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Elasticsearch vs Solr: What are the differences?
Introduction
Elasticsearch and Solr are both widely used open-source search engines used for full-text search, analytics, and distributed search and analytics. While they have similar functionalities, there are key differences between the two. This article will highlight six key differences between Elasticsearch and Solr.
Query Types: Elasticsearch supports a broader range of query types than Solr. Elasticsearch has a rich query language that allows for more complex queries, including nested queries, fuzzy queries, and wildcard queries, among others. Solr, on the other hand, has a more limited set of query types and is generally more focused on keyword-based search.
Scalability and Distributed Search: Elasticsearch is built on a distributed architecture and is designed for scalability and distributed search out of the box. It uses automatic sharding and replication, making it easier to scale horizontally. Solr has added distributed capabilities over the years but requires manual configuration for scaling out and distributed search.
Document Oriented: Elasticsearch is document-oriented, meaning it stores and indexes whole documents rather than individual fields. This makes it more suitable for scenarios where the entire document needs to be searched and retrieved. Solr, on the other hand, is traditionally field-oriented, meaning it focuses more on individual fields within a document.
Data Replication and High Availability: Elasticsearch comes with built-in support for data replication and high availability. It automatically replicates data across nodes, ensuring that there are copies of the data available in case of node failures. Solr, while it has some support for replication and high availability, requires manual setup and configuration.
Real-time Analytics: Elasticsearch has better support for real-time analytics compared to Solr. It offers near real-time search, meaning that documents are indexed and made searchable almost immediately. Solr, on the other hand, has a delay in indexing and can take some time before new documents are searchable.
Ecosystem and Community: Elasticsearch has a larger and more active community compared to Solr. This means that there are more resources, plugins, and community support available for Elasticsearch. Additionally, Elasticsearch has a broader ecosystem of tools and integrations, making it easier to integrate with other systems.
In summary, Elasticsearch offers a more extensive query language, better scalability and distributed search capabilities, supports document-oriented indexing, provides built-in data replication and high availability mechanisms, offers near real-time search, and has a larger ecosystem and community compared to Solr.
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 Solr
- Powerful35
- Indexing and searching22
- Scalable20
- Customizable19
- Enterprise Ready13
- Restful5
- Apache Software Foundation5
- Great Search engine4
- Security built-in2
- Easy Operating1
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of Solr
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