[[size-your-shards]] == How to size your shards ++++ Size your shards ++++ To protect against hardware failure and increase capacity, {es} 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. [discrete] [[create-a-sharding-strategy]] === Create a sharding strategy 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 https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing[quantitative cluster sizing video]. As you test different shard configurations, use {kib}'s {kibana-ref}/elasticsearch-metrics.html[{es} 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 <>. [discrete] [[shard-sizing-considerations]] === Sizing considerations Keep the following things in mind when building your sharding strategy. [discrete] [[single-thread-per-shard]] ==== Searches run on a single thread per shard 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 <>. This can result in low throughput and slow search speeds. [discrete] [[each-shard-has-overhead]] ==== Each shard has overhead 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. {es} keeps segment metadata in <> so it can be quickly retrieved for searches. As a shard grows, its segments are <> into fewer, larger segments. This decreases the number of segments, which means less metadata is kept in heap memory. [discrete] [[shard-auto-balance]] ==== {es} automatically balances shards within a data tier A cluster's nodes are grouped into <>. Within each tier, {es} attempts to spread an index's shards across as many nodes as possible. When you add a new node or a node fails, {es} automatically rebalances the index's shards across the tier's remaining nodes. [discrete] [[shard-size-best-practices]] === Best practices Where applicable, use the following best practices as starting points for your sharding strategy. [discrete] [[delete-indices-not-documents]] ==== Delete indices, not documents Deleted documents aren't immediately removed from {es}'s file system. Instead, {es} marks the document as deleted on each related shard. The marked document will continue to use resources until it's removed during a periodic <>. When possible, delete entire indices instead. {es} can immediately remove deleted indices directly from the file system and free up resources. [discrete] [[use-ds-ilm-for-time-series]] ==== Use data streams and {ilm-init} for time series data <> let you store time series data across multiple, time-based backing indices. You can use <> to automatically manage these backing indices. [role="screenshot"] image:images/ilm/index-lifecycle-policies.png[] One advantage of this setup is <>, 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-init} to automatically delete it and free up resources. {ilm-init} also makes it easy to change your sharding strategy over time: * *Want to decrease the shard count for new indices?* + Change the <> setting in the data stream's <>. * *Want larger shards?* + Increase your {ilm-init} policy's <>. * *Need indices that span shorter intervals?* + Offset the increased shard count by deleting older indices sooner. You can do this by lowering the `min_age` threshold for your policy's <>. Every new backing index is an opportunity to further tune your strategy. [discrete] [[shard-size-recommendation]] ==== Aim for shard sizes between 10GB and 50GB Shards larger than 50GB may make a cluster less likely to recover from failure. When a node fails, {es} 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. [discrete] [[shard-count-recommendation]] ==== Aim for 20 shards or fewer per GB of heap memory The number of shards a node can hold is proportional to the node's <>. 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 <> to check the number of shards per node. [source,console] ---- GET _cat/shards ---- // TEST[setup:my_index] 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 <>. [discrete] [[avoid-node-hotspots]] ==== Avoid node hotspots 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 setting to explicitly limit the number of shards on a single node. You can configure `index.routing.allocation.total_shards_per_node` using the <>. [source,console] -------------------------------------------------- PUT /my-index-000001/_settings { "index" : { "routing.allocation.total_shards_per_node" : 5 } } -------------------------------------------------- // TEST[setup:my_index] [discrete] [[fix-an-oversharded-cluster]] === Fix an oversharded cluster If your cluster is experiencing stability issues due to oversharded indices, you can use one or more of the following methods to fix them. [discrete] [[reindex-indices-from-shorter-periods-into-longer-periods]] ==== Create time-based indices that cover longer periods 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-init}, you can do this by increasing the `max_age` threshold for the <>. 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. [discrete] [[delete-empty-indices]] ==== Delete empty or unneeded indices If you're using {ilm-init} 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 <>. [source,console] ---- GET /_cat/count/my-index-000001?v ---- // TEST[setup:my_index] Once you have a list of empty indices, you can delete them using the <>. You can also delete any other unneeded indices. [source,console] ---- DELETE /my-index-* ---- // TEST[setup:my_index] [discrete] [[force-merge-during-off-peak-hours]] ==== Force merge during off-peak hours If you no longer write to an index, you can use the <> to <> 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. [source,console] ---- POST /my-index-000001/_forcemerge ---- // TEST[setup:my_index] [discrete] [[shrink-existing-index-to-fewer-shards]] ==== Shrink an existing index to fewer shards If you no longer write to an index, you can use the <> to reduce its shard count. [source,console] ---- POST /my-index-000001/_shrink/my-shrunken-index-000001 ---- // TEST[s/^/PUT my-index-000001\n{"settings":{"index.number_of_shards":2,"blocks.write":true}}\n/] {ilm-init} also has a <> for indices in the warm phase. [discrete] [[combine-smaller-indices]] ==== Combine smaller indices You can also use the <> 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. [source,console] ---- POST /_reindex { "source": { "index": "my-index-2099.10.*" }, "dest": { "index": "my-index-2099.10" } } ----