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How can you manage pagination and rate limiting in API data collection?

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If you are a data engineer, you might need to collect data from various APIs to build your data pipelines. However, APIs often have limitations on how much and how fast you can access their data. Pagination and rate limiting are two common challenges that you need to handle when working with APIs. In this article, you will learn what pagination and rate limiting are, why they are important, and how you can manage them in your data collection process.

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1 What is pagination?

Pagination is a technique that divides a large amount of data into smaller chunks, called pages, that can be retrieved separately. For example, if an API has 1000 records, it might return only 100 records per page, and you need to make 10 requests to get all the data. Pagination helps to reduce the load on the server and the network, and to improve the performance and usability of the API. However, pagination also adds some complexity to your data collection code, as you need to keep track of the page number, the page size, and the total number of pages.

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Piotr Kurzynoga

Principal Cloud Solutions Engineer at Oracle
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There is a lot of talk around pagination when it comes to performance on the execution of the request itself but it is key to remember that pagination often goes hand in hand with lazy loading and caching. - Do we want to retrieve specific records -> Pagination - Is my data static -> Caching - Is there an always growing data set -> Lazy loading Most of the time it's worth making the user wait a little longer on the first load to benefit from caching larger data sets and having much smoother pagination!

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Bhavesh Shah

Technology Architect @ Oracle | Helping Customers Realize Digital Goals with Oracle Cloud ERP, SCM, OCI, Gen AI, PaaS, SaaS | TOGAF? 9 Certified Enterprise Architect
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In my opinion using API's to collect large data sets is a wrong approach to the problem. Look for ways to bulk export data within any software products. They are better suited for data collection than API's. Use of API's should be reserved for transactional systems that handle low data volumes but where data consistency, transaction control and data integrity (read ACID!) are of utmost importance.

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2 What is rate limiting?

Rate limiting is a mechanism that controls the number of requests that an API can handle in a given time period. For example, an API might limit you to 60 requests per minute, or 1000 requests per day. Rate limiting helps to prevent abuse, overload, and denial-of-service attacks on the API. However, rate limiting also imposes some restrictions on your data collection speed, as you need to respect the API's quota and avoid exceeding it.

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Jeff Smith

Product Manager | Databases | Blogger | Software Development | Cloud | Social | Community Management | Product Marketing
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Rate limiting can also have a commercial application. It is often used to provide a premium service level for subscribers, pay $, get higher or even no rate limit.

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3 How to handle pagination?

When implementing pagination with an API, you need to refer to the specific documentation for details. Common methods include using query parameters such as page and per_page , offset and limit parameters such as offset and limit , or a cursor or token parameter such as cursor or token . For instance, a query with the parameters page=1&per_page=100 would return the first 100 records, while a query with the parameters offset=0&limit=100 would return the same set of records. Similarly, a query with a cursor or token parameter would return a set of records and a new cursor value for the next page. You must increment the page number, increase the offset, or use the new cursor value until you get an empty response or a null cursor. Reporting and documenting these steps will help inform stakeholders and improve your cloud security posture.

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Tony Kipkemboi

Sr. Developer Advocate - AI Agents @ CrewAI
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You'll run into pagination especially when consuming a large dataset. When you make the initial API call, you will often receive a response that contains info that indicates that there are more pages of data available. I typically use a `while` loop to iterate through the pages of data until I've retrieved all the data I need. Just be aware that if you're paying for the API use per request and the dataset is humongous, you might end up with a hefty bill.

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Jeff Smith

Product Manager | Databases | Blogger | Software Development | Cloud | Social | Community Management | Product Marketing
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Instead of relying on getting the 'new cursor value' to see if the response is empty or null, you can instead say for a page size of 25, fetch 26 or 27 rows instead. Your API then knows that there is most definitely a 'Next' page to follow, or not.

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4 How to handle rate limiting?

When using an API, it is important to check the documentation for the rate limiting rules. Common methods for enforcing rate limiting include using headers such as X-Rate-Limit-Limit , X-Rate-Limit-Remaining , and X-Rate-Limit-Reset , or using status codes like 429 Too Many Requests or 503 Service Unavailable . Additionally, if no explicit information about the rate limit is given, you may need to implement a backoff and retry strategy. This involves increasing the wait time between failed requests until you succeed or reach a maximum limit. Monitoring these headers and adjusting your request frequency accordingly is essential for API rate limiting.

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5 Best practices for API data collection

API data collection is a crucial skill for data engineers, yet it can be difficult due to challenges like pagination and rate limiting. To manage these effectively, you should read the API documentation, use a library or tool that can handle pagination and rate limiting, test your code with a small sample of data before scaling up, use logging and error handling to track your progress, and respect the API's terms of service. By following these best practices, you can collect data from APIs more efficiently and reliably.

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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Experimenting and monitoring the consuming operations of API, so then you can detect behavior of API based in metadata like datetime, business season o setup parameters; and implement contigent plans to continue use the API with new values for rate limit and paginations. use or implement a tool that it can help this process.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Jeff Smith

Product Manager | Databases | Blogger | Software Development | Cloud | Social | Community Management | Product Marketing
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For your APIs to become widely adopted, pay careful attention to consistency, reliability, and developer/user friendliness. Requests, responses should be uniform across your APIs. Errors should always be reported as FAILED vs SUCECSS (but oh read the details), and they need to be well documented, with lots of examples! Do this, and your community will build amazing solutions you never even dreamed of, all because you supplied great APIs.

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How can you manage pagination and rate limiting in API data collection?

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1 What is pagination?

2 What is rate limiting?

3 How to handle pagination?

4 How to handle rate limiting?

5 Best practices for API data collection

6 Here’s what else to consider

Data Engineering

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How can you manage pagination and rate limiting in API data collection?

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1 What is pagination?

2 What is rate limiting?

3 How to handle pagination?

4 How to handle rate limiting?

5 Best practices for API data collection

6 Here’s what else to consider

Data Engineering

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