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How to Scrape Algolia Search

Here is a 2000+ word blog post on "How to Scrape Algolia Search":


Algolia is a popular search API that powers the search functionality for many websites across the internet. It allows websites to implement fast and relevant search without needing to run complex search infrastructure themselves.

Some examples of popular websites using Algolia include:

  • Reddit
  • Medium
  • GitHub
  • StackOverflow
  • HackerNews

The goal of this post is to explain:

  1. What is Algolia and how it works
  2. How to scrape Algolia search results using Python
  3. Techniques to scrape Algolia efficiently at scale
  4. How to avoid getting blocked while scraping Algolia

By the end, you‘ll understand how to build a scalable Algolia web scraper for any website using it.

What is Algolia?

Algolia is a hosted search API that provides services like indexing, search, and recommendations. It‘s often referred to as a Search-as-a-Service (SaaS) provider.

The key value propositions of Algolia include:

  • Fast search – Algolia claims to be able to search over billions of records in under 100ms. This is orders of magnitude faster than doing search on your own infrastructure.

  • Relevant search – Algolia handles things like typo tolerance, synonyms, and learning based on user behavior to return the most relevant results.

  • Hosted service – Algolia takes care of things like scaling and redundancy. There‘s no infrastructure for you to manage.

  • API access – The search functionality can be accessed via API which allows for easy integration into websites, mobile apps, etc.

Algolia provides client libraries for most major languages and frameworks that handle the API communication. On the front-end, developers add JavaScript code to interface with Algolia‘s API.

So in summary, Algolia provides hosted and scalable search via API. This allows websites to build great search quickly without having to build complex systems themselves.

Scraping Algolia Search with Python

Now that we understand what Algolia is, let‘s look at how we can scrape Algolia search results using Python.

Scraping Algolia is straightforward since the API is public and documented. We simply need to:

  1. Identify the API endpoint and parameters
  2. Extract any access keys
  3. Send search requests and parse the JSON response

Let‘s go through a complete example of scraping an Algolia-powered website.

Finding the API Endpoint

First, we need to find the API endpoint used by the website for search. The easiest way is to open the site in your browser, run a search query, and check the network requests in the developer tools.

For example, on HackerNews we see a request made to:

The /api/v1/search path gives away that this is the Algolia search API. We also see the search term python passed as a query parameter.

By checking the response, we can see it returns JSON with the results. We now know the API endpoint and search parameter to use.

Getting the API Keys

Next, we need to get the API key which is required to authenticate. Again checking the network request, we can see it passed via the X-Algolia-API-Key header.

We can extract this API key and add it to our requests. Some additional reverse engineering may be required if the key is obfuscated in JavaScript.

Making Search Requests

With the endpoint and API key, we can now make search requests in Python:

import requests 

api_key = "abc123" # Extracted key 

search_url = ""

params = {
  ‘query‘: ‘python‘,
  ‘hitsPerPage‘: 100, 
  ‘attributesToSnippet‘: [‘title:10‘]

headers = {
  "X-Algolia-API-Key": api_key

response = requests.get(search_url, params=params, headers=headers)
data = response.json()


We make a GET request to the API endpoint passing our search term, hits per page, and the API key header. The result contains the search hits as JSON which we can parse and process as needed.

And we now have a basic Algolia scraper!

Scraping Additional Pages

One limitation is that the API only returns the first page of results. To get additional pages, we need to pass the page parameter incrementing from 0:

# First page
params[‘page‘] = 0 

# Second page
params[‘page‘] = 1 

# Third page
params[‘page‘] = 2

To scrape all pages, we can loop over making requests incrementing the page number until no more results are returned.

Putting this together:

from typing import Iterator

def scrape_search(search_term: str) -> Iterator[dict]:

  params = {
    ‘query‘: search_term,
    ‘hitsPerPage‘: 100,

  page = 0
  while True:
    params[‘page‘] = page
    resp = requests.get(search_url, params=params, headers=headers)
    data = resp.json()

    if not data[‘hits‘]:

    yield from data[‘hits‘]

    page += 1

This iterates over pages and yields all the results.

To collect all results:

results = []

for result in scrape_search("python"):


And we now have a complete paginator to scrape all Algolia search results!

Scraping Algolia at Scale

The basic scraper above works but isn‘t optimized for large scale scraping. Issues you may run into:

  • Slow – Synchronous requests make it slow to scrape 100s of pages.
  • Fragile – One failure breaks the entire scrape process.
  • Banned – Scraping from one IP risks getting blocked.

Let‘s look at how to address these issues for robust large scale scraping.

Asynchronous Requests

To speed up scraping we can leverage asynchronous requests. This allows us to have many requests in flight simultaneously.

For example with the asyncio module:

import asyncio

async def fetch_page(page):
  params[‘page‘] = page
  resp = await asyncio.to_thread(requests.get, search_url, params=params) 
  return resp.json()

async def async_scrape():
  page = 0 
  while True:
    tasks = [asyncio.create_task(fetch_page(page + i)) for i in range(10)]
    results = await asyncio.gather(*tasks)

    for data in results:
      if not data[‘hits‘]:

      for hit in data[‘hits‘]:
        yield hit

    page += 10

pages = async_scrape()  

This fetches 10 pages concurrently on each iteration. With async requests the scraper is an order of magnitude faster.

Retries and Fault Tolerance

Network requests are prone to intermittent failures. We can add retries to handle errors gracefully:

from time import sleep

async def fetch_page(page):

  for retry in range(3):

      return await asyncio.to_thread(requests.get, search_url, params=params) 
    except Exception as e:
      print(f"Error: {e}, retrying")

  print(f"Failed to fetch page {page} after {retries} retries")
  return {‘hits‘: []} # Return empty result

This simply retries up to 3 times on any failure. Other improvements like exponential backoff could also be added.

For further resilience, we can wrap the overall scraping loop in a try/except and retry on any unexpected crashes.

With retries at multiple levels, the scraper can recover from various faults and keep running.

Rotating Proxies

Scraping too much from a single IP risks getting blocked. To prevent this, we can route requests through different proxies using modules like requests-proxy-killer:

from proxy_killer import KillerScraper

scraper = KillerScraper(use_cache=False, max_retries=3)

async def fetch_page(page):

  for retry in range(3): 
      proxy = scraper.get_proxy() # Rotate proxy
      resp = scraper.get(search_url, proxies=proxy, params=params)
      return resp.json()
    except Exception as e:
      print(f"Error: {e}, retrying")

# Remainder same as above

By routing each request through a different proxy IP, we can scrape at scale without worrying about blocks.

The steps above allow us to build a robust, high performance, large scale Algolia scraper in Python. The same principles apply to any language.

Avoiding Blocks while Scraping Algolia

The final issue to address is avoiding blocks from the Algolia service itself. If making too many aggressive requests, Algolia may block your IP or throttle requests.

Here are some tips to scrape politely and minimize blocks:

  • Limit rate: Don‘t overwhelm the API with 100s of concurrent requests. Start small and increase gradually.

  • Use proxies: Rotate different IPs to distribute load and avoid concentrated requests.

  • Randomize user-agents: Vary the user-agent header between requests.

  • Follow robots.txt: Make sure your scraper obeys robots.txt rules.

  • Use retry logic: Exponential backoff if you get rate limited or blocked.

  • Scrape during low traffic periods: Target weekday nights when load is lower.

  • Monitor carefully: Check for increasing failures or throttling.

With proper care, you can build sustainable long-running Algolia scrapers. But be sure to closely monitor and adapt your approach over time.

Scraping Helper Libraries

Manually handling all the complexity of scaling and resilience can be cumbersome. Various commercial tools exist to simplify web scraping.

For example:

  • ScrapingBee – Handles proxies, CAPTCHAs, and browsers.
  • ScraperAPI – Browser API with auto proxy rotation.
  • ProxyCrawl – Residential proxies with headless browser.

These tools make it easier to build robust scrapers without having to code complex logic yourself. See my guide on how and when to use a scraping API.

Wrapping Up

Here are the key takeaways:

  • Algolia provides hosted search via API for easy integration into sites.
  • The search API is public and can be scraped by extracting the endpoint and keys.
  • Scraping at scale requires asynchronous requests and proxy rotation.
  • Monitor carefully and scrape politely to avoid blocks.
  • Commercial scraping services can simplify large scraping jobs.

I hope this post provides a good overview of how to effectively scrape Algolia search API at scale with Python. The same principles apply to other languages as well.

Let me know if you have any other questions!


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