DAOS is an open-source software-defined scale-out object store that provides high bandwidth and high IOPS storage containers to applications and enables next-generation data-centric workflows combining simulation, data analytics, and machine learning.

Unlike the traditional storage stacks that were primarily designed for rotating media, DAOS is architected from the ground up to exploit new NVM technologies and is extremely lightweight since it operates End-to-End (E2E) in user space with full OS bypass. DAOS offers a shift away from an I/O model designed for block-based and high-latency storage to one that inherently supports fine-grained data access and unlocks the performance of the next-generation storage technologies.

Unlike traditional Burst Buffers, DAOS is a high-performant independent and fault-tolerant storage tier that does not rely on a third-party tier to manage metadata and data resilience.

DAOS Features

DAOS relies on OFI for low-latency communications and stores data on both storage-class memory and NVMe storage. DAOS presents a native key-array-value storage interface that offers a unified storage model over which domain-specific data models are ported, such as HDF5, MPI-IO, and Apache Arrow. A POSIX I/O emulation layer implementing files and directories over the native DAOS API is also available.

DAOS I/O operations are logged and then inserted into a persistent index maintained in SCM. Each I/O is tagged with a particular timestamp called epoch and is associated with a particular version of the dataset. No read-modify-write operations are performed internally. Write operations are non-destructive and not sensitive to alignment. Upon read request, the DAOS service walks through the persistent index and creates a complex scatter-gather Remote Direct Memory Access (RDMA) descriptor to reconstruct the data at the requested version directly in the buffer provided by the application.

The SCM storage is memory-mapped directly into the address space of the DAOS service that manages the persistent index via direct load/store. Depending on the I/O characteristics, the DAOS service can decide to store the I/O in either SCM or NVMe storage. As represented in Figure 2-1, latency-sensitive I/Os, like application metadata and byte-granular data, will typically be stored in the former, whereas checkpoints and bulk data will be stored in the latter. This approach allows DAOS to deliver the raw NVMe bandwidth for bulk data by streaming the data to NVMe storage and maintaining internal metadata index in SCM. The Persistent Memory Development Kit (PMDK)[^1] allows managing transactional access to SCM and the Storage Performance Development Kit (SPDK)[^2] enables user-space I/O to NVMe devices.

Figure 2-1. DAOS Storage

DAOS aims at delivering:

  • High throughput and IOPS at arbitrary alignment and size

  • Fine-grained I/O operations with true zero-copy I/O to SCM

  • Support for massively distributed NVM storage via scalable collective communications across the storage servers

  • Non-blocking data and metadata operations to allow I/O and computation to overlap

  • Advanced data placement taking into account fault domains

  • Software-managed redundancy supporting both replication and erasure code with an online rebuild

  • End-to-end data integrity

  • Scalable distributed transactions with guaranteed data consistency and automated recovery

  • Dataset snapshot

  • Security framework to manage access control to storage pools

  • Software-defined storage management to provision, configure, modify and monitor storage pools over COTS hardware

  • Native support for Hierarchical Data Format (HDF)5, MPI-IO and POSIX namespace over the DAOS data model

  • Tools for disaster recovery

  • Seamless integration with the Lustre parallel filesystem

  • Mover agent to migrate datasets among DAOS pools and from parallel filesystems to DAOS and vice versa

DAOS Components

A data center may have hundreds of thousands of compute nodes interconnected via a scalable high-performance fabric, where all, or a subset of the nodes called storage nodes, have direct access to NVM storage. A DAOS installation involves several components that can be either collocated or distributed.

DAOS Target, Server and System

The DAOS server is a multi-tenant daemon running on a Linux instance (i.e. natively on the physical node or in a VM or container) of each storage node and exporting through the network the locally-attached NVM storage. It listens to a management port, addressed by an IP address and a TCP port number, plus one or more fabric endpoints, addressed by network URIs. The DAOS server is configured through a YAML file and can be integrated with different daemon management or orchestration frameworks (e.g., a systemd script, a Kubernetes service or even via a parallel launcher like pdsh or srun).

A DAOS system is identified by a system name and consists of a set of DAOS servers connected to the same fabric. Membership of the DAOS servers is recorded into the system map that assigns a unique integer rank to each server. Two different systems comprise two disjoint sets of servers and do not coordinate with each other.

Inside a DAOS server, the storage is statically partitioned across multiple targets to optimize concurrency. To avoid contention, each target has its private storage, own pool of service threads and dedicated network context that can be directly addressed over the fabric independently of the other targets hosted on the same storage node. A target is typically associated with a single-ported SCM module and NVMe SSD attached to a single storage node. Moreover, a target does not implement any internal data protection mechanism against storage media failure. As a result, a target is a single point of failure. A dynamic state is associated with each target and is set to either up and running, or down and not available.

A target is the unit of performance. Hardware components associated with the target, such as the backend storage medium, the server, and the network, have limited capability and capacity.

The number of targets exported by a DAOS server instance is configurable and depends on the underlying hardware (i.e., the number of SCM modules, CPUs, NVMe SSDs ...). A target is the unit of fault.

Storage API, Application Interface and Tools

Applications, users, and administrators can interact with a DAOS system through two different client APIs. The management API offers the ability to administrate a DAOS system. It is intended to be integrated with different vendor-specific storage management or open-source orchestration frameworks. The dmg CLI tool is built over the DAOS management API. On the other hand, the DAOS library (libdaos) implements the DAOS storage model. It is primarily targeted at application and I/O middleware developers who want to store datasets in a DAOS system. User utilities like the daos command are also built over the API to allow users to manage datasets from a CLI.

Applications can access datasets stored in DAOS either directly through the native DAOS API, through an I/O middleware library (e.g. POSIX emulation, MPI-IO, HDF5) or through frameworks like Spark or TensorFlow that have already been integrated with the native DAOS storage model.


The DAOS agent is a daemon residing on the client nodes that interacts with the DAOS library to authenticate the application processes. It is a trusted entity that can sign the DAOS Client credentials using certificates. The agent can support different authentication frameworks, and uses a Unix Domain Socket to communicate with the client library.