Time-Series Data in NewSQL

1. Introduction

Time-series data is a sequence of data points indexed in time order, often used to track changes over time. NewSQL databases are designed to handle the scalability of big data while maintaining the ACID properties of traditional SQL databases. This lesson explores how to effectively manage time-series data in NewSQL environments.

2. Key Concepts

3. NewSQL Overview

NewSQL databases are designed to handle the needs of modern applications that require high availability and scalability. They provide:

• Horizontal scalability.
• ACID transactions.
• SQL compatibility.

4. Time-Series in NewSQL

NewSQL databases offer features that are particularly useful for managing time-series data:

• Optimized for high write throughput.
• Support for partitioning and sharding to distribute data.
• Time-series specific data types and indexing methods.

4.1 Example of Storing Time-Series Data

Here’s a simple example of how to insert time-series data into a NewSQL database:

INSERT INTO temperature_readings (timestamp, temperature)
VALUES (CURRENT_TIMESTAMP, 72.5);
        

4.2 Querying Time-Series Data

To query the average temperature over a given period:

SELECT AVG(temperature) 
FROM temperature_readings 
WHERE timestamp BETWEEN '2023-10-01' AND '2023-10-31';
        

5. Best Practices

When managing time-series data in NewSQL databases, consider the following best practices:

• Design schemas that separate time-series data from other data types.
• Utilize indexing strategies that optimize for time-based queries.
• Regularly archive old data to maintain performance.

6. FAQ