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Amazon Elasticsearch Service vs Azure Search: What are the differences?
# Introduction #
- Deployment: Amazon Elasticsearch Service is a fully managed service, which means Amazon takes care of cluster management, scaling, and maintenance, while Azure Search requires more manual configuration and management from the user's end.
- Pricing: Amazon Elasticsearch Service charges for the resources you use, while Azure Search has a flat pricing structure based on the service tier chosen, causing potential cost differences depending on usage patterns.
- Integration: Amazon Elasticsearch Service seamlessly integrates with other AWS services, providing a broader ecosystem for users, while Azure Search integrates well with other Microsoft Azure services, offering a similar ecosystem but within the Azure cloud environment.
- Indexing: Amazon Elasticsearch Service supports automatic indexing of JSON documents, eliminating the need for manual indexing, whereas Azure Search requires explicit indexing of documents, which can be a manual task for developers.
- Querying: Amazon Elasticsearch Service allows the use of Elasticsearch Query DSL (Domain Specific Language) for more complex queries, giving users more powerful search capabilities than Azure Search, which has its own query syntax that may be more limited in functionality.
- Scalability: Amazon Elasticsearch Service offers seamless scalability with the ability to add or remove nodes dynamically, while Azure Search may require manual scaling operations that could introduce downtime during high traffic periods.
In Summary, Amazon Elasticsearch Service and Azure Search differ in deployment, pricing, integration, indexing, querying, and scalability capabilities, which should be considered when choosing a search service for your project.
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!
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.
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.
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.
Pros of Amazon Elasticsearch Service
- Easy setup, monitoring and scaling10
- Kibana7
- Document-oriented7
Pros of Azure Search
- Easy to set up4
- Auto-Scaling3
- Managed3
- Easy Setup2
- More languages2
- Lucene based search criteria2
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