How to create a search engine

Think of a search engine as a librarian, there to help you find the information you’re looking for. You tell them the problem you’re trying to solve or the question you’re trying to answer, and the librarian can point you to the books and resources that are most likely to help you. They might not always get it right, but it’s far more efficient than blindly thumbing through books, hoping to get lucky.

Search engines are made up of four main components — web servers, data ingestion, an index, and the results page. Before you build your search engine, it’s important to understand what each of these does.

If the search engine is the librarian, the web servers are the library itself. This is where you store all the data you need to return meaningful results to the user. These web servers are commonly cloud-based because of the scalability, accessibility, security, and performance they give you. For a web search engine, this will be the location of HTML pages, images, videos, and other assets across different websites. For a social media site, this will be the titles, descriptions, metadata, and other information needed for the content on that platform.

Just like libraries need to curate and collect different books, a search engine needs to collect the data from somewhere. This is why data ingestion is such an important part of building a search engine. For web search engines, this data ingestion is done using a web crawler. The crawler uses sophisticated algorithms to scan websites and identify what the content is and where it can be found.

Integrating with other services via an API is another type of data ingestion. These integrations make it possible to cherry pick where your data comes from, which can make your search engine much better at finding specific data. For example, if you’re building a search engine for video, you might want to display results from multiple providers, such as YouTube, Netflix, and Disney+.

Similarly, you can use connectors to bring in information from one or more data sources. These are often pre-built modules or code snippets you can use to connect to specific databases, applications, or APIs. They give you plenty of flexibility, without having to broaden your scope too far.

Just like in a library, you need to have an index of your content, otherwise it would be impossible to know where everything is. An index does this by organizing and storing information from your data sources to ensure it can be retrieved efficiently. For your search engine to work well, it needs to be able to identify, rank, and serve content quickly.

Because you’re likely trying to index vast amounts of data, this index can’t be simply copied verbatim from the source. Instead, the index needs to process this content, breaking it down into key elements like:

• Keywords: Words and phrases found on the page

• Embeddings: Multidimensional vectors representing text data

• Metadata: Titles, descriptions, and other structured data embedded in the page

• Content analysis: Understanding of the page's topic, entities, and overall meaning

• Backlinks: Links from other websites pointing to the content

The final piece to this puzzle is actually displaying the search results to the user. The search engine results page takes all the hard work you’ve done with the servers, data ingestion, and index, culminating in a list of useful results for the user to pick from.

How this looks will vary from search engine to search engine, but you’ll likely have a title, link, description, and some sort of pagination on SERPs. You might also have more advanced filtering and faceting, so the user can refine the results easily based on common parameters. But the important thing is that the results are displayed clearly, so it’s easy to find the best, most relevant choice.

Your search relevance is important because it fosters trust by showing the user you understand their intent, assuring them they've landed on the right path. This ultimately guides them seamlessly toward their desired information — a combination that builds loyalty and fuels long-lasting engagement.

Now that you’ve got a better understanding of the key components of a search engine, let’s dive into the process of building your own and the challenges you might face. 

For a start, you want to think about the scale of your search engine. It might be tempting to aim for the stars and try to build the new Google. But crawling billions of web pages requires a massive amount of infrastructure and computing power — not to mention the capacity to store all the data. 

You should also bear in mind your existing knowledge and skill set before you start building. The better you know the data source, the easier it will be to leverage it for your search engine. Similarly, try to stick to a tech stack you already have experience with. If you’re proficient in Python, consider using that to build your search engine.

The first step in building your search engine is to decide what problem your search engine is going to solve. This decision will influence everything else you’re going to build, from the data source(s), to the indexing, to how you display your results. So think about who you’re building your search engine for, and ask yourself these questions:

• Why are they looking for this information/content?

• What information do you need to know to decide if something is relevant?

• How will you decide which results are better than others?

• How will you present the results to make them as useful as possible?

Once you’ve answered these questions, you’ll be in a much better place to make key decisions throughout this building process — from what data sources to use to whether you should have images on your search engine results pages. The clearer these answers are in your mind, the better you’ll be at meeting user needs and expectations.

Once you know what your search engine requirements are, the next step is ingesting the data you need. If you’re planning to use integrations or connectors, you’ll need to get access to those sources and ensure you can access the data whenever you need for indexing. If the data source belongs to you, this shouldn’t be a problem. But remember that any external data source comes with some risk. The owner of the data source could revoke access or make changes to the data at any point, which could cause you some problems down the line. You can schedule data refreshes to combat this, but if there are changes to the structure or architecture of the data, it can still cause issues. 

If you’re creating a web search engine, you’ll need to use a web crawler to fetch the data you want to index. The time this takes will depend entirely on the scope of your search engine. In theory, you could build your own crawler, but that will take a lot of work. Instead, it’s much quicker and easier to use an existing tool such as the Elastic web crawler. This will scan whatever websites you like, and you can schedule automatic recrawls so your search engine is always up to date.

It doesn't matter if you use a crawler, API, or connector — you still need somewhere to store your collected information. But you shouldn’t rush into picking any old database. You need to consider things like data volume and growth, performance requirements, data structure, scalability, reliability, security, and analytics. You also need to consider the cost of storing this data in both the short and long term.

As we mentioned earlier, it’s also useful to consider your own skill set. For example, if you’ve mostly used Elasticsearch® before in your dev, that’s probably the best option for you now. But if you’re comfortable with a few different types of databases, you should base your decision on the factors listed above.

The next thing you need to do is index the data you’ve collected and stored. This is what will let you give your users the most relevant results to their query. Luckily, indexing is included as part of the Elastic web crawler, which will make your life easier. But you’ll still need to consider things like data granularity, attribute indexing, and data compression while configuring your index structure.

There will probably be some trial and error on the way, but the target should be to help users:

• Find relevant information quickly

• Refine their search and filter results

• Discover related content

Using an out-of-the-box search UI will make all of this much easier, as you can have your search engine UI up and running quickly. This enables you to test your search engine, review the indexing, and make tweaks and changes to improve your search engine. This includes things like filtering and sorting, pagination, and search-as-you-type.

The ultimate goal when building any search engine is to deliver the most useful and relevant results. But it’s unlikely you’ll be able to do that out of the gate. Instead, you need to constantly work to refine your search engine to get closer to achieving that goal. Things like keyword matching, a vector database, hybrid search techniques, relevance scoring, link analysis, and synonyms can all make big improvements.

You can also look to machine learning and AI to enhance your search capabilities. This can make your search engine much more powerful, as it can learn from user behavior, include more advanced personalization, and even better understand the user’s intent and tone. This does come with its own challenges, though. You’ll need to make sure bias doesn’t creep into your search engine, and you need to take privacy and security very seriously.

Building your first search engine can feel like a daunting task, but hopefully these steps have shown you that it’s actually very achievable. And Elastic can help with the process at every step. It simplifies data ingestion with tools like the web crawler, empowers indexing with its scalable and flexible architecture, and fuels relevance with its machine learning capabilities.

Whether you're building website search or a specialized search engine, Elasticsearch gives you a comprehensive set of tools for creating an efficient and user-friendly search experience, from scratch.

Whenever you're ready, here are four ways we can help you bring better search experiences to your business:

1. Start a free trial and see how Elastic can help your business.

2. Tour our solutions to see how the Elasticsearch Platform works and how our solutions will fit your needs.

3. Learn how to set up your Elasticsearch cluster and get started on data collection and ingestion with our 45-minute webinar. 

4. Share this article with someone you know who'd enjoy reading it via email, LinkedIn, Twitter, or Facebook.