Web Scraping with Kotlin in 2026: Complete Guide

Kotlin gives you type safety, null-safe selectors, and coroutine-powered concurrency for building scrapers that don't crash in production. In this guide, you'll learn how to build web scrapers in Kotlin—from basic HTML parsing to parallel processing with rate limiting.

What is Web Scraping with Kotlin?

Web scraping with Kotlin involves programmatically extracting data from websites using the Kotlin programming language. You send HTTP requests to target URLs, parse the returned HTML, and extract the specific data you need using CSS selectors or XPath expressions.

Kotlin runs on the JVM and gives you access to the entire Java ecosystem. This means you can use battle-tested libraries like Jsoup and OkHttp while writing cleaner, safer code. The language's null safety catches missing elements at compile time rather than crashing your scraper at 3 AM.

Here's why developers choose Kotlin over Python or Java for scraping in 2026:

• Null safety forces explicit handling of missing DOM elements
• Coroutines make parallel scraping trivial without callback hell
• Data classes provide free serialization for scraped data
• Type inference reduces boilerplate while keeping type safety
• Full interoperability with Java libraries

Setting Up Your Kotlin Scraping Environment

Before writing any code, you need a working development environment. The setup is straightforward and takes about 10 minutes.

Prerequisites

You'll need three things installed:

JDK 21 or newer — Download the latest LTS version from Oracle or use SDKMAN for version management. Kotlin runs on the JVM, so this is non-negotiable.

Gradle 8.5+ — The preferred build tool for Kotlin projects. It supports Kotlin DSL for build scripts and handles dependencies cleanly.

IntelliJ IDEA — JetBrains' IDE offers the best Kotlin support. The Community Edition is free and works perfectly for scraping projects.

Creating a New Project

Open your terminal and create a new Kotlin project:

mkdir kotlin-scraper && cd kotlin-scraper
gradle init --type kotlin-application

During initialization, select Kotlin for the build script DSL. Name your package something like com.scraper and accept the defaults for other options.

Adding Dependencies

Open build.gradle.kts in the app folder and add these dependencies:

dependencies {
    // HTTP client for making requests
    implementation("com.squareup.okhttp3:okhttp:5.0.0-alpha.14")
    
    // HTML parser with CSS selector support
    implementation("org.jsoup:jsoup:1.18.1")
    
    // Kotlin coroutines for parallel scraping
    implementation("org.jetbrains.kotlinx:kotlinx-coroutines-core:1.8.1")
    
    // CSV export for scraped data
    implementation("com.github.doyaaaaaken:kotlin-csv-jvm:1.10.0")
}

Run ./gradlew build to download dependencies. You're now ready to write your first Kotlin scraper.

Building Your First Kotlin Web Scraper

Let's build a scraper that extracts product data from the Books to Scrape sandbox site. This example covers fetching pages, parsing HTML, and extracting structured data.

Fetching the Page

The first step in web scraping with Kotlin is sending an HTTP request and getting the HTML response. OkHttp handles this cleanly:

import okhttp3.OkHttpClient
import okhttp3.Request

fun fetchPage(url: String): String? {
    val client = OkHttpClient()
    
    val request = Request.Builder()
        .url(url)
        .header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36")
        .build()
    
    return client.newCall(request).execute().use { response ->
        if (response.isSuccessful) {
            response.body?.string()
        } else {
            null
        }
    }
}

The use block automatically closes the response body. Always set a realistic User-Agent header—many sites block requests that look like bots.

Parsing HTML with Jsoup

Once you have the HTML, Jsoup turns it into a queryable document:

import org.jsoup.Jsoup
import org.jsoup.nodes.Document

fun parseHtml(html: String, baseUrl: String): Document {
    return Jsoup.parse(html, baseUrl)
}

The baseUrl parameter is important. It allows Jsoup to resolve relative URLs into absolute ones when you extract links and images.

Extracting Product Data

Now define a data class to hold scraped products and extract them from the page:

data class Book(
    val title: String,
    val price: String,
    val availability: String,
    val rating: String,
    val url: String
)

fun extractBooks(doc: Document): List<Book> {
    return doc.select("article.product_pod").map { card ->
        Book(
            title = card.selectFirst("h3 a")?.attr("title") ?: "Unknown",
            price = card.selectFirst(".price_color")?.text() ?: "N/A",
            availability = card.selectFirst(".availability")?.text()
                ?.replace(Regex("\\s+"), " ")?.trim() ?: "Unknown",
            rating = extractRating(card.selectFirst("p.star-rating")),
            url = card.selectFirst("h3 a")?.absUrl("href") ?: ""
        )
    }
}

private fun extractRating(element: org.jsoup.nodes.Element?): String {
    if (element == null) return "Unknown"
    return element.classNames()
        .firstOrNull { it in listOf("One", "Two", "Three", "Four", "Five") }
        ?: "Unknown"
}

Notice how Kotlin's null-safe operators (?., ?:) handle missing elements gracefully. This is crucial for web scraping with Kotlin—pages change constantly, and selectors fail.

Complete Basic Scraper

Here's the full working scraper:

import okhttp3.OkHttpClient
import okhttp3.Request
import org.jsoup.Jsoup

data class Book(
    val title: String,
    val price: String,
    val availability: String,
    val rating: String,
    val url: String
)

fun main() {
    val targetUrl = "https://books.toscrape.com/"
    val html = fetchPage(targetUrl) ?: error("Failed to fetch page")
    val doc = Jsoup.parse(html, targetUrl)
    
    val books = extractBooks(doc)
    
    books.forEach { book ->
        println("${book.title} - ${book.price}")
    }
    println("\nTotal books scraped: ${books.size}")
}

fun fetchPage(url: String): String? {
    val client = OkHttpClient()
    val request = Request.Builder()
        .url(url)
        .header("User-Agent", "Mozilla/5.0 (compatible; KotlinScraper/1.0)")
        .build()
    
    return client.newCall(request).execute().use { response ->
        if (response.isSuccessful) response.body?.string() else null
    }
}

fun extractBooks(doc: org.jsoup.nodes.Document): List<Book> {
    return doc.select("article.product_pod").mapNotNull { card ->
        val titleEl = card.selectFirst("h3 a") ?: return@mapNotNull null
        
        Book(
            title = titleEl.attr("title"),
            price = card.selectFirst(".price_color")?.text() ?: "N/A",
            availability = card.selectFirst(".availability")?.text()
                ?.replace(Regex("\\s+"), " ")?.trim() ?: "Unknown",
            rating = card.selectFirst("p.star-rating")?.classNames()
                ?.firstOrNull { it in listOf("One", "Two", "Three", "Four", "Five") }
                ?: "Unknown",
            url = titleEl.absUrl("href")
        )
    }
}

Run it with ./gradlew run and you'll see 20 books printed to the console.

Handling Pagination in Kotlin Scrapers

Most websites spread data across multiple pages. The sandbox site has a "Next" button linking to additional pages.

Detecting the Next Page

Inspect the pagination element on the target site. You'll find a li.next a selector that contains the relative URL to the next page.

fun getNextPageUrl(doc: org.jsoup.nodes.Document): String? {
    return doc.selectFirst("li.next a")?.absUrl("href")
}

This returns null on the last page when there's no next button.

Crawling Multiple Pages

Now loop through all pages until pagination ends:

fun scrapeAllPages(startUrl: String): List<Book> {
    val allBooks = mutableListOf<Book>()
    var currentUrl: String? = startUrl
    var pageCount = 0
    
    while (currentUrl != null) {
        pageCount++
        println("Scraping page $pageCount: $currentUrl")
        
        val html = fetchPage(currentUrl) ?: break
        val doc = Jsoup.parse(html, currentUrl)
        
        val books = extractBooks(doc)
        allBooks.addAll(books)
        
        currentUrl = getNextPageUrl(doc)
        
        // Polite delay between requests
        if (currentUrl != null) {
            Thread.sleep(1000)
        }
    }
    
    println("Finished scraping $pageCount pages, ${allBooks.size} total books")
    return allBooks
}

The Thread.sleep(1000) adds a 1-second delay between requests. This is basic rate limiting—sites will ban you if you hit them too fast.

Parallel Web Scraping with Kotlin Coroutines

Sequential scraping is slow. If you need to scrape 1,000 pages, waiting 1 second between each takes over 16 minutes.

Kotlin coroutines let you scrape multiple pages concurrently with built-in rate limiting. This is where Kotlin really shines for web scraping.

Setting Up Coroutines

First, add the coroutines dependency (already included in our setup). Then create a rate-limited parallel scraper:

import kotlinx.coroutines.*
import kotlinx.coroutines.sync.Semaphore
import kotlinx.coroutines.sync.withPermit

class RateLimitedScraper(
    private val maxConcurrent: Int = 5,
    private val delayMs: Long = 500
) {
    private val semaphore = Semaphore(maxConcurrent)
    private val client = OkHttpClient()
    
    suspend fun scrapeUrls(urls: List<String>): List<Book> = coroutineScope {
        urls.map { url ->
            async {
                semaphore.withPermit {
                    delay(delayMs)
                    scrapeUrl(url)
                }
            }
        }.awaitAll().flatten()
    }
    
    private fun scrapeUrl(url: String): List<Book> {
        val request = Request.Builder()
            .url(url)
            .header("User-Agent", "Mozilla/5.0 (compatible; KotlinScraper/1.0)")
            .build()
        
        return try {
            client.newCall(request).execute().use { response ->
                if (response.isSuccessful) {
                    val doc = Jsoup.parse(response.body?.string() ?: "", url)
                    extractBooks(doc)
                } else {
                    emptyList()
                }
            }
        } catch (e: Exception) {
            println("Error scraping $url: ${e.message}")
            emptyList()
        }
    }
}

The Semaphore limits concurrent requests to 5. The delay(delayMs) adds a pause before each request. This pattern scales beautifully—pass in 1,000 URLs and it handles everything.

Using the Parallel Scraper

fun main() = runBlocking {
    val scraper = RateLimitedScraper(maxConcurrent = 10, delayMs = 300)
    
    // Generate page URLs
    val pageUrls = (1..50).map { page ->
        if (page == 1) "https://books.toscrape.com/"
        else "https://books.toscrape.com/catalogue/page-$page.html"
    }
    
    val startTime = System.currentTimeMillis()
    val books = scraper.scrapeUrls(pageUrls)
    val duration = System.currentTimeMillis() - startTime
    
    println("Scraped ${books.size} books from ${pageUrls.size} pages in ${duration}ms")
}

This scrapes 50 pages in parallel with rate limiting. What would take 50+ seconds sequentially now completes in under 10 seconds.

Error Handling with Sealed Classes

Web scraping with Kotlin benefits from the language's sealed classes for structured error handling. Instead of try-catch everywhere, model your results explicitly:

sealed class ScrapeResult {
    data class Success(val books: List<Book>) : ScrapeResult()
    data class HttpError(val url: String, val statusCode: Int) : ScrapeResult()
    data class NetworkError(val url: String, val exception: Exception) : ScrapeResult()
    data class ParseError(val url: String, val exception: Exception) : ScrapeResult()
}

fun scrapeWithResult(url: String): ScrapeResult {
    return try {
        val request = Request.Builder()
            .url(url)
            .header("User-Agent", "Mozilla/5.0")
            .build()
        
        OkHttpClient().newCall(request).execute().use { response ->
            if (!response.isSuccessful) {
                return ScrapeResult.HttpError(url, response.code)
            }
            
            val html = response.body?.string() ?: ""
            try {
                val doc = Jsoup.parse(html, url)
                val books = extractBooks(doc)
                ScrapeResult.Success(books)
            } catch (e: Exception) {
                ScrapeResult.ParseError(url, e)
            }
        }
    } catch (e: Exception) {
        ScrapeResult.NetworkError(url, e)
    }
}

Now calling code can handle each case explicitly:

when (val result = scrapeWithResult(url)) {
    is ScrapeResult.Success -> processBooks(result.books)
    is ScrapeResult.HttpError -> logHttpError(result.url, result.statusCode)
    is ScrapeResult.NetworkError -> retryLater(result.url)
    is ScrapeResult.ParseError -> alertHtmlChanged(result.url)
}

This approach makes your scraper more robust and easier to debug.

Alternative Libraries for Kotlin Scraping

Jsoup and OkHttp cover most use cases, but you have other options depending on your needs.

skrape{it} — Native Kotlin DSL

skrape{it} provides a Kotlin-first API with a fluent DSL:

import it.skrape.core.htmlDocument
import it.skrape.fetcher.HttpFetcher
import it.skrape.fetcher.skrape

val books = skrape(HttpFetcher) {
    request {
        url = "https://books.toscrape.com/"
    }
    response {
        htmlDocument {
            "article.product_pod" {
                findAll {
                    map { 
                        it.findFirst("h3 a").attribute("title")
                    }
                }
            }
        }
    }
}

The DSL is clean but has a learning curve. It's best for developers who want a purely Kotlin experience.

kdriver — CDP-Based Automation

For JavaScript-heavy sites, kdriver uses Chrome DevTools Protocol directly:

import kdriver.Browser

suspend fun main() {
    val browser = Browser.launch()
    val page = browser.newPage()
    
    page.navigate("https://example.com")
    page.waitForSelector(".dynamic-content")
    
    val html = page.content()
    // Parse with Jsoup
    
    browser.close()
}

kdriver is faster than Selenium and harder for sites to detect. It's the go-to choice for scraping SPAs and sites with heavy JavaScript rendering.

Ktor Client

If you're already using Ktor for web applications, its HTTP client works well for scraping:

val client = HttpClient(CIO) {
    install(ContentNegotiation)
}

val response: String = client.get("https://example.com").bodyAsText()

Ktor integrates naturally with coroutines and provides async-first HTTP operations.

Handling Anti-Bot Protections

Real-world sites implement protections against scrapers. Here are practical techniques for avoiding blocks.

Rotating User Agents

Don't use the same User-Agent for every request:

val userAgents = listOf(
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15",
    "Mozilla/5.0 (X11; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/119.0"
)

fun randomUserAgent(): String = userAgents.random()

Using Proxies

Rotating IP addresses prevents IP-based bans. If you need reliable residential proxies, services like Roundproxies.com offer residential, datacenter, ISP, and mobile proxy options for scraping at scale.

Configure a proxy with OkHttp:

import java.net.InetSocketAddress
import java.net.Proxy

val proxy = Proxy(
    Proxy.Type.HTTP,
    InetSocketAddress("proxy.example.com", 8080)
)

val client = OkHttpClient.Builder()
    .proxy(proxy)
    .build()

Respecting robots.txt

Check the site's robots.txt before scraping. While not legally binding everywhere, it signals the site owner's preferences:

fun isAllowed(robotsTxt: String, path: String): Boolean {
    // Basic parser - use a proper library for production
    val disallowed = robotsTxt.lines()
        .filter { it.startsWith("Disallow:") }
        .map { it.removePrefix("Disallow:").trim() }
    
    return disallowed.none { path.startsWith(it) }
}

Exporting Scraped Data

Once you've collected data, export it to a useful format.

CSV Export

Using kotlin-csv:

import com.github.doyaaaaaken.kotlincsv.dsl.csvWriter

fun exportToCsv(books: List<Book>, filename: String) {
    csvWriter().open(filename) {
        writeRow("Title", "Price", "Availability", "Rating", "URL")
        books.forEach { book ->
            writeRow(book.title, book.price, book.availability, book.rating, book.url)
        }
    }
    println("Exported ${books.size} books to $filename")
}

JSON Export

Using kotlinx.serialization:

import kotlinx.serialization.*
import kotlinx.serialization.json.*

@Serializable
data class Book(
    val title: String,
    val price: String,
    val availability: String,
    val rating: String,
    val url: String
)

fun exportToJson(books: List<Book>, filename: String) {
    val json = Json { prettyPrint = true }
    val content = json.encodeToString(books)
    File(filename).writeText(content)
}

Common Mistakes and How to Avoid Them

These are the pitfalls I've seen trip up developers new to web scraping with Kotlin.

Not handling null selectors — A selector that works today might return null tomorrow when the site updates. Always use ?. and ?: operators.

Ignoring rate limits — Hammering a server with requests gets your IP banned fast. Add delays between requests and use a semaphore for concurrent scraping.

Hardcoding selectors — Store CSS selectors in configuration files or constants. When the site changes, you only update one place.

Missing timeout configuration — Long-running requests tie up resources. Set timeouts on your HTTP client:

val client = OkHttpClient.Builder()
    .connectTimeout(10, TimeUnit.SECONDS)
    .readTimeout(30, TimeUnit.SECONDS)
    .build()

Not logging failures — Silent failures hide problems. Log every error with the URL that caused it so you can investigate later.

Advanced Techniques for Production Scrapers

Once you've mastered the basics, these techniques will make your Kotlin scrapers more robust and maintainable.

Retry Logic with Exponential Backoff

Network failures are inevitable. Rather than failing immediately, implement retries with increasing delays:

suspend fun fetchWithRetry(
    url: String,
    maxAttempts: Int = 3,
    initialDelayMs: Long = 1000
): String? {
    var currentDelay = initialDelayMs
    
    repeat(maxAttempts) { attempt ->
        try {
            val result = fetchPage(url)
            if (result != null) return result
        } catch (e: Exception) {
            println("Attempt ${attempt + 1} failed for $url: ${e.message}")
        }
        
        if (attempt < maxAttempts - 1) {
            delay(currentDelay)
            currentDelay *= 2 // Exponential backoff
        }
    }
    
    return null
}

The delay doubles after each failed attempt. This gives overloaded servers time to recover and avoids hammering them during outages.

Caching Responses

Don't re-scrape pages you've already fetched. Implement a simple file-based cache:

import java.io.File
import java.security.MessageDigest

class PageCache(private val cacheDir: String = ".cache") {
    init {
        File(cacheDir).mkdirs()
    }
    
    private fun urlToFilename(url: String): String {
        val digest = MessageDigest.getInstance("MD5")
        val hash = digest.digest(url.toByteArray())
            .joinToString("") { "%02x".format(it) }
        return "$cacheDir/$hash.html"
    }
    
    fun get(url: String): String? {
        val file = File(urlToFilename(url))
        return if (file.exists()) file.readText() else null
    }
    
    fun put(url: String, content: String) {
        File(urlToFilename(url)).writeText(content)
    }
}

// Usage
val cache = PageCache()
val html = cache.get(url) ?: fetchPage(url)?.also { cache.put(url, it) }

This saves bandwidth and speeds up development when you're iterating on selectors.

Session Management with Cookies

Some sites require login or session cookies. OkHttp handles cookies automatically with a cookie jar:

import okhttp3.Cookie
import okhttp3.CookieJar
import okhttp3.HttpUrl

class InMemoryCookieJar : CookieJar {
    private val cookies = mutableMapOf<String, List<Cookie>>()
    
    override fun saveFromResponse(url: HttpUrl, cookies: List<Cookie>) {
        this.cookies[url.host] = cookies
    }
    
    override fun loadForRequest(url: HttpUrl): List<Cookie> {
        return cookies[url.host] ?: emptyList()
    }
}

val client = OkHttpClient.Builder()
    .cookieJar(InMemoryCookieJar())
    .build()

After logging in, subsequent requests automatically include the session cookie.

Handling Dynamic Content Detection

Before using a headless browser, check if the site actually needs JavaScript:

fun requiresJavaScript(url: String): Boolean {
    val html = fetchPage(url) ?: return true
    val doc = Jsoup.parse(html)
    
    // Check for common SPA indicators
    val hasEmptyBody = doc.body().text().length < 100
    val hasReactRoot = doc.selectFirst("#root, #app, [data-reactroot]") != null
    val hasNoscriptWarning = doc.selectFirst("noscript")?.text()
        ?.contains("enable javascript", ignoreCase = true) == true
    
    return hasEmptyBody || (hasReactRoot && hasEmptyBody) || hasNoscriptWarning
}

This check saves you from loading heavy browser automation when it's not needed.

Scraping JavaScript-Heavy Sites

When sites require JavaScript execution, you have several options in the Kotlin ecosystem.

Using Playwright with Kotlin

Playwright offers excellent browser automation. While there's no official Kotlin SDK, you can use the Java version:

import com.microsoft.playwright.*

fun scrapeWithPlaywright(url: String): String {
    Playwright.create().use { playwright ->
        val browser = playwright.chromium().launch()
        val page = browser.newPage()
        
        page.navigate(url)
        page.waitForSelector(".dynamic-content", 
            Page.WaitForSelectorOptions().setTimeout(10000.0))
        
        val html = page.content()
        browser.close()
        
        return html
    }
}

Add the Playwright dependency:

implementation("com.microsoft.playwright:playwright:1.42.0")

The first run downloads browser binaries automatically.

HtmlUnit for Lightweight JavaScript

HtmlUnit is a headless browser written in Java. It's lighter than Playwright but supports less JavaScript:

import com.gargoylesoftware.htmlunit.WebClient
import com.gargoylesoftware.htmlunit.html.HtmlPage

fun scrapeWithHtmlUnit(url: String): String {
    val webClient = WebClient().apply {
        options.isJavaScriptEnabled = true
        options.isCssEnabled = false
        options.isThrowExceptionOnScriptError = false
    }
    
    val page: HtmlPage = webClient.getPage(url)
    webClient.waitForBackgroundJavaScript(5000)
    
    return page.asXml()
}

HtmlUnit works well for basic JavaScript but struggles with modern React/Vue applications.

Structuring Large Scraping Projects

As your scraping project grows, organization becomes critical.

Project Structure

src/main/kotlin/
├── scraper/
│   ├── core/
│   │   ├── HttpClient.kt
│   │   ├── HtmlParser.kt
│   │   └── RateLimiter.kt
│   ├── extractors/
│   │   ├── BookExtractor.kt
│   │   └── ProductExtractor.kt
│   ├── models/
│   │   ├── Book.kt
│   │   └── ScrapeResult.kt
│   ├── exporters/
│   │   ├── CsvExporter.kt
│   │   └── JsonExporter.kt
│   └── Main.kt
└── resources/
    ├── selectors.yaml
    └── user-agents.txt

Configuration-Driven Selectors

Store selectors in configuration files rather than code:

# selectors.yaml
books:
  container: "article.product_pod"
  title: "h3 a[title]"
  price: ".price_color"
  availability: ".availability"
  rating: "p.star-rating"

Load and use them dynamically:

import com.charleskorn.kaml.Yaml

data class SelectorConfig(
    val container: String,
    val title: String,
    val price: String,
    val availability: String,
    val rating: String
)

val config = Yaml.default.decodeFromString(
    SelectorConfig.serializer(),
    File("selectors.yaml").readText()
)

When the site changes its HTML structure, update the YAML file instead of recompiling code.

Performance Benchmarks

I ran benchmarks comparing different approaches for web scraping with Kotlin.

Test setup: Scraping 100 pages from a local test server, measured in seconds.

Parallel scraping with coroutines is dramatically faster. The memory tradeoff is minimal for most use cases.

Library comparison for HTML parsing:

Jsoup remains the fastest and most memory-efficient option for pure HTML parsing.

Legal and Ethical Considerations

Web scraping with Kotlin (or any language) comes with responsibilities.

Respect Terms of Service — Many sites explicitly prohibit scraping in their ToS. Violating them can lead to legal action, especially if you're scraping competitors or reselling data.

Check robots.txt — While not legally binding everywhere, it shows the site owner's intentions. Ignoring it invites technical countermeasures.

Don't overload servers — Aggressive scraping can disrupt service for other users. Always rate limit your requests.

Handle personal data carefully — GDPR, CCPA, and similar regulations apply to scraped data. If you're collecting personal information, understand your legal obligations.

Use data responsibly — Just because you can scrape something doesn't mean you should. Consider the source's interests and your own reputation.

FAQ

Is Kotlin good for web scraping?

Yes, Kotlin is excellent for web scraping. It combines Java's mature ecosystem (Jsoup, OkHttp, Selenium) with modern features like null safety and coroutines. The language catches common scraping bugs at compile time and makes parallel scraping trivial.

Can Kotlin scrape JavaScript-rendered websites?

Kotlin can scrape JavaScript websites using headless browsers. Libraries like kdriver (CDP-based) or Selenium WebDriver render JavaScript before you extract data. For simple dynamic content, consider checking if the data is available via API calls first.

What's the best HTML parser for Kotlin?

Jsoup is the best HTML parser for most Kotlin projects. It's fast, memory-efficient, and supports CSS selectors. For a more Kotlin-native experience, skrape{it} provides a fluent DSL built specifically for Kotlin.

How do I avoid getting blocked when scraping?

Avoid blocks by rotating User-Agents, using proxy services for IP rotation, respecting rate limits with delays between requests, and honoring robots.txt rules. Using residential proxies from providers like Roundproxies.com helps avoid IP-based bans at scale.

Is web scraping with Kotlin faster than Python?

Kotlin can be faster for concurrent scraping due to coroutines and JVM optimizations. For simple scripts, Python is often faster to write. For production scrapers that need reliability and performance, Kotlin's type safety and parallel processing give it an edge.

Conclusion

Web scraping with Kotlin gives you type safety, null-safe selectors, and coroutine-powered parallelism. You get access to mature Java libraries while writing cleaner, shorter code.

Start with the basic Jsoup + OkHttp stack for static sites. Add coroutines when you need to scale up. Use kdriver or a headless browser for JavaScript-heavy pages.

The key is building scrapers that handle errors gracefully and respect the sites you're scraping. Rate limit your requests, rotate user agents, and always check robots.txt.

For production scraping at scale, consider using proxy services to rotate IPs and avoid bans. The combination of Kotlin's safety features and proper infrastructure makes building reliable scrapers straightforward.