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Elasticsearch vs MemSQL: What are the differences?
## Key Differences between Elasticsearch and MemSQL
Elasticsearch is a search engine based on the Lucene library, while MemSQL is a distributed in-memory database.
Elasticsearch is optimized for full-text search and complex search queries, making it ideal for use cases like log analysis and text searching. In contrast, MemSQL is designed for real-time analytics and transactional workloads, providing high performance for data processing and retrieval.
Elasticsearch uses a document-oriented data model, where data is stored in JSON format and organized into indexes and types. It provides robust indexing and querying capabilities for unstructured data. On the other hand, MemSQL follows a relational database model with tables, rows, and columns, making it suitable for structured data storage and processing.
Elasticsearch supports distributed search capabilities and horizontal scalability through its cluster-based architecture, allowing for efficient data distribution and processing across multiple nodes. In comparison, MemSQL employs a distributed architecture for high availability and fault tolerance, enabling seamless scaling of data across clusters and automatic data redundancy.
Elasticsearch offers advanced text analysis features like tokenization, stemming, and synonym expansion, which are essential for accurate full-text search. MemSQL, on the other hand, provides support for SQL queries and ACID transactions, ensuring data consistency and integrity in large-scale data operations.
Elasticsearch provides powerful analytics and aggregation capabilities through its aggregation framework, enabling users to perform complex data analysis on large datasets. In contrast, MemSQL offers in-memory processing capabilities and real-time analytics, allowing for instant insights and decision-making on streaming data.
In Summary, Elasticsearch is optimized for full-text search and complex search queries, while MemSQL is designed for real-time analytics and transactional workloads, catering to different use cases and data processing requirements.
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 api327
- Great search engine315
- Open source230
- Restful214
- Near real-time search199
- Free97
- Search everything84
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Highly Available3
- Awesome, great tool3
- Great docs3
- Easy to scale3
- Fast2
- Easy setup2
- Great customer support2
- Intuitive API2
- Great piece of software2
- Reliable2
- Potato2
- Nosql DB2
- Document Store2
- Not stable1
- Scalability1
- Open1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Easy to get hot data1
- Community0
Pros of MemSQL
- Distributed8
- Realtime4
- Sql3
- Concurrent3
- JSON3
- Columnstore3
- Scalable2
- Ultra fast2
- Availability Group1
- Mixed workload1
- Pipeline1
- Unlimited Storage Database1
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of MemSQL
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