- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.10
- Set up Elasticsearch
- Installing Elasticsearch
- Run Elasticsearch locally
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Miscellaneous cluster settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- Health Diagnostic settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging
- Machine learning settings
- Monitoring settings
- Node
- Networking
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Advanced configuration
- Important system configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- All permission check
- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Attachment
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- Geo-grid
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Redact
- Registered domain
- Remove
- Rename
- Reroute
- Script
- Set
- Set security user
- Sort
- Split
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Aliases
- Search your data
- Collapse search results
- Filter search results
- Highlighting
- Long-running searches
- Near real-time search
- Paginate search results
- Retrieve inner hits
- Retrieve selected fields
- Search across clusters
- Search multiple data streams and indices
- Search shard routing
- Search templates
- Search with synonyms
- Sort search results
- kNN search
- Semantic search
- Searching with query rules
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Frequent item sets
- Geo-distance
- Geohash grid
- Geohex grid
- Geotile grid
- Global
- Histogram
- IP prefix
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Random sampler
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Time series
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Change point
- Cumulative cardinality
- Cumulative sum
- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
- Min bucket
- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- Geospatial analysis
- EQL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
- Concepts
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Data tiers
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
- Manually configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- User profiles
- Realms
- Realm chains
- Security domains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- JWT authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Looking up users without authentication
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Role restriction
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Watcher
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- How to
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
- Total number of shards per node has been reached
- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
- Fix other role nodes out of disk
- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Multiple deployments writing to the same snapshot repository
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat component templates
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Prevalidate node removal
- Nodes reload secure settings
- Nodes stats
- Cluster Info
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Create or update desired nodes
- Get desired nodes
- Delete desired nodes
- Get desired balance
- Reset desired balance
- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- Features APIs
- Fleet APIs
- Find structure API
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
- Clone index
- Close index
- Create index
- Create or update alias
- Create or update component template
- Create or update index template
- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Unfreeze index
- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Ingest APIs
- Info API
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Upgrade model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Update data frame analytics jobs
- Machine learning trained model APIs
- Clear trained model deployment cache
- Create or update trained model aliases
- Create part of a trained model
- Create trained models
- Create trained model vocabulary
- Delete trained model aliases
- Delete trained models
- Get trained models
- Get trained models stats
- Infer trained model
- Start trained model deployment
- Stop trained model deployment
- Update trained model deployment
- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Script APIs
- Search APIs
- Search Application APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get service accounts
- Get service account credentials
- Get token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- Query API key information
- Update API key
- Bulk update API keys
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Activate user profile
- Disable user profile
- Enable user profile
- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- 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:
response = client.cat.nodes( v: true, s: 'master,name', h: 'name,master,node.role,heap.percent,disk.used_percent,cpu' ) puts 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:
response = client.cat.allocation( v: true, s: 'node', h: 'node,shards,disk.percent,disk.indices,disk.used' ) puts 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:
response = client.nodes.stats( human: true, filter_path: 'nodes.*.name,nodes.*.indices.indexing' ) puts 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:
response = client.cat.thread_pool( thread_pool_patterns: 'write,search', v: true, s: 'n,nn', h: 'n,nn,q,a,r,c' ) puts 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:
response = client.indices.stats( level: 'shards', human: true, expand_wildcards: 'all', filter_path: 'indices.*.total.indexing.index_total' ) puts 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:
response = client.indices.stats( metric: 'indexing,search', level: 'shards', human: true, expand_wildcards: 'all' ) puts 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,
response = client.cat.tasks( v: true, s: 'time:desc', h: 'type,action,running_time,node,cancellable' ) puts 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,
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.