Before you dig into reading this guide, have you tried asking OpsGPT what this log means? You’ll receive a customized analysis of your log.
Try OpsGPT now for step-by-step guidance and tailored insights into your OpenSearch operation.
Briefly, this error occurs when OpenSearch is trying to close all shards during a shutdown or restart process, but some shards are still open even after waiting for a specified period. This could be due to slow network, heavy load, or unresponsive nodes. To resolve this, you can increase the timeout period to allow more time for the shards to close. Alternatively, you can investigate the cause of the delay, such as checking for network issues or overloaded nodes. If a specific shard is consistently problematic, consider relocating it or checking it for corruption.
For a complete solution to your to your search operation, try for free AutoOps for Elasticsearch & OpenSearch . With AutoOps and Opster’s proactive support, you don’t have to worry about your search operation – we take charge of it. Get improved performance & stability with less hardware.
This guide will help you check for common problems that cause the log ” Not all shards are closed yet; waited {}sec – stopping service ” to appear. To understand the issues related to this log, read the explanation below about the following OpenSearch concepts: shards, indices.
Overview
Data in an OpenSearch index can grow to massive proportions. In order to keep it manageable, it is split into a number of shards. Each OpenSearch shard is an Apache Lucene index, with each individual Lucene index containing a subset of the documents in the OpenSearch index. Splitting indices in this way keeps resource usage under control. An Apache Lucene index has a limit of 2,147,483,519 documents.
Examples
The number of shards is set when an index is created, and this number cannot be changed later without reindexing the data. When creating an index, you can set the number of shards and replicas as properties of the index using:
PUT /sensor
{
"settings" : {
"index" : {
"number_of_shards" : 6,
"number_of_replicas" : 2
}
}
}The ideal number of shards should be determined based on the amount of data in an index. Generally, an optimal shard should hold 30-50GB of data. For example, if you expect to accumulate around 300GB of application logs in a day, having around 10 shards in that index would be reasonable.
During their lifetime, shards can go through a number of states, including:
- Initializing: An initial state before the shard can be used.
- Started: A state in which the shard is active and can receive requests.
- Relocating: A state that occurs when shards are in the process of being moved to a different node. This may be necessary under certain conditions, such as when the node they are on is running out of disk space.
- Unassigned: The state of a shard that has failed to be assigned. A reason is provided when this happens. For example, if the node hosting the shard is no longer in the cluster (NODE_LEFT) or due to restoring into a closed index (EXISTING_INDEX_RESTORED).
In order to view all shards, their states, and other metadata, use the following request:
GET _cat/shards
To view shards for a specific index, append the name of the index to the URL, for example:
sensor: GET _cat/shards/sensor
This command produces output, such as in the following example. By default, the columns shown include the name of the index, the name (i.e. number) of the shard, whether it is a primary shard or a replica, its state, the number of documents, the size on disk, the IP address, and the node ID.
sensor 5 p STARTED 0 283b 127.0.0.1 ziap sensor 5 r UNASSIGNED sensor 2 p STARTED 1 3.7kb 127.0.0.1 ziap sensor 2 r UNASSIGNED sensor 3 p STARTED 3 7.2kb 127.0.0.1 ziap sensor 3 r UNASSIGNED sensor 1 p STARTED 1 3.7kb 127.0.0.1 ziap sensor 1 r UNASSIGNED sensor 4 p STARTED 2 3.8kb 127.0.0.1 ziap sensor 4 r UNASSIGNED sensor 0 p STARTED 0 283b 127.0.0.1 ziap sensor 0 r UNASSIGNED
Notes and good things to know
- Having shards that are too large is simply inefficient. Moving huge indices across machines is both a time- and labor-intensive process. First, the Lucene merges would take longer to complete and would require greater resources. Moreover, moving the shards across the nodes for rebalancing would also take longer and recovery time would be extended. Thus by splitting the data and spreading it across a number of machines, it can be kept in manageable chunks and minimize risks.
- Having the right number of shards is important for performance. It is thus wise to plan in advance. When queries are run across different shards in parallel, they execute faster than an index composed of a single shard, but only if each shard is located on a different node and there are sufficient nodes in the cluster. At the same time, however, shards consume memory and disk space, both in terms of indexed data and cluster metadata. Having too many shards can slow down queries, indexing requests, and management operations, and so maintaining the right balance is critical.
How to reduce your OpenSearch costs by optimizing your shards
Watch the video below to learn how to save money on your deployment by optimizing your shards.
Log Context
Log “Not all shards are closed yet; waited {}sec – stopping service” classname is IndicesService.java.
We extracted the following from OpenSearch source code for those seeking an in-depth context :
}
});
}
try {
if (latch.await(shardsClosedTimeout.seconds(); TimeUnit.SECONDS) == false) {
logger.warn("Not all shards are closed yet; waited {}sec - stopping service"; shardsClosedTimeout.seconds());
}
} catch (InterruptedException e) {
// ignore
} finally {
indicesStopExecutor.shutdown();
