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Elasticsearch vs Sumo Logic: What are the differences?
Introduction
This Markdown document outlines the key differences between Elasticsearch and Sumo Logic. Elasticsearch is an open-source search engine that allows for real-time distributed search and analysis of data, while Sumo Logic is a cloud-based log management and analytics service.
Scalability: Elasticsearch is designed to handle massive amounts of data and can scale horizontally by adding more nodes to distribute the workload. Sumo Logic also offers scalability, but it is a cloud-based service that relies on Sumo Logic's infrastructure for scaling.
Data Source: Elasticsearch is commonly used for indexing and searching structured and unstructured data, including documents, logs, and metrics. On the other hand, Sumo Logic is primarily focused on log management and analysis, making it more suitable for monitoring and troubleshooting applications and infrastructure.
Architecture: Elasticsearch is built on top of Lucene, a full-text search library, and is part of the ELK stack (Elasticsearch, Logstash, and Kibana). It allows for real-time querying and analysis of data across distributed nodes. Sumo Logic, on the other hand, is a cloud-native solution that collects data from various sources and provides centralized log management and analytics.
Deployment Options: Elasticsearch can be deployed as a self-managed on-premises solution or as a managed service in the cloud, such as Elasticsearch Service provided by Elastic. Sumo Logic is primarily a cloud-based service and does not offer a self-managed option, making it convenient for organizations looking for a fully managed log management solution.
Querying and Visualization: Elasticsearch provides a powerful query language called Query DSL that allows for complex querying and aggregation of data. It also integrates with Kibana, a visualization tool that provides a user-friendly interface for exploring and visualizing data. Sumo Logic also offers querying capabilities, but its focus is more on providing pre-built dashboards and visualizations for log analysis.
Pricing Model: Elasticsearch follows an open-source model, where the core software is free to use, but additional features and support may require a subscription. Sumo Logic, being a cloud-based service, offers different pricing tiers based on the volume of data ingested and the number of features required.
In Summary, Elasticsearch and Sumo Logic differ in terms of scalability, data source, architecture, deployment options, querying and visualization capabilities, and pricing model.
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 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
Pros of Sumo Logic
- Search capabilities11
- Live event streaming5
- Pci 3.0 compliant3
- Easy to setup2
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of Sumo Logic
- Expensive2
- Occasionally unreliable log ingestion1
- Missing Monitoring1