Before you begin reading this guide, we recommend you run Elasticsearch Error Check-Up which can resolve issues that cause many errors.
This guide will help you check for common problems that cause the log ” Unable to close Groovy loader ” to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts: Index and Memory.
Advanced users might want to skip right to the common problems section in each concept or try running the Check-Up which analyses ES to pinpoint the cause of many errors and provides suitable actionable recommendations how to resolve them (free tool that requires no installation).
Elasticsearch memory requirements
The Elasticsearch process is very memory intensive. Elasticsearch uses a JVM (Java Virtual Machine), and close to 50% of the memory available on a node should be allocated to JVM. The JVM machine uses memory because the Lucene process needs to know where to look for index values on disk. The other 50% is required for the file system cache which keeps data that is regularly accessed in memory.
For a full explanation of JVM management, please see: Heap Size Usage and JVM Garbage Collection in ES – A Detailed Guide.
It is also common to receive warnings from the different types of circuit breakers. This is discussed here: How to Handle Circuit Breaker Exceptions in Elasticsearch.
jvm.mem
The most important memory section is the JVM heap.
GET _nodes/stats/jvm
The output could look like this:
"jvm" : {
"timestamp" : 1603351829573,
"uptime_in_millis" : 150932107,
"mem" : {
"heap_used_in_bytes" : 258714272,
"heap_used_percent" : 24,
"heap_committed_in_bytes" : 1073741824,
"heap_max_in_bytes" : 1073741824,
"non_heap_used_in_bytes" : 192365488,
"non_heap_committed_in_bytes" : 209186816,Note that the heap_used_in_bytes in a healthy JVM will follow a saw tooth pattern due to the garbage collection process, increasing steadily up to around 70%, then reducing sharply to 30% when garbage collection occurs.
The JVM heap_max will depend on the value set in jvm.options file, and you should set it to be 50% of the RAM available for your container or server.
For more information on JVM heap issues, please see: Heap Size Usage and JVM Garbage Collection in ES – A Detailed Guide.
Explaining the different types of memory statistics
When looking at the memory statistics, we need to be aware that many Elasticsearch applications are running inside containers on much larger machines. This is typical if you are using a hosted service such as AWS Elasticsearch service or Elastic cloud, or if you are running Elasticsearch on Docker or Kubernetes. In such cases, it’s important to be careful to interpret the memory statistics available to us.
There are various memory statistics available from the Elasticsearch monitoring APIs, as explained below:
GET _nodes/stats/os
"os" : {
"timestamp" : 1603350306857,
"cpu" : {
"percent" : 13,
"load_average" : {
"1m" : 3.37,
"5m" : 3.18,
"15m" : 2.8
}
},
"mem" : {
"total_in_bytes" : 16703369216,
"free_in_bytes" : 361205760,
"used_in_bytes" : 16342163456,
"free_percent" : 2,
"used_percent" : 98
},
"swap" : {
"total_in_bytes" : 1023406080,
"free_in_bytes" : 1302528,
"used_in_bytes" : 1022103552
},
"cgroup" : {
"cpuacct" : {
"control_group" : "/",
"usage_nanos" : 2669636420088
},
"cpu" : {
"control_group" : "/",
"cfs_period_micros" : 100000,
"cfs_quota_micros" : -1,
"stat" : {
"number_of_elapsed_periods" : 0,
"number_of_times_throttled" : 0,
"time_throttled_nanos" : 0
}
},
"memory" : {
"control_group" : "/",
"limit_in_bytes" : "9223372036854771712",
"usage_in_bytes" : "4525641728"
}
}
}
The above statistics are for a development node running on docker. Let’s interpret the sections we received here:
os.mem
The first section “mem” refers to the host server where the machine is running. In this case we are running on docker, so 16GB refers to the memory of the host machine where the container is running. Note that it is quite normal for a machine to be using close to 100% of its memory, and this does not indicate a problem.
os.swap
The swap section again refers to the host machine. In this case, we can see that the host machine allows swapping. This is quite normal when we are running a container inside a host with swapping still enabled. We can double check that inside the docker container no swap is permitted by running:
GET _nodes?filter_path=**.mlockall
os.cgroup
Finally, we can see the cgroup section which refers to the container itself. In this case the container is using 4GB of memory.
process.mem
We also have access to a statistic virtual memory:
GET _nodes/stats/process
"process" : {
"timestamp" : 1603352751181,
"open_file_descriptors" : 436,
"max_file_descriptors" : 1048576,
"cpu" : {
"percent" : 0,
"total_in_millis" : 1964850
},
"mem" : {
"total_virtual_in_bytes" : 5035376640
}
Note that total_virtual_in_bytes includes the entire memory available to the process including virtual memory available via mmap.
Log Context
Log “Unable to close Groovy loader” classname is GroovyScriptEngineService.java
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
Override
public Void run() {
try {
loader.close();
} catch (IOException e) {
logger.warn("Unable to close Groovy loader"; e);
}
return null;
}
});
}
Run the Check-Up to get customized recommendations like this:
Run Check-Up
The high disk watermark threshold is about to be reached in specific nodes
Description
There are various “watermark” thresholds on each Elasticsearch cluster. When the high disk watermark threshold has been exceeded, it means disk space is running out. The node will…
Recommendations
Based on your specific ES deployment you should…
X-PUT curl -H [a customized code snippet to resolve the issue]