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Elasticsearch vs Paw: What are the differences?
# Introduction
1. **Scalability**: Elasticsearch is a distributed search and analytics engine, designed for horizontal scalability, allowing it to efficiently handle large amounts of data and query loads. On the other hand, Paw is an HTTP client tool primarily used for testing APIs and services, focusing on individual requests rather than handling massive amounts of data or distributed systems.
2. **Full-Text Search**: One of the key differences between Elasticsearch and Paw is that Elasticsearch specializes in full-text search capabilities, supporting complex queries, relevance scoring, and text analysis features out of the box. In contrast, Paw does not have the same level of full-text search functionality, as it is primarily focused on HTTP request and response handling.
3. **Real-Time Data Search**: Elasticsearch excels in real-time data search and analytics, providing near-instant search results even on large datasets, making it suitable for applications that require up-to-date information retrieval. In comparison, Paw is not designed for real-time search operations but rather for manual testing and debugging of API calls and responses.
4. **Data Aggregation and Analytics**: Elasticsearch offers robust aggregation capabilities, allowing users to extract and summarize data from various sources, perform analytics, and generate insights. Paw, on the other hand, lacks advanced data aggregation features, as its main purpose is to assist developers in working with API endpoints and payloads.
5. **Open-Source vs. Commercial Tool**: Elasticsearch is an open-source project with a strong community backing, offering flexibility and customization options without licensing fees. In contrast, Paw is a commercial tool that requires a paid license for full access to its features and support, catering to professionals and organizations with specific API testing requirements.
6. **Integration Ecosystem**: Elasticsearch has a broad integration ecosystem, with plugins and extensions to support various use cases, including data visualization tools, security plugins, and connectors to other systems. Paw, while extensible through custom scripts and configurations, does not have the same level of integration options as Elasticsearch.
In Summary, Elasticsearch and Paw differ in scalability, full-text search capabilities, real-time data search performance, data aggregation features, licensing models, and integration ecosystems.
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 Paw
- Great interface46
- Easy to use37
- More stable and performant than the others25
- Saves endpoints list for testing16
- Supports environment variables13
- Integrations12
- Multi-Dimension Environment Settings9
- Paste curl commands into Paw4
- Creates code for any language or framework2
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of Paw
- It's not free3
- MacOS Only2
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