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Elasticsearch vs Google Cloud Datastore: What are the differences?
Introduction
Elasticsearch and Google Cloud Datastore are two popular data storage and retrieval systems. While they both serve similar purposes, there are several key differences between them. In this article, we will explore these differences in detail.
Scalability: One key difference between Elasticsearch and Google Cloud Datastore is their scalability. Elasticsearch is highly scalable, allowing you to distribute your data across multiple nodes and handle large volumes of data and high loads efficiently. On the other hand, Google Cloud Datastore has limited scalability and is more suitable for small to medium-sized workloads.
Querying and Search Capabilities: Elasticsearch is built specifically for searching and provides powerful querying capabilities. It supports full-text search, aggregations, filtering, and ranked search results. Google Cloud Datastore, on the other hand, has limited querying and search capabilities. It is primarily a NoSQL document datastore with basic filtering and sorting options.
Data Consistency: Another difference between Elasticsearch and Google Cloud Datastore is their approach to data consistency. Elasticsearch sacrifices some level of data consistency to achieve high availability and fast search performance. It uses eventual consistency, where changes to the data may take some time to propagate across all nodes in the cluster. In contrast, Google Cloud Datastore guarantees strong data consistency, ensuring that all read operations return the most up-to-date data.
Schema Flexibility: Elasticsearch is schema-less, allowing you to index and search any JSON document without the need for a predefined schema. This makes it highly flexible and suitable for applications with evolving data structures. Google Cloud Datastore, on the other hand, requires a predefined schema for each kind (entity type). Any changes to the schema require updates and migrations.
Indexing and Data Retrieval: Elasticsearch excels in indexing and data retrieval speed, making it a great choice for real-time search applications. It uses inverted indices for efficient searching and retrieval. Google Cloud Datastore, while capable of fast retrieval, may not perform as well as Elasticsearch for high-speed search scenarios.
Operational Complexity: While Elasticsearch offers powerful search capabilities, it comes with a higher level of operational complexity. Setting up and managing Elasticsearch clusters require expertise in distributed systems and can be challenging. Google Cloud Datastore, on the other hand, is a fully managed service, abstracting away the complexity of infrastructure management.
In summary, Elasticsearch and Google Cloud Datastore differ in terms of scalability, querying capabilities, data consistency, schema flexibility, indexing speed, and operational complexity. Depending on your specific use case and requirements, you can choose the one that best suits your needs.
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 Google Cloud Datastore
- High scalability7
- Serverless2
- Ability to query any property2
- Pay for what you use1
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of Google Cloud Datastore
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