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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on ArangoDB and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 405.6K views
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AlgoliaAlgoliaElasticsearchElasticsearch
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FirebaseFirebase

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!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 305.2K views
Recommends
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AlgoliaAlgolia

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.

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Mike Endale
Recommends
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Cloud FirestoreCloud Firestore

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.

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Pros of ArangoDB
Pros of Elasticsearch
  • 37
    Grahps and documents in one DB
  • 26
    Intuitive and rich query language
  • 25
    Good documentation
  • 25
    Open source
  • 21
    Joins for collections
  • 15
    Foxx is great platform
  • 14
    Great out of the box web interface with API playground
  • 6
    Good driver support
  • 6
    Low maintenance efforts
  • 6
    Clustering
  • 5
    Easy microservice creation with foxx
  • 4
    You can write true backendless apps
  • 2
    Managed solution available
  • 0
    Performance
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Awesome, great tool
  • 4
    Great docs
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Nosql DB
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Reliable
  • 2
    Potato
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Great piece of software
  • 1
    Open
  • 1
    Scalability
  • 1
    Not stable
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 0
    Community

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Cons of ArangoDB
Cons of Elasticsearch
  • 3
    Web ui has still room for improvement
  • 2
    No support for blueprints standard, using custom AQL
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale

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What is ArangoDB?

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

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May 21 2019 at 12:20AM

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What are some alternatives to ArangoDB and Elasticsearch?
Neo4j
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
PostgreSQL
PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
OrientDB
It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records.
See all alternatives