Before you begin reading this guide, we recommend you run Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many errors.
Briefly, this error occurs when the requested filter cache size is too large and needs to be reduced to the maximum allowed size, which can cause issues with the operation of Elasticsearch. To resolve this issue, you can try to reduce the requested filter cache size to the maximum allowed size, or check the system configuration for errors.
To easily locate the root cause and resolve this issue try AutoOps for Elasticsearch & OpenSearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them.
This guide will help you check for common problems that cause the log ” Reducing requested filter cache size of to the maximum allowed size of ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: cache, filter and indices.
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
}
}
}
]
}
}
}Overview
A filter in Elasticsearch is all about applying some conditions inside the query that are used to narrow down the matching result set.
What it is used for
When a query is executed, Elasticsearch by default calculates the relevance score of the matching documents. But in some conditions it does not require scores to be calculated, for instance if a document falls in the range of two given timestamps. For all these Yes/No criteria, a filter clause is used.
Examples
Return all the results of a given index that falls between a date range:
GET my_index/_search
{
"query": {
"bool": {
"filter": {
"range": {
"created_at": {
"gte": "2020-01-01",
"lte": "2020-01-10"
}
}
}
}
}
}Notes
- Queries are used to find out how relevant a document is to a particular query by calculating a score for each document, whereas filters are used to match certain criteria and are cacheable to enable faster execution.
- Filters do not contribute to scoring and thus are faster to execute.
- There are major changes introduced in Elasticsearch version 2.x onward related to how query and filters are written and performed internally.
Common problems
- The most common problem with filters is incorrect use inside the query. If filters are not used correctly, query performance can be significantly affected. So filters must be used wherever there is scope of not calculating the score.
- Another problem often arises when using date range filters, if “now” is used to represent the current time. It has to be noted that “now” is continuously changing the timestamp and thus Elasticsearch cannot use caching of the response since the data set will keep changing.
Overview
In Elasticsearch, an index (plural: indices) contains a schema and can have one or more shards and replicas. An Elasticsearch index is divided into shards and each shard is an instance of a Lucene index.
Indices are used to store the documents in dedicated data structures corresponding to the data type of fields. For example, text fields are stored inside an inverted index whereas numeric and geo fields are stored inside BKD trees.
Examples
Create index
The following example is based on Elasticsearch version 5.x onwards. An index with two shards, each having one replica will be created with the name test_index1
PUT /test_index1?pretty
{
"settings" : {
"number_of_shards" : 2,
"number_of_replicas" : 1
},
"mappings" : {
"properties" : {
"tags" : { "type" : "keyword" },
"updated_at" : { "type" : "date" }
}
}
}List indices
All the index names and their basic information can be retrieved using the following command:
GET _cat/indices?v
Index a document
Let’s add a document in the index with the command below:
PUT test_index1/_doc/1
{
"tags": [
"opster",
"elasticsearch"
],
"date": "01-01-2020"
}Query an index
GET test_index1/_search
{
"query": {
"match_all": {}
}
}Query multiple indices
It is possible to search multiple indices with a single request. If it is a raw HTTP request, index names should be sent in comma-separated format, as shown in the example below, and in the case of a query via a programming language client such as python or Java, index names are to be sent in a list format.
GET test_index1,test_index2/_search
Delete indices
DELETE test_index1
Common problems
- It is good practice to define the settings and mapping of an Index wherever possible because if this is not done, Elasticsearch tries to automatically guess the data type of fields at the time of indexing. This automatic process may have disadvantages, such as mapping conflicts, duplicate data and incorrect data types being set in the index. If the fields are not known in advance, it’s better to use dynamic index templates.
- Elasticsearch supports wildcard patterns in Index names, which sometimes aids with querying multiple indices, but can also be very destructive too. For example, It is possible to delete all the indices in a single command using the following commands:
DELETE /*
To disable this, you can add the following lines in the elasticsearch.yml:
action.destructive_requires_name: true
Log Context
Log “reducing requested filter cache size of [{}] to the maximum allowed size of [{}]” classname is IndicesFilterCache.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
}
private void computeSizeInBytes() {
long sizeInBytes = MemorySizeValue.parseBytesSizeValueOrHeapRatio(size).bytes();
if (sizeInBytes > ByteSizeValue.MAX_GUAVA_CACHE_SIZE.bytes()) {
logger.warn("reducing requested filter cache size of [{}] to the maximum allowed size of [{}]"; new ByteSizeValue(sizeInBytes);
ByteSizeValue.MAX_GUAVA_CACHE_SIZE);
sizeInBytes = ByteSizeValue.MAX_GUAVA_CACHE_SIZE.bytes();
// Even though it feels wrong for size and sizeInBytes to get out of
// sync we don't update size here because it might cause the cache
// to be rebuilt every time new settings are applied.
