Best practices for large Weather API queries

February 2, 2026

When retrieving weather data for long time periods or many locations, breaking the work into smaller independent requests can make your application more reliable and easier to manage.

This technique, known as query segmentation, can help reduce the impact of timeouts and failed requests, simplify retries and debugging, and make large weather data retrieval jobs easier to control.

In this article, we’ll look at when you should consider segmenting Visual Crossing Timeline Weather API requests and the best ways to divide large weather queries.

What is query segmentation?

Query segmentation means taking a large weather data request and dividing it into multiple smaller requests that can be processed independently.

For example, suppose you need 20 years of historical weather data for a location.

Rather than making one request for the entire period:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2006-01-01/2025-12-31?key=YOUR_API_KEY

you could divide the request into smaller date ranges:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2006-01-01/2010-12-31?key=YOUR_API_KEY

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2011-01-01/2015-12-31?key=YOUR_API_KEY

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2016-01-01/2020-12-31?key=YOUR_API_KEY

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2021-01-01/2025-12-31?key=YOUR_API_KEY

Each request produces a self-contained portion of the complete dataset.

The same principle applies when retrieving weather for many locations. The standard Timeline Weather API requests one location at a time, so a large list of locations can naturally be processed as a series of independent requests.

Why segment large weather queries?

There is no single request size at which segmentation becomes mandatory. The best strategy depends on the number of locations, date range, type of data requested, and how your application processes the results.

However, larger retrieval jobs often benefit from segmentation for several reasons.

Plan usage limits

Visual Crossing Weather API plans include usage limits such as request volume, concurrency, and other plan-specific allowances. Applications should be designed to operate within the limits of the selected plan rather than attempting to exceed them through aggressive parallelization or repeated requests. When processing large workloads, use controlled concurrency, queue additional work as necessary, and monitor usage so that requests remain within your account limits. If your application regularly requires greater capacity, consider upgrading to a plan that better matches the expected workload.

Easier retries

One of the most important benefits of segmentation is that a failed request does not require the entire job to be restarted.

For example, imagine retrieving ten years of historical data as ten one-year requests. If the request for one year fails because of a temporary network problem, you only need to retry that year.

With a single ten-year request, the entire request may need to be repeated.

This is particularly useful for automated ETL processes, scheduled jobs, and other large-scale data retrieval workflows.

More predictable request times

Large requests naturally take longer to process and transfer than smaller requests.

Request duration can also be affected by the type and amount of weather data requested. A request for daily values contains far fewer records than a request for hourly data covering the same period.

By using smaller segments, applications can work with requests that have more predictable execution times and smaller response sizes.

This can also reduce the likelihood that a request encounters a timeout elsewhere in the network path, such as within an HTTP client, proxy, VPN, gateway, or other infrastructure.

Easier debugging

Smaller requests are easier to inspect and troubleshoot.

If an application is requesting data for hundreds or thousands of locations, processing the locations independently makes it much easier to determine which location or request caused a problem.

Likewise, dividing a long historical period into smaller date ranges can help isolate unexpected data or application processing issues.

Easier recovery and checkpointing

Segmentation makes it possible to record which parts of a large retrieval job have already completed.

For example, a process loading historical weather into a database might record that data has successfully been retrieved for:

2020
2021
2022
2023

If the process stops while retrieving 2024, it can resume from that point rather than retrieving the earlier years again.

This approach can be especially valuable for long-running imports and scheduled data processing.

Smaller responses

Requesting only the data your application needs reduces both processing and network transfer requirements.

In addition to segmenting a large date range, you can use the Timeline Weather API’s include and elements parameters to reduce the amount of data returned.

For example, if you only require daily temperature and precipitation:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2025-01-01/2025-12-31?key=YOUR_API_KEY&include=days&elements=datetime,tempmax,tempmin,temp,precip

This can often be just as important as choosing an appropriate date segment.

Segmenting queries by location

The standard Timeline Weather API retrieves weather for a single location:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/[location]/[start-date]/[end-date]?key=YOUR_API_KEY

If you need weather for many locations, the recommended approach for large retrieval jobs is therefore to process the locations individually.

For example:

/timeline/London,UK/2025-01-01/2025-12-31

/timeline/Paris,France/2025-01-01/2025-12-31

/timeline/Berlin,Germany/2025-01-01/2025-12-31

This approach has several advantages:

  • A problem with one location does not affect the others.
  • Individual locations can be retried independently.
  • Results can be processed or stored as soon as each request completes.
  • The application can control how many requests are running at the same time.

For database imports and other automated applications, this is generally preferable to creating one very large combined request.

Using the Multiple Location Timeline Weather API

Visual Crossing also provides a Multiple Location Timeline Weather API for situations where it is more convenient to retrieve a small number of locations in a single request.

For example:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timelinemulti?key=YOUR_API_KEY&locations=London%2CUK%7CParis%2CFrance%7CBerlin%2CGermany&datestart=2025-01-01&dateend=2025-01-31

The Multiple Location Timeline API is useful when an application benefits from receiving one combined result, particularly tools such as Microsoft Excel, Power BI, and Google Sheets.

It is not intended as a replacement for segmented single-location requests when retrieving large datasets.

For larger processing jobs, use individual Timeline API requests and process the locations in controlled batches.

Segmenting queries by date

Long historical date ranges can also be divided into smaller periods.

For example, instead of retrieving ten years in one request:

/timeline/London,UK/2016-01-01/2025-12-31

you could retrieve one year at a time:

/timeline/London,UK/2016-01-01/2016-12-31

/timeline/London,UK/2017-01-01/2017-12-31

/timeline/London,UK/2018-01-01/2018-12-31

and continue until the complete period has been retrieved.

Timeline date ranges are inclusive of both the start and end dates, so adjacent segments should not overlap.

For example:

2024-01-01 to 2024-12-31
2025-01-01 to 2025-12-31

rather than:

2024-01-01 to 2025-01-01
2025-01-01 to 2025-12-31

The latter would retrieve January 1, 2025 twice.

How large should each segment be?

There is no universal segment size that is correct for every application.

A useful segment size depends primarily on:

  • whether you are requesting daily, hourly, or sub-hourly data;
  • the number of weather elements requested;
  • the length of the date range;
  • the response format;
  • the speed and reliability requirements of your application.

A year of daily weather contains only hundreds of daily records, while a year of hourly weather contains thousands of hourly records. The same date segment can therefore represent very different response sizes.

For many historical data workflows, natural calendar periods such as months or years make convenient segments because they are easy to generate, identify, store, and retry.

The goal is not to create the smallest possible requests. Instead, choose segments that are reasonably sized and easy for your application to manage.

Retry failed requests

Temporary failures can occur in any application that depends on network services.

A segmented weather retrieval process should therefore be designed so that an individual request can be retried without restarting the entire job.

A typical workflow is:

  1. Create a list of location and date-range segments.
  2. Submit each segment to the Timeline Weather API.
  3. Store the result when the request succeeds.
  4. Record the segment as complete.
  5. Retry an individual segment if it fails because of a temporary error.
  6. Continue until all segments have completed.

Applications should avoid immediately retrying a failed request repeatedly. For temporary failures, using a delay between retries and increasing that delay for repeated failures is generally preferable.

Also distinguish between temporary failures and request errors. For example, correcting an invalid API URL or parameter is more appropriate than continuously retrying the same invalid request.

Combining CSV results

If your application requests CSV data, segmented results can be combined into one dataset.

For example:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2025-01-01/2025-01-31?key=YOUR_API_KEY&include=days&contentType=csv

returns a CSV file containing a header followed by the requested records.

When concatenating multiple CSV segments, you normally want the header from only the first result.

The Timeline Weather API provides the noheaders option for this purpose:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK/2025-02-01/2025-02-28?key=YOUR_API_KEY&include=days&contentType=csv&options=noheaders

You can retrieve the first segment normally and add options=noheaders to subsequent requests before appending their results.

Applications that parse CSV directly can alternatively discard the header row when processing subsequent segments.

JSON results

JSON results generally do not need to be physically concatenated before processing.

Applications can parse each Timeline result independently and insert the resulting records into a database, data frame, or other internal data structure.

This is often preferable for large data jobs because a complete combined result never needs to be held in memory at one time.

Location identifiers can simplify processing

When processing weather for locations from an existing application or database, you may want to associate each API result with your application’s own location identifier.

The Timeline Weather API supports the locationNames parameter for this purpose.

For example:

https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/38.9697,-77.385/2025-01-01/2025-01-31?key=YOUR_API_KEY&locationNames=STORE_1234

Using an existing store, customer, asset, or site identifier can make it easier to associate weather results with the correct record in your system.

Avoid requesting unnecessary data

Before increasing segmentation, also consider whether the original request is retrieving more information than necessary.

The Timeline Weather API provides two important parameters for reducing the response:

include

Use include to select which sections of weather data should be returned.

For example:

&include=days

or:

&include=hours

elements

Use elements to request only the individual weather fields your application requires.

For example:

&elements=datetime,temp,humidity,precip,windspeed

Reducing unnecessary data makes each request smaller and can improve both API and application performance.

Choosing a segmentation strategy

For most large Timeline Weather API retrieval jobs, use the following general approach:

Many locations

Process each location using an individual Timeline Weather API request and use controlled concurrency.

Long historical period

Divide the date range into convenient non-overlapping periods such as months or years.

Many locations and a long period

Segment on both dimensions. Process one location at a time and divide each location into manageable date ranges where necessary.

Small number of locations where a combined result is important

Consider the Multiple Location Timeline Weather API.

Large CSV export

Retrieve independent segments and use options=noheaders for subsequent CSV responses when concatenating the files.

Summary

Query segmentation is a simple way to make large weather data retrieval jobs easier to operate and more resilient.

Rather than concentrating a large amount of work into a single request, divide the job into independent location or date-range segments that can be processed, stored, monitored, and retried separately.

When designing a large Timeline Weather API workflow:

  • Use individual Timeline requests for large lists of locations.
  • Divide long date ranges into manageable non-overlapping periods when appropriate.
  • Limit responses using include and elements.
  • Use controlled concurrency rather than submitting every request simultaneously.
  • Make individual segments independently retryable.
  • Track completed segments so interrupted jobs can resume.
  • Use the Multiple Location Timeline API when a small combined result is more useful than separate calls.
  • Use options=noheaders when combining CSV segments.

These practices make large weather data retrieval jobs more predictable while keeping the implementation simple and easy to recover when individual requests fail.