Need advice about which tool to choose?Ask the StackShare community!
Elasticsearch vs Pilosa: What are the differences?
Data Model: Elasticsearch uses a document-oriented data model where data is stored in JSON format, while Pilosa is a bitmap-indexing data model that stores data as binary values. This difference in data models impacts how data is indexed and queried in each system.
Querying: Elasticsearch provides complex full-text search capabilities, aggregations, and filtering through its query DSL (Domain Specific Language). Pilosa focuses on set-based operations like unions, intersections, and differences which are efficient for analyzing relationships between data points.
Scalability: Elasticsearch is designed for horizontal scalability, with built-in features like sharding and replication that allow it to handle large quantities of data across multiple nodes. Pilosa, on the other hand, is designed for massive scalability by partitioning and distributing bitmaps across a cluster of nodes.
Indexing Speed: Elasticsearch is known for its fast indexing speed, making it suitable for near real-time data indexing and searching. Pilosa, with its bitmap-based indexing, provides rapid response times for set-based queries but may not be as fast for indexing large volumes of data compared to Elasticsearch.
Use Cases: Elasticsearch is commonly used for full-text search, log analytics, and real-time analytics due to its powerful search capabilities and scalability. Pilosa is typically used for analytical workloads that involve complex set calculations, particularly in scenarios where relationships between data points need to be analyzed efficiently.
Consistency Model: Elasticsearch provides eventual consistency by default, allowing for quick data availability but potential for stale results. Pilosa offers strong consistency where updates are immediately reflected across all nodes in the cluster, ensuring real-time accuracy but potentially impacting latency.
In Summary, Key differences between Elasticsearch and Pilosa lie in their data models, querying capabilities, scalability approaches, indexing speed, preferred use cases, and consistency models.
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 Pilosa
Sign up to add or upvote prosMake informed product decisions
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4