Streamlining infrastructure monitoring

Tencent consolidated nine backend tools into SurrealDB to build a graph-first monitoring platform managing 8 million nodes and 50 million edges at 10,000+ QPS.

IndustryTechnology

OutcomeSimplified stack from 9 tools to one to build a unified graph-first monitoring platform for real-time incident analysis

01 |KEY CHALLENGES

What they needed to solve

The technical and operational hurdles the team faced before adopting SurrealDB.

Fragmented toolchain

A patchwork of nine systems including MySQL, Elasticsearch, MongoDB, Dgraph, and Flink created operational complexity.

Decision overhead for analysts

Users had to choose the right storage or compute engine before answering any question, slowing investigations.

High operational cost

More systems meant more upgrades, failure modes, governance enforcement, and ongoing maintenance burden.

Graph-shaped monitoring workflows

The team needed to model process behaviour, parent-child derivations, and traffic relationships as a continuously updated graph.

02 |SOLUTIONS

How SurrealDB helped

The architecture decisions and SurrealDB capabilities that addressed each challenge.

Multi-model context graph

Consolidated nine tools into a single system that natively supports document data and graph relationships.

Native graph fault analysis

Processes become nodes, relationships become edges, enabling multi-hop dependency traversal during incidents.

Versioned temporal data

SurrealKV enables point-in-time investigations and replay of how process trees evolve across graph states.

Production-scale deployment

A cluster of 9 storage nodes and 6 compute nodes sustains 10,000+ QPS across 8 million nodes and 50 million edges.

03 |RESULTS

The impact

Measurable outcomes delivered after moving to SurrealDB.

9 1

Consolidated toolchain

Replaced MySQL, Elasticsearch, MongoDB, Dgraph, Flink, and four other tools with a single platform.

FEWER TOOLS

Simplified operations

Fewer upgrades, less governance, fewer cross-system inconsistencies, and a smaller failure surface.

10K+

QPS

Production-grade scale

Manages 8 million nodes and 50 million edges with continuous real-time graph updates.

50M+

EDGES

Knowledge graph ready

50 million edges across 8 million nodes form the foundation for a broader knowledge graph.

MORE CASE STUDIES

See what other teams have shipped

From knowledge graphs to AI assistants - how enterprise teams are building on the context layer.

Samsung

Unlocking insights with knowledge graphs

Samsung Ads uses SurrealDB to build dynamic, real-time knowledge graphs for smarter campaign execution - collapsing three legacy data stores into one.

Read case study

Verizon

AI assistant empowering 10,000 technicians

Verizon uses SurrealDB to power a generative AI assistant for 10,000 field technicians, delivering instant access to documentation, outage updates, and workflows.

Read case study

Tencent