The Google Ads API enforces rate limiting by queries per second (QPS) on both customer IDs and Google Cloud projects independently. The Google Ads API uses a token bucket algorithm to meter requests and determine an appropriate QPS limit, so the exact limit will vary depending on the overall server load at any given time.
The purpose of imposing rate limits is to prevent one user from disrupting service for other users by (either intentionally or unintentionally) overwhelming the Google Ads API servers with a high volume of requests.
Запросы, нарушающие ограничения скорости, будут отклонены с ошибкой: RESOURCE_TEMPORARILY_EXHAUSTED .
Вы можете взять под контроль свое приложение и снизить ограничения скорости запросов, активно уменьшая количество запросов и ограничивая количество запросов в секунду на стороне клиента.
There are a number of ways to reduce the chances of exceeding the rate limit. Becoming familiar with Enterprise Integration Patterns (EIP) concepts such as messaging, re-delivery, and throttling can help you build a more robust client app.
Ниже представлены рекомендуемые методы, упорядоченные по степени сложности, начиная с более простых стратегий и заканчивая более надежными, но сложными архитектурами:
- Ограничьте количество одновременно выполняемых задач.
- Пакетная обработка запросов
- Регулирование скорости и ограничители скорости
- Очереди
Ограничьте количество одновременно выполняемых задач.
One root cause of exceeding rate limits is that the client app is spawning an excessive number of parallel tasks. While we don't limit the number of parallel requests a client app can have, this can exceed the requests per second limit at the Google Cloud project level.
Setting a reasonable upper bound for the total number of concurrent tasks that are going to make requests (across all processes and machines), and adjusting upward to optimize your throughput without exceeding the rate limit is recommended.
Кроме того, вы можете рассмотреть возможность ограничения частоты запросов в секунду (QPS) на стороне клиента (подробнее см. раздел «Ограничение частоты запросов и лимитеры скорости »).
Пакетная обработка запросов
Consider batching multiple operations into a single request. This is most applicable on Mutate calls for various services. For example, if you're updating status for multiple instances of AdGroupAd , you can call MutateAdGroupAds once, and pass in multiple operations instead of calling MutateAdGroupAds once for each AdGroupAd . Refer to our batch operations guidance for some additional examples.
While batching requests reduces the total number of requests and mitigates the rate limits on requests per minute , it may trigger the operations per minute rate limit if you perform a large number of operations against a single account.
Регулирование скорости и ограничители скорости
In addition to limiting the total number of threads in your application, you can also implement rate limiters on the client side. This can ensure all the threads across your processes and / or clusters are governed by a specific QPS limit from the client side.
You can check out Guava Rate Limiter , or implement your own token bucket based algorithm for a clustered environment. For example, you could generate tokens and store them in a shared transactional storage such as a database, and each client would have to acquire and consume a token before it processes the request. If the tokens were used up, the client would have to wait until the next batch of tokens is generated.
Очереди
A message queue is the solution for operation load distribution, while also controlling request and consumer rates. There are a number of message queue options available — some open source, some proprietary — and many of them can work with different languages.
When using message queues, you can have multiple producers pushing messages to the queue and multiple consumers processing those messages. Throttles can be implemented at the consumer side by limiting the number of concurrent consumers, or implement rate limiters or throttlers for either the producers or consumers.
For example, if a message consumer encounters a rates limit error, that consumer can return the request to the queue to be retried. At the same time, that consumer can also notify all other consumers to pause processing for a number of seconds to recover from the error.