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Scalability
TimescaleDB offers excellent horizontal and vertical scalability, which allows it to handle large volumes of data efficiently. Its architecture is designed to accommodate growth by distributing and efficiently managing data shards.
Time-Series Data Optimization
Specifically optimized for time-series data, TimescaleDB provides features like hypertables and continuous aggregates that speed up queries and optimize storage for time-based data.
SQL Compatibility
As an extension of PostgreSQL, TimescaleDB offers full SQL support, making it familiar to developers and allowing easy integration with existing SQL-based systems and applications.
Retention Policies
TimescaleDB includes built-in data retention policies, enabling automatic management of historical data and freeing up storage by performing automatic data roll-ups or deletes.
Integration with the PostgreSQL Ecosystem
It benefits from PostgreSQL's rich ecosystem of extensions, tools, and optimizations, allowing for versatile use cases beyond just time-series data while maintaining robust reliability and performance.
We have collected here some useful links to help you find out if TimescaleDB is good.
Check the traffic stats of TimescaleDB on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of TimescaleDB on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of TimescaleDB's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of TimescaleDB on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about TimescaleDB on Reddit. This can help you find out how popualr the product is and what people think about it.
(:alert: I work for Timescale :alert:) It's funny, we hear this more and more "we did some research and landed on Influx and ... Help it's confusing". We actually wrote an article about what we think, you can find it here: https://www.timescale.com/blog/what-influxdb-got-wrong/ As the QuestDB folks mentioned if you want a drop in replacement for Influx then they would be an option, it kinda sounds that's not what... - Source: Hacker News / almost 3 years ago
If you like PostgreSQL, I'd recommend starting with that. Additionally, you can try TimescaleDB (it's a PostgreSQL extension for time-series data with full SQL support) it has many features that are useful even on a small-scale, things like:. Source: almost 4 years ago
I have built a Django server which serves up the JSON configuration, and I'd also like the server to store and render sensor graphs & event data for my Thing. In future, I'd probably use something like timescale.com as it is a database suited for this application. However right now I only have a handful of devices, and don't want to spend a lot of time configuring my back end when the Thing is my focus. So I'm... Source: almost 5 years ago
I've seen a lot of benchmark results on timescale on the web but they all come from timescale.com so I just want to ask if those are accurate. Source: almost 5 years ago
Ryan from Timescale here. We (TimescaleDB) just launched the second annual State of PostgreSQL survey, which asks developers across the globe about themselves, how they use PostgreSQL, their experiences with the community, and more. Source: over 5 years ago
TimescaleDB, a leading player in the time-series databases sector, has gained considerable traction due to its deep integration with PostgreSQL, offering both robustness and familiarity for developers accustomed to SQL-based systems. Public perception of TimescaleDB, as gathered from various sources and dialogues, points to a combination of satisfaction with its capabilities and curiosity about its bold claims, particularly in performance comparisons.
PostgreSQL Integration: A significant factor contributing to TimescaleDB's positive reception is its seamless integration with PostgreSQL. Leveraging PostgreSQL's mature ecosystem allows TimescaleDB users to access existing tools and plugins, ensuring reliability and flexibility. The transition from other databases like MongoDB to PostgreSQL-based systems, as noted in discussions, often highlights the enhanced scalability and performance attributed to SQL compliance, traits that TimescaleDB inherits.
SQL Support and Usability: TimescaleDB's full support for SQL is frequently mentioned as a distinct advantage. This compatibility facilitates ease of use for those already familiar with SQL, mitigates the learning curve associated with adopting new database systems, and integrates smoothly into existing infrastructures.
Comprehensive Features: Public opinion acknowledges the breadth of features TimescaleDB offers, even at smaller scales. Its design accommodates both large enterprise applications and smaller projects, which is appealing to a wide range of developers. The database is often recommended for projects involving time-series data, such as IoT applications, where its performance in handling high-ingress data and complex queries is beneficial.
Performance Claims: Despite the many positive views, there is cautious skepticism regarding performance claims made by TimescaleDB. Users often point to the need for third-party validation of benchmark results to confirm the claims against other competitors like InfluxDB or ClickHouse. While TimescaleDB's own publications provide insights, independent assessments are valued for objectivity.
Decision-Making Difficulty: For developers and companies evaluating time-series databases, the decision between TimescaleDB and its competitors like InfluxDB, QuestDB, or even non-time-series specific databases like MongoDB, often centers around specific project requirements and anticipated data architecture. Public discussions frequently reference the challenges in choosing the right fit, given the overlap in features among these systems.
Scalability Concerns: While TimescaleDB is appreciated for its capabilities, conversations occasionally hint at concerns regarding how well it scales relative to certain NoSQL databases known for handling vast quantities of unstructured data. Developers working with exceedingly large datasets might look for empirical evidence underscoring TimescaleDB's scalability in comparison.
The prevailing sentiment surrounding TimescaleDB is largely favorable, especially for projects where PostgreSQL integration, SQL support, and a robust feature set are desirable. However, prospective users are advised to weigh TimescaleDBโs proclaimed advantages against their specific technical needs and expect to verify performance claims with independent testing. As the database landscape evolves, TimescaleDBโs enduring success will likely hinge on its ability to substantiate its competitive assertions and adapt to emerging technological trends.
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Is TimescaleDB good? This is an informative page that will help you find out. Moreover, you can review and discuss TimescaleDB here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
