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Elasticsearch vs OpenTSDB: What are the differences?
Introduction
In this Markdown code, we will discuss the key differences between Elasticsearch and OpenTSDB. Elasticsearch is a distributed search and analytics engine, while OpenTSDB is a scalable time series database.
Scalability: One key difference between Elasticsearch and OpenTSDB is their scalability. Elasticsearch is designed to handle large amounts of data and can be easily scaled horizontally by adding more nodes to the cluster. On the other hand, OpenTSDB is built specifically for storing and querying time series data, making it highly scalable for handling time-based data.
Querying and Aggregation: Elasticsearch offers a powerful query and aggregation framework that allows users to perform complex queries and aggregations on their data. It supports full-text search, filtering, sorting, and aggregations like statistical, histogram, and date range aggregations. OpenTSDB, on the other hand, focuses on time-based querying and aggregation, providing functions like time-based downsampling and roll-ups for efficient time series data analysis.
Schema and Data Model: Elasticsearch is schema-less, meaning you don't need to define a fixed schema for your data before indexing it. This flexibility allows you to easily change the structure of your data without any schema migration. OpenTSDB, on the contrary, has a fixed data model that requires you to define a metric, timestamp, and tags for your time series data before storing it. This helps in efficiently storing and querying time series data without any schema changes.
Data Replication and Resilience: Elasticsearch provides automatic data replication and sharding for high availability and resilience. It uses a distributed architecture where each shard has replicas, ensuring that data is replicated across multiple nodes for failover and data reliability. OpenTSDB also supports data replication and resilience, but it uses a different approach called the HBase write-ahead log for durability and replication.
Data Ingestion and Integration: Elasticsearch provides a rich set of APIs and plugins for data ingestion and integration with various data sources and systems. It supports batch indexing, real-time indexing, and streaming data ingestion. OpenTSDB is mainly designed for time series data ingestion and integrates well with monitoring and metrics collection systems like Prometheus and StatsD.
Community and Ecosystem: Elasticsearch has a large and active open-source community with a wide range of plugins and libraries available for various use cases. It integrates well with popular visualization tools like Kibana and Grafana for data exploration and monitoring. OpenTSDB also has an active community, but it is more focused on time series data analysis and monitoring.
In summary, Elasticsearch and OpenTSDB differ in terms of scalability, querying and aggregation capabilities, schema and data model, data replication and resilience, data ingestion and integration options, as well as their community and ecosystem support.
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 OpenTSDB
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4