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What is the best way to store time series data in MongoDB?
TimescaleDB outperforms both methods of storing time-series data in MongoDB, by between 69% (vs. Mongo-naive) and 160% (vs. Mongo-recommended). Mongo-naive shows better write performance (154% the ingest rate) and uses less disk space (85% as much disk) than Mongo-recommended.
A time-series database (TSDB) is a computer system that is designed to store and retrieve data records that are part of a “time series,” which is a set of data points that are associated with timestamps. The timestamps provide a critical context for each of the data points in how they are related to others.
Is NoSQL good for time series data?
NoSQL for time series use cases. Gaining high performance for time series from a SQL database requires significant customization and configuration. Without that, unless you’re working with a very small dataset, a SQL-based database will simply not work properly.
Is MongoDB suitable for time series data?
MongoDB may be a great non-relational document store, but it just isn’t that great for time-series data. So for time-series data with TimescaleDB, you get all the benefits of a reliable relational database (i.e., PostgreSQL) with better performance than a popular NoSQL solution like MongoDB.
Why is time series data important?
A time series is a data set that tracks a sample over time. In particular, a time series allows one to see what factors influence certain variables from period to period. Time series analysis can be useful to see how a given asset, security, or economic variable changes over time.
What can be done with time series data?
Best suited for predictive modeling and forecasting. Internet of Things (IoT) Data collected by IoT devices is a natural fit for time series storage and analysis. The incoming data is inserted and rarely, if ever, updated.
When to use NoSQL to store time series data?
Lesson learned: use a NoSQL solution for storing time-series data when you care about ingesting speed and throughput as much as possible, and for data that is not at the core of your application.
How to handle time series data in DynamoDB?
Best Practices for Handling Time-Series Data in DynamoDB. General design principles in DynamoDB recommend that you keep the number of tables you use to a minimum. For most applications, a single table is all you need. However, for time-series data, you can often best handle it by using one table per application per period.
Which is the best database for financial time series?
PostgreSQL turned out to be a pretty solid choice as a general purpose database, which means that both customers data and financial time-series data live in the same database, with strong guarantees of referential integrity. ForecastCycles is a SaaS built with React, Semantic-UI and PostgreSQL.