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Algolia vs Elasticsearch: What are the differences?
Developers describe Algolia as "Developer-friendly API and complete set of tools for building search". Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard. On the other hand, Elasticsearch is detailed as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Algolia and Elasticsearch can be categorized as "Search as a Service" tools.
Some of the features offered by Algolia are:
- Database search
- Multi-attributes
- Search as you type
On the other hand, Elasticsearch provides the following key features:
- Distributed and Highly Available Search Engine.
- Multi Tenant with Multi Types.
- Various set of APIs including RESTful
"Ultra fast", "Super easy to implement" and "Easy setup, fast and relevant" are the key factors why developers consider Algolia; whereas "Powerful api", "Great search engine" and "Open source" are the primary reasons why Elasticsearch is favored.
Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.
Uber Technologies, Instacart, and Slack are some of the popular companies that use Elasticsearch, whereas Algolia is used by Medium, StackShare, and Product Hunt. Elasticsearch has a broader approval, being mentioned in 2000 company stacks & 976 developers stacks; compared to Algolia, which is listed in 258 company stacks and 54 developer stacks.
I want to design a search engine which can search with PAYMENT-ID, ORDER-ID, CUSTOMER-NAME, CUSTOMER-PHONE, STORE-NAME, STORE-NUMBER, RETAILER-NAME, RETAILER-NUMBER, RETAILER-ID, RETAILER-MARKETPLACE-ID.
All these details are stored in different tables like ORDERS, PAYMENTS, RETAILERS, STORES, CUSTOMERS, and INVOICES with relations. Right now we have only 10MBs of data with 20K records. So I need a scalable solution that can handle the search from all the tables mentioned and how can I make a dataset with so many tables with relations for search.
What e-commerce platform or framework are you using?
A lot of this depends on what your infrastructure already supports. Either of the options are a great choice so it comes down to what will be easiest to integrate and which search service is most affordable.
Elastic search is open source but you will need to configure and maintain it on your server. It may be more difficult to set up depending on the platform your app is built on.
Algolia has great documentation and is normally pretty easy to integrate but it can be pretty expensive.
I've never used Typsense but it seems like it would be a great option as well.
Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are: - Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON - Allow a strict match mode - Perform the search through all the JSON values (it can reach 6 nesting levels) - Ignore all Keys of the JSON; I'm interested only in the values.
The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!
The Amazon Elastic Search service will certainly help you do most of the heavy lifting and you won't have to maintain any of the underlying infrastructure. However, elastic search isn't trivial in nature. Typically, this will mean several days worth of work.
Over time and projects, I've over the years leveraged another solution called Algolia Search. Algolia is a fully managed, search as a service solution, which also has SDKs available for most common languages, will answer your fuzzy search requirements, and also cut down implementation and maintenance costs significantly. You should be able to get a solution up and running within a couple of minutes to an hour.
I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.
I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.
Maybe you can do it with storing on S3, and query via Amazon Athena en AWS Glue. Don't know about the performance though. Fuzzy search could otherwise be done with storing a soundex value of the fields you want to search on in a MongoDB. In DynamoDB you would need indexes on every searchable field if you want it to be efficient.
Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?
(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.
Thank you!
Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.
To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.
Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.
For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.
Hope this helps.
We originally had used Algolia for our search features. It's a great service, however the cost was getting to be unmanageable for us. Algolia's pricing model is based around the number of search requests and the number of records. So if you produce a large number of small records the price can quickly get out of hand even if your actual dataset doesn't take up that much space on disk. Spikes in internet traffic can also lead to unexpected increases in billing (even if the traffic comes from bots)
After migrating to Typesense Cloud we have been able to save A LOT of money without losing out on any of the performance we got from Algolia. I do not exaggerate when I say that our overhead for search is less than 25% of what it used to be. Typesense also has the following advantages:
Their cloud offering lets you configure your Typesense nodes and specify how many you want to spin up. This allows you to manage costs in a manner that is way more predictable. (You basically pay for servers/nodes instead of records and requests)
It's completely open source. We can spin up a cluster on our own servers or run it locally.
The new pricing model Algolia introduced really sealed the deal for us on this one - much closer to pay-as-you-go. And didn't want to spend time learning more about hosting/optimizing Elasticsearch when that isn't our core business problem - would much rather pay others to solve that problem for us.