How to size your shards

To 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.

The 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.

Keep the following things in mind when building your sharding strategy.

Most 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.

Every 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.

By default, Elasticsearch attempts to spread an index’s shards across as many nodes as possible. When a node fails or you add a new node, Elasticsearch automatically rebalances shards across the remaining nodes.

You can use shard allocation awareness to ensure shards for particular indices only move to specified nodes. If you use Elasticsearch for time series data, you can use shard allocation to decrease storage costs by assigning shards for older indices to nodes with less expensive hardware.

Where applicable, use the following best practices as starting points for your sharding strategy.

Deleted 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.

Data 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:

Every new backing index is an opportunity to further tune your strategy.

Shards 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 cluster’s remaining nodes. Shards larger than 50GB can be harder to move across a network and may tax node resources.

The 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

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 Setting the heap size.

If 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
  }
}

If your cluster is experiencing stability issues due to oversharded indices, you can use one or more of the following methods to fix them.

For 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.

If 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

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-*

If 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

If 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.

You 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"
  }
}