Search-Heavy Content Platform
Search-Heavy Applicationhigh complexity
Deterministic topology derived from YAML knowledge entities. Nodes represent workloads, datastores, patterns, and risk components. Edges show typed relationships with propagation direction.
13
Components
6
Connections
4
Failure Modes
1
Propagation Paths
Max exposure: high· 1 high-risk node in this topology
Topology Graph13 nodes · 6 edges
1 high-risk nodeClick a failure mode below to trace propagation
Workload
Datastore
Cache
Event stream
Pattern
Risk node
Risk path
Failure Propagation Trace
Topology Notes
- ·PostgreSQL is the system of record. All writes go to PostgreSQL first. Elasticsearch is always a derived, eventually consistent replica of the PostgreSQL data. No application logic should write to Elasticsearch directly without a corresponding PostgreSQL record.
- ·WAL CDC connector (e.g., Debezium) reads from PostgreSQL logical replication slot and publishes change events to Kafka. An Elasticsearch sink connector (or custom consumer) consumes from Kafka and performs bulk index operations. The CDC connector lag is a first-class SLA metric (target: < 10s under normal load).
- ·Redis caches search result pages and hot content objects. Cache keys are scoped to query parameters plus a content version token to enable targeted invalidation on content updates. TTL should be short (30–120s) for trending content queries and longer (10–30min) for stable catalog queries.
- ·Elasticsearch indexes use explicit field mappings: dynamic mapping must be disabled in production to prevent mapping explosion from user-generated content. All analyzed text fields must have explicit analyzer configuration (language-specific or custom).
- ·Index aliases provide the zero-downtime reindex capability. The application always queries through an alias (e.g., content_v1_alias), not directly against the versioned index name. Reindex to content_v2, then atomically flip the alias.