The Decision System For Shard Allocation in Elasticsearch

How does it work and what can we learn from it?


Shard allocation plays an important role in the data distribution of an Elasticsearch cluster. Having all the shards allocated ensures the good health of your cluster and avoids potential data loss. That’s why it’s important to avoid unassigned shards and avoid yellow cluster. But as a software engineer or as a database administrator (DBA), do you know how the decision is made internally? How does Elasticsearch know which node should be used? In this article, we are going to study this part by exploring the decider system.

After reading this article, you will understand:

  • The responsibility of allocation service and allocation deciders.
  • The structure of these 19 deciders.
  • How do they make decisions?
  • The deciders’ position in the lifecycle of an ES node.
  • How to test them?
  • How to go further from this article?

And hopefully, this article will help you better understand the internal mechanism or inspire you to make your decision system based on a similar architecture. Note that this article is written with Elasticsearch 7.16.2 (latest), which may be different from what you are using. Now, let’s get started!


Allocation deciders are part of the allocation service in Elasticsearch. This service manages the shard allocation of a cluster. For this reason, the AllocationService contains an instance of AllocationDeciders to choose nodes for shard allocation. This class also manages new nodes joining the cluster and the rerouting of shards. If none of the nodes accepts the allocation, the shard will remain unassigned. Having unassigned shard leads to under-replicated shards. It will probably result to a yellow cluster and increase the risk of data loss.

Which actions require decisions?

We can find this information from the abstract class AllocationDecider. An allocation decider can decide for many actions: allocation, rebalancing, keeping the shard in the current node (canRemain), and auto-expanding the shards of a given index. Each method returns a decision so that the allocation service knows how to react. Each action respects the naming convention can{Action} or should{Action}. Here is a more detailed version:

➜  elasticsearch git:(v7.16.2 u=) ✗ grep "public Decision" server/src/main/java/org/elasticsearch/cluster/routing/allocation/decider/ | sort
    public Decision canAllocate(IndexMetadata indexMetadata, RoutingNode node, RoutingAllocation allocation) {
    public Decision canAllocate(ShardRouting shardRouting, RoutingAllocation allocation) {
    public Decision canAllocate(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {
    public Decision canAllocateReplicaWhenThereIsRetentionLease(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {
    public Decision canForceAllocateDuringReplace(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {
    public Decision canForceAllocatePrimary(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {
    public Decision canRebalance(RoutingAllocation allocation) {
    public Decision canRebalance(ShardRouting shardRouting, RoutingAllocation allocation) {
    public Decision canRemain(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {
    public Decision shouldAutoExpandToNode(IndexMetadata indexMetadata, DiscoveryNode node, RoutingAllocation allocation) {

Deciders Structure

In Elasticsearch 7.16, there are 19 deciders for the shard allocation. You can find them as follows:

➜  elasticsearch git:(v7.16.2 u=) rg -l --sort-files "extends AllocationDecider" server/src/main | sed 's/.*\///g'

Among these deciders, there is a root decider, the AllocationDeciders, which contains the references of all other deciders. When making a decision, it will ask those deciders to decide for the shard allocation, and then assemble them to make a global decision. We will discuss more details in the following section. As for other deciders, you can see their purposes from their filenames, such as for awareness key-value pairs (e.g. availability zone), cluster rebalancing, concurrent rebalancing, disk threshold, and much more.

Making Decisions

There are two types of decision in the decider system: single decision and multi-decision. A single decision represents a decision made on one dimension, e.g. awareness or disk threshold. While a multi-decision represents a decision container that contains a list of child decisions. Let’s take a closer look at both of them.

Single Decision

DiskThresholdDecider is a good example of making a single decision. Let’s check its method for shard allocation: canAllocate(...). First of all, it retrieves the settings from the class member diskThresholdSettings for the low and high thresholds, then it retrieves the disk usage from the given information (node, routing allocation, usages), and finally compare both of them to make the decision. Depending on the situation, we will either get a YES or a NO decision. Here is the code snippet:

public Decision canAllocate(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {

    // retrieve settings
    final double usedDiskThresholdLow = 100.0 - diskThresholdSettings.getFreeDiskThresholdLow();
    final double usedDiskThresholdHigh = 100.0 - diskThresholdSettings.getFreeDiskThresholdHigh();

    // checks for exact byte comparisons
    if (freeBytes < diskThresholdSettings.getFreeBytesThresholdLow().getBytes()) {

    // checks for percentage comparisons
    if (freeDiskPercentage < diskThresholdSettings.getFreeDiskThresholdLow()) {


Note that YES and NO are not the only types for the decision. It also exists a THROTTLE type, which allows users to control the shard allocation based on the concurrency. We can consider it as a “temporary NO”, as the allocation is not allowed right now, but may be YES in the future.


Now let’s take a look at the AllocationDeciders to see how it makes a multi-decision. When starting the decision, it accepts information about the shard routing, node routing, and the current allocation. Then, it either ignores the shard or delegates the decision-making to its child deciders and assembles them at the end. All child deciders make a decision synchronously, probably because they don’t require additional information (such as HTTP requests) from an external service. Once the decision is made by a child decider, the result is processed with the fail-fast strategy. That is, whenever one child decision is negative (NO), we consider the whole decision as NO without asking the remaining deciders. It’s a short circuit. We do that unless we are in the debug mode, in which case, we gather all the decisions, including the NO decisions, before returning the final result. If my explanation is a bit confusing for you, here is the source code of AllocationDeciders, hopefully, it makes things clearer.

    public Decision canAllocate(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation) {
        if (allocation.shouldIgnoreShardForNode(shardRouting.shardId(), node.nodeId())) {
            return Decision.NO;
        Decision.Multi ret = new Decision.Multi();
        for (AllocationDecider allocationDecider : allocations) {
            Decision decision = allocationDecider.canAllocate(shardRouting, node, allocation);
            // short track if a NO is returned.
            if (decision.type() == Decision.Type.NO) {
                if (logger.isTraceEnabled()) {
                // short circuit only if debugging is not enabled
                if (allocation.debugDecision() == false) {
                    return Decision.NO;
                } else {
            } else {
                addDecision(ret, decision, allocation);
        return ret;

Now if we go further into the structure of the value class Decision.Multi, we can see that a multi-decision is a decision container, it contains multiple child decisions inside it:

    public static class Multi extends Decision implements ToXContentFragment {

        private final List<Decision> decisions = new ArrayList<>();

Unlike single decisions, it does not have a label and explanation. More precisely, getting a label from a multi-decision returns null, and getting an explanation throws an unsupported operation exception. In my opinion, the Decision base class is too vague and we shouldn’t return null or raise an exception. But that’s a design choice and probably does not matter as this decider system is internal to Elasticsearch.


When are deciders created? I.e. where are deciders positioned in the lifecycle of an Elasticsearch cluster?

When starting a new node, the class Bootstrap is called. Before starting the node, it creates a new Node instance, which creates and adds a list of modules. One of these modules is called ClusterModule and it contains the deciders. So the whole logic happens at the early stage of the lifecycle, more precisely, the creation happens before the startup of a node.

- boostrap
  - construct node
    - create modules
      - create master module
        - create deciders
        - create root decider (AllocationDeciders)
        - create allocation service
        - ...
    - add modules
  - start node

Now another question is: when updating a setting of a cluster, do we need to restart the node to take the new value into account?

I don’t think so. I handled such cases many times in production and it never requires a restart. These settings are dynamic. When looking into Elasticsearch’s source code, such as awareness allocation decider and disk threshold decider, we can find out that the settings are passed from the constructor of the deciders. Here are the code snippets:

public AwarenessAllocationDecider(Settings settings, ClusterSettings clusterSettings) { ... }
public DiskThresholdDecider(Settings settings, ClusterSettings clusterSettings) {
    this.diskThresholdSettings = new DiskThresholdSettings(settings, clusterSettings);
    assert Version.CURRENT.major < 9 : "remove enable_for_single_data_node in 9";
    this.enableForSingleDataNode = ENABLE_FOR_SINGLE_DATA_NODE.get(settings);

And the decider is subscribed to the settings update. In other words, when a setting is updated, the decider is notified and its setting is updated as well. Here is the code for subscribing to the settings update for disk thresholds (low watermark, high watermark, flood-stage) in class DiskThresholdSettings:

public DiskThresholdSettings(Settings settings, ClusterSettings clusterSettings) {
    clusterSettings.addSettingsUpdateConsumer(CLUSTER_ROUTING_ALLOCATION_LOW_DISK_WATERMARK_SETTING, this::setLowWatermark);
    clusterSettings.addSettingsUpdateConsumer(CLUSTER_ROUTING_ALLOCATION_HIGH_DISK_WATERMARK_SETTING, this::setHighWatermark);
    clusterSettings.addSettingsUpdateConsumer(CLUSTER_ROUTING_ALLOCATION_DISK_FLOOD_STAGE_WATERMARK_SETTING, this::setFloodStage);

where you can see that when those cluster settings are updated, the class DiskThresholdSettings sets the new value into its instance.


There are three types of testing related to the deciders: 1) the tests for the single deciders which make one decision at a time; 2) the tests for the root decider which gathers information from multiple single decisions and makes a multi-decision; 3) other services that depend on deciders since deciders are part of the cluster module. And … we are going to discuss three of them right now.

Testing a single-decision decider. To understand this, let’s take the disk threshold decider as an example (DiskThresholdDeciderTests). As we saw in the previous sections “Responsibility” and “Making Decisions”, the allocation is made using the index metadata, shard routing, routing allocation, etc. All these data are fetched eagerly and passed as input parameters. Therefore, it makes the test quite simple: we just need to provide this information to a decider without mocking anything as there are no external calls. More precisely, in a test case, we:

  • prepare settings that are relevant to this decider
  • create a decider instance using these settings
  • prepare a cluster state (including index metadata, shard routing, etc) which are necessary for making the decision
  • create an allocation service that encapsulates the decider under test
  • require re-routing the shards by calling the service, which asks the deciders to make the decision behind the screen
  • finally assert the result

Testing a multi-decision decider. To understand this, let’s take AllocationDecidersTests as an example. In this test suite, it tests the debug mode and the early termination. Because these expectations are not tight to any specific child deciders, the setup simply uses some mocked deciders instantiated as anonymous classes.

final AllocationDeciders allocationDeciders = new AllocationDeciders(Arrays.asList(
    new AllocationDecider() { ... }, new AllocationDecider() { ... }));

Testing a service that depends on the deciders. In this case, the testing is more complex to set up because we need to prepare the deciders. Depending on the needs of that service, it may plug some decider during the setup phase. The easiest setup is to prepare a no-operation decider system, which requires an empty all-deciders decider.

public class ClusterAllocationExplainActionTests extends ESTestCase {

    private static final AllocationDeciders NOOP_DECIDERS
        = new AllocationDeciders(Collections.emptyList());

Going Further

How to go further from here?


In this article, we saw the decider system for shard allocation. More precisely, we saw that the allocation service is responsible for allocating shards and balancing shards; we saw that there are 19 deciders in Elasticsearch 7.16 and there are two types of decisions: single decision and multi-decision. When making a decision, a decider relies on information from the ES settings and from cluster state (index metadata, node, routing allocation info). The deciders are created early in the lifecycle during node bootstrap. We also saw how the testing works for testing a single-decision decider, a multi-decision decider, and other services that depend on deciders. Finally, I suggested some resources for you so that you can go further from this article. Interested to know more? You can subscribe to the feed of my blog, follow me on Twitter or GitHub. Hope you enjoy this article, see you the next time!