- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.10
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Setting JVM options
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- HTTP
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
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- Logging
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- Node
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- Transforms settings
- Transport
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- Important Elasticsearch 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
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- Bootstrap Checks for X-Pack
- Starting Elasticsearch
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- Discovery and cluster formation
- Add and remove nodes in your cluster
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- Configuring X-Pack Java Clients
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- Upgrade Elasticsearch
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- Text analysis
- Overview
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- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
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- Apostrophe
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- Synonym graph
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- Search your data
- Query DSL
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- EQL
- SQL access
- Overview
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- ILM: Manage the index lifecycle
- Overview
- Concepts
- Automate rollover
- Manage Filebeat time-based indices
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Resolve lifecycle policy execution errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Monitor a cluster
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- Set up a cluster for high availability
- Snapshot and restore
- Secure a cluster
- Overview
- Configuring security
- User authentication
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- Realms
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- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
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- PKI user authentication
- SAML authentication
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- Integrating with other authentication systems
- Enabling anonymous access
- 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
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- Granting privileges for data streams and index aliases
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- Submitting requests on behalf of other users
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- Customizing roles and authorization
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- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Some settings are not returned via the nodes settings API
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- Cluster APIs
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- Add index alias
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Create trained models
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- Get data frame analytics jobs
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- Get trained models stats
- Start data frame analytics jobs
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- Migration APIs
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- Authenticate
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- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- Transform APIs
- Usage API
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- Definitions
- Migration guide
- Release notes
- Elasticsearch version 7.10.2
- Elasticsearch version 7.10.1
- Elasticsearch version 7.10.0
- Elasticsearch version 7.9.3
- Elasticsearch version 7.9.2
- Elasticsearch version 7.9.1
- Elasticsearch version 7.9.0
- Elasticsearch version 7.8.1
- Elasticsearch version 7.8.0
- Elasticsearch version 7.7.1
- Elasticsearch version 7.7.0
- Elasticsearch version 7.6.2
- Elasticsearch version 7.6.1
- Elasticsearch version 7.6.0
- Elasticsearch version 7.5.2
- Elasticsearch version 7.5.1
- Elasticsearch version 7.5.0
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
- Dependencies and versions
How to size your shards
editHow to size your shards
editTo protect against hardware failure and increase capacity, Elasticsearch stores copies of an index’s data across multiple shards on multiple nodes. The number and size of these shards can have a significant impact on your cluster’s health. One common problem is oversharding, a situation in which a cluster with a large number of shards becomes unstable.
Create a sharding strategy
editThe best way to prevent oversharding and other shard-related issues is to create a sharding strategy. A sharding strategy helps you determine and maintain the optimal number of shards for your cluster while limiting the size of those shards.
Unfortunately, there is no one-size-fits-all sharding strategy. A strategy that works in one environment may not scale in another. A good sharding strategy must account for your infrastructure, use case, and performance expectations.
The best way to create a sharding strategy is to benchmark your production data on production hardware using the same queries and indexing loads you’d see in production. For our recommended methodology, watch the quantitative cluster sizing video. As you test different shard configurations, use Kibana’s Elasticsearch monitoring tools to track your cluster’s stability and performance.
The following sections provide some reminders and guidelines you should consider when designing your sharding strategy. If your cluster has shard-related problems, see Fix an oversharded cluster.
Sizing considerations
editKeep the following things in mind when building your sharding strategy.
Searches run on a single thread per shard
editMost searches hit multiple shards. Each shard runs the search on a single CPU thread. While a shard can run multiple concurrent searches, searches across a large number of shards can deplete a node’s search thread pool. This can result in low throughput and slow search speeds.
Each shard has overhead
editEvery shard uses memory and CPU resources. In most cases, a small set of large shards uses fewer resources than many small shards.
Segments play a big role in a shard’s resource usage. Most shards contain several segments, which store its index data. Elasticsearch keeps segment metadata in heap memory so it can be quickly retrieved for searches. As a shard grows, its segments are merged into fewer, larger segments. This decreases the number of segments, which means less metadata is kept in heap memory.
Elasticsearch automatically balances shards within a data tier
editA cluster’s nodes are grouped into data tiers. Within each tier, Elasticsearch attempts to spread an index’s shards across as many nodes as possible. When you add a new node or a node fails, Elasticsearch automatically rebalances the index’s shards across the tier’s remaining nodes.
Best practices
editWhere applicable, use the following best practices as starting points for your sharding strategy.
Delete indices, not documents
editDeleted documents aren’t immediately removed from Elasticsearch’s file system. Instead, Elasticsearch marks the document as deleted on each related shard. The marked document will continue to use resources until it’s removed during a periodic segment merge.
When possible, delete entire indices instead. Elasticsearch can immediately remove deleted indices directly from the file system and free up resources.
Use data streams and ILM for time series data
editData streams let you store time series data across multiple, time-based backing indices. You can use index lifecycle management (ILM) to automatically manage these backing indices.
One advantage of this setup is
automatic rollover, which creates
a new write index when the current one meets a defined max_age
, max_docs
, or
max_size
threshold. You can use these thresholds to create indices based on
your retention intervals. When an index is no longer needed, you can use
ILM to automatically delete it and free up resources.
ILM also makes it easy to change your sharding strategy over time:
-
Want to decrease the shard count for new indices?
Change theindex.number_of_shards
setting in the data stream’s matching index template. -
Want larger shards?
Increase your ILM policy’s rollover threshold. -
Need indices that span shorter intervals?
Offset the increased shard count by deleting older indices sooner. You can do this by lowering themin_age
threshold for your policy’s delete phase.
Every new backing index is an opportunity to further tune your strategy.
Aim for shard sizes between 10GB and 50GB
editShards larger than 50GB may make a cluster less likely to recover from failure. When a node fails, Elasticsearch rebalances the node’s shards across the data tier’s remaining nodes. Shards larger than 50GB can be harder to move across a network and may tax node resources.
Aim for 20 shards or fewer per GB of heap memory
editThe number of shards a node can hold is proportional to the node’s heap memory. For example, a node with 30GB of heap memory should have at most 600 shards. The further below this limit you can keep your nodes, the better. If you find your nodes exceeding more than 20 shards per GB, consider adding another node. You can use the cat shards API to check the number of shards per node.
GET _cat/shards?v=true
To use compressed pointers and save memory, we recommend each node have a maximum heap size of 32GB or 50% of the node’s available memory, whichever is lower. See Heap size settings.
Avoid node hotspots
editIf too many shards are allocated to a specific node, the node can become a hotspot. For example, if a single node contains too many shards for an index with a high indexing volume, the node is likely to have issues.
To prevent hotspots, use the
index.routing.allocation.total_shards_per_node
index
setting to explicitly limit the number of shards on a single node. You can
configure index.routing.allocation.total_shards_per_node
using the
update index settings API.
PUT /my-index-000001/_settings { "index" : { "routing.allocation.total_shards_per_node" : 5 } }
Fix an oversharded cluster
editIf your cluster is experiencing stability issues due to oversharded indices, you can use one or more of the following methods to fix them.
Create time-based indices that cover longer periods
editFor time series data, you can create indices that cover longer time intervals. For example, instead of daily indices, you can create indices on a monthly or yearly basis.
If you’re using ILM, you can do this by increasing the max_age
threshold for the rollover action.
If your retention policy allows it, you can also create larger indices by
omitting a max_age
threshold and using max_docs
and/or max_size
thresholds instead.
Delete empty or unneeded indices
editIf you’re using ILM and roll over indices based on a max_age
threshold,
you can inadvertently create indices with no documents. These empty indices
provide no benefit but still consume resources.
You can find these empty indices using the cat count API.
GET /_cat/count/my-index-000001?v=true
Once you have a list of empty indices, you can delete them using the delete index API. You can also delete any other unneeded indices.
DELETE /my-index-*
Force merge during off-peak hours
editIf you no longer write to an index, you can use the force merge API to merge smaller segments into larger ones. This can reduce shard overhead and improve search speeds. However, force merges are resource-intensive. If possible, run the force merge during off-peak hours.
POST /my-index-000001/_forcemerge
Shrink an existing index to fewer shards
editIf you no longer write to an index, you can use the shrink index API to reduce its shard count.
POST /my-index-000001/_shrink/my-shrunken-index-000001
ILM also has a shrink action for indices in the warm phase.
Combine smaller indices
editYou can also use the reindex API to combine indices
with similar mappings into a single large index. For time series data, you could
reindex indices for short time periods into a new index covering a
longer period. For example, you could reindex daily indices from October with a
shared index pattern, such as my-index-2099.10.11
, into a monthly
my-index-2099.10
index. After the reindex, delete the smaller indices.
POST /_reindex { "source": { "index": "my-index-2099.10.*" }, "dest": { "index": "my-index-2099.10" } }
On this page
- Create a sharding strategy
- Sizing considerations
- Searches run on a single thread per shard
- Each shard has overhead
- Elasticsearch automatically balances shards within a data tier
- Best practices
- Delete indices, not documents
- Use data streams and ILM for time series data
- Aim for shard sizes between 10GB and 50GB
- Aim for 20 shards or fewer per GB of heap memory
- Avoid node hotspots
- Fix an oversharded cluster
- Create time-based indices that cover longer periods
- Delete empty or unneeded indices
- Force merge during off-peak hours
- Shrink an existing index to fewer shards
- Combine smaller indices