Neo4j Kafka Skill

Neo4j Kafka Skill

When to Use

• Writing Kafka events into Neo4j (sink connector — Cypher, Pattern, CDC, CUD strategies)
• Streaming Neo4j changes to Kafka topics (source connector — CDC or query-based)
• Querying Neo4j change events natively without Kafka (db.cdc.query)
• Configuring Confluent Cloud managed Neo4j sink connector
• Setting up schema registry (Avro/JSON Schema) for typed Kafka messages
• Enabling exactly-once semantics or dead-letter queue on sink

When NOT to Use

• Cypher query authoring → neo4j-cypher-skill
• Bulk CSV/JSON file import → neo4j-import-skill
• GDS algorithms → neo4j-gds-skill
• Live app write patterns → neo4j-cypher-skill

Decision Table — Which connector strategy?

Prerequisites

• Neo4j Connector for Kafka ≥ 5.5.2 (download from neo4j.com/labs/kafka or Confluent Hub) — 5.5.2 processes incoming messages per partition instead of per topic; 5.5.1 forces static labels in generated queries
• Kafka Connect ≥ 3.x or Confluent Platform ≥ 7.x
• For CDC source/sink: Neo4j 5.13+ Enterprise Edition, AuraDB Business Critical, or AuraDB VDC
• For query source: any Neo4j edition
• Java 11+

Core Connection Config (all connectors)

{
  "neo4j.uri": "neo4j+s://your-instance.databases.neo4j.io:7687",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "${file:/opt/secrets.properties:neo4j.password}",
  "neo4j.database": "neo4j"
}

Authentication types: BASIC | BEARER | KERBEROS | CUSTOM | NONE

Never hardcode passwords — use Kafka Connect secrets provider (${file:...} or ${env:...}).

Sink Connector

Strategy 1 — Cypher

Connector auto-prepends UNWIND $events AS __value — write query using __value:

{
  "connector.class": "org.neo4j.connectors.kafka.sink.Neo4jConnector",
  "topics": "person-creates,person-updates",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "secret",
  "neo4j.cypher.topic.person-creates":
    "MERGE (p:Person {id: __value.id}) SET p += __value.properties",
  "neo4j.cypher.topic.person-updates":
    "MATCH (p:Person {id: __value.id}) SET p += __value.properties",
  "neo4j.cypher.bind-value-as": "__value",
  "neo4j.cypher.bind-key-as": "__key",
  "neo4j.cypher.bind-header-as": "__header"
}

MERGE pattern — idempotent upsert:

MERGE (p:Person {id: __value.id})
ON CREATE SET p.createdAt = datetime(), p += __value.properties
ON MATCH  SET p.updatedAt = datetime(), p += __value.properties

Strategy 2 — Pattern

No Cypher needed — map message fields to graph via pattern syntax:

{
  "neo4j.pattern.topic.users": "(:User{!userId, name, email})",
  "neo4j.pattern.topic.friendships":
    "(:User{!userId: from.userId})-[:KNOWS{since}]->(:User{!userId: to.userId})"
}

Pattern rules:

• !prop = key property (used for MERGE)
• prop: field.path = map from nested message field
• * = map all message fields
• -prop = exclude property (cannot mix with inclusions)

Strategy 3 — CDC (mirror another Neo4j)

{
  "neo4j.cdc.schema.topics": "neo4j-cdc-events"
}

Or with source-id tracking (stores elementId as property):

{
  "neo4j.cdc.source-id.topics": "neo4j-cdc-events",
  "neo4j.cdc.source-id.label-name": "SourceEvent",
  "neo4j.cdc.source-id.property-name": "sourceId"
}

Exactly-Once Semantics (EOS)

Requires: connector ≥ 5.3.0 (Cypher/Pattern/CDC strategies), connector ≥ 5.3.1 (CUD strategy), Kafka broker EOS support, and a NODE KEY constraint.

Step 1 — Create constraint:

CREATE CONSTRAINT kafka_offset_key IF NOT EXISTS
FOR (n:__KafkaOffset)
REQUIRE (n.strategy, n.topic, n.partition) IS NODE KEY;

Step 2 — Add to connector config:

{
  "neo4j.eos-offset-label": "__KafkaOffset"
}

Without EOS: connector provides at-least-once — write idempotent Cypher (MERGE, not CREATE).

Error Handling / DLQ

{
  "errors.tolerance": "all",
  "errors.log.enable": "true",
  "errors.log.include.messages": "true",
  "errors.deadletterqueue.topic.name": "neo4j-dlq",
  "errors.deadletterqueue.context.headers.enable": "true",
  "errors.deadletterqueue.topic.replication.factor": "3"
}

errors.tolerance=none (default) — stops on first error. Use all + DLQ for production.

Source Connector

CDC-Based Source (recommended, Neo4j 5.13+)

{
  "connector.class": "org.neo4j.connectors.kafka.source.Neo4jConnector",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "secret",
  "neo4j.source-strategy": "CDC",
  "neo4j.start-from": "NOW",
  "neo4j.cdc.poll-interval": "1s",
  "neo4j.cdc.poll-duration": "5s",
  "neo4j.cdc.topic.person-creates.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-creates.patterns.0.operation": "CREATE",
  "neo4j.cdc.topic.person-updates.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-updates.patterns.0.operation": "UPDATE",
  "neo4j.cdc.topic.person-deletes.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-deletes.patterns.0.operation": "DELETE"
}

neo4j.start-from options: NOW | EARLIEST | a specific cursor string

Multiple patterns per topic — indexed 0, 1, 2...:

{
  "neo4j.cdc.topic.all-changes.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.all-changes.patterns.1.pattern": "(:Organization)"
}

Cursor warning: after DB restore from backup, CDC cursors are invalidated. Reconfigure neo4j.start-from.

Query-Based Source (legacy / any edition)

{
  "neo4j.source-strategy": "QUERY",
  "neo4j.query": "MATCH (p:Person) WHERE p.updatedAt > $lastCheck RETURN p.id AS id, p.name AS name, p.updatedAt AS updatedAt",
  "neo4j.query.streaming-property": "updatedAt",
  "neo4j.query.topic": "person-changes",
  "neo4j.query.polling-interval": "5s",
  "neo4j.query.polling-duration": "10s"
}

$lastCheck is auto-injected by connector. neo4j.query.streaming-property must be returned by the query and should be indexed.

Native CDC API (no Kafka required)

Requires: Neo4j 5.13+ Enterprise, AuraDB BC, or AuraDB VDC.

Enable CDC first (self-managed — set in neo4j.conf):

db.cdc.enabled=true

On Aura: enabled by default on eligible tiers.

Cursor Bootstrap

// Get cursor for "right now" — start tracking from this point forward
CALL db.cdc.current() YIELD id RETURN id AS cursor;

// Get earliest available cursor (replay from history start)
CALL db.cdc.earliest() YIELD id RETURN id AS cursor;

Cursors are exclusive: db.cdc.current() does NOT include the transaction it points to.

Query Changes

// All changes since cursor
CALL db.cdc.query($cursor, []) YIELD id, txId, seq, metadata, event
RETURN id, txId, seq, metadata, event
ORDER BY txId, seq;

Filtered — nodes with label Person, CREATE only:

CALL db.cdc.query($cursor, [
  {select: 'n', labels: ['Person'], operation: 'c'}
]) YIELD id, txId, seq, event
RETURN id, event.state.after.properties AS newProps
ORDER BY txId, seq;

Filtered — specific relationship type with property change tracking:

CALL db.cdc.query($cursor, [
  {select: 'r', type: 'KNOWS', changesTo: ['since', 'strength']}
]) YIELD id, txId, seq, event
RETURN id, event.state.before AS before, event.state.after AS after;

Selector Reference

Event Structure

{
  id:       STRING,           // cursor for this event (use as next $cursor)
  txId:     INTEGER,          // transaction ID
  seq:      INTEGER,          // ordering within transaction
  metadata: {
    executingUser:     STRING,
    authenticatedUser: STRING,
    captureMode:       STRING,  // "DIFF" or "FULL"
    txStartTime:       DATETIME,
    txCommitTime:      DATETIME,
    txMetadata:        MAP
  },
  event: {
    elementId:  STRING,
    eventType:  STRING,         // "n" or "r"
    operation:  STRING,         // "c", "u", "d"
    labels:     [STRING],       // nodes only
    type:       STRING,         // relationships only
    keys:       MAP,
    state: {
      before: { properties: MAP },  // null on CREATE
      after:  { properties: MAP }   // null on DELETE
    }
  }
}

Cursor-Loop Pattern (Python)

from neo4j import GraphDatabase

driver = GraphDatabase.driver("neo4j+s://...", auth=("neo4j", "password"))

def poll_changes(cursor: str, selectors: list) -> tuple[list, str]:
    records, _, _ = driver.execute_query(
        "CALL db.cdc.query($cursor, $selectors) YIELD id, txId, seq, event "
        "RETURN id, txId, seq, event ORDER BY txId, seq",
        cursor=cursor, selectors=selectors,
        database_="neo4j"
    )
    events = [r.data() for r in records]
    # Advance cursor to last event id; keep current if no events
    next_cursor = events[-1]["id"] if events else cursor
    return events, next_cursor

# Bootstrap
with driver.session(database="neo4j") as s:
    cursor = s.run("CALL db.cdc.current() YIELD id RETURN id").single()["id"]

selectors = [{"select": "n", "labels": ["Person"]}]

import time
while True:
    events, cursor = poll_changes(cursor, selectors)
    for e in events:
        print(e["event"]["operation"], e["event"]["elementId"])
    time.sleep(1)

Confluent Cloud Managed Connector

Confluent Cloud hosts the Neo4j Sink connector as a fully managed service (no JAR upload needed).

Config differences vs self-managed:

• No connector.class field — selected in UI/API
• Credentials via Confluent Cloud secret manager or direct JSON
• Private endpoints supported (AWS PrivateLink, Azure Private Link, GCP PSC)
• Managed upgrades — pin connector version explicitly if needed

Required Confluent Cloud fields:

{
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "...",
  "kafka.api.secret": "...",
  "input.data.format": "JSON",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "..."
}

One strategy per topic — cannot mix Cypher and Pattern on same topic.

Schema Registry (Avro / JSON Schema)

Source connector always generates messages with schema support — must configure converters:

{
  "key.converter": "io.confluent.connect.avro.AvroConverter",
  "key.converter.schema.registry.url": "https://your-schema-registry",
  "value.converter": "io.confluent.connect.avro.AvroConverter",
  "value.converter.schema.registry.url": "https://your-schema-registry"
}

For JSON Schema:

{
  "value.converter": "io.confluent.connect.json.JsonSchemaConverter",
  "value.converter.schema.registry.url": "https://..."
}

Sink converter must match source — Avro sink cannot consume JSON schema source messages.

Common Errors

References

• Full connector config reference — all neo4j.* properties, defaults, types
• CDC API patterns — cursor loop, selector examples, event structure detail
• Neo4j Connector for Kafka docs
• CDC docs

Checklist

• CDC availability confirmed (Neo4j 5.13+ EE / Aura BC / VDC) if using CDC source or sink
• Uniqueness/NODE KEY constraints created before sink import (MERGE uses them)
• EOS constraint created if using neo4j.eos-offset-label
• Credentials via secrets provider — not hardcoded in config
• Cypher sink queries use MERGE (not CREATE) for idempotency
• errors.tolerance=all + DLQ configured for production sink
• Source: neo4j.query.streaming-property indexed
• Schema registry converters match on both source and sink sides
• After DB restore: CDC cursor reconfigured (neo4j.start-from)
• CDC cursor-loop: advance cursor only after successful processing