Top 10 Cypher Tuning Tips & Tricks

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Top 10 Cypher
(Tuning) Tips & Tricks
Michael Hunger,
Senior Director, User
Innovation

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Why should I care?
1 2
Efficiency Readability
3 4
Resources Network

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Dataset - (All of) StackOverflow
https://demo.neo4jlabs.com:7473
username/password/database: stackoverflow
51M nodes
124M relationships

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1. PROFILE

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1. Profile
• PROFILE and EXPLAIN show you the query plan
• All Operators – Index lookups, Expand, Projection, …
• Cardinality (rows) - multiplicative
• DB-Hits (measure of cost)
• Avoid Eager for Updates
• Shows Memory Usage
• Best if you can stream through and shortcut (e.g. LIMIT)
• avoid aggregation / sorting / collect if you can

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PROFILE
PROFILE
MATCH (t:Tag)<-[:TAGGED]-(q:Question)
RETURN t.name, count(*) AS c
ORDER BY c DESC LIMIT 10
// Cypher version: CYPHER 4.4,
// planner: COST,
// runtime: PIPELINED.
// 97726446 total db hits
// in 29387 ms

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2. Properties

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2. Properties
• Property reads are more expensive than they should be
• Avoid them if possible, or defer to the latest moment
• When you have narrowed down the data to what you want to return
• Elevate properties to labels or rel-types can speed up a lot
• E.g. boolean, status properties as labels
• Sub-Structure of rel-properties to Rel-Types (e.g. year of a datetime)
• Use indexes, properties can be read from the index (except for points)
which is much faster (need indicate type)

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PROFILE
PROFILE
MATCH (t:Tag)<-[:TAGGED]-(q:Question)
WITH t, count(*) AS c
RETURN t.name, c
// 97526566 total db hits
// in 29831 ms.

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3. Relationships

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Relationships
• Avoid traversing relationships if you can, each is O(1) but all are O(n)
• Always provide rel-type and direction
• Don’t traverse across supernodes, check “against” them
• Use get-degree (size(pattern)) without end label to get fast reads
• Elevate properties to rel-type for speedups
• Use relationship-indexes for global lookups or
• Add source/target key/property to index for node-centric indexes

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PROFILE
PROFILE
MATCH (t:Tag)
WITH t, size([p=(t)<-[:TAGGED]-()|p]) AS c
RETURN t.name, c
// 104911 total db hits in 195 ms

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4. Indexes & Constraints

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PROFILE
PROFILE
MATCH (t:Tag)
RETURN t.name, t.count AS c
// 157356 total db hits in 66 ms

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PROFILE
PROFILE
MATCH (t:Tag)
// type triggers index usage
WHERE t.count > 0
// 32 total db hits in 1 ms.

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Types & Uses of indexes and constraints
1 2
Lookup
Ranges
Type Scans
3 4
Sorting
Aggregation
Uniqueness
Existence

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Indexes & Constraints
• show indexes
• show constraints
• create <type> index/constraint <name> IF NOT EXISTS ON (x) FOR
(prop)
• todo docs

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Indexes & Constraints
• lookup
• lookup in list
• lookup range
• don't read from property store
• sorting / aggregation
• compound indexes (one range, rest equality)
◦ NOM type
• relationship(type) indexes
◦ global lookup by attribute / range
◦ simulate node-centric indexes index start/end-node id or property

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neo4j.com/docs/cypher-manual/current/query-tuning/using/
5. Query Hints

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Query Hints
• shouldn't be needed
• but planner is not smart enough (yet)
• use rarely, first try without
• USING INDEX [SEEK] - force index usage (also for relationships)
• USING JOIN ON - force hash join for two high-cardinality sides
◦ example with LDBC
• USING SCAN ON - force label scan if very selective label

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INDEX Hint
PROFILE
MATCH (t:Tag)
WHERE t.count > 0
USING INDEX :Tag(count)
// 32 total db hits in 1 ms.

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4. Var Length Queries

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4. Var Length Queries
• Traverse arbitrary length paths / trees
• Limit length if possible
• Pruning Var-Length Expand ( on distinct end nodes )
• Shortest Path ( can also be used as condition )

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Var Length Example
profile
MATCH (u:User {name:'meistermeier'})-
[:POSTED|ACCEPTED|ANSWERED*..5]-(u2:User)
RETURN count(u)
// 569.130 total db hits

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Var Length Example
profile
MATCH (u:User {name:'meistermeier'})-
[:POSTED|ACCEPTED|ANSWERED*..5]-(u2:User)
RETURN count(distinct u)
// 217.347 total db hits

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Triadic Closure Example
profile
MATCH (u:User {name:'meistermeier‘})
--()--(u2:User)
WHERE NOT (u)--(u2)
RETURN count(distinct u)
// 810 total db total db hits

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5. Fun with Lists

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5. Fun with Lists
• Like in Python
• [ elem IN list WHERE pred(elem) | expr(elem) ]
• Result is a list again
• Quantors
• all/single/none/any(x IN list WHERE pred(element))
• List into rows: UNWIND list AS elem
• Rows into list: collect(elem) AS list

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6. Map Projections

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5. Map Projections
• Idea borrowed from GraphQL
• Map expression - node/relationship/map as expression
• var { .* } // all properties
• var {.property1, .property2}
• var {value} // key = „value“
• var {name: expression} // any expression, incl. map/list
• collect map expression to get list of documents
• You can use map { .*, foo : null } to remove properties from a map
(e.g. for LOAD CSV)

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5. Map Projections
-[:POSTED]->()-[:ANSWERED]->(q)-[:TAGGED]->(tag)
WITH q, collect(tag.name) as tags
RETURN q { .title, .answers, .views, tags, } as question

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7. Pattern
Comprehensions

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6. Pattern Comprehensions
• Idea borrowed from GraphQL
• Pattern comprehension = like list comprehension just for patterns
• Pattern (can introduce new local variables)
• Filter
• Expression
• [ (pattern)-[of:SOME]->(nodes) WHERE filter | expression ]
• Result is list of the expression
• Expressions can be pattern comprehensions again or map expressions
• Not well planned (indexes etc)
• No sorting / pagination / pushdown

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6. Pattern Comprehensions
RETURN [(u)-[:POSTED]->()-[:ANSWERED]->(q) |
q { .title, .answers, .views }] as questions

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8. Subqueries

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6. Subqueries
• Added in 4.0/4.1
• CALL { WITH var MATCH … RETURN var2 } …
• Existential subqueries EXISTS {}
• Can not shadow variables in RETURN
• Dependent or independent
• Still participate in cardinality but can change it
• Properly planned, unlike pattern comprehensions -> can rewrite

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9. Batched Updates

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7. Batched Updates
• Avoid transaction sizes exceed available memory
• Update in smaller chunks e.g. 50k updates per tx
• (2/3G heap per 1M updates in a tx)
• 3 options
◦ Send chunks (list of dicts) from client: UNWIND $data AS row …
◦ CALL {} IN TRANSACTIONS [OF 50000 ROWS]
◦ APOC apoc.periodic.iterate
• USING PERIODIC COMMIT is deprecated
• Avoid the Eager

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7. Avoid the Eager
• To isolate dependent reads from writes
• Cypher injects an Eager operator that
• pulls ALL rows through that happen before the operator
• Which can use a lot of memory,
• and can disable transaction batching
• Avoided through:
• Multi-pass

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9. Might help to
• To isolate dependent reads from writes
• Cypher injects an Eager operator that
• pulls ALL rows through that happen before the operator
• Which can use a lot of memory, and can disable batching

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10. User Defined
Procedures & Functions

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10. User Defined Procedures & Functions
• Lego blocks to combine inside a Cypher query
• Procedure libraries like APOC cover a wide range of utilities (have a look)
• Refactoring
• Data integration
• Import
• Map/collection/datetime functions
• SHOW PROCEDURES / FUNCTIONS / call apoc.help(„text“)
• CALL procedure(arg1, arg2) YIELD col1, col2

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10. User Defined Procedures & Functions

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11. Upcoming – Path
Filters

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11. Upcoming – Path Filters
• Working towards GQL support
• Inline WHERE predicates for nodes, relationships (5.0)
• Future: predicates for pattern
• Quantifier for patterns / subpatterns

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Learn more
1 2
Docs
neo4j.com/docs/cypher-
manual/current/query-tuning/
Course
neo4j.com/graphacademy/training-cqt-40/
3 4
Refcard
neo4j.com/docs/cypher-refcard
Community
community.neo4j.com

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Q&A

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Thank you!
Contact us at
sales@neo4j.com

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