Advanced everything in Backend Dev, page 3
Modularizing routes with express.Router
Create a Router instance in users.js, attach routes to it, export it, then mount it under a base path with app.use in the main file.
http.Agent and connection pooling
The agent pools and keeps sockets alive, avoiding repeated TCP and TLS handshakes, controlled by keepAlive and maxSockets.
Backpressure in Node.js streams
Backpressure pauses the readable when the writable buffer fills, pipe and pipeline manage it automatically, pipeline also propagates errors and cleans up.
Scaling across cores with cluster and os
Os.cpus gives core count, the primary forks one worker per core, all workers share the listening port, the OS load-balances connections.
Async iterators and for await...of for streaming
Async iterators yield values lazily over time; for await...of consumes them sequentially with backpressure, keeping memory bounded.
Promise.all vs Promise.allSettled
All rejects on the first failure; allSettled always fulfills with a status/value or reason per input. Use allSettled when partial success is acceptable.
Bounded concurrency for many async requests
Chunk the array and await Promise.all per chunk, or run a fixed worker pool pulling from a shared index; cap in-flight requests.
Monorepo workspaces vs private npm packages
Workspaces give atomic cross-service changes and instant local linking but couple release cadence; private packages give versioned isolation but add publish overhead and version drift.
Diamond dependencies and nested node_modules
Npm hoists one version to the top and nests the conflicting version under the dependent package; both coexist on disk.
Circular dependencies in CommonJS modules
When A requires B which requires A, the cache returns A's partial exports; fields defined later are undefined at that moment.
Tuning HNSW for recall vs latency
ANN trades exactness for speed, and HNSW knobs M and efConstruction shape graph quality while efSearch trades query latency for recall at runtime.
TSM-Tree vs LSM-Tree storage engines
Both buffer writes in memory and flush sorted immutable files, but TSM organizes by series and time with columnar, heavily compressed blocks tuned for ordered appends and range scans.
Iceberg vs Delta Lake metadata and ACID
Iceberg uses a tree of metadata and manifest files with atomic pointer swaps and optimistic concurrency; Delta uses an ordered transaction log of JSON commits with optimistic concurrency.
The small files problem in data lakes
Many tiny files create per-file overhead and metadata pressure, hurting scans; fix via compaction, batching writes, and tuning partitioning.
Multi-region active-passive DR with Aurora
Async global replication to a passive region, promote and repoint traffic on failover, and fence the old primary to prevent split-brain.
Aurora vs Spanner architecture contrast
Aurora is single-writer with a shared distributed log-based storage and quorum, scaling reads; Spanner shards data with Paxos and TrueTime for global writes.
Fixing an ORM's inefficient aggregation query
Drop to raw SQL or a view for the heavy report, or restructure the ORM query and add indexes. Raw SQL is fast but couples to the schema; tuning keeps portability.
Unit of Work / Session pattern in ORMs
The Unit of Work tracks new, dirty, and deleted objects, then flushes them as one batched transaction at commit.
Defense-in-depth against SQL injection
Beyond parameterization, apply least-privilege accounts, stored procedures, input allowlisting, and monitoring to shrink blast radius.
Diagnosing degradation with normal CPU and memory
When CPU and memory look fine, sessions are waiting, not computing; examine wait statistics, lock and latch contention, I/O waits, and buffer pool hit ratio.
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