Globant Interview Questions (2026): Real, With Answers

Expect a mix of short technical items tied to the role you applied for and situational or behavioral prompts, not a two-hour algorithm marathon. Globant markets this stage as an objective, AI-assisted first pass meant to reduce recruiter workload across a very high volume of applications, so it tends to favor breadth over depth: a handful of questions across several competencies rather than one deep technical problem.

Treat it the way you'd treat any automated screen. Read every prompt twice, don't rush the situational judgment section assuming it doesn't matter, and if a coding portion shows up, write code that actually runs rather than pseudocode, since some of these tools do execute submissions.

Yes, at least directionally. Junior and entry-level applicants describe more foundational technical checks, data structures, basic syntax, simple logic puzzles. Senior candidates report the automated stage leaning harder into scenario and judgment questions, with the real technical depth pushed into the live interview with an engineer or hiring manager instead of the automated tool.

The practical takeaway is the same either way: this stage is a gate, not the interview itself. Passing it gets you a human conversation. It rarely determines the offer on its own.

Candidate reports cluster around one to two weeks total, though a meaningful share describe it stretching closer to a month when a specific client account needs to sign off on staffing before an offer goes out. Globant's own site describes onboarding as the third stage after the interview rounds close, so once you clear the technical and hiring manager conversations, the paperwork moves fairly fast.

If you haven't heard back after roughly two weeks, a short follow-up email to the recruiter is normal and expected, not pushy, since a Globant recruiter is typically juggling multiple open reqs across multiple accounts at once.

Walk the list once, keeping three pointers: the previous node, the current node, and a temporary reference to the current node's next before you overwrite it. At each step, point the current node's next back at the previous node, then advance previous and current forward by one.

Node reverse(Node head) {
  Node prev = null;
  Node curr = head;
  while (curr != null) {
    Node next = curr.next;
    curr.next = prev;
    prev = curr;
    curr = next;
  }
  return prev;
}

One pass through the list, so O(n) time. No extra data structure beyond a few pointers, so O(1) space. Interviewers usually follow up by asking you to trace it on a three or four node example on the whiteboard, which is worth practicing out loud, not just in your head.

Floyd's cycle detection, two pointers moving at different speeds through the same list. A slow pointer advances one node at a time, a fast pointer advances two. If there's a cycle, the fast pointer eventually laps the slow one and they land on the same node. If there's no cycle, the fast pointer hits null first.

boolean hasCycle(Node head) {
  Node slow = head, fast = head;
  while (fast != null && fast.next != null) {
    slow = slow.next;
    fast = fast.next.next;
    if (slow == fast) return true;
  }
  return false;
}

Two pointers, one starting at each end, moving inward. At each step, skip past any character that isn't a letter or digit, then compare the two remaining characters case-insensitively. Mismatch means it's not a palindrome, and pointers crossing without a mismatch means it is.

def is_palindrome(s):
  left, right = 0, len(s) - 1
  while left < right:
    while left < right and not s[left].isalnum():
      left += 1
    while left < right and not s[right].isalnum():
      right -= 1
    if s[left].lower() != s[right].lower():
      return False
    left += 1
    right -= 1
  return True

The naive approach checks every pair, O(n²). The faster answer walks the array once, keeping a hash map of values seen so far. At each element, check whether target minus the current value already exists in the map. If it does, you've found your pair immediately, if not, add the current value to the map and keep going.

def two_sum(nums, target):
  seen = {}
  for i, n in enumerate(nums):
    complement = target - n
    if complement in seen:
      return [seen[complement], i]
    seen[n] = i
  return []

O(n) time, O(n) space, one pass. This exact problem shows up often enough as an opening warm-up that being slow on it reads as a red flag more than the problem itself being hard.

A stack is last-in, first-out. The most recent item added is the first one removed. A queue is first-in, first-out, the oldest item added is the first one removed.

A stack fits an undo feature in an editor, or the call stack tracking function returns during recursion. A queue fits a print spooler or a task processor working through jobs in the order they arrived, or a breadth-first traversal of a tree or graph, where you need to visit nodes level by level rather than depth first.

var is function-scoped and gets hoisted with an initial value of undefined, which lets you reference it before its declaration line without an error, just an unhelpful value. let and const are block-scoped, confined to the nearest enclosing curly braces, and both sit in a "temporal dead zone" until their declaration line actually executes, so referencing them earlier throws a ReferenceError instead of silently returning undefined.

const additionally prevents reassignment of the binding itself, though it doesn't freeze the value. A const array or object can still have its contents mutated, push, pop, or a property change, just not be pointed at an entirely new array or object.

=== compares both value and type with no coercion. == coerces one or both operands to a common type before comparing, which produces surprising results like "" == 0 being true, or null == undefined being true while null === undefined is false.

The honest answer on appropriateness: almost never in application code. The one common exception is checking for both null and undefined at once with a single comparison, if (value == null), which deliberately relies on that specific coercion rule and is arguably clearer than writing out both checks.

Encapsulation bundles data and the methods that operate on it, hiding internal state behind a controlled interface, a BankAccount class exposing deposit and withdraw methods rather than a public balance field anyone can set directly. Abstraction hides implementation complexity behind a simpler interface, a PaymentProcessor interface with a single charge method that hides whether it's calling Stripe or a bank API underneath.

Inheritance lets a class reuse and extend behavior from a parent, a SavingsAccount extending a base Account class and adding interest calculation. Polymorphism lets different classes respond to the same method call in their own way, calling draw on a list of mixed Shape objects, circles and squares each render themselves differently through the same method signature.

An INNER JOIN returns only rows where the join condition matches in both tables. A LEFT JOIN returns every row from the left table regardless of whether a match exists in the right table, filling in NULLs for the right table's columns when there's no match.

SELECT c.name, o.order_id
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id;
-- every customer appears at least once, even those with zero orders

Vertical scaling adds more resources, CPU, memory, to a single existing server. Horizontal scaling adds more servers running the same workload behind a load balancer. Vertical scaling has a hard ceiling, eventually you run out of bigger machines to buy, and a single point of failure the whole time. Horizontal scaling avoids both problems but requires the application to be stateless, or state to live somewhere shared, since any given request could land on any instance.

Continuous integration means every code change gets automatically built and tested as soon as it's pushed, catching integration problems early instead of at a big merge later. Continuous delivery or deployment extends that automation through packaging and release, so a change that passes every stage can go to staging or production with little or no manual step.

A typical pipeline runs: build, unit tests, static analysis or linting, integration tests, then deploy to a staging environment, followed by either an automated or a manual gate before production. Fast feedback at every stage, so a broken build fails in minutes, not after a full end-to-end run.

Supervised learning trains a model on labeled examples, input paired with the correct output, so the model learns to predict that output for new inputs, spam classification or price prediction being classic cases. Unsupervised learning works on unlabeled data and looks for structure on its own, clustering similar customers together, or reducing a high-dimensional dataset down to its most important patterns without being told what "correct" looks like ahead of time.

A sprint is a fixed time box, commonly two weeks, that starts with sprint planning, where the team pulls a set of backlog items it commits to finishing. Daily standups keep the team synced on progress and blockers in a few minutes each morning. The sprint ends with a review demoing completed work to stakeholders, followed by a retrospective where the team reflects on what went well and what to change next sprint.

Pick a specific example and walk through how you translated the technical detail into business impact, what it means for the timeline, the cost, or the user, rather than the mechanism itself. The strongest answers describe checking for understanding along the way, not just delivering a monologue and assuming it landed.

This question is a stand-in for whether you're safe to put in front of a client without supervision, which at a services company is a real and immediate concern from day one on a new account, not a hypothetical future skill.

A weak answer talks generically about "innovation" or "culture" without anything specific. A strong answer names something real about the work: exposure to a wide variety of client problems and industries in a short career span, the ability to work across different tech stacks rather than being locked into one product's codebase for years, or a specific studio or practice area (AI, cloud, a particular industry vertical) that matches your own interests.

Being able to name a specific client industry or type of project Globant is known for in your area of interest signals you actually researched the company rather than treating this as a generic "why us" template answer.

Given how frequently Globant staff move between client accounts and tech stacks, this question is not really about the specific technology. It is about your learning process itself. Describe concretely how you got up to speed, official docs first, then a small throwaway project to test understanding before touching real code, pairing with someone already familiar with the stack, rather than a vague "I just figured it out."

Close with how you applied what you learned and what the outcome was. A candidate who can describe their learning process specifically reads as lower risk to staff onto an unfamiliar stack quickly, which is exactly the scenario this question is simulating.

Give a researched range rather than a single number, anchored to market data for your role, level, and location, not just what you currently make. It's fair to ask what budget range the specific role or account has in mind before committing to a number yourself, especially at a company like Globant where compensation can vary somewhat by which client account and region you're ultimately staffed against.

Always have at least two or three ready, and make at least one specific to the actual team or account you'd join rather than something generic you could ask any company. Good options at a staffing-model company like Globant: what does onboarding onto a new client account typically look like, how often do engineers rotate between accounts, and what does the path from your level to the next one actually look like in practice.

Skipping this question entirely, or asking only about vacation days and benefits, reads as low engagement at this late a stage in the process.

Two strings are anagrams if they contain exactly the same characters with the same frequencies, just rearranged. The cleanest check counts character frequency in each string and compares the two frequency maps, or sorts both strings and compares the sorted results.

def is_anagram(a, b):
  if len(a) != len(b):
    return False
  counts = {}
  for ch in a:
    counts[ch] = counts.get(ch, 0) + 1
  for ch in b:
    if ch not in counts or counts[ch] == 0:
      return False
    counts[ch] -= 1
  return True

The counting version runs in O(n) time versus O(n log n) for the sort-and-compare approach, worth mentioning if the interviewer pushes on efficiency after your first pass.

A tree is height-balanced if, for every node, the height difference between its left and right subtree is at most one. The efficient approach computes height and checks balance in a single bottom-up pass, returning negative one as a sentinel the moment an imbalance is found anywhere below, so you don't waste time computing heights for a subtree you already know fails.

boolean isBalanced(TreeNode root) {
  return height(root) != -1;
}

int height(TreeNode node) {
  if (node == null) return 0;
  int left = height(node.left);
  if (left == -1) return -1;
  int right = height(node.right);
  if (right == -1) return -1;
  if (Math.abs(left - right) > 1) return -1;
  return Math.max(left, right) + 1;
}

A naive version recomputes height from scratch at every node, O(n²) worst case. The sentinel-based version above is O(n), and interviewers specifically watch whether you catch that inefficiency without being prompted.

Breadth-first search, since in an unweighted graph the first time BFS reaches a node is guaranteed to be via the shortest path to it. Track visited nodes to avoid revisiting, and keep a parent map alongside the queue so you can reconstruct the actual path once you hit the target, not just report the distance.

from collections import deque

def shortest_path(graph, start, end):
  visited = {start}
  queue = deque([[start]])
  while queue:
    path = queue.popleft()
    node = path[-1]
    if node == end:
      return path
    for neighbor in graph.get(node, []):
      if neighbor not in visited:
        visited.add(neighbor)
        queue.append(path + [neighbor])
  return None

useEffect runs asynchronously after the browser has painted the updated DOM, so the user sees the new frame before your effect's code executes. useLayoutEffect runs synchronously right after DOM mutations but before the browser paints, blocking the paint until it finishes.

useEffect(() => {
 // runs after paint, fine for data fetching, subscriptions
 fetchData();
}, []);

useLayoutEffect(() => {
 // runs before paint, use when you need to measure or adjust
 // the DOM before the user sees a flash of the wrong layout
 const rect = ref.current.getBoundingClientRect();
}, []);

Reach for useLayoutEffect specifically when a visible flicker would otherwise happen, measuring an element's size to position a tooltip is the classic case. Default to useEffect for everything else, since blocking paint unnecessarily hurts perceived performance.

React builds a virtual DOM tree on every render and diffs it against the previous tree. For elements of the same type at the same position, React updates only the changed attributes rather than recreating the DOM node. For a list, React uses the key prop to match elements across renders, if a key persists between renders, React reuses that DOM node and its internal state instead of unmounting and remounting it.

That's exactly why using an array index as a key causes bugs when the list reorders or items get inserted in the middle: React matches by position, sees the same index pointing at different data, and can misattribute state, like a text input's cursor position, to the wrong row.

Node runs JavaScript on a single thread, but offloads I/O work, file reads, network calls, timers, to the underlying system (via libuv) and keeps a queue of callbacks to run once that work finishes. The event loop repeatedly checks whether the call stack is empty and, if so, pulls the next completed callback off the queue and runs it.

console.log("1");
setTimeout(() => console.log("2"), 0);
Promise.resolve().then(() => console.log("3"));
console.log("4");
// logs: 1, 4, 3, 2
// synchronous code first, then microtasks (promises), then macrotasks (timers)

The order in that snippet trips up a lot of candidates who know the event loop exists but haven't internalized that microtasks (promise callbacks) always drain before the next macrotask (a timer callback), even a zero-millisecond one.

Debouncing delays a function's execution until a certain amount of time has passed with no new calls. Each new call cancels the pending timer from the previous call and starts a fresh one, so the wrapped function only actually runs once input has been quiet for the full delay.

function debounce(fn, delayMs) {
 let timer;
 return (...args) => {
  clearTimeout(timer);
  timer = setTimeout(() => fn(...args), delayMs);
 };
}

const debouncedSearch = debounce((query) => fetchResults(query), 300);
input.addEventListener("input", (e) => debouncedSearch(e.target.value));

Follow-up worth being ready for: debounce versus throttle. Debounce waits for a pause before firing once. Throttle guarantees the function runs at most once per fixed interval regardless of how continuously the event keeps firing, which fits a scroll handler better than a search box.

An abstract class can hold shared implementation and state alongside abstract methods that subclasses must implement, but a class can only extend one abstract class. An interface (in most languages that distinguish the two) traditionally held only method signatures, though modern Java and C# now allow default implementations in interfaces too, and a class can implement several interfaces at once.

Choose an abstract class when related types share meaningful common code you don't want duplicated. Choose an interface when you're defining a contract, a capability like Comparable or Serializable, that unrelated classes across the codebase might need to implement independently.

The straightforward version orders salaries descending, skips the top row, and limits to one. A version that also handles duplicate salaries correctly (so two people tied for first don't both count as "the highest") uses a subquery excluding the maximum.

SELECT MAX(salary) AS second_highest
FROM Employees
WHERE salary < (SELECT MAX(salary) FROM Employees);

That version returns NULL cleanly if there's no second distinct salary, which is the correct edge-case behavior, versus an ORDER BY / OFFSET approach that can silently return the wrong row when duplicates exist at the top.

Dependency injection means a class receives the objects it depends on from the outside, usually through its constructor, instead of constructing those dependencies itself internally. That inversion is the whole point: it decouples a class from the concrete implementations of what it uses, which makes swapping a real database connection for a test double trivial without touching the class's own code.

class OrderService {
  private final PaymentGateway gateway;

  OrderService(PaymentGateway gateway) { // injected, not "new StripeGateway()" inside
    this.gateway = gateway;
  }
}

A VM virtualizes an entire machine, including its own guest operating system kernel, running on top of a hypervisor. A container shares the host machine's kernel and only packages the application plus its dependencies, which makes containers dramatically lighter and faster to start, seconds instead of minutes, at the cost of weaker isolation than a full VM boundary.

FROM node:20-slim
WORKDIR /app
COPY package*.json./
RUN npm ci --production
COPY..
CMD ["node", "server.js"]

Infrastructure as Code means defining servers, networking, and configuration in version-controlled files, Terraform or CloudFormation being the common tools, rather than clicking through a cloud console by hand. The environment becomes reproducible, reviewable in a pull request like any other code change, and recoverable, if a region goes down you can stand the whole environment back up from the same definition instead of trying to remember every manual setting someone applied over the years.

resource "aws_instance" "web" {
 ami      = "ami-0c55b159cbfafe1f0"
 instance_type = "t3.micro"
}

Overfitting happens when a model learns the training data's noise and idiosyncrasies too closely, performing well on data it's already seen but poorly on new data, because it memorized specifics instead of learning the underlying pattern.

Regularization (penalizing overly large model weights) and cross-validation (repeatedly training and testing on different splits of the data to catch a model that only performs well on one particular split) are two standard fixes. Simplifying the model, gathering more training data, and early stopping during training are also common practical levers.

At each step, the model computes a probability distribution over its entire vocabulary given everything generated so far, then samples or picks the next token from that distribution. It's not looking anything up or reasoning in a symbolic sense, it's a statistical prediction of what token is most likely to come next, repeated one token at a time, with each new token feeding back in as additional context for the next prediction.

RAG retrieves relevant documents from an external knowledge source at query time and feeds them into the model's prompt alongside the user's question, so the model answers grounded in that retrieved context instead of relying purely on what it memorized during training. Fine-tuning bakes new knowledge into the model's weights directly through additional training.

RAG wins when the underlying data changes often, a product catalog, internal documentation, since updating a search index is far cheaper than retraining a model. Fine-tuning wins when you need the model to change its behavior or style consistently rather than just recall new facts. Plenty of production AI systems use both together.

Precision measures, of everything the model flagged as positive, how much of it was actually positive. Recall measures, of everything that was actually positive, how much of it the model actually caught. There's a real trade-off between them, tightening the model to reduce false positives usually costs you some true positives too.

Optimize for recall when missing a real positive is expensive, fraud detection or a medical screening tool, where a false negative can be far more costly than a false positive that just gets reviewed and dismissed. Optimize for precision when a false positive is the expensive mistake, a spam filter that wrongly buries an important email erodes trust fast.

Strong consistency guarantees every read sees the most recent write, immediately, everywhere, which usually means coordinating across replicas before confirming a write, costing latency and availability during network issues. Eventual consistency allows a brief window where different replicas can return stale data, but replicas converge to the same value given enough time without new writes, trading a short-term correctness guarantee for lower latency and higher availability.

Pick strong consistency for anything where stale data causes real harm, an account balance. Eventual consistency is fine, and usually the better performance trade, for something like a social media like-count where a brief lag between what two different users see costs nothing real.

Scrum's whole design intent is to protect a sprint's scope once it's committed, so the honest first move is surfacing the change to the product owner rather than silently absorbing it into the current sprint. If it's genuinely urgent, that's a conversation about what gets dropped from the current sprint to make room, not an assumption that the team can just add scope for free.

For anything that isn't a true emergency, the change goes into the backlog for prioritization ahead of the next sprint planning session. This is as much a client-management skill as a process one at a company like Globant, where the "product owner" is often sitting on the client's side of the table, not internally.

A scrum master serves the team, removing blockers, facilitating ceremonies, protecting the team from scope creep mid-sprint, and coaching the team on the process itself, without directing the actual work. A project manager typically owns scope, timeline, and budget more directly, and in a traditional structure has authority over what gets prioritized and delivered.

In practice at a services company staffing client teams, these roles blur more than the textbook definitions suggest, and a Globant engineer is likely to work under both titles depending on the specific account's setup.

Story points estimate relative effort and complexity, not hours directly, commonly using a Fibonacci-like scale (1, 2, 3, 5, 8...) precisely because the gaps between numbers force a real conversation about complexity rather than false precision. Planning poker, where each team member estimates independently and reveals simultaneously, surfaces disagreement early instead of anchoring everyone to whoever spoke first.

When an estimate turns out wrong mid-sprint, the immediate move is flagging it to the team and product owner as soon as it's clear, not waiting for sprint review to surface a miss. Long term, that story becomes a data point for calibrating future estimates on similar work, which is the entire value of tracking velocity over multiple sprints rather than trusting any single sprint's numbers.

This is an intentionally uncomfortable variant of "what's your weakness," designed to see whether you can be honest and self-aware under a slightly adversarial framing instead of retreating into a rehearsed non-answer. The strongest response names something real, a genuine gap or growth area, and immediately follows it with concrete evidence of how you're addressing it, not a humble-brag disguised as a flaw.

What interviewers are actually screening for here is composure. Anyone can prepare a clean answer to "what's your weakness." Far fewer can take a pointed, slightly confrontational version of the same question and answer it steadily without getting defensive or over-explaining.

Structure this with specifics: what the deadline actually was, why it was tight, what tradeoffs you made explicit to the team or client rather than quietly cutting corners, and what the outcome was. The strongest version of this answer shows you communicated the tradeoff proactively, "we can hit the date if we descope X" said out loud before the deadline arrived, rather than silently absorbing scope and hoping it worked out.

Avoid a story where the resolution is just "I worked longer hours." That answer signals a lack of prioritization or communication skill more than it signals dedication, and at a company where you're routinely representing the firm to an external client, the communication half of that answer matters as much as the delivery half.

Pick a real example where you raised the disagreement directly and professionally, with a specific reason grounded in the work, not personality. Then be honest about the outcome, whether you ultimately convinced them, compromised, or accepted their call and executed it fully anyway. All three are legitimate outcomes to describe as long as the story shows you engaged rather than either staying silent or digging in past the point of usefulness.

Interviewers at a client-services company specifically listen for whether you escalated appropriately versus went around someone, and whether you were still able to execute cleanly once a final decision was made, even one you disagreed with.

The honest, senior answer here resists the urge to immediately propose a rewrite. Describe how you'd spend real time understanding why the current state exists before criticizing it, since a decision that looks obviously wrong from the outside sometimes has a real constraint behind it, a client requirement, a legacy integration, that isn't visible on day one.

Then talk about how you'd introduce improvement incrementally, small, low-risk changes that build trust and demonstrate judgment, rather than a big proposal on week one that a team who's lived with the codebase for two years might reasonably resist.

Every JavaScript object has an internal link to another object, its prototype, and property lookups walk up that chain until they find a match or hit the end. Classical inheritance, the kind Java or C# use, defines a fixed class hierarchy at compile time. Prototypal inheritance is dynamic, an object's prototype can be reassigned at runtime, and objects inherit directly from other objects rather than from a class blueprint.

const animal = {
 speak() { return `${this.name} makes a sound`; }
};

const dog = Object.create(animal);
dog.name = "Rex";
dog.speak(); // "Rex makes a sound", found via the prototype chain

The class syntax introduced in ES6 doesn't change any of this underneath, it's sugar over the same prototype chain. A candidate who can explain that class in JavaScript still resolves through prototypes, not a separate mechanism, is showing real depth here.

Normalization organizes a schema to eliminate redundant data and update anomalies, typically by splitting information into related tables connected by foreign keys, following forms like 1NF, 2NF, and 3NF that each remove a specific class of redundancy. A normalized schema keeps data consistent because each fact lives in exactly one place.

Denormalization deliberately reintroduces some redundancy, duplicating a customer's name onto every order row instead of always joining back to the customers table, to trade write-time consistency risk for read-time speed. It's the right call on a reporting table or a read-heavy path where join cost genuinely hurts latency, and the wrong call on a table with frequent updates, since now every update has to touch multiple copies of the same fact.

Start with an offline evaluation set, real or realistic examples with known good outputs, and measure the model against it with metrics that actually match the task, accuracy for classification, something like relevance or faithfulness scoring for generative answers, not just a vibe check from a handful of manual tests. Then run it against a shadow or canary slice of real traffic before a full rollout, comparing key business metrics, not just model metrics, against the existing baseline.

Just as important: define what "good enough" means before you look at the results, and decide what your fallback behavior is for the cases the model gets wrong, since no evaluation set catches every failure mode a live, adversarial user population will find.

Core need: given a long URL, generate and store a short, unique code, and redirect the short code back to the original URL fast. Generate the code either by hashing the URL and taking a truncated portion, checking for collisions, or by base-62 encoding an auto-incrementing ID, which guarantees uniqueness without a collision check at all.

Store the mapping in a key-value store optimized for fast reads, since redirects vastly outnumber creations in a real system. Cache hot URLs in front of the database, and consider what happens if the service needs to scale across regions, whether the ID generator needs to be distributed too, since a single auto-incrementing counter becomes a bottleneck and a single point of failure once you're running more than one instance.

The token bucket algorithm is the common answer: each client gets a bucket that refills at a fixed rate up to a maximum capacity, and each request consumes one token. If the bucket's empty, the request gets rejected or queued. It allows short bursts up to the bucket size while still enforcing a steady long-term rate, which fits real traffic patterns better than a strict fixed window.

For a distributed system with multiple API servers, the bucket state has to live somewhere shared, Redis is the typical choice, since each server enforcing its own local limit independently would let a client blow past the intended total rate by simply hitting different servers.

Add a circuit breaker in front of the call to that downstream service. Once failures cross a threshold, the breaker trips open and starts failing fast for a cooldown window instead of letting every request pile up waiting on a timeout against a service that's already down. Pair that with a sensible fallback, cached data, a default response, or a clearly degraded feature, rather than surfacing a hard error to the end user for something non-critical.

Timeouts on every outbound call matter just as much as the circuit breaker itself. A downstream service that's slow rather than fully down is often the more dangerous case, since it can quietly exhaust your own service's thread pool or connection pool while everything looks "up" from the outside.

Decouple the trigger from the delivery. When an event happens (an order ships, say), publish a message to a queue rather than calling the email and SMS providers synchronously inline. A pool of worker processes consumes from that queue and handles the actual send, retrying with backoff on transient provider failures, which absorbs a traffic spike without the triggering service itself getting blocked or overwhelmed.

Partition the queue by notification type or priority so a flood of low-priority marketing emails can't starve a time-sensitive password-reset SMS. Track delivery status per message so you can retry or alert on failures, and rate-limit against each third-party provider's own API limits, since your million-notification spike becomes their problem the moment you fire it all at once.

Comparing every document against every other document is O(n²) and doesn't scale past a small dataset. The standard approach uses a fingerprinting technique like MinHash or SimHash to compress each document into a small, fixed-size signature such that similar documents produce similar signatures, then buckets documents by signature using locality-sensitive hashing, comparing full documents only within a bucket rather than across the entire dataset.

That reduces the problem from an all-pairs comparison down to comparisons within much smaller candidate groups, which is the difference between a job that finishes in minutes versus one that never finishes at the scale of millions of documents.