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Elasticsearch vs MarkLogic: What are the differences?
Key Differences Between Elasticsearch and MarkLogic
Elasticsearch and MarkLogic are both popular search and data management platforms, but they have distinct differences. Here are six key differences between the two:
Scalability: Elasticsearch is highly scalable and optimized for horizontal scaling, making it suitable for handling large-scale data and heavy search workloads. On the other hand, while MarkLogic can also handle large quantities of data, it is generally considered to be more suitable for smaller to medium-sized applications.
Data Model: Elasticsearch uses a document-oriented data model, where data is indexed and stored as JSON documents. MarkLogic, on the other hand, uses a flexible, multi-model approach, allowing you to work with a variety of data models including document, relational, and graph data.
Search Capabilities: Elasticsearch is specifically designed for full-text search and offers powerful search capabilities out of the box, including ranked results, aggregations, and filtering options. MarkLogic also supports full-text search, but it offers more advanced features such as faceted search, semantic search, and entity extraction.
Data Management: MarkLogic provides a comprehensive set of features for managing data, including ACID-compliant transactions, robust security controls, and built-in governance capabilities. Elasticsearch, while offering some data management functionalities, focuses more on search and scalability rather than comprehensive data management.
Integration and Ecosystem: Elasticsearch has a rich ecosystem of plugins and integrations, making it easy to connect with other systems and tools. It integrates seamlessly with popular tools like Kibana, Logstash, and Beats. MarkLogic, on the other hand, offers a more integrated and unified platform, with a wide range of built-in capabilities for data ingestion, transformation, analysis, and visualization.
Commercial vs Open Source: Elasticsearch is an open-source search and analytics engine that can be freely used and extended. While there is a commercial version available from Elastic, the core functionality is open source. MarkLogic, on the other hand, is a commercial product that requires a paid license for full use. This can impact the decision-making process, particularly for organizations with specific budget constraints.
In summary, Elasticsearch excels in scalability, document-oriented data model, and search capabilities, with a large ecosystem of integrations. MarkLogic, on the other hand, offers a flexible multi-model approach, comprehensive data management features, and a more integrated and unified platform. The choice between the two will depend on the specific requirements and priorities of the project or organization.
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 MarkLogic
- RDF Triples5
- JSON3
- Marklogic is absolutely stable and very fast3
- REST API3
- JavaScript3
- Enterprise3
- Semantics2
- Multi-model DB2
- Bitemporal1
- Tiered Storage1
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of MarkLogic
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