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ArangoDB vs Elasticsearch: What are the differences?
ArangoDB and Elasticsearch are both popular database management systems used for different purposes. While ArangoDB focuses on multi-model capabilities and Elasticsearch specializes in full-text search and analytics, there are several key differences between these two systems that set them apart.
Data Model: ArangoDB supports a flexible multi-model approach, allowing users to store and query data as key-value pairs, documents, or graphs. On the other hand, Elasticsearch follows a document-oriented approach, where data is primarily stored as JSON documents.
Query Language: ArangoDB uses its own query language called AQL (ArangoDB Query Language), which provides a unified way to query data across different data models. Elasticsearch, on the other hand, utilizes a query DSL (Domain Specific Language) to perform more advanced and specialized searches on JSON documents.
Scalability: ArangoDB offers horizontal scalability through sharding and replication, allowing users to distribute data and workload across multiple servers. Elasticsearch is designed with scalability in mind and uses horizontal scaling by default, making it easy to add more nodes to handle increasing data volumes and search queries.
Search Capabilities: While both databases offer search functionality, Elasticsearch excels at full-text search and advanced search capabilities, such as filtering, faceted search, and relevance scoring. ArangoDB also supports full-text search but is not as specialized as Elasticsearch in this aspect.
Data Storage: ArangoDB stores data in collections, providing features like transactions and indexes for efficient data retrieval. Elasticsearch organizes data into indices, which are further divided into shards for distribution. This division allows Elasticsearch to handle large volumes of data efficiently.
Data Replication: ArangoDB supports synchronous and asynchronous data replication between different database servers in a cluster, ensuring high availability and fault tolerance. Elasticsearch also provides data replication but focuses more on distributing data across multiple nodes for improved performance and fault tolerance.
In Summary, ArangoDB and Elasticsearch differ in their data models, query languages, scalability approaches, search capabilities, data storage methods, and data replication strategies.
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 ArangoDB
- Grahps and documents in one DB37
- Intuitive and rich query language26
- Good documentation25
- Open source25
- Joins for collections21
- Foxx is great platform15
- Great out of the box web interface with API playground14
- Good driver support6
- Low maintenance efforts6
- Clustering6
- Easy microservice creation with foxx5
- You can write true backendless apps4
- Managed solution available2
- Performance0
Pros of Elasticsearch
- Powerful api329
- 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
- Awesome, great tool4
- Great docs4
- Highly Available3
- Easy to scale3
- Nosql DB2
- Document Store2
- Great customer support2
- Intuitive API2
- Reliable2
- Potato2
- Fast2
- Easy setup2
- Great piece of software2
- Open1
- Scalability1
- Not stable1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Community0
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Cons of ArangoDB
- Web ui has still room for improvement3
- No support for blueprints standard, using custom AQL2
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4