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Amazon CloudSearch vs Amazon Elasticsearch Service: What are the differences?
Introduction:
When it comes to choosing between Amazon CloudSearch and Amazon Elasticsearch Service for search solutions in the cloud, there are several key differences to consider.
Data Structure and Querying Abilities: Amazon CloudSearch offers a more simplistic and limited data structure compared to Amazon Elasticsearch Service. CloudSearch is schema-less, which means less control over indexing and querying, while Elasticsearch allows for more complex data structures and querying capabilities using its powerful query language.
Scalability and Flexibility: Amazon Elasticsearch Service provides more scalability and flexibility in terms of cluster configuration, allowing users to customize hardware specifications, instance types, and storage options based on their specific requirements. CloudSearch has more restrictions in terms of cluster size and scaling options.
Integration with Other AWS Services: While both services integrate well with other AWS services, Amazon Elasticsearch Service has more extensive integrations with AWS services like Kinesis, CloudWatch, and IAM, allowing for seamless data pipelines and monitoring capabilities. CloudSearch also integrates with AWS services but may have limitations compared to Elasticsearch.
Pricing Model: Amazon CloudSearch has a simpler pricing model based on instance types and document batch uploads, making it easier to estimate costs for smaller workloads. In contrast, Amazon Elasticsearch Service pricing is more complex and based on cluster instance hours, storage, data transfer, and additional features like dedicated master nodes, which can be more cost-effective for larger workloads.
Management and Monitoring Tools: Amazon Elasticsearch Service provides more advanced management and monitoring tools, such as Kibana for data visualization, Elasticsearch API and console for cluster management, and integration with AWS CloudWatch for monitoring performance metrics. These tools make it easier to manage and monitor Elasticsearch clusters compared to CloudSearch.
In Summary, when choosing between Amazon CloudSearch and Amazon Elasticsearch Service, consider factors like data structure, scalability, integrations, pricing, and management tools to determine the best fit for your search solution in the cloud.
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 CloudSearch
- Managed11
- Auto-Scaling7
- Compound Queries5
- Easy Setup3
Pros of Amazon Elasticsearch Service
- Easy setup, monitoring and scaling10
- Kibana7
- Document-oriented7
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