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- Elasticsearch version 8.16.4
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- Elasticsearch version 8.16.2
- Elasticsearch version 8.16.1
- Elasticsearch version 8.16.0
- Elasticsearch version 8.15.5
- Elasticsearch version 8.15.4
- Elasticsearch version 8.15.3
- Elasticsearch version 8.15.2
- Elasticsearch version 8.15.1
- Elasticsearch version 8.15.0
- Elasticsearch version 8.14.3
- Elasticsearch version 8.14.2
- Elasticsearch version 8.14.1
- Elasticsearch version 8.14.0
- Elasticsearch version 8.13.4
- Elasticsearch version 8.13.3
- Elasticsearch version 8.13.2
- Elasticsearch version 8.13.1
- Elasticsearch version 8.13.0
- Elasticsearch version 8.12.2
- Elasticsearch version 8.12.1
- Elasticsearch version 8.12.0
- Elasticsearch version 8.11.4
- Elasticsearch version 8.11.3
- Elasticsearch version 8.11.2
- Elasticsearch version 8.11.1
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- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
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- Elasticsearch version 8.9.1
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- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Hot spotting
editHot spotting
editComputer hot spotting may occur in Elasticsearch when resource utilizations are unevenly distributed across nodes. Temporary spikes are not usually considered problematic, but ongoing significantly unique utilization may lead to cluster bottlenecks and should be reviewed.
Detect hot spotting
editHot spotting most commonly surfaces as significantly elevated
resource utilization (of disk.percent
, heap.percent
, or cpu
) among a
subset of nodes as reported via cat nodes. Individual spikes aren’t
necessarily problematic, but if utilization repeatedly spikes or consistently remains
high over time (for example longer than 30 seconds), the resource may be experiencing problematic
hot spotting.
For example, let’s show case two separate plausible issues using cat nodes:
resp = client.cat.nodes( v=True, s="master,name", h="name,master,node.role,heap.percent,disk.used_percent,cpu", ) print(resp)
response = client.cat.nodes( v: true, s: 'master,name', h: 'name,master,node.role,heap.percent,disk.used_percent,cpu' ) puts response
const response = await client.cat.nodes({ v: "true", s: "master,name", h: "name,master,node.role,heap.percent,disk.used_percent,cpu", }); console.log(response);
GET _cat/nodes?v&s=master,name&h=name,master,node.role,heap.percent,disk.used_percent,cpu
Pretend this same output pulled twice across five minutes:
name master node.role heap.percent disk.used_percent cpu node_1 * hirstm 24 20 95 node_2 - hirstm 23 18 18 node_3 - hirstmv 25 90 10
Here we see two significantly unique utilizations: where the master node is at
cpu: 95
and a hot node is at disk.used_percent: 90%
. This would indicate
hot spotting was occurring on these two nodes, and not necessarily from the same
root cause.
Causes
editHistorically, clusters experience hot spotting mainly as an effect of hardware, shard distributions, and/or task load. We’ll review these sequentially in order of their potentially impacting scope.
Hardware
editHere are some common improper hardware setups which may contribute to hot spotting:
- Resources are allocated non-uniformly. For example, if one hot node is given half the CPU of its peers. Elasticsearch expects all nodes on a data tier to share the same hardware profiles or specifications.
- Resources are consumed by another service on the host, including other Elasticsearch nodes. Refer to our dedicated host recommendation.
- Resources experience different network or disk throughputs. For example, if one node’s I/O is lower than its peers. Refer to Use faster hardware for more information.
- A JVM that has been configured with a heap larger than 31GB. Refer to Set the JVM heap size for more information.
- Problematic resources uniquely report memory swapping.
Shard distributions
editElasticsearch indices are divided into one or more shards which can sometimes be poorly distributed. Elasticsearch accounts for this by balancing shard counts across data nodes. As introduced in version 8.6, Elasticsearch by default also enables desired balancing to account for ingest load. A node may still experience hot spotting either due to write-heavy indices or by the overall shards it’s hosting.
Node level
editYou can check for shard balancing via cat allocation, though as of version 8.6, desired balancing may no longer fully expect to balance shards. Kindly note, both methods may temporarily show problematic imbalance during cluster stability issues.
For example, let’s showcase two separate plausible issues using cat allocation:
resp = client.cat.allocation( v=True, s="node", h="node,shards,disk.percent,disk.indices,disk.used", ) print(resp)
response = client.cat.allocation( v: true, s: 'node', h: 'node,shards,disk.percent,disk.indices,disk.used' ) puts response
const response = await client.cat.allocation({ v: "true", s: "node", h: "node,shards,disk.percent,disk.indices,disk.used", }); console.log(response);
GET _cat/allocation?v&s=node&h=node,shards,disk.percent,disk.indices,disk.used
Which could return:
node shards disk.percent disk.indices disk.used node_1 446 19 154.8gb 173.1gb node_2 31 52 44.6gb 372.7gb node_3 445 43 271.5gb 289.4gb
Here we see two significantly unique situations. node_2
has recently
restarted, so it has a much lower number of shards than all other nodes. This
also relates to disk.indices
being much smaller than disk.used
while shards
are recovering as seen via cat recovery. While node_2
's shard
count is low, it may become a write hot spot due to ongoing ILM
rollovers. This is a common root cause of write hot spots covered in the next
section.
The second situation is that node_3
has a higher disk.percent
than node_1
,
even though they hold roughly the same number of shards. This occurs when either
shards are not evenly sized (refer to Aim for shards of up to 200M documents, or with sizes between 10GB and 50GB) or when
there are a lot of empty indices.
Cluster rebalancing based on desired balance does much of the heavy lifting of keeping nodes from hot spotting. It can be limited by either nodes hitting watermarks (refer to fixing disk watermark errors) or by a write-heavy index’s total shards being much lower than the written-to nodes.
You can confirm hot spotted nodes via the nodes stats API, potentially polling twice over time to only checking for the stats differences between them rather than polling once giving you stats for the node’s full node uptime. For example, to check all nodes indexing stats:
resp = client.nodes.stats( human=True, filter_path="nodes.*.name,nodes.*.indices.indexing", ) print(resp)
response = client.nodes.stats( human: true, filter_path: 'nodes.*.name,nodes.*.indices.indexing' ) puts response
const response = await client.nodes.stats({ human: "true", filter_path: "nodes.*.name,nodes.*.indices.indexing", }); console.log(response);
GET _nodes/stats?human&filter_path=nodes.*.name,nodes.*.indices.indexing
Index level
editHot spotted nodes frequently surface via cat thread pool's
write
and search
queue backups. For example:
resp = client.cat.thread_pool( thread_pool_patterns="write,search", v=True, s="n,nn", h="n,nn,q,a,r,c", ) print(resp)
response = client.cat.thread_pool( thread_pool_patterns: 'write,search', v: true, s: 'n,nn', h: 'n,nn,q,a,r,c' ) puts response
const response = await client.cat.threadPool({ thread_pool_patterns: "write,search", v: "true", s: "n,nn", h: "n,nn,q,a,r,c", }); console.log(response);
GET _cat/thread_pool/write,search?v=true&s=n,nn&h=n,nn,q,a,r,c
Which could return:
n nn q a r c search node_1 3 1 0 1287 search node_2 0 2 0 1159 search node_3 0 1 0 1302 write node_1 100 3 0 4259 write node_2 0 4 0 980 write node_3 1 5 0 8714
Here you can see two significantly unique situations. Firstly, node_1
has a
severely backed up write queue compared to other nodes. Secondly, node_3
shows
historically completed writes that are double any other node. These are both
probably due to either poorly distributed write-heavy indices, or to multiple
write-heavy indices allocated to the same node. Since primary and replica writes
are majorly the same amount of cluster work, we usually recommend setting
index.routing.allocation.total_shards_per_node
to
force index spreading after lining up index shard counts to total nodes.
We normally recommend heavy-write indices have sufficient primary
number_of_shards
and replica number_of_replicas
to evenly spread across
indexing nodes. Alternatively, you can reroute shards to
more quiet nodes to alleviate the nodes with write hot spotting.
If it’s non-obvious what indices are problematic, you can introspect further via the index stats API by running:
resp = client.indices.stats( level="shards", human=True, expand_wildcards="all", filter_path="indices.*.total.indexing.index_total", ) print(resp)
response = client.indices.stats( level: 'shards', human: true, expand_wildcards: 'all', filter_path: 'indices.*.total.indexing.index_total' ) puts response
const response = await client.indices.stats({ level: "shards", human: "true", expand_wildcards: "all", filter_path: "indices.*.total.indexing.index_total", }); console.log(response);
GET _stats?level=shards&human&expand_wildcards=all&filter_path=indices.*.total.indexing.index_total
For more advanced analysis, you can poll for shard-level stats, which lets you compare joint index-level and node-level stats. This analysis wouldn’t account for node restarts and/or shards rerouting, but serves as overview:
resp = client.indices.stats( metric="indexing,search", level="shards", human=True, expand_wildcards="all", ) print(resp)
response = client.indices.stats( metric: 'indexing,search', level: 'shards', human: true, expand_wildcards: 'all' ) puts response
const response = await client.indices.stats({ metric: "indexing,search", level: "shards", human: "true", expand_wildcards: "all", }); console.log(response);
GET _stats/indexing,search?level=shards&human&expand_wildcards=all
You can for example use the third-party JQ tool,
to process the output saved as indices_stats.json
:
cat indices_stats.json | jq -rc ['.indices|to_entries[]|.key as $i|.value.shards|to_entries[]|.key as $s|.value[]|{node:.routing.node[:4], index:$i, shard:$s, primary:.routing.primary, size:.store.size, total_indexing:.indexing.index_total, time_indexing:.indexing.index_time_in_millis, total_query:.search.query_total, time_query:.search.query_time_in_millis } | .+{ avg_indexing: (if .total_indexing>0 then (.time_indexing/.total_indexing|round) else 0 end), avg_search: (if .total_search>0 then (.time_search/.total_search|round) else 0 end) }'] > shard_stats.json # show top written-to shard simplified stats which contain their index and node references cat shard_stats.json | jq -rc 'sort_by(-.avg_indexing)[]' | head
Task loads
editShard distribution problems will most-likely surface as task load as seen above in the cat thread pool example. It is also possible for tasks to hot spot a node either due to individual qualitative expensiveness or overall quantitative traffic loads.
For example, if cat thread pool reported a high
queue on the warmer
thread pool, you would
look-up the effected node’s hot threads.
Let’s say it reported warmer
threads at 100% cpu
related to
GlobalOrdinalsBuilder
. This would let you know to inspect
field data’s global ordinals.
Alternatively, let’s say cat nodes shows a hot spotted master node
and cat thread pool shows general queuing across nodes.
This would suggest the master node is overwhelmed. To resolve
this, first ensure hardware high availability
setup and then look to ephemeral causes. In this example,
the nodes hot threads API reports multiple threads in
other
which indicates they’re waiting on or blocked by either garbage collection
or I/O.
For either of these example situations, a good way to confirm the problematic tasks
is to look at longest running non-continuous (designated [c]
) tasks via
cat task management. This can be supplemented checking longest
running cluster sync tasks via cat pending tasks. Using
a third example,
resp = client.cat.tasks( v=True, s="time:desc", h="type,action,running_time,node,cancellable", ) print(resp)
response = client.cat.tasks( v: true, s: 'time:desc', h: 'type,action,running_time,node,cancellable' ) puts response
const response = await client.cat.tasks({ v: "true", s: "time:desc", h: "type,action,running_time,node,cancellable", }); console.log(response);
GET _cat/tasks?v&s=time:desc&h=type,action,running_time,node,cancellable
This could return:
type action running_time node cancellable direct indices:data/read/eql 10m node_1 true ...
This surfaces a problematic EQL query. We can gain further insight on it via the task management API,
resp = client.tasks.list( human=True, detailed=True, ) print(resp)
response = client.tasks.list( human: true, detailed: true ) puts response
const response = await client.tasks.list({ human: "true", detailed: "true", }); console.log(response);
GET _tasks?human&detailed
Its response contains a description
that reports this query:
indices[winlogbeat-*,logs-window*], sequence by winlog.computer_name with maxspan=1m\n\n[authentication where host.os.type == "windows" and event.action:"logged-in" and\n event.outcome == "success" and process.name == "svchost.exe" ] by winlog.event_data.TargetLogonId
This lets you know which indices to check (winlogbeat-*,logs-window*
), as well
as the EQL search request body. Most likely this is
SIEM related.
You can combine this with audit logging as needed to
trace the request source.