Python vs Go vs Java vs Ruby: Picking the Right Language for Production Web Scraping

Every scraping tutorial starts the same way: install BeautifulSoup, fetch a page, parse the HTML, done. Twenty minutes from zero to working prototype. What none of them cover is what happens when that script needs to process 100,000 pages daily, rotate through paid mobile proxies, and stay running without memory leaks for weeks at a time.

Language choice barely matters for a proof of concept. It matters a lot for production infrastructure.

The Comparison Nobody Makes

Most language comparisons for scraping focus on syntax and library availability. Python has BeautifulSoup and Scrapy, Go has Colly, Java has Jsoup, Ruby has Nokogiri. All of them can parse HTML. That part is solved.

The real differences show up in concurrency models, memory behavior under sustained load, and how each language handles proxy connection pooling across tens of thousands of requests. These are the things that determine whether a scraper runs reliably at scale or falls apart after a few hours.

Here's the summary before the details:

	Python	Go	Java	Ruby
Concurrency	Threading / Asyncio	Goroutines	Threads / Virtual Threads	Threads (GIL limited)
I/O throughput	Baseline	4-5x faster	2-3x faster	Similar to baseline
Memory at 10k pages	800-900 MB	200-250 MB	1-1.5 GB	700-800 MB
Best fit	Prototyping, ML pipelines	High-volume production	Enterprise compliance	Rails-integrated automation

Python: Fastest to Build, First to Break at Scale

Python is the default for a reason. Scrapy handles retries, middleware, and pipelines out of the box. Selenium covers JavaScript-heavy sites. The ecosystem is massive and well-documented.

The problem is the GIL (Global Interpreter Lock). It limits Python to executing one thread of Python code at a time, which means true parallelism requires multiprocessing, not threading. Asyncio helps with I/O-bound workloads when configured correctly, but CPU-bound HTML parsing still runs single-threaded.

At moderate scale, around a few thousand pages daily, this doesn't matter much. Past the 10,000 page threshold, memory creep becomes visible. Long-running Scrapy jobs tend to climb in RAM usage over extended sessions. Running 20 concurrent Selenium instances for JavaScript rendering eats 6+ GB of memory.

Python is the right choice when the priority is getting a scraper working quickly, when the team includes data scientists who need direct access to the output, or when the volume stays under 10k pages daily. Past that, the optimization effort starts to outweigh the development speed advantage.

Go: Built for Exactly This Problem

Go's Colly framework combined with goroutines handles high-concurrency scraping with minimal resource overhead. Goroutines are cheap to spawn, thousands of them can run simultaneously without the thread overhead that Python or Java would require.

The performance difference at scale is significant. Go typically delivers 4-5x the throughput of Python for I/O-bound scraping workloads while using roughly 75% less memory. Processing hundreds of thousands of pages, memory stays flat where Python's would climb steadily.

c := colly.NewCollector(
    colly.Async(true),
    colly.MaxDepth(2),
)

c.Limit(&colly.LimitRule{
    DomainGlob:  "*",
    Parallelism: 100,
    RandomDelay: 2 * time.Second,
})

c.SetProxyFunc(func(_ *http.Request) (*url.URL, error) {
    return url.Parse("http://proxy.example.com:8080")
})

c.OnHTML("div.product", func(e *colly.HTMLElement) {
    // Parse product data
})

Go's connection pooling also handles proxy rotation more efficiently than Python's requests library, which tends to create new connections unless sessions are configured carefully. Over 10k+ requests, that connection overhead adds up in both latency and wasted proxy bandwidth.

The tradeoff is development speed. Go takes more time upfront, but that time comes back in reduced operational overhead later.

Java: The Enterprise Option

Java gets dismissed for scraping because of boilerplate verbosity. Fair point for small projects. For enterprise environments that need audit trails, structured logging, and integration with existing JVM infrastructure, it's a different conversation.

Virtual threads in Java 21 changed the concurrency story significantly. Handling 50,000+ concurrent connections is now practical without the memory overhead of traditional thread pools. For teams already running JVM-based systems, adding a scraping layer that plugs into existing monitoring and compliance infrastructure is more practical than spinning up a separate Go or Python service.

Java's Selenium implementation is also more mature than most alternatives, with better resource management for long-running browser automation tasks. The throughput sits at roughly 2-3x Python's baseline for I/O-bound workloads.

It's the right pick when compliance, audit logging, and JVM ecosystem integration matter more than raw development speed.

Ruby: Fine Until It Isn't

Nokogiri parses HTML cleanly. If a Rails application already exists and moderate-scale scraping needs to feed data into it, Ruby makes sense. The syntax is clean, integration with ActiveRecord is direct, and developer productivity is high.

The ceiling is low though. Ruby has its own GIL, and performance plateaus around 8,000 pages daily regardless of how many threads get thrown at it. Only one thread executes Ruby code at a time, so additional concurrency adds overhead without proportional throughput gains.

Ruby works for Rails-integrated automation at moderate volume. It's not a contender for high-throughput production scraping.

Proxy Rotation: The Part That Actually Determines Success Rate

Language performance is secondary if the proxy layer is slow or gets detected. A scraper running on datacenter IPs from AWS or GCP will hit rate limits within minutes on any major e-commerce site, regardless of how fast the language processes responses.

Mobile carrier proxies from real 4G/5G networks perform better because the traffic is indistinguishable from legitimate smartphone users. CGNAT means the IPs are shared with thousands of real mobile users, so platforms can't block the ranges without blocking actual customers.

How proxy rotation gets configured matters too. Sticky sessions (holding the same IP for 10-30 minutes) work better for sites that track session behavior. Rotating per request reduces rate limiting risk but triggers more CAPTCHA challenges. The interaction between rotation strategy and language-level connection pooling is where performance differences compound.

Go and Java handle connection pooling natively and efficiently. Python's requests library needs explicit session management to avoid creating new connections on every request. Over tens of thousands of requests, poor connection handling wastes proxy bandwidth on failed connections and retries.

Enterprise proxy providers like Bright Data and Oxylabs offer large shared mobile pools that work well for high-volume data collection. For scraping workflows that need dedicated IPs with longer session stability and programmatic proxy management, smaller specialized providers like VoidMob offer dedicated mobile proxies on carrier infrastructure with MCP server access for agent-level control over rotation and session handling.

Memory Behavior Over Time

This is the factor that kills long-running scrapers silently. A scraper that works fine for an hour can fall apart after twelve.

Python's garbage collector struggles with circular references in complex scraping pipelines. Memory tends to climb gradually during extended sessions before stabilizing at a higher baseline. Multiprocessing sidesteps this but adds complexity managing shared state.

Go's garbage collector is tuned for low-latency workloads. Memory stays flat across hundreds of thousands of pages. This is Go's strongest argument for production scraping, not raw speed, but predictable resource usage over days and weeks of continuous operation.

Java's GC is configurable but requires tuning. Default settings can cause occasional pauses that disrupt request timing on strict rate-limited targets.

Ruby's memory profile grows steadily during long scraping sessions, likely from how string allocations accumulate during HTML parsing.

When to Pick What

Go when throughput and resource efficiency are the priority. Scrapers running 24/7 processing 100k+ pages justify the steeper learning curve through lower infrastructure costs and predictable memory behavior.

Python when rapid prototyping matters, when the team includes data scientists, or when volume stays under 10k pages daily. Scrapy's ecosystem is mature and the development speed advantage is real.

Java when the scraper needs to integrate with existing enterprise JVM systems, when compliance and audit logging are requirements, or when virtual threads can replace what would otherwise be a complex async architecture.

Ruby when there's an existing Rails application and the scraping volume stays moderate. Don't try to scale it past 8k pages daily.

The language gets the data. The proxy infrastructure determines whether the data keeps flowing. Both decisions matter, but most teams spend too long on the first one and not enough on the second.