Node.js
Summary - Distributed systems
Combine CAP, consensus and tracing to write a distributed service.
Summary - Distributed systems
In this group we saw the core concepts of distributed systems: CAP theorem, consensus, distributed tracing, event sourcing and CQRS. Each shapes a distributed Node.js application.
CAP theorem forces a choice between consistency and availability during a partition. Consensus (Raft/Paxos) makes nodes agree on one value. Distributed tracing (OpenTelemetry) ties a request's path with a trace ID. Event sourcing builds state from immutable events. CQRS separates command and query models. In the final lesson we combine them in a report function.
The report function
The final task combines all distributed system concepts into one report. The distributedReport() function joins five info strings with ' | '. Output: CAP: AP | consensus: Raft | tracing: OpenTelemetry | event sourcing: replay | CQRS: split.
This report summarizes five core concepts in one string: the CAP choice (AP), the consensus algorithm (Raft), the tracing standard (OpenTelemetry), the event method (replay) and the CQRS split. Inside the function you place each piece in a variable, then join them with +. This lets you see what you learned on the console; you model the architecture without standing up a real distributed system.
Exercise Write the distributed systems report
Write function distributedReport(). Define const cap = 'CAP: AP', const cons = 'consensus: Raft', const trace = 'tracing: OpenTelemetry', const es = 'event sourcing: replay' and const cqrs = 'CQRS: split'. Return cap + ' | ' + cons + ' | ' + trace + ' | ' + es + ' | ' + cqrs. Print console.log(distributedReport()).
Show example solution
function distributedReport() {
const cap = 'CAP: AP';
const cons = 'consensus: Raft';
const trace = 'tracing: OpenTelemetry';
const es = 'event sourcing: replay';
const cqrs = 'CQRS: split';
return cap + ' | ' + cons + ' | ' + trace + ' | ' + es + ' | ' + cqrs;
}
console.log(distributedReport());
Test yourself
-
Which three guarantees does CAP theorem balance?
- Consistency, availability, partition tolerance
- Consistency, availability, speed
- Consistency, security, cost
- Speed, cost, security
Show answer
Consistency, availability, partition tolerance — CAP: consistency, availability, partition tolerance.
-
Match each concept to its role
- CAP
- Choice during partition
- Raft
- Consensus algorithm
- OpenTelemetry
- Tracing standard
- CQRS
- Command and query split
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