Terminology first:

Backend Provider is a designed-in abstraction and, simultaneously, an API-supported option that allows to delineate between “remote” and “local” buckets with respect to a given AIS cluster.

AIStore natively integrates with multiple backend providers:

Backend Schema(s) Description
ais ais://, ais://@remote_uuid AIStore bucket provider, can also refer to remote AIS cluster
aws aws://, s3:// Amazon Cloud Storage
azure azure://, az:// Azure Cloud Storage
gcp gcp://, gs:// Google Cloud Storage
hdfs hdfs:// Hadoop Distributed File System
ht ht:// HTTP(S) based dataset

Native integration, in turn, implies:

  • utilizing vendor’s SDK libraries to operate on the respective remote backends;
  • providing unified namespace (where, e.g., two same-name buckets from different backends can co-exist with no conflicts);
  • on-the-fly populating AIS own bucket metadata with the properties of remote buckets.

The last bullet deserves a little more explanation. First, there’s a piece of cluster-wide metadata that we call BMD. Like every other type of metadata, BMD is versioned, checksummed, and replicated - the process that is carried out by the currently elected primary.

BMD contains all bucket definitions and per-bucket configurable management policies - local and remote.

Here’s what happens upon the very first (read or write or list, etc.) access to a remote bucket that is not yet in the BMD:

  1. Behind the scenes, AIS will try to confirm the bucket’s existence and accessibility.
  2. If confirmed, AIS will atomically add the bucket to the BMD (along with its remote properties).
  3. Once all of the above is set and done, AIS will go ahead to perform that original (read or write or list, etc.) operation

There are advanced-usage type options to skip Steps 1. and 2. above - see e.g. LisObjsMsg flags

The full taxonomy of the supported backends is shown below (and note that AIS supports itself on the back as well):

Supported Backends


Remote AIS cluster

In addition to the listed above 3rd party Cloud storages and non-Cloud HTTP(S) and HDFS-based backends, any given pair of AIS clusters can be organized in a way where one cluster would be providing fully-accessible backend to another.


Term Comment
attach remote cluster Allow one cluster to see remote datasets, cache those datasets on demand, copy remote buckets, list, create, and destroy remote buckets, read and write remote buckets, etc.
detach remote cluster Operation that (as the name implies) removes the corresponding attachment
alias An optional user-friendly alias that can be assigned at attachment time and be further used in all subsequent operations instead of the remote cluster’s UUID
global namespace Refers to the capability to unambiguously indicate and access any dataset in an arbitrary network (or DAG, to be precise) of AIS clusters whereby some clusters are attached to another ones. By attaching AIS clusters we are, effectively and ad-hoc, forming a unified global namespace of all individually hosted datasets.

Example working with remote AIS cluster (as well as easy-to-use scripts) can be found in the README for developers.

Unified Global Namespace

Examples first. The following two commands attach and then show remote cluster at the addressmy.remote.ais:51080:

$ ais cluster remote-attach alias111=http://my.remote.ais:51080
Remote cluster (alias111=http://my.remote.ais:51080) successfully attached
$ ais show remote-cluster
UUID      URL                     Alias     Primary         Smap  Targets  Online
eKyvPyHr  my.remote.ais:51080     alias111  p[80381p11080]  v27   10       yes

Notice two aspects of this:

  • user-defined aliasing whereby a user can assign an arbitrary name (aka alias) to a given remote cluster
  • the remote cluster does not have to be online at attachment time; offline or currently not reachable clusters are shown as follows:
$ ais show remote-cluster
UUID        URL                       Alias     Primary         Smap  Targets  Online
eKyvPyHr    my.remote.ais:51080       alias111  p[primary1]     v27   10       no
<alias222>  <other.remote.ais:51080>            n/a             n/a   n/a      no

Notice the difference between the first and the second lines in the printout above: while both clusters appear to be currently offline (see the rightmost column), the first one was accessible at some earlier time and therefore we do show that it has (in this example) 10 storage nodes and other details.

To detach any of the previously configured association, simply run:

$ ais cluster remote-detach alias111
$ ais show remote-cluster
UUID        URL                       Alias     Primary         Smap  Targets  Online
<alias222>  <other.remote.ais:51080>            n/a             n/a   n/a      no

Configuration-wise, the following two examples specify a single-URL and multi-URL attachments that can be also be configured prior to runtime (or can be added at runtime via the ais remote attach CLI as shown above):

  • Example: single URL

      "backend": {
        "ais": {
          "remote-cluster-alias": [""]
  • Example: multiple URL

      "backend": {
        "ais": {
          "remote-cluster-alias": [

Multiple remote URLs can be provided for the same typical reasons that include fault tolerance. However, once connected we will rely on the remote cluster map to retry upon connection errors and load balance.

For more usage examples, please see:

And one more comment:

You can run ais cluster remote-attach and/or ais show remote-cluster CLI to refresh remote configuration: check availability and reload remote cluster maps.

In other words, repeating the same ais cluster remote-attach command will have the side effect of refreshing all the currently configured attachments. Or, use ais show remote-cluster CLI for the same exact purpose.

Cloud object storage

Cloud-based object storage include:

In each case, we use the vendor’s own SDK/API to provide transparent access to Cloud storage with the additional capability of persistently caching all read data in the AIStore’s remote buckets.

The term “persistent caching” is used to indicate much more than what’s conventionally understood as “caching”: irrespectively of its origin and source, all data inside an AIStore cluster is end-to-end checksummed and protected by the storage services configured both globally and on a per bucket basis. For instance, both remote buckets and ais buckets can be erasure coded, etc.

Notwithstanding, remote buckets will often serve as a fast cache or a fast tier in front of a given 3rd party Cloud storage.

Note as well that AIS provides 5 (five) easy ways to populate its remote buckets - including, but not limited to conventional on-demand caching (aka cold GET).

HDFS Provider

Hadoop and HDFS is well known and widely used software for distributed processing of large datasets using MapReduce model. For years, it has been considered as a standard for big data.

HDFS backend provider is a way to access files contained inside the HDFS cluster from AIStore. Here we will talk about standard configuration and usages (see also full tutorial on HDFS provider).


Before we jump to functionalities, let’s first focus on configuration. AIStore needs to know the address of NameNode server and the username for the requests. Important note here is that the NameNode and DataNode addresses must be accessible from the AIStore, otherwise the connection will fail.

Example of HDFS provider configuration:

"backend": {
  "hdfs": {
    "user": "root",
    "addresses": ["localhost:8020"],
    "use_datanode_hostname": false
  • user specifies which HDFS user the client will act as.
  • addresses specifies the namenode(s) to connect to.
  • use_datanode_hostname specifies whether the client should connect to the datanodes via hostname (which is useful in multi-homed setups) or IP address, which may be required if DNS isn’t available.


After the HDFS is set up, and the binary is built with HDFS provider, we can see everything in action.

$ ais create hdfs://yt8m --props="extra.hdfs.ref_directory=/part1/video"
"hdfs://yt8m" bucket created
$ ais ls hdfs://
HDFS Buckets (1)
$ ais put 1.mp4 hdfs://yt8m/1.mp4
PUT "1.mp4" into bucket "hdfs://yt8m"
$ ais ls hdfs://yt8m
1.mp4	 76.31KiB
$ ais get hdfs://yt8m/1.mp4 video.mp4
GET "1.mp4" from bucket "hdfs://yt8m" as "video.mp4" [76.31KiB]

The first thing to notice is --props="extra.hdfs.ref_directory=/part1/video". Here we specify the required path the hdfs://yt8m bucket will refer to (the directory must exist on bucket creation). It means that when accessing object hdfs://yt8m/1.mp4 the path will be resolved to /part1/video/1.mp4 (/part1/video + 1.mp4).

HTTP(S) based dataset

AIS bucket may be implicitly defined by HTTP(S) based dataset, where files such as, for instance:

  • https://a/b/c/imagenet/train-000000.tar
  • https://a/b/c/imagenet/train-123456.tar
  • ...
  • https://a/b/c/imagenet/train-999999.tar

would all be stored in a single AIS bucket that would have a protocol prefix ht:// and a bucket name derived from the directory part of the URL Path (“a/b/c/imagenet”, in this case).

WARNING: Currently HTTP(S) based datasets can only be used with clients which support an option of overriding the proxy for certain hosts (for e.g. curl ... --noproxy=$(curl -s G/v1/cluster?what=target_ips)). If used otherwise, we get stuck in a redirect loop, as the request to target gets redirected via proxy.