Briefly, this error occurs when Elasticsearch cannot find a specific shard. This could be due to a node failure, network issues, or disk corruption. To resolve this issue, you can try to restart the Elasticsearch node, check the network connectivity between nodes, or restore the shard from a snapshot. If the shard is permanently lost and no snapshot is available, you may need to reindex your data.
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Briefly, this error occurs when Elasticsearch cannot find the specified shard. The solution is to check the shard name or ID and ensure that it exists and is spelled correctly.
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This guide will help you check for common problems that cause the log ” missing shard ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: shard, plugin.
Missing Shard
A missing shard error indicates that one or more indices does not have a primary shard available, and furthermore cannot promote a replica shard to replace the missing one. It is usually associated with a cluster red status.
Why this error occurs
There can be several reasons why this error occurs:
1. There are no replicas available to promote
A missing shard error will only happen if there are no available replicas. If there were an available replica, then it would be promoted to primary shard. This may happen because users only have one node, or, if by design users intentionally specified number_of_replicas:0.
2. Node crashes
If more than one node becomes overwhelmed or stops operating for any reason, for example, due to “out of memory” errors, then users will end up with missing shards.
3. Networking issues
If nodes are not able to reach each other reliably, then the nodes will lose contact with one another and the cluster will consider all of the shards on the lost node to be missing. Users may be able to detect this situation by repeated messages in the logs about nodes leaving or rejoining the cluster.
Finding the cause of missing shards
Users can use the cluster allocation API:
GET /_cluster/allocation/explain
By running the command above, users will get an explanation of the allocation status of the first unallocated shard found.
{
"index" : "my_index",
"shard" : 0,
"primary" : false,
"current_state" : "unassigned",
"unassigned_info" : {
"reason" : "NODE_LEFT",
"at" : "2017-01-04T18:53:59.498Z",
"details" : "node_left[G92ZwuuaRY-9n8_tc-IzEg]",
"last_allocation_status" : "no_attempt"
},
"can_allocate" : "allocation_delayed",
"allocate_explanation" : "cannot allocate because the cluster is still waiting 59.8s for the departed node holding a replica to rejoin, despite being allowed to allocate the shard to at least one other node",
"configured_delay" : "1m",
"configured_delay_in_millis" : 60000,
"remaining_delay" : "59.8s",
"remaining_delay_in_millis" : 59824,
"node_allocation_decisions" : [
{
"node_id" : "pmnHu_ooQWCPEFobZGbpWw",
"node_name" : "node_t2",
"transport_address" : "127.0.0.1:9402",
"node_decision" : "yes"
},
{
"node_id" : "3sULLVJrRneSg0EfBB-2Ew",
"node_name" : "node_t0",
"transport_address" : "127.0.0.1:9400",
"node_decision" : "no",
"store" : {
"matching_size" : "4.2kb",
"matching_size_in_bytes" : 4325
},
"deciders" : [
{
"decider" : "same_shard",
"decision" : "NO",
"explanation" : "the shard cannot be allocated to the same node on which a copy of the shard already exists [[my_index][0], node[3sULLVJrRneSg0EfBB-2Ew], [P], s[STARTED], a[id=eV9P8BN1QPqRc3B4PLx6cg]]"
}
]
}
]
}The above api returns:
“unassigned_info” => The reason why the shard became unassigned.
“node_allocation_decision” => A list of explanations for each node, which explain whether it could potentially receive the shard.
“deciders” => The decision and explanation.
How to recover a missing primary shard
A lost primary shard is usually recovered automatically by promoting its replica. However, the cluster allocation explain API may indicate that this is not possible.
{
"index" : "test",
"shard" : 0,
"primary" : true,
"current_state" : "unassigned",
"unassigned_info" : {
"reason" : "NODE_LEFT",
"at" : "2017-01-04T18:03:28.464Z",
"details" : "node_left[OIWe8UhhThCK0V5XfmdrmQ]",
"last_allocation_status" : "no_valid_shard_copy"
},
"can_allocate" : "no_valid_shard_copy",
"allocate_explanation" : "cannot allocate because a previous copy of the primary shard existed but can no longer be found on the nodes in the cluster"
}In this case, the following options are available:
Wait for the node to come back online
If the lost node went down or restarted, it may be a matter of time before the node is restarted and the shard becomes available again.
Restore a snapshot
It is generally preferable to restore a snapshot in a known state (eg. 30 minutes ago) than to try to recover corrupted data in an unknown state.
Restore from corrupted shard
As a last resort, if there is no way of recovering the node and if no snapshot is available, it might be possible to promote a stale shard. However, this means that data will be lost, and in the event that the lost node recovers, data will be overwritten with the stale data.
The command to restore is:
POST /_cluster/reroute
{
"commands" : [
{
"allocate_stale_primary" : {
"index" : "test", "shard" : 0,
"node" : "es01",
"accept_data_loss":"true"
}
}
]
}Overview
Data in an Elasticsearch index can grow to massive proportions. In order to keep it manageable, it is split into a number of shards. Each Elasticsearch shard is an Apache Lucene index, with each individual Lucene index containing a subset of the documents in the Elasticsearch 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 Elasticsearch 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 “missing shard” class name is TransportDownsampleIndexerAction.java. We extracted the following from Elasticsearch source code for those seeking an in-depth context :
long numIndexed = 0;
int successfulShards = 0;
for (int i = 0; i < shardsResponses.length(); i++) {
Object shardResponse = shardsResponses.get(i);
if (shardResponse == null) {
throw new ElasticsearchException("missing shard");
} else if (shardResponse instanceof DownsampleIndexerAction.ShardDownsampleResponse r) {
successfulShards++;
numIndexed += r.getNumIndexed();
} else if (shardResponse instanceof Exception e) {
throw new ElasticsearchException(e);
