Search the practice set
126 questions, 454 terms and 400 topics in 20 areas.
60 results for “Application Performance Monitoring”
Application Performance Monitoring
Instrumentation inside the application that attributes latency and errors to specific code paths, queries and dependencies.
Connection Pool
A fixed set of reusable database connections shared by an application's requests, and one of the most common hidden capacity ceilings.
Application Discovery
Establishing what applications exist, what they depend on, who owns them and whether anyone uses them — the step whose absence makes every later step a guess.
Application Portfolio Management
Maintaining an inventory of every application with its owner, cost, business value and technical health, and using it to decide what to invest in, replace or retire.
Web Application Firewall
A filter in front of an application that inspects HTTP requests and blocks those matching known attack patterns — useful as a layer, dangerous as a substitute.
Bloom Filter
A compact probabilistic structure that answers "is this key definitely absent, or possibly present?" — no false negatives, tunable false positives.
Bloom Filter Cache Guard
Placing a Bloom filter in front of an expensive lookup so that keys which certainly do not exist never reach it.
Blue-Green Database Schema
The constraint that makes fast rollback actually work — both application versions must be able to run against one schema at the same time.
Burn Rate Alerting
Paging when the error budget is being consumed fast enough to matter, rather than when a component crosses a threshold.
Burstable Instance
An instance that provides a low baseline CPU allocation and accrues credits while idle, spendable for short periods of full performance.
Cache Invalidation
The problem of removing or refreshing cached data when the underlying source changes, and the reason caching is harder than it looks.
Caching Strategy
The chosen pattern for how a cache is populated, read and invalidated — cache-aside, read-through, write-through or write-behind.
Cardinality
The number of distinct time series produced by a metric, which is the product of the distinct values of all its labels — and the main driver of monitoring cost.
Cardinality Estimation
The planner's prediction of how many rows each step of a query will produce — the input that determines every other choice it makes.
Change Data Capture
Publishing a stream of a database's row-level changes by reading its replication log, without modifying the application that owns it.
Concurrency
The number of operations in progress at once — distinct from parallelism, which is how many are literally executing simultaneously.
Covering Index
An index that contains every column a query needs, so the query is answered from the index without reading the table at all.
DORA Metrics
Four measures of software delivery performance — deployment frequency, lead time for change, change failure rate, and time to restore service.
Database Index
A secondary structure that lets the engine find rows without scanning, trading write cost and storage for read speed.
Denormalisation
Deliberately duplicating data across records to make reads cheap, accepting the write-time cost of keeping copies in step.
Field-Level Encryption
Encrypting specific sensitive fields in the application before they reach the datastore, so the store never holds plaintext.
Foreign Key Constraint
A database-enforced rule that a referencing value must exist in the referenced table — referential integrity that no application bug can violate.
Golden Signals
The four measurements that cover most of what matters for a request-driven service: latency, traffic, errors and saturation.
Google Maps and Planetary-Scale Spatial Serving
Map serving is fast because almost nothing is computed on request — the world is precomputed into a pyramid of tiles, and space is indexed onto a one-dimensional curve.
Hedged Request
Sending a duplicate of a request to a second replica after a short delay and using whichever response returns first, to cut tail latency.
Horizontal vs Vertical Scaling
Adding more machines versus making one machine bigger — and the fact that vertical is underrated for stateful tiers.
Indexing Strategy
Choosing the set of indexes a table carries by working backwards from its actual queries, and accepting the write cost that each one adds.
Join Strategies
The three ways a database combines two row sets — nested loop, hash join and merge join — and the conditions under which each is correct.
Kubernetes Operator
A custom controller that encodes operational knowledge for a specific application, reconciling a custom resource towards a desired state the same way built-in controllers do.
Layered Architecture
Organising code into horizontal layers — presentation, application, domain, data — where each layer may only call the one beneath it.
Little's Law
In a stable system, the average number of items in it equals the arrival rate times the average time each spends in it — L = λW.
Design the network layout for a three-tier application in one cloud region. What are the decisions you cannot easily change later?
What the interviewer is testing Whether you know which network decisions are cheap and which are effectively permanent. This is a knowledge question with a clea
One availability zone becomes unavailable. Walk through what happens to a typical three-tier application and what you would have changed.
What survives and what does not Load balancer — regional, survives, and stops routing to targets in the failed zone once health checks fail. Note the detection
A dashboard query that took 200ms now takes 40 seconds. The table has grown to 200 million rows. Walk me through diagnosis and fix, including what you would not do.
What the interviewer is testing Whether you diagnose with evidence before changing anything, and whether you know the costs of the fixes you propose. Diagnosis,
A product catalogue page does 40,000 reads per second against a database that can serve 5,000. Walk me through the caching design, including what happens at 3 AM when the cache is empty.
What the interviewer is testing Whether you can design a cache including its failure modes, rather than saying "put Redis in front of it". The base design Cache
A table has 14 indexes and writes have become slow. How do you decide which to remove?
The approach 1. Get usage statistics, not opinions. Every major engine reports index scan counts — PostgreSQL's pg stat user indexes , SQL Server's sys.dm db in
Application Performance Monitoring
Attributing latency to code paths, queries and dependencies.
Application Architecture
The application estate as a designed portfolio rather than an accumulation.
Application Decomposition
Finding seams in a monolith, starting from the data.
Application Portfolio Management
Inventory, ownership, cost and health for every application.
Application Rationalisation
Retire, consolidate, replatform — and why retirement is under-applied.
Caching for Performance
Layer choice, hit ratio as a first-class metric, and cold-cache recovery.
Database Performance
Plans, indexes, contention and the pool in front of the database.
LLM Application Architecture
The shape of a production system with a model in the request path.
Network Performance
Latency floors, bandwidth-delay product, and what no code change fixes.
Network Performance Tuning
Keep-alive, compression, payload size and round-trip elimination.
Performance & Capacity
General material on performance and capacity engineering.
Performance Budgets
Targets enforced in CI so regressions fail the build.
Performance vs Cost
Buying latency, and knowing what the last millisecond is worth.
SLO Monitoring
Burn-rate alerting that fires on user impact rather than on thresholds.
Bottleneck Analysis
Finding the constraint, and expecting a second one behind it.
Capacity Modelling
Arithmetic before load tests, and headroom for failure as well as peak.
Cloud Migration
Per-application disposition, sequencing and the capability change underneath.
Concurrency
Operations in flight, and the limits that are the real capacity ceiling.
Connection Pooling
The most common hidden ceiling, and the metric nobody collects.
EA Domains
Business, application, data and technology architecture as viewpoints.
Encryption
At rest, in transit, and at the application layer — three different threats.
Horizontal vs Vertical Scaling
Scale out for stateless, scale up first for stateful.
Latency
Distributions rather than averages, and the floors physics imposes.