Elasticsearch Breaker

Elasticsearch Breaker

Last Update: July 2020

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Breaker in Elasticsearch


Elasticsearch has the concept of circuit breakers to deal with OutOfMemory errors that cause nodes to crash. When a request reaches Elasticsearch nodes, the circuit breakers first estimate the amount of memory needed to load the required data. They then compare the estimated size with the configured heap size limit. If the estimated size is greater than the heap size, the query is terminated and an exception is thrown to avoid the node loading more than the available heap size.

What it is used for

Elasticsearch has several circuit breakers available such as fielddata, requests, network, indices and script compilation. Each breaker is used to limit the memory an operation can use. In addition, Elasticsearch has a parent circuit breaker which is used to limit the combined memory used by all the other circuit breakers.

Examples

Increasing circuit breaker size for fielddata limit – The default limit for fielddata breakers is 40%. The following command can be used to increase it to 60%:

PUT /_cluster/settings
{
  "persistent": {
    "indices.breaker.fielddata.limit": "60%"
  }
}
Notes
  • Each breaker ships with default limits and their limits can be modified as well. But this is an expert setting and you should understand the pitfalls carefully before changing the limits, otherwise the node may start OOM exceptions.
  • Sometimes it is better to fail a query instead of getting OOM, because when OOM appears JVM becomes unresponsive.
  • It is important to keep indices.breaker.request.limit lower than indices.breaker.total.limit so that request circuit breakers trip before the total circuit breaker.
Common Problems
  • The most common error  resulting from circuit breakers is “data too large” with 429 status code. The application should be ready to handle such exceptions.
  • If the application starts throwing exceptions because of circuit breaker limits, it is important to review the queries and memory requirements. In most cases, a scaling is required by adding more resources to the cluster.

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