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Azure Search vs Elasticsearch: What are the differences?
Introduction: Azure Search and Elasticsearch are both popular search platforms that offer powerful search capabilities. While they have some similarities, there are also key differences between the two. In this article, we will explore six key differences between Azure Search and Elasticsearch.
Architecture: Azure Search is a managed service provided by Microsoft Azure, which means that most of the infrastructure and management tasks are handled by Azure. On the other hand, Elasticsearch is an open-source search engine that needs to be self-managed. This means that users have more control and flexibility over the deployment and management of Elasticsearch.
Scalability: Azure Search is designed to be highly scalable and can handle large volumes of data. It can automatically scale up or down based on the demand and offers horizontal scaling. Elasticsearch also supports scalability, but it requires more manual configuration and management compared to Azure Search.
Full-text Search: Both Azure Search and Elasticsearch offer full-text search capabilities. However, the way they handle full-text search is different. Azure Search uses a language-specific analyzer to process and tokenize text, which can be customized based on specific requirements. Elasticsearch, on the other hand, uses its own text analysis engine called Lucene, which provides a wide range of built-in analyzers and filters.
Data Ingestion: Azure Search provides built-in connectors to various data sources such as Azure SQL Database, Cosmos DB, and Azure Blob Storage, which makes data ingestion easier and faster. Elasticsearch also supports data ingestion from multiple sources, but it requires more manual configuration and development effort to set up the data pipeline.
Query Language: Azure Search uses a query language called OData for querying the search index. It provides a simple and intuitive way to build search queries using standard query operators. On the other hand, Elasticsearch uses its own query DSL (Domain-Specific Language), which offers more advanced querying capabilities and flexibility compared to OData.
Analytics and Monitoring: Azure Search provides built-in analytics and monitoring capabilities, which allow users to track search performance, query patterns, and other important metrics. It provides a user-friendly interface to view and analyze the search analytics data. Elasticsearch also offers analytics and monitoring features, but it requires more manual configuration and integration with third-party tools for visualization and analysis.
In summary, Azure Search is a managed service provided by Microsoft Azure, offering ease of deployment and management, built-in connectors, and a user-friendly query language. Elasticsearch, on the other hand, is an open-source search engine that provides more control and flexibility over deployment, powerful querying capabilities, and a wide range of integrations.
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!
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.
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.
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.
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.
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.