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Elasticsearch vs Manticore Search: What are the differences?
Elasticsearch and Manticore Search are both powerful search engines that can be used to index and analyze large amounts of data, but they have some key differences.
Data Structures and Indexing: Elasticsearch uses an inverted index, which allows for quick searching and retrieval of data. Manticore Search, on the other hand, uses a hybrid data structure known as a inverted index with a disk-based hash table. This provides a more efficient indexing process and allows for faster data retrieval.
Full-text Search Features: Elasticsearch offers a wide range of full-text search features, including tokenization, stemming, and relevance scoring. Manticore Search, however, takes full-text search to another level with support for advanced features like faceted search, infix search, and indexing of custom data types.
Scalability: Both Elasticsearch and Manticore Search are designed to be scalable, but Elasticsearch has a more mature and robust clustering mechanism. It provides built-in features for horizontal scaling and automatic load balancing, making it better suited for larger deployments and high-volume search applications.
Query Language: Elasticsearch uses the Query DSL (Domain Specific Language) for querying, which is a powerful and flexible way to construct complex queries. Manticore Search, on the other hand, uses a simplified version of the SQL language, making it easier for developers familiar with SQL to get started.
Faceting and Aggregation: Elasticsearch has extensive support for faceting and aggregation, allowing users to extract valuable insights from their data. Manticore Search also supports faceting and aggregation, but the functionality is not as comprehensive as Elasticsearch's.
Logging and Monitoring: Elasticsearch provides a comprehensive logging and monitoring system, which includes built-in tools like the Elasticsearch Monitoring API and the Elastic Stack. Manticore Search, while it does have some logging and monitoring capabilities, does not have the same level of built-in tools and integration options as Elasticsearch.
In Summary, Elasticsearch and Manticore Search differ in terms of data structures and indexing, full-text search features, scalability, query language, faceting and aggregation capabilities, and logging and monitoring options.
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.
Pros of Elasticsearch
- Powerful api328
- Great search engine315
- Open source231
- Restful214
- Near real-time search200
- Free98
- Search everything85
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Great docs4
- Awesome, great tool4
- Highly Available3
- Easy to scale3
- Potato2
- Document Store2
- Great customer support2
- Intuitive API2
- Nosql DB2
- Great piece of software2
- Reliable2
- Fast2
- Easy setup2
- Open1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Not stable1
- Scalability1
- Community0
Pros of Manticore Search
- Easy to scale2
- Free2
- Distributed2
- Easy to get started2
- Real-time inserts2
- Lightweight2
- Open source2
- Low RAM consumption2
- JSON over HTTP2
- MySQL/PostgreSQL/ODBC/xml/csv sync out of the box2
- SQL syntax2
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4