	`RETURN lpad('5', 3, '0')` → `'005'`
`rpad(s, len, [pad])`	Pad right to length	`RETURN rpad('5', 3, '0')` → `'500'`

Math Functions¶

Function	Description	Example
`abs(n)`	Absolute value	`RETURN abs(-5)` → `5`
`ceil(n)`	Round up	`RETURN ceil(4.2)` → `5`
`floor(n)`	Round down	`RETURN floor(4.8)` → `4`
`round(n)`	Round to nearest	`RETURN round(4.5)` → `5`
`sqrt(n)`	Square root	`RETURN sqrt(16)` → `4`
`sign(n)`	Sign (-1, 0, 1)	`RETURN sign(-5)` → `-1`
`log(n)`	Natural logarithm	`RETURN log(2.718)` → `~1`
`log10(n)`	Base-10 logarithm	`RETURN log10(100)` → `2`
`exp(n)`	e^n	`RETURN exp(1)` → `~2.718`
`power(base, exp)` / `pow(base, exp)`	Exponentiation	`RETURN power(2, 3)` → `8`
`sin(n)`, `cos(n)`, `tan(n)`	Trigonometric	`RETURN sin(0)` → `0`

List Functions¶

Function	Description	Example
`size(list)`	Length of list/string/map	`RETURN size([1,2,3])` → `3`
`head(list)`	First element	`RETURN head([1,2,3])` → `1`
`tail(list)`	All but first element	`RETURN tail([1,2,3])` → `[2,3]`
`last(list)`	Last element	`RETURN last([1,2,3])` → `3`
`keys(map)`	Keys of a map	`RETURN keys({a:1, b:2})` → `['a','b']`
`range(start, end, [step])`	Generate number sequence	`RETURN range(1, 5)` → `[1,2,3,4,5]`

Path Functions¶

Function	Description	Example
`length(path)`	Number of relationships in path	`RETURN length(path)`
`nodes(path)`	List of nodes in path	`RETURN nodes(path)`
`relationships(path)`	List of relationships in path	`RETURN relationships(path)`

Type Conversion Functions¶

Function	Description	Example
`toInteger(x)`	Convert to integer	`RETURN toInteger('42')` → `42`
`toFloat(x)`	Convert to float	`RETURN toFloat('3.14')` → `3.14`
`toString(x)`	Convert to string	`RETURN toString(42)` → `'42'`
`toBoolean(x)`	Convert to boolean	`RETURN toBoolean('true')` → `true`

Null Handling Functions¶

Function	Description	Example
`coalesce(x, y, ...)`	First non-null value	`RETURN coalesce(null, 'default')` → `'default'`
`nullif(a, b)`	Return null if a = b	`RETURN nullif(5, 5)` → `null`

Example Usage¶

// String manipulation
MATCH (p:Paper)
RETURN toUpper(p.title) AS upper_title,
       substring(p.abstract, 0, 100) AS abstract_preview

// Math operations
MATCH (p:Paper)
RETURN p.title, round(p.citations / 10.0) * 10 AS rounded_citations

// List operations
MATCH (a:Author)
WHERE size(a.affiliations) > 1
RETURN a.name, head(a.affiliations) AS primary_affiliation

// Type conversion
MATCH (p:Paper)
WHERE toInteger(p.year_str) > 2020
RETURN p.title

ORDER BY, SKIP, LIMIT¶

Sorting¶

// Ascending (default)
ORDER BY p.year

// Descending
ORDER BY p.year DESC

// Multiple columns
ORDER BY p.year DESC, p.title ASC

// By alias
RETURN p.title, p.citations AS cites
ORDER BY cites DESC

Pagination¶

// First 10 results
LIMIT 10

// Skip first 20, take next 10
SKIP 20
LIMIT 10

// Combined
RETURN p.title
ORDER BY p.year DESC
SKIP 100
LIMIT 25

CREATE Clause¶

Create new nodes and relationships.

Creating Nodes¶

// Simple node
CREATE (p:Paper {title: 'My Paper'})

// With properties
CREATE (p:Paper {
  title: 'New Research',
  year: 2024,
  venue: 'ArXiv',
  citations: 0
})
RETURN p

// Multiple nodes
CREATE (a:Author {name: 'Alice'}), (b:Author {name: 'Bob'})

Creating Relationships¶

// Between existing nodes
MATCH (p:Paper {title: 'Paper A'}), (a:Author {name: 'Alice'})
CREATE (p)-[:AUTHORED_BY {position: 1}]->(a)

// Create node and relationship together
CREATE (p:Paper {title: 'New Paper'})-[:AUTHORED_BY]->(a:Author {name: 'New Author'})

Returning Created Elements¶

CREATE (p:Paper {title: 'My Paper', year: 2024})
RETURN p.title, p.year

WITH Clause¶

The WITH clause chains query parts together.

Intermediate Processing¶

// Filter after aggregation
MATCH (p:Paper)-[:AUTHORED_BY]->(a:Author)
WITH a, COUNT(p) AS paper_count
WHERE paper_count > 5
RETURN a.name, paper_count

Subquery-like Behavior¶

// First find top authors, then their papers
MATCH (a:Author)<-[:AUTHORED_BY]-(p:Paper)
WITH a, COUNT(p) AS papers
ORDER BY papers DESC
LIMIT 10
MATCH (a)<-[:AUTHORED_BY]-(recent:Paper)
WHERE recent.year >= 2022
RETURN a.name, recent.title

UNWIND Clause¶

Expand a list into individual rows.

// Expand literal list
UNWIND [1, 2, 3] AS num
RETURN num

// Expand list property
MATCH (p:Paper)
UNWIND p.keywords AS keyword
RETURN keyword, COUNT(*) AS usage
ORDER BY usage DESC

// Create multiple from list
UNWIND ['Alice', 'Bob', 'Charlie'] AS name
CREATE (a:Author {name: name})

CALL Clause (Procedures)¶

Invoke built-in procedures.

Vector Search¶

// Basic vector search
CALL db.idx.vector.query('Paper', 'embedding', $query_vector, 10)
YIELD node, distance
RETURN node.title, distance

// With threshold
CALL db.idx.vector.query('Paper', 'embedding', $query_vector, 100, 0.3)
YIELD node, distance
WHERE distance < 0.2
RETURN node.title, distance

Schema Introspection¶

// List labels
CALL db.labels()
YIELD label
RETURN label

// List relationship types
CALL db.relationshipTypes()
YIELD relationshipType
RETURN relationshipType

// List indexes
CALL db.indexes()
YIELD name, type, labelsOrTypes, properties
RETURN *

Index Management¶

Create Indexes¶

// Scalar index
CREATE INDEX ON :Paper(year)

// Named index
CREATE INDEX paper_year FOR (p:Paper) ON (p.year)

// Vector index
CREATE VECTOR INDEX paper_embeddings
FOR (p:Paper) ON p.embedding
OPTIONS {index_type: 'hnsw', metric: 'cosine'}

// JSON Full-Text index
CREATE JSON FULLTEXT INDEX article_fts
FOR (a:Article) ON _doc
OPTIONS {with_positions: true}

Drop Indexes¶

DROP INDEX paper_year

Show Indexes¶

SHOW INDEXES

Query Parameters¶

Use parameters to avoid injection and improve plan caching.

Parameter Syntax¶

// In query
MATCH (p:Paper)
WHERE p.year = $year AND p.venue = $venue
RETURN p.title

CLI Usage¶

uni query "MATCH (p:Paper) WHERE p.year = \$year RETURN p" \
    --params '{"year": 2023}' \
    --path ./storage

Session Variables¶

Session variables provide scoped context that's automatically available in all queries within a session. This is ideal for multi-tenant applications.

Creating a Session¶

// Rust API
let session = db.session()
    .set("tenant_id", "acme-corp")
    .set("user_id", "user-123")
    .set("granted_tags", vec!["public", "team:eng"])
    .build();

Using Session Variables¶

Access session variables with the $session. prefix:

// Automatic tenant filtering
MATCH (d:Document)
WHERE d.tenant_id = $session.tenant_id
RETURN d.title

// Multiple session variables
MATCH (e:Event)
WHERE e.tenant_id = $session.tenant_id
  AND e.created_by = $session.user_id
RETURN e

// Combining session variables with query parameters
MATCH (d:Document)
WHERE d.tenant_id = $session.tenant_id
  AND d.status = $status
RETURN d

Session Variable Benefits¶

Benefit	Description
Reduced boilerplate	No need to pass tenant_id to every query
Security	Variables are immutable after session creation
Consistency	Same context applied to all queries in session
Multi-tenancy	Natural pattern for SaaS applications

Session Queries vs Regular Queries¶

// Without session (repetitive)
db.query_with("MATCH (d:Document) WHERE d.tenant = $t RETURN d")
    .param("t", "acme")
    .fetch_all().await?;

// With session (cleaner)
let session = db.session().set("tenant", "acme").build();
session.query("MATCH (d:Document) WHERE d.tenant = $session.tenant RETURN d").await?;

Temporal Queries¶

Uni provides functions for querying temporal (time-based) data with validity ranges.

validAt Function¶

Check if a node or edge was valid at a specific point in time:

// Basic validAt usage
MATCH (e:Event)
WHERE uni.validAt(e, 'valid_from', 'valid_to', datetime($time))
RETURN e.name, e.valid_from, e.valid_to

Parameters: - e: Node or edge to check - 'valid_from': Property name for start time - 'valid_to': Property name for end time - datetime($time): Point in time to check

Semantics: valid_from <= time < valid_to (half-open interval)

VALID_AT Macro¶

For convenience, use the VALID_AT macro with default property names:

// Macro form (uses 'valid_from' and 'valid_to' by default)
MATCH (e:Event)
WHERE e VALID_AT datetime('2024-06-15T12:00:00Z')
RETURN e.name

Custom Property Names¶

Specify custom property names if your schema differs:

// With custom property names
MATCH (c:Contract)
WHERE c VALID_AT(datetime($check_time), 'start_date', 'end_date')
RETURN c.name, c.value

Temporal Query Patterns¶

Find currently valid records:

MATCH (e:Employee)
WHERE uni.validAt(e, 'hire_date', 'termination_date', datetime())
RETURN e.name, e.department

Find records valid at a historical point:

MATCH (p:Price)
WHERE p.product_id = $product_id
  AND uni.validAt(p, 'effective_from', 'effective_to', datetime('2024-01-01'))
RETURN p.amount

Temporal edges:

MATCH (e:Employee)-[r:WORKS_IN]->(d:Department)
WHERE uni.validAt(r, 'start_date', 'end_date', datetime($as_of_date))
RETURN e.name, d.name

Open-ended validity (NULL end date):

// Records with NULL valid_to are considered currently valid
MATCH (m:Membership)
WHERE m.valid_to IS NULL
  OR m.valid_to > datetime()
RETURN m

DELETE Clause¶

Remove nodes and relationships from the graph.

Deleting Relationships¶

// Delete a specific relationship
MATCH (a:Author)-[r:AUTHORED]->(p:Paper)
WHERE a.name = 'Alice' AND p.title = 'Old Paper'
DELETE r

// Delete all relationships of a type
MATCH ()-[r:DEPRECATED]->()
DELETE r

Deleting Nodes¶

// Delete a node (must have no relationships)
MATCH (p:Paper {title: 'Deleted Paper'})
DELETE p

// Delete node and all its relationships (DETACH)
MATCH (p:Paper {title: 'Deleted Paper'})
DETACH DELETE p

// Delete multiple nodes
MATCH (p:Paper)
WHERE p.year < 1990
DETACH DELETE p

Conditional Deletion¶

// Delete only if condition is met
MATCH (p:Paper)
WHERE p.citations = 0 AND p.year < 2000
DETACH DELETE p
RETURN COUNT(*) AS deleted_count

UNION Clause¶

Combine results from multiple queries.

UNION (Distinct)¶

// Combine results, removing duplicates
MATCH (a:Author {affiliation: 'MIT'})
RETURN a.name AS name
UNION
MATCH (a:Author {affiliation: 'Stanford'})
RETURN a.name AS name

UNION ALL (Keep Duplicates)¶

// Combine results, keeping all rows
MATCH (p:Paper)-[:CITES]->(:Paper {title: 'Paper A'})
RETURN p.title AS title
UNION ALL
MATCH (p:Paper)-[:CITES]->(:Paper {title: 'Paper B'})
RETURN p.title AS title

Multi-way UNION¶

// Combine multiple queries
MATCH (a:Author) WHERE a.h_index > 50
RETURN a.name AS name, 'high_impact' AS category
UNION
MATCH (a:Author) WHERE a.papers > 100
RETURN a.name AS name, 'prolific' AS category
UNION
MATCH (a:Author) WHERE a.citations > 10000
RETURN a.name AS name, 'highly_cited' AS category

WITH RECURSIVE Clause¶

Execute recursive queries using Common Table Expressions (CTEs).

Basic Recursive Query¶

// Find all transitive citations (papers cited by cited papers)
WITH RECURSIVE citation_chain AS (
  // Base case: direct citations
  MATCH (p:Paper {title: 'Root Paper'})-[:CITES]->(cited:Paper)
  RETURN cited
  UNION
  // Recursive case: citations of citations
  MATCH (c:citation_chain)-[:CITES]->(next:Paper)
  RETURN next
)
MATCH (p:citation_chain)
RETURN p.title

Recursive Path Finding¶

// Find all ancestors in a hierarchy
WITH RECURSIVE ancestors AS (
  MATCH (n:Category {name: 'Machine Learning'})
  RETURN n
  UNION
  MATCH (a:ancestors)-[:PARENT]->(parent:Category)
  RETURN parent
)
MATCH (a:ancestors)
RETURN a.name AS category_hierarchy

EXPLAIN Clause¶

Inspect the query execution plan without running the query.

Basic EXPLAIN¶

// Show query plan
EXPLAIN MATCH (p:Paper)-[:CITES]->(cited:Paper)
WHERE p.year > 2020
RETURN cited.title, COUNT(*) AS citation_count
ORDER BY citation_count DESC
LIMIT 10

Understanding the Plan¶

The EXPLAIN output shows: - Scan operations: How data is accessed (index scan, full scan) - Filter operations: Where predicates are applied - Join operations: How patterns are matched - Aggregation: Grouping and aggregation steps - Sort/Limit: Final ordering and pagination - Index usage: Which indexes are used and why - Cost estimates: Estimated rows and query cost

EXPLAIN Output Example¶

Plan:
├─ Scan: Paper
│  ├─ Filter: year > 2020
│  │  └─ Index: paper_year_btree (BTREE) ✓
│  ├─ Estimated rows: 500
│  └─ Cost: 12.5
├─ Expand: CITES (OUTGOING)
│  ├─ Estimated paths: 2500
│  └─ Cost: 125.0
└─ Target: Paper
   └─ Estimated rows: 2500

Index Usage:
  ✓ Paper.year → paper_year_btree (BTREE)
  ✗ Paper.venue → paper_venue_idx (not in filter)

Total estimated cost: 137.5

PROFILE Clause¶

Execute the query with runtime statistics collection.

Basic PROFILE¶

// Execute and collect statistics
PROFILE MATCH (p:Paper)-[:CITES]->(cited:Paper)
WHERE p.year > 2020
RETURN cited.title, COUNT(*) AS citation_count
ORDER BY citation_count DESC
LIMIT 10

PROFILE Output¶

PROFILE returns detailed execution metrics:

Execution Profile:
├─ Scan: Paper
│  ├─ Actual rows: 423
│  ├─ Time: 12.3ms
│  └─ Index hits: 423
├─ Expand: CITES
│  ├─ Actual paths: 1892
│  └─ Time: 45.6ms
└─ Project
   ├─ Actual rows: 1892
   └─ Time: 1.2ms

Summary:
  Total time: 59.1ms
  Peak memory: 2.4MB
  Rows returned: 1892

Using PROFILE for Optimization¶

// Compare two query approaches
PROFILE MATCH (a:Author)-[:AUTHORED]->(p:Paper)
WHERE a.name = 'Alice' AND p.year > 2020
RETURN p.title

// vs

PROFILE MATCH (p:Paper)
WHERE p.year > 2020
MATCH (a:Author {name: 'Alice'})-[:AUTHORED]->(p)
RETURN p.title

Common Query Patterns¶

Find Connected Nodes¶

MATCH (start:Paper {title: 'Attention Is All You Need'})
MATCH (start)-[:CITES]->(cited)
RETURN cited.title, cited.year
ORDER BY cited.year DESC

Bidirectional Relationships¶

// Papers that cite each other
MATCH (a:Paper)-[:CITES]->(b:Paper)-[:CITES]->(a)
RETURN a.title, b.title

Shortest Path (Future)¶

// Shortest path between two nodes
MATCH path = shortestPath((a:Author {name: 'Alice'})-[:COAUTHOR*]-(b:Author {name: 'Bob'}))
RETURN path

Degree Counting¶

// Count outgoing relationships
MATCH (p:Paper)-[c:CITES]->()
RETURN p.title, COUNT(c) AS out_degree
ORDER BY out_degree DESC

Top-K with Ties¶

MATCH (p:Paper)
WITH p, p.citations AS cites
ORDER BY cites DESC
LIMIT 10
RETURN p.title, cites

Graph Algorithms¶

Uni provides 36 high-performance graph algorithms available via the algo namespace.

Centrality Algorithms¶

Algorithm	Procedure	Description
PageRank	`algo.pageRank`	Importance based on incoming links
Betweenness	`algo.betweenness`	Nodes on shortest paths
Closeness	`algo.closeness`	Average distance to all nodes
Degree Centrality	`algo.degreeCentrality`	Connection count
Harmonic Centrality	`algo.harmonicCentrality`	Harmonic mean distance
Eigenvector Centrality	`algo.eigenvectorCentrality`	Influence via neighbors
Katz Centrality	`algo.katzCentrality`	Weighted path influence

// PageRank
CALL algo.pageRank(['Paper'], ['CITES'])
YIELD nodeId, score
RETURN nodeId, score
ORDER BY score DESC

// Betweenness Centrality
CALL algo.betweenness(['Author'], ['COAUTHOR'], true, 100)
YIELD nodeId, score

// Closeness Centrality
CALL algo.closeness(['Station'], ['CONNECTS'])
YIELD nodeId, score

Community Detection Algorithms¶

Algorithm	Procedure	Description
Louvain	`algo.louvain`	Modularity-based community detection
Label Propagation	`algo.labelPropagation`	Fast community detection
WCC	`algo.wcc`	Weakly connected components
SCC	`algo.scc`	Strongly connected components
K-Core	`algo.kCore`	K-core decomposition
Triangle Count	`algo.triangleCount`	Count triangles per node

// Louvain
CALL algo.louvain(['User'], ['INTERACTS'])
YIELD nodeId, communityId

// Label Propagation
CALL algo.labelPropagation(['User'], ['INTERACTS'])
YIELD nodeId, communityId

// Weakly Connected Components
CALL algo.wcc(['User'], ['INTERACTS'])
YIELD nodeId, componentId

// Strongly Connected Components
CALL algo.scc(['Task'], ['DEPENDS_ON'])
YIELD nodeId, componentId

// K-Core Decomposition
CALL algo.kCore(['User'], ['FRIEND'], 3)
YIELD nodeId, coreNumber

// Triangle Count
CALL algo.triangleCount(['User'], ['FRIEND'])
YIELD nodeId, triangleCount

Path Finding Algorithms¶

Algorithm	Procedure	Description
Dijkstra	`algo.dijkstra`	Shortest weighted path
Bellman-Ford	`algo.bellmanFord`	Shortest path with negative weights
A*	`algo.astar`	Heuristic shortest path
Bidirectional Dijkstra	`algo.bidirectionalDijkstra`	Two-way shortest path
K-Shortest Paths	`algo.kShortestPaths`	Multiple shortest paths
All Simple Paths	`algo.allSimplePaths`	All paths between nodes
APSP	`algo.allPairsShortestPath`	All pairs shortest path

// Dijkstra shortest path
CALL algo.dijkstra(['Station'], ['CONNECTS'], $startId, $endId, 'distance')
YIELD path, cost

// K-Shortest Paths
CALL algo.kShortestPaths(['Station'], ['CONNECTS'], $startId, $endId, 5)
YIELD path, cost

// All Pairs Shortest Path
CALL algo.allPairsShortestPath(['Station'], ['CONNECTS'])
YIELD sourceNodeId, targetNodeId, distance

Similarity & Traversal Algorithms¶

Algorithm	Procedure	Description
Node Similarity	`algo.nodeSimilarity`	Jaccard similarity
Random Walk	`algo.randomWalk`	Random graph traversal
Maximal Cliques	`algo.maximalCliques`	Find all maximal cliques
Graph Coloring	`algo.graphColoring`	Vertex coloring

// Node Similarity (Jaccard)
CALL algo.nodeSimilarity(['Product'], ['PURCHASED'])
YIELD node1, node2, similarity

// Random Walk
CALL algo.randomWalk(['Page'], ['LINKS'], 10, 5)
YIELD path

Structural Analysis Algorithms¶

Algorithm	Procedure	Description
Topological Sort	`algo.topologicalSort`	DAG ordering
Cycle Detection	`algo.cycleDetection`	Find cycles
Bipartite Check	`algo.bipartiteCheck`	Test bipartiteness
Bridges	`algo.bridges`	Find bridge edges
Articulation Points	`algo.articulationPoints`	Find cut vertices
Elementary Circuits	`algo.elementaryCircuits`	Find all simple cycles

// Topological sort (for DAGs)
CALL algo.topologicalSort(['Task'], ['DEPENDS_ON'])
YIELD nodeId, order

// Find articulation points
CALL algo.articulationPoints(['Router'], ['CONNECTS'])
YIELD nodeId

Flow & Matching Algorithms¶

Algorithm	Procedure	Description
Maximum Matching	`algo.maxMatching`	Maximum cardinality matching
Minimum Spanning Tree	`algo.mst`	MST using Prim/Kruskal
Dinic's Algorithm	`algo.dinic`	Maximum flow
Ford-Fulkerson	`algo.fordFulkerson`	Maximum flow

// Minimum Spanning Tree
CALL algo.mst(['City'], ['ROAD'], 'distance')
YIELD edge, cost

// Maximum Matching
CALL algo.maxMatching(['Worker', 'Job'], ['CAN_DO'])
YIELD node1, node2

Schema DDL¶

Uni supports Data Definition Language (DDL) statements for managing schema.

Create Label (Vertex Type)¶

// Create a label with properties
CREATE LABEL Paper (
  title STRING NOT NULL,
  year INT32,
  abstract STRING,
  embedding VECTOR(768)
)

// Create label with default values
CREATE LABEL Author (
  name STRING NOT NULL,
  h_index INT32 DEFAULT 0,
  active BOOLEAN DEFAULT true
)

Create Edge Type¶

// Create edge type with source/destination constraints
CREATE EDGE TYPE AUTHORED FROM Author TO Paper (
  position INT32,
  corresponding BOOLEAN DEFAULT false
)

// Edge type between multiple label types
CREATE EDGE TYPE COLLABORATES FROM Author TO Author (
  papers_count INT32
)

Alter Schema¶

// Add a property to an existing label
ALTER LABEL Paper ADD abstract STRING

// Drop a property
ALTER LABEL Paper DROP deprecated_field

// Rename a property
ALTER LABEL Paper RENAME old_name TO new_name

Drop Schema Elements¶

// Drop a label (must have no vertices)
DROP LABEL TempLabel

// Drop an edge type
DROP EDGE TYPE OLD_RELATIONSHIP

Constraints¶

Define and manage data integrity constraints.

Create Constraints¶

// Unique constraint
CREATE CONSTRAINT paper_doi_unique
FOR (p:Paper) REQUIRE p.doi IS UNIQUE

// Existence constraint (property must be present)
CREATE CONSTRAINT author_name_exists
FOR (a:Author) REQUIRE a.name IS NOT NULL

// Check constraint (custom validation)
CREATE CONSTRAINT paper_year_valid
FOR (p:Paper) REQUIRE p.year >= 1900 AND p.year <= 2100

Show Constraints¶

// List all constraints
SHOW CONSTRAINTS

// Filter by label
SHOW CONSTRAINTS FOR :Paper

Drop Constraints¶

DROP CONSTRAINT paper_doi_unique

Database Management¶

Administrative commands and procedures for database maintenance.

BACKUP¶

Create a backup of the database to a specified destination.

// Backup to local path
BACKUP TO './backups/backup_2026_01_17'

// Backup to S3
BACKUP TO 's3://my-bucket/backups/backup_2026_01_17'

// Backup with options
BACKUP TO './backups/full' WITH incremental = false, compress = true

VACUUM¶

Reclaim storage space by removing deleted data and compacting files.

// Perform vacuum operation
VACUUM

// Note: VACUUM may take time on large databases
// and temporarily increases I/O load

CHECKPOINT¶

Force a checkpoint to flush all pending writes to durable storage.

// Force checkpoint
CHECKPOINT

// Useful before taking external backups
// or when durability guarantee is needed immediately

Compaction¶

// Trigger storage compaction
CALL db.compact()
YIELD fragmentsCompacted, bytesReclaimed, duration

// Check compaction status
CALL db.compactionStatus()
YIELD status, progress, estimatedRemaining

Snapshots¶

// Create a named snapshot
CALL db.snapshot.create('before_migration')
YIELD name, created, size

// List available snapshots
CALL db.snapshot.list()
YIELD name, created, size

// Restore to a snapshot
CALL db.snapshot.restore('before_migration')

Schema Introspection¶

// List all labels
CALL db.labels()
YIELD label, propertyCount, nodeCount, indexCount
RETURN label, nodeCount

// List all relationship types
CALL db.relationshipTypes()
YIELD type, sourceLabels, targetLabels, propertyCount
RETURN type

// List all edge types (alias)
CALL db.edgeTypes()
YIELD type, sourceLabels, targetLabels, propertyCount

// List all indexes
CALL db.indexes()
YIELD name, type, label, properties, state
WHERE type = 'VECTOR'
RETURN name, label

// List all constraints
CALL db.constraints()
YIELD name, type, label, properties, enabled
RETURN name, type

// Get detailed label info
CALL db.schema.labelInfo('Paper')
YIELD property, dataType, nullable, indexed, unique
RETURN property, dataType

Schema DDL Procedures¶

Create and modify schema at runtime via procedures:

// Create a new label
CALL db.createLabel('Product', {
  properties: {
    name: { type: 'STRING', nullable: false },
    price: { type: 'FLOAT64' },
    embedding: { type: 'VECTOR', dimensions: 128 }
  }
})

// Create an edge type
CALL db.createEdgeType('PURCHASED', ['Customer'], ['Product'], {
  properties: {
    quantity: { type: 'INT32' },
    timestamp: { type: 'TIMESTAMP' }
  }
})

// Create an index
CALL db.createIndex('Product', 'name', { type: 'BTREE' })

// Create a vector index
CALL db.createIndex('Product', 'embedding', {
  type: 'VECTOR',
  metric: 'cosine',
  index_type: 'hnsw',
  m: 16,
  ef_construction: 200
})

// Create a constraint
CALL db.createConstraint('Product', 'UNIQUE', ['sku'])

// Drop operations
CALL db.dropLabel('TempLabel')
CALL db.dropEdgeType('OLD_RELATIONSHIP')
CALL db.dropIndex('Product', 'old_index')
CALL db.dropConstraint('Product', 'constraint_name')

CRDT Properties¶

Uni supports CRDT (Conflict-free Replicated Data Type) properties for distributed, eventually-consistent data. CRDT properties can be defined in the schema and manipulated via Cypher.

Schema Definition¶

Define CRDT properties in your schema:

{
  "properties": {
    "Paper": {
      "title": { "type": "String", "nullable": false },
      "view_count": { "type": "Crdt", "crdt_type": "GCounter" },
      "tags": { "type": "Crdt", "crdt_type": "ORSet" },
      "metadata": { "type": "Crdt", "crdt_type": "LWWMap" }
    }
  }
}

Supported CRDT Types¶

Type	Description	Cypher Operations
`GCounter`	Grow-only counter	Increment only
`GSet`	Grow-only set	Add only
`ORSet`	Add/remove set (add-wins)	Add, remove
`LWWRegister`	Single value (last-writer-wins)	Set value
`LWWMap`	Key-value map (per-key LWW)	Put, remove keys
`Rga`	Ordered sequence	Insert, delete at position

Querying CRDT Properties¶

// Read CRDT values (returns materialized value)
MATCH (p:Paper {id: 'paper_001'})
RETURN p.view_count, p.tags

// GCounter returns total count
// ORSet returns list of visible elements
// LWWMap returns map of non-tombstoned entries

Updating CRDT Properties¶

CRDT updates use special syntax to ensure conflict-free merging:

// Increment a GCounter
MATCH (p:Paper {id: 'paper_001'})
SET p.view_count = crdt.increment(p.view_count, 1)

// Add to an ORSet
MATCH (p:Paper {id: 'paper_001'})
SET p.tags = crdt.add(p.tags, 'machine-learning')

// Remove from an ORSet
MATCH (p:Paper {id: 'paper_001'})
SET p.tags = crdt.remove(p.tags, 'deprecated-tag')

// Update LWWMap
MATCH (p:Paper {id: 'paper_001'})
SET p.metadata = crdt.put(p.metadata, 'reviewed', true)

CRDT Functions¶

Function	CRDT Types	Description
`crdt.increment(counter, n)`	GCounter	Increment by n
`crdt.add(set, elem)`	GSet, ORSet	Add element
`crdt.remove(set, elem)`	ORSet	Remove element
`crdt.set(register, value)`	LWWRegister	Set value
`crdt.put(map, key, value)`	LWWMap	Put key-value
`crdt.delete(map, key)`	LWWMap	Delete key
`crdt.insert(rga, index, elem)`	Rga	Insert at position
`crdt.deleteAt(rga, index)`	Rga	Delete at position

Distributed Sync¶

CRDT properties enable conflict-free synchronization across distributed nodes:

┌─────────────┐         ┌─────────────┐
│   Node A    │◄───────►│   Node B    │
│             │         │             │
│ increment   │         │ increment   │
│ view_count  │         │ view_count  │
│    +5       │         │    +3       │
└──────┬──────┘         └──────┬──────┘
       │                       │
       └───────────┬───────────┘
                   │
                   ▼
            ┌─────────────┐
            │   Merged    │
            │ view_count  │
            │     = 8     │
            └─────────────┘

For detailed CRDT semantics and merge behavior, see CRDT Types.

Cypher Feature Status¶

Feature	Status
MATCH (basic patterns)	Stable
WHERE (comparisons, boolean)	Stable
RETURN, ORDER BY, LIMIT, SKIP	Stable
Aggregations (COUNT, SUM, AVG, MIN, MAX, COLLECT)	Stable
Window Functions (OVER clause)	Stable
Scalar Functions (40+ functions)	Stable
CREATE (nodes and relationships)	Stable
WITH clause	Stable
UNWIND	Stable
CALL procedures	Stable
Index management	Stable
JSON Full-Text Search (CONTAINS)	Stable
Variable-length paths (`*1..3`)	Stable
MERGE	Stable
SET / REMOVE	Stable
DELETE / DETACH DELETE	Stable
UNION / UNION ALL	Stable
WITH RECURSIVE (CTEs)	Stable
EXPLAIN (with index usage)	Stable
PROFILE (runtime statistics)	Stable
Session Variables (`$session.*`)	Stable
Temporal Queries (`uni.validAt`, `VALID_AT`)	Stable
Schema DDL (CREATE/ALTER/DROP LABEL)	Stable
Schema DDL Procedures (`db.createLabel`, etc.)	Stable
Constraints (UNIQUE, EXISTS, CHECK)	Stable
Composite Key Constraints	Stable
Graph Algorithms (36 algorithms)	Stable
CRDT Properties	Stable
Database Management (BACKUP, VACUUM, CHECKPOINT)	Stable
Snapshots (create, list, restore)	Stable
Schema Introspection (db.labels, db.indexes, etc.)	Stable
Inverted Index (ANY IN pattern)	Stable
OPTIONAL MATCH	Planned
Subqueries	Planned

Next Steps¶

Vector Search — Semantic similarity queries
Data Ingestion — Bulk import and streaming writes
Performance Tuning — Query optimization strategies
Troubleshooting — Common issues and solutions