S3 repository

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You can use AWS S3 as a repository for Snapshot/Restore.

If you are looking for a hosted solution of Elasticsearch on AWS, please visit https://www.elastic.co/cloud/.

Getting started

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To register an S3 repository, specify the type as s3 when creating the repository. The repository defaults to using ECS IAM Role credentials for authentication. You can also use Kubernetes service accounts for authentication.

The only mandatory setting is the bucket name:

resp = client.snapshot.create_repository(
    name="my_s3_repository",
    repository={
        "type": "s3",
        "settings": {
            "bucket": "my-bucket"
        }
    },
)
print(resp)
const response = await client.snapshot.createRepository({
  name: "my_s3_repository",
  repository: {
    type: "s3",
    settings: {
      bucket: "my-bucket",
    },
  },
});
console.log(response);
PUT _snapshot/my_s3_repository
{
  "type": "s3",
  "settings": {
    "bucket": "my-bucket"
  }
}

Client settings

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The client that you use to connect to S3 has a number of settings available. The settings have the form s3.client.CLIENT_NAME.SETTING_NAME. By default, s3 repositories use a client named default, but this can be modified using the repository setting client. For example:

resp = client.snapshot.create_repository(
    name="my_s3_repository",
    repository={
        "type": "s3",
        "settings": {
            "bucket": "my-bucket",
            "client": "my-alternate-client"
        }
    },
)
print(resp)
const response = await client.snapshot.createRepository({
  name: "my_s3_repository",
  repository: {
    type: "s3",
    settings: {
      bucket: "my-bucket",
      client: "my-alternate-client",
    },
  },
});
console.log(response);
PUT _snapshot/my_s3_repository
{
  "type": "s3",
  "settings": {
    "bucket": "my-bucket",
    "client": "my-alternate-client"
  }
}

Most client settings can be added to the elasticsearch.yml configuration file with the exception of the secure settings, which you add to the Elasticsearch keystore. For more information about creating and updating the Elasticsearch keystore, see Secure settings.

For example, if you want to use specific credentials to access S3 then run the following commands to add these credentials to the keystore.

bin/elasticsearch-keystore add s3.client.default.access_key
bin/elasticsearch-keystore add s3.client.default.secret_key
# a session token is optional so the following command may not be needed
bin/elasticsearch-keystore add s3.client.default.session_token

If instead you want to use the instance role or container role to access S3 then you should leave these settings unset. You can switch from using specific credentials back to the default of using the instance role or container role by removing these settings from the keystore as follows:

bin/elasticsearch-keystore remove s3.client.default.access_key
bin/elasticsearch-keystore remove s3.client.default.secret_key
# a session token is optional so the following command may not be needed
bin/elasticsearch-keystore remove s3.client.default.session_token

All client secure settings of this repository type are reloadable. You can define these settings before the node is started, or call the Nodes reload secure settings API after the settings are defined to apply them to a running node.

After you reload the settings, the internal s3 clients, used to transfer the snapshot contents, will utilize the latest settings from the keystore. Any existing s3 repositories, as well as any newly created ones, will pick up the new values stored in the keystore.

In-progress snapshot/restore tasks will not be preempted by a reload of the client’s secure settings. The task will complete using the client as it was built when the operation started.

The following list contains the available client settings. Those that must be stored in the keystore are marked as "secure" and are reloadable; the other settings belong in the elasticsearch.yml file.

access_key (Secure, reloadable)
An S3 access key. If set, the secret_key setting must also be specified. If unset, the client will use the instance or container role instead.
secret_key (Secure, reloadable)
An S3 secret key. If set, the access_key setting must also be specified.
session_token (Secure, reloadable)
An S3 session token. If set, the access_key and secret_key settings must also be specified.
endpoint
The S3 service endpoint to connect to. This defaults to s3.amazonaws.com but the AWS documentation lists alternative S3 endpoints. If you are using an S3-compatible service then you should set this to the service’s endpoint.
protocol
The protocol to use to connect to S3. Valid values are either http or https. Defaults to https. When using HTTPS, this repository type validates the repository’s certificate chain using the JVM-wide truststore. Ensure that the root certificate authority is in this truststore using the JVM’s keytool tool. If you have a custom certificate authority for your S3 repository and you use the Elasticsearch bundled JDK, then you will need to reinstall your CA certificate every time you upgrade Elasticsearch.
proxy.host
The host name of a proxy to connect to S3 through.
proxy.port
The port of a proxy to connect to S3 through.
proxy.scheme
The scheme to use for the proxy connection to S3. Valid values are either http or https. Defaults to http. This setting allows to specify the protocol used for communication with the proxy server
proxy.username (Secure, reloadable)
The username to connect to the proxy.host with.
proxy.password (Secure, reloadable)
The password to connect to the proxy.host with.
read_timeout
(time value) The maximum time Elasticsearch will wait to receive the next byte of data over an established, open connection to the repository before it closes the connection. The default value is 50 seconds.
max_connections
The maximum number of concurrent connections to S3. The default value is 50.
max_retries
The number of retries to use when an S3 request fails. The default value is 3.
use_throttle_retries
Whether retries should be throttled (i.e. should back off). Must be true or false. Defaults to true.
path_style_access
Whether to force the use of the path style access pattern. If true, the path style access pattern will be used. If false, the access pattern will be automatically determined by the AWS Java SDK (See AWS documentation for details). Defaults to false.

In versions 7.0, 7.1, 7.2 and 7.3 all bucket operations used the now-deprecated path style access pattern. If your deployment requires the path style access pattern then you should set this setting to true when upgrading.

disable_chunked_encoding
Whether chunked encoding should be disabled or not. If false, chunked encoding is enabled and will be used where appropriate. If true, chunked encoding is disabled and will not be used, which may mean that snapshot operations consume more resources and take longer to complete. It should only be set to true if you are using a storage service that does not support chunked encoding. See the AWS Java SDK documentation for details. Defaults to false.
region
Allows specifying the signing region to use. Specificing this setting manually should not be necessary for most use cases. Generally, the SDK will correctly guess the signing region to use. It should be considered an expert level setting to support S3-compatible APIs that require v4 signatures and use a region other than the default us-east-1. Defaults to empty string which means that the SDK will try to automatically determine the correct signing region.
signer_override
Allows specifying the name of the signature algorithm to use for signing requests by the S3 client. Specifying this setting should not be necessary for most use cases. It should be considered an expert level setting to support S3-compatible APIs that do not support the signing algorithm that the SDK automatically determines for them. See the AWS Java SDK documentation for details. Defaults to empty string which means that no signing algorithm override will be used.

Repository settings

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The s3 repository type supports a number of settings to customize how data is stored in S3. These can be specified when creating the repository. For example:

resp = client.snapshot.create_repository(
    name="my_s3_repository",
    repository={
        "type": "s3",
        "settings": {
            "bucket": "my-bucket",
            "another_setting": "setting-value"
        }
    },
)
print(resp)
const response = await client.snapshot.createRepository({
  name: "my_s3_repository",
  repository: {
    type: "s3",
    settings: {
      bucket: "my-bucket",
      another_setting: "setting-value",
    },
  },
});
console.log(response);
PUT _snapshot/my_s3_repository
{
  "type": "s3",
  "settings": {
    "bucket": "my-bucket",
    "another_setting": "setting-value"
  }
}

The following settings are supported:

bucket

(Required) Name of the S3 bucket to use for snapshots.

The bucket name must adhere to Amazon’s S3 bucket naming rules.

client
The name of the S3 client to use to connect to S3. Defaults to default.
base_path

Specifies the path to the repository data within its bucket. Defaults to an empty string, meaning that the repository is at the root of the bucket. The value of this setting should not start or end with a /.

Don’t set base_path when configuring a snapshot repository for Elastic Cloud Enterprise. Elastic Cloud Enterprise automatically generates the base_path for each deployment so that multiple deployments may share the same bucket.

chunk_size
(byte value) The maximum size of object that Elasticsearch will write to the repository when creating a snapshot. Files which are larger than chunk_size will be chunked into several smaller objects. Elasticsearch may also split a file across multiple objects to satisfy other constraints such as the max_multipart_parts limit. Defaults to 5TB which is the maximum size of an object in AWS S3.
compress
When set to true metadata files are stored in compressed format. This setting doesn’t affect index files that are already compressed by default. Defaults to true.
max_restore_bytes_per_sec
(Optional, byte value) Maximum snapshot restore rate per node. Defaults to unlimited. Note that restores are also throttled through recovery settings.
max_snapshot_bytes_per_sec
(Optional, byte value) Maximum snapshot creation rate per node. Defaults to 40mb per second. Note that if the recovery settings for managed services are set, then it defaults to unlimited, and the rate is additionally throttled through recovery settings.
readonly

(Optional, Boolean) If true, the repository is read-only. The cluster can retrieve and restore snapshots from the repository but not write to the repository or create snapshots in it.

Only a cluster with write access can create snapshots in the repository. All other clusters connected to the repository should have the readonly parameter set to true.

If false, the cluster can write to the repository and create snapshots in it. Defaults to false.

If you register the same snapshot repository with multiple clusters, only one cluster should have write access to the repository. Having multiple clusters write to the repository at the same time risks corrupting the contents of the repository.

server_side_encryption
When set to true files are encrypted on server side using AES256 algorithm. Defaults to false.
buffer_size
(byte value) Minimum threshold below which the chunk is uploaded using a single request. Beyond this threshold, the S3 repository will use the AWS Multipart Upload API to split the chunk into several parts, each of buffer_size length, and to upload each part in its own request. Note that setting a buffer size lower than 5mb is not allowed since it will prevent the use of the Multipart API and may result in upload errors. It is also not possible to set a buffer size greater than 5gb as it is the maximum upload size allowed by S3. Defaults to 100mb or 5% of JVM heap, whichever is smaller.
max_multipart_parts
(integer) The maximum number of parts that Elasticsearch will write during a multipart upload of a single object. Files which are larger than buffer_size × max_multipart_parts will be chunked into several smaller objects. Elasticsearch may also split a file across multiple objects to satisfy other constraints such as the chunk_size limit. Defaults to 10000 which is the maximum number of parts in a multipart upload in AWS S3.
canned_acl
The S3 repository supports all S3 canned ACLs : private, public-read, public-read-write, authenticated-read, log-delivery-write, bucket-owner-read, bucket-owner-full-control. Defaults to private. You could specify a canned ACL using the canned_acl setting. When the S3 repository creates buckets and objects, it adds the canned ACL into the buckets and objects.
storage_class
Sets the S3 storage class for objects written to the repository. Values may be standard, reduced_redundancy, standard_ia, onezone_ia and intelligent_tiering. Defaults to standard. See S3 storage classes for more information.
delete_objects_max_size
(integer) Sets the maxmimum batch size, betewen 1 and 1000, used for DeleteObjects requests. Defaults to 1000 which is the maximum number supported by the AWS DeleteObjects API.
max_multipart_upload_cleanup_size
(integer) Sets the maximum number of possibly-dangling multipart uploads to clean up in each batch of snapshot deletions. Defaults to 1000 which is the maximum number supported by the AWS ListMultipartUploads API. If set to 0, Elasticsearch will not attempt to clean up dangling multipart uploads.

The option of defining client settings in the repository settings as documented below is considered deprecated, and will be removed in a future version.

In addition to the above settings, you may also specify all non-secure client settings in the repository settings. In this case, the client settings found in the repository settings will be merged with those of the named client used by the repository. Conflicts between client and repository settings are resolved by the repository settings taking precedence over client settings.

For example:

resp = client.snapshot.create_repository(
    name="my_s3_repository",
    repository={
        "type": "s3",
        "settings": {
            "client": "my-client",
            "bucket": "my-bucket",
            "endpoint": "my.s3.endpoint"
        }
    },
)
print(resp)
const response = await client.snapshot.createRepository({
  name: "my_s3_repository",
  repository: {
    type: "s3",
    settings: {
      client: "my-client",
      bucket: "my-bucket",
      endpoint: "my.s3.endpoint",
    },
  },
});
console.log(response);
PUT _snapshot/my_s3_repository
{
  "type": "s3",
  "settings": {
    "client": "my-client",
    "bucket": "my-bucket",
    "endpoint": "my.s3.endpoint"
  }
}

This sets up a repository that uses all client settings from the client my_client_name except for the endpoint that is overridden to my.s3.endpoint by the repository settings. `

S3 storage classes

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Amazon S3 supports a variety of storage classes, each of which offers different operational characteristics. For instance, some classes cost less per byte stored per month, but cost more per request, and other classes may vary in terms of their availability guarantees.

You may specify the storage class that Elasticsearch uses to store data objects with the storage_class repository setting.

Changing the storage_class setting on an existing repository only affects the storage class for newly created objects, resulting in a mixed usage of storage classes.

You may use an S3 Lifecycle Policy to adjust the storage class of existing objects in your repository, but you must not transition objects to an unsupported class such as the Glacier classes, and you must not expire objects. If you use a Glacier storage class, or another unsupported storage class, or object expiry, then you may permanently lose access to your repository contents.

You may use the intellligent_tiering storage class to automatically manage the class of objects, but you must not enable the optional Archive Access or Deep Archive Access tiers. If you use these tiers then you may permanently lose access to your repository contents.

For more information about S3 storage classes, see AWS Storage Classes Guide.

Recommended S3 permissions

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In order to restrict the Elasticsearch snapshot process to the minimum required resources, we recommend using Amazon IAM in conjunction with pre-existing S3 buckets. Here is an example policy which will allow the snapshot access to an S3 bucket named "snaps.example.com". This may be configured through the AWS IAM console, by creating a Custom Policy, and using a Policy Document similar to this (changing snaps.example.com to your bucket name).

{
  "Statement": [
    {
      "Action": [
        "s3:ListBucket",
        "s3:GetBucketLocation",
        "s3:ListBucketMultipartUploads",
        "s3:ListBucketVersions"
      ],
      "Effect": "Allow",
      "Resource": [
        "arn:aws:s3:::snaps.example.com"
      ]
    },
    {
      "Action": [
        "s3:GetObject",
        "s3:PutObject",
        "s3:DeleteObject",
        "s3:AbortMultipartUpload",
        "s3:ListMultipartUploadParts"
      ],
      "Effect": "Allow",
      "Resource": [
        "arn:aws:s3:::snaps.example.com/*"
      ]
    }
  ],
  "Version": "2012-10-17"
}

You may further restrict the permissions by specifying a prefix within the bucket, in this example, named "foo".

{
  "Statement": [
    {
      "Action": [
        "s3:ListBucket",
        "s3:GetBucketLocation",
        "s3:ListBucketMultipartUploads",
        "s3:ListBucketVersions"
      ],
      "Condition": {
        "StringLike": {
          "s3:prefix": [
            "foo/*"
          ]
        }
      },
      "Effect": "Allow",
      "Resource": [
        "arn:aws:s3:::snaps.example.com"
      ]
    },
    {
      "Action": [
        "s3:GetObject",
        "s3:PutObject",
        "s3:DeleteObject",
        "s3:AbortMultipartUpload",
        "s3:ListMultipartUploadParts"
      ],
      "Effect": "Allow",
      "Resource": [
        "arn:aws:s3:::snaps.example.com/foo/*"
      ]
    }
  ],
  "Version": "2012-10-17"
}

The bucket needs to exist to register a repository for snapshots. If you did not create the bucket then the repository registration will fail.

Using IAM roles for Kubernetes service accounts for authentication
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If you want to use Kubernetes service accounts for authentication, you need to add a symlink to the $AWS_WEB_IDENTITY_TOKEN_FILE environment variable (which should be automatically set by a Kubernetes pod) in the S3 repository config directory, so the repository can have the read access for the service account (a repository can’t read any files outside its config directory). For example:

mkdir -p "${ES_PATH_CONF}/repository-s3"
ln -s $AWS_WEB_IDENTITY_TOKEN_FILE "${ES_PATH_CONF}/repository-s3/aws-web-identity-token-file"

The symlink must be created on all data and master eligible nodes and be readable by the elasticsearch user. By default, Elasticsearch runs as user elasticsearch using uid:gid 1000:0.

If the symlink exists, it will be used by default by all S3 repositories that don’t have explicit client credentials.

AWS VPC bandwidth settings

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AWS instances resolve S3 endpoints to a public IP. If the Elasticsearch instances reside in a private subnet in an AWS VPC then all traffic to S3 will go through the VPC’s NAT instance. If your VPC’s NAT instance is a smaller instance size (e.g. a t2.micro) or is handling a high volume of network traffic your bandwidth to S3 may be limited by that NAT instance’s networking bandwidth limitations. Instead we recommend creating a VPC endpoint that enables connecting to S3 in instances that reside in a private subnet in an AWS VPC. This will eliminate any limitations imposed by the network bandwidth of your VPC’s NAT instance.

Instances residing in a public subnet in an AWS VPC will connect to S3 via the VPC’s internet gateway and not be bandwidth limited by the VPC’s NAT instance.

S3-compatible services

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There are a number of storage systems that provide an S3-compatible API, and the s3 repository type allows you to use these systems in place of AWS S3. To do so, you should set the s3.client.CLIENT_NAME.endpoint setting to the system’s endpoint. This setting accepts IP addresses and hostnames and may include a port. For example, the endpoint may be 172.17.0.2 or 172.17.0.2:9000.

By default Elasticsearch communicates with your storage system using HTTPS, and validates the repository’s certificate chain using the JVM-wide truststore. Ensure that the JVM-wide truststore includes an entry for your repository. If you wish to use unsecured HTTP communication instead of HTTPS, set s3.client.CLIENT_NAME.protocol to http.

MinIO is an example of a storage system that provides an S3-compatible API. The s3 repository type allows Elasticsearch to work with MinIO-backed repositories as well as repositories stored on AWS S3. Other S3-compatible storage systems may also work with Elasticsearch, but these are not covered by the Elasticsearch test suite.

There are many systems, including some from very well-known storage vendors, which claim to offer an S3-compatible API despite failing to emulate S3’s behaviour in full. If you are using such a system for your snapshots, consider using a shared filesystem repository based on a standardized protocol such as NFS to access your storage system instead. The s3 repository type requires full compatibility with S3. In particular it must support the same set of API endpoints, with the same parameters, return the same errors in case of failures, and offer consistency and performance at least as good as S3 even when accessed concurrently by multiple nodes. You will need to work with the supplier of your storage system to address any incompatibilities you encounter. Please do not report Elasticsearch issues involving storage systems which claim to be S3-compatible unless you can demonstrate that the same issue exists when using a genuine AWS S3 repository.

You can perform some basic checks of the suitability of your storage system using the repository analysis API. If this API does not complete successfully, or indicates poor performance, then your storage system is not fully compatible with AWS S3 and therefore unsuitable for use as a snapshot repository. However, these checks do not guarantee full compatibility.

Most storage systems can be configured to log the details of their interaction with Elasticsearch. If you are investigating a suspected incompatibility with AWS S3, it is usually simplest to collect these logs and provide them to the supplier of your storage system for further analysis. If the incompatibility is not clear from the logs emitted by the storage system, configure Elasticsearch to log every request it makes to the S3 API by setting the logging level of the com.amazonaws.request logger to DEBUG.

To prevent leaking sensitive information such as credentials and keys in logs, Elasticsearch rejects configuring this logger at high verbosity unless insecure network trace logging is enabled. To do so, you must explicitly enable it on each node by setting the system property es.insecure_network_trace_enabled to true.

Once enabled, you can configure the com.amazonaws.request logger:

resp = client.cluster.put_settings(
    persistent={
        "logger.com.amazonaws.request": "DEBUG"
    },
)
print(resp)
response = client.cluster.put_settings(
  body: {
    persistent: {
      'logger.com.amazonaws.request' => 'DEBUG'
    }
  }
)
puts response
const response = await client.cluster.putSettings({
  persistent: {
    "logger.com.amazonaws.request": "DEBUG",
  },
});
console.log(response);
PUT /_cluster/settings
{
  "persistent": {
    "logger.com.amazonaws.request": "DEBUG"
  }
}

Collect the Elasticsearch logs covering the time period of the failed analysis from all nodes in your cluster and share them with the supplier of your storage system along with the analysis response so they can use them to determine the problem. See the AWS Java SDK documentation for further information, including details about other loggers that can be used to obtain even more verbose logs. When you have finished collecting the logs needed by your supplier, set the logger settings back to null to return to the default logging configuration and disable insecure network trace logging again. See Logger and Cluster update settings for more information.

Linearizable register implementation

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The linearizable register implementation for S3 repositories is based on the strongly consistent semantics of the multipart upload API. Elasticsearch first creates a multipart upload to indicate its intention to perform a linearizable register operation. Elasticsearch then lists and cancels all other multipart uploads for the same register. Elasticsearch then attempts to complete the upload. If the upload completes successfully then the compare-and-exchange operation was atomic.