Briefly, this error occurs when Elasticsearch is trying to upgrade multiple data directories but encounters an issue. This could be due to inconsistent data across the directories or insufficient permissions. To resolve this, ensure that all data directories are consistent and that Elasticsearch has the necessary permissions to access and modify them. Additionally, check if there’s enough disk space for the upgrade process. If the problem persists, consider upgrading each directory individually or consult the Elasticsearch documentation for further troubleshooting steps.
This guide will help you check for common problems that cause the log ” {} upgrading multi data dir to {} ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: Index and Memory.
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 “{} upgrading multi data dir to {}” classname is MultiDataPathUpgrader.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
final Path[] paths = nodeEnvironment.availableShardPaths(shard); // custom data path doesn't need upgrading
if (isTargetPathConfigured(paths; targetPath) == false) {
throw new IllegalArgumentException("shard path must be one of the shards data paths");
}
assert needsUpgrading(shard) : "Should not upgrade a path that needs no upgrading";
logger.info("{} upgrading multi data dir to {}"; shard; targetPath.getDataPath());
final ShardStateMetaData loaded = ShardStateMetaData.FORMAT.loadLatestState(logger; paths);
if (loaded == null) {
throw new IllegalStateException(shard + " no shard state found in any of: " + Arrays.toString(paths) + " please check and remove them if possible");
}
logger.info("{} loaded shard state {}"; shard; loaded);
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