Downsampling (data processing)

Downsampling is a data processing technique used to reduce the resolution or granularity of time-series data. The process involves taking larger time intervals and summarizing or aggregating the data points that fall within those intervals. This technique is particularly useful in the analysis of large datasets, where capturing trends or general patterns over time is more important than retaining the fine detail of the original high-resolution data.

In the context of data analysis, downsampling helps condense the data by computing statistics such as the minimum, maximum, and average values over specified time intervals, thus facilitating trend analysis and efficient storage.

Use Cases and Benefits​

Downsampling is instrumental in scenarios where the volume of data is immense, and fine granularity is not required for long-term analysis:

• Data Storage Optimization: Downsampling reduces the size of datasets, thereby saving storage space and associated costs.

• Performance Improvement: Working with lower-resolution data can improve the performance of queries and analyses by reducing computational overhead.

• Trend Analysis: Aggregated data from downsampling accentuates macro-level trends, which can be obscured by noise in high-resolution data.

• Resource Load Balancing: Downsampling eases the load on systems responsible for real-time monitoring by offloading detailed analysis to downstream systems.

Downsampling in Practice​

To perform downsampling, a data source is queried to produce aggregate metrics over defined intervals. For instance, if hourly summaries of heart rate data from a sensor are desired, a query will be written to calculate the minimum, maximum, and average heart rate for each hour:

SELECT
    min(heartrate) AS min_heartrate,
    max(heartrate) AS max_heartrate,
    avg(heartrate) AS avg_heartrate,
    ts
FROM
    heart_rate
WHERE
    sensorId = 1000
SAMPLE BY 1h
FILL(NULL, NULL, PREV);

The results of downsampling can be persisted into another table or dataset to be readily available for analysis, providing a simplified view of data that the data science team can use for creating models or further examination.

Data Management Strategies​

Database management systems, like QuestDB, provide SQL functions and constructs that facilitate downsampling. By creating a separate table for the downsampled data, the operation can be executed periodically to keep the summary data up-to-date with the latest information:

CREATE TABLE sampled_data (
    ts timestamp,
    min_heartrate double,
    max_heartrate double,
    avg_heartrate double,
    sensorId long
) timestamp(ts);

INSERT INTO sampled_data
SELECT ts, min(heartrate), max(heartrate), avg(heartrate), sensorId
FROM heart_rate
SAMPLE BY 1h
FILL(NULL, NULL, PREV);

Downsampling is a pragmatic balance between data retention and usability, allowing teams to harness large volumes of data effectively.