Briefly, this error occurs when Elasticsearch fails to perform a maintenance task on the snapshot blob cache due to an issue with the cluster state update event source. This could be due to network issues, insufficient permissions, or a problem with the underlying storage system. To resolve this, you can try restarting the Elasticsearch cluster, checking the network connectivity, ensuring the user has the correct permissions, or verifying the health of the storage system. If the problem persists, consider increasing the logging level to get more detailed error messages.
This guide will help you check for common problems that cause the log ” snapshot blob cache maintenance task failed for cluster state update [” + event.source() + “] ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin, task, cache, snapshot, cluster.
Overview
A task is an Elasticsearch operation, which can be any request performed on an Elasticsearch cluster, such as a delete by query request, a search request and so on. Elasticsearch provides a dedicated Task API for the task management which includes various actions, from retrieving the status of current running tasks to canceling any long running task.
Examples
Get all currently running tasks on all nodes of the cluster
Apart from other information, the response of the below request contains task IDs of all the tasks which can be used to get detailed information about the particular task in question.
GET _tasks
Get detailed information of a particular task
Where clQFAL_VRrmnlRyPsu_p8A:1132678759 is the ID of the task in below request
GET _tasks/clQFAL_VRrmnlRyPsu_p8A:1132678759
Get all the current tasks running on particular nodes
GET _tasks?nodes=nodeId1,nodeId2
Cancel a task
Where clQFAL_VRrmnlRyPsu_p8A:1132678759 is the ID of the task in the below request
POST /_tasks/clQFAL_VRrmnlRyPsu_p8A:1132678759/_cancel?pretty
Notes
- The Task API will be most useful when you want to investigate the spike of resource utilization in the cluster or want to cancel an operation.
Overview
Elasticsearch uses three types of caches to improve the efficiency of operation.
- Node request cache
- Shard data cache
- Field data cache
How they work
Node request cache maintains the results of queries used in a filter context. The results are evicted on a least recently used basis.
Shard data cache maintains the results of frequently used queries where size=0, particularly the results of aggregations. This cache is particularly relevant for logging use cases where data is not updated on old indices, and regular aggregations can be kept in cache to be reused.
The field data cache is used for sorting and aggregations. To keep these operations quick Elasticsearch loads these values into memory.
Examples
Elasticsearch usually manages cache behind the scenes, without the need for any specific settings. However, it is possible to monitor and limit the amount of memory being used on each node for a given cache type by putting the following in elasticsearch.yml :
indices.queries.cache.size: 10% indices.fielddata.cache.size: 30%
Note, the above values are in fact the defaults, and there is no need to set them specifically. The default values are good for most use cases, and should rarely be modified.
You can monitor the use of caches on each node like this:
GET /_nodes/stats/indices/fielddata GET /_nodes/stats/indices/query_cache GET /_nodes/stats/indices/request_cache
Notes and good things to know
Construct your queries with reusable filters. There are certain parts of your query which are good candidates to be reused across a large number of queries, and you should design your queries with this in mind. Anything thing that does not need to be scored should go in the filter section of a bool query. For example, time ranges, language selectors, or clauses that exclude inactive documents are all likely to be excluded in a large number of queries, and should be included in filter parts of the query so that they can be cached and reused.
In particular, take care with time filters. “now-15m” cannot be reused, because “now” will continually change as the time window moves on. On the other hand “now-15/m” will round to the nearest minute, and can be re-used (via cache) for 60 seconds before rolling over to the next minute.
For example when a user enters the search term “brexit”, we may want to also filter on language and time period to return relevant articles. The query below leaves only the query term “brexit” in the “must” part of the query, because this is the only part which should affect the relevance score. The time filter and language filter can be reused time and time again for new queries for different searches.
POST results/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"message": {
"query": "brexit"
}
}
}
],
"filter": [
{
"range": {
"@timestamp": {
"gte": "now-10d/d"
}
}
},
{
"term": {
"lang.keyword": {
"value": "en",
"boost": 1
}
}
}
]
}
}
}Limit the use of field data. Be careful about using fielddata=true in your mapping where the number of terms will result in a high cardinality. If you must use fielddata=true, you can also reduce the requirement of fielddata cache by limiting the requirements for fielddata for a given index using a field data frequency filter.
POST results/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"message": {
"query": "brexit"
}
}
}
],
"filter": [
{
"range": {
"@timestamp": {
"gte": "now-10d/d"
}
}
},
{
"term": {
"lang.keyword": {
"value": "en",
"boost": 1
}
}
}
]
}
}
}Log Context
Log “snapshot blob cache maintenance task failed for cluster state update [” + event.source() + “]” classname is BlobStoreCacheMaintenanceService.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
}));
}
@Override
public void onFailure(Exception e) {
logger.warn(() -> "snapshot blob cache maintenance task failed for cluster state update [" + event.source() + "]"; e);
}
}
/**
* A maintenance task that periodically cleans up unused cache entries from the blob store cache index.
