1.Lustre

2.TrueNAS

3.Gluster

4.BeeGFS

5.Ceph

6.OpenStack Swift

7.DAOS

8.MinIO

9.ZFS

10.HDFS

11.Lustre

[Figure 1] schedule of China storage system Company

[Figure 1] financing records of China storage system Company (2019-2023)

background

Lustre is an open-source distributed parallel file system designed for high-performance computing (HPC) and large-scale data storage. It was originally developed by Carnegie Mellon University in 1999 and released in 2003. It is widely used in supercomputing centers and national laboratories, and is especially suitable for scenarios that need to process ultra-large data sets and high concurrent access. Lustre supports hundreds of PB of storage capacity and provides a throughput of several TB per second to meet the needs of data-intensive applications.

Lustre uses a client-server architecture and provides a global single namespace. It manages data and metadata through object storage targets (OST) and metadata targets (MDT). Its core technologies include the efficient network transmission protocol LNet, which can run on various hardware platforms and provide high availability and reliability. Lustre's modular design supports running on a variety of storage devices to adapt to different workloads and environments.

The latest Lustre version (2.15, up to 2023) has added new functions such as OST over-stripe and self-expanding layout, further improving performance and flexibility. Lustre still maintains a wide range of applications and leading positions in the global HPC and big data storage fields.

Benefits

high performance: Lustre uses parallel I/O and high-speed interconnection technologies (such as InfiniBand, Ethernet, and Omni-Path) to provide TB of aggregate throughput per second in large-scale data processing scenarios. It is suitable for data-intensive applications such as scientific computing, simulation and genomics.

Scalability: Lustre's modular design allows linear expansion by adding object storage servers (OSS) and metadata servers (MDS), which can be supported from hundreds of TB to hundreds of PB of data. The global single namespace simplifies the management of large-scale clusters.

High Availability: Lustre provides failover through redundant metadata servers to reduce the risk of single point of failure. Object storage targets (OST) process file locks to ensure data consistency. The log file system enhances the recovery capability and data integrity of the system.

Wide applicability: Lustre is applicable to various environments from small HPC clusters to global supercomputers, and is widely used in scientific research, oil and gas, manufacturing, media, finance, medical and other industries, it is the most commonly used file system in the world's Top 500 supercomputers.

Disadvantages

metadata performance bottleneck: Lustre separates metadata from data storage, which may cause the metadata server (MDS) to become a performance bottleneck. When processing a large number of small files or metadata-intensive operations under high load conditions, MDS may be overloaded, resulting in performance degradation. In addition, the inode allocation system may quickly run out when processing a large number of small files, although there is still more physical storage.

Configuration and management complexity: the configuration of Lustre is complex, and the number of MDS and OSS servers needs to be accurately planned according to workload requirements. Incorrect configurations may affect performance and reliability. At the same time, the system needs regular maintenance and optimization, such as managing directory size and limiting the number of files in each directory to ensure optimal performance.

Limited support for small files: Lustre is mainly optimized for large files and high throughput scenarios. It does not support applications that process large numbers of small files or require low-latency metadata operations. This makes Lustre unsuitable for modern applications that require frequent small data packet processing, such as IoT or some analysis tasks.

Dependency on network performance: Lustre's performance is highly dependent on network throughput and latency. Network problems directly affect the speed of data access, which reduces the reliability of Lustre in the environment with fluctuating network conditions.

Single point of failure risk: Although Lustre supports high availability configuration, insufficient redundant configuration may lead to single point of failure, resulting in significant downtime and data access problems. Robust failover mechanisms are needed to reduce these risks.

Limited cross-platform support: Lustre mainly runs on Linux operating system, which limits its compatibility on other platforms such as Windows or macOS. This requires additional configuration on the client machine to correctly access the Lustre file system.

User feedback

performance Comparison and small file processing: Lustre performs well in processing large-scale data, but users generally report performance problems when processing small files. Specifically, Lustre has a low efficiency in processing files smaller than 4KB, which is especially prominent in modern data-intensive applications. Lustre still performs well in large file processing and high throughput environments. Its throughput can exceed 10 TB/s, and the throughput of a single client can reach 4.5 Gb/s under optimal conditions.

Metadata performance and inode: the metadata operation performance of Lustre is significantly lower than that of local file systems, such as XFS or ext4, which is about 26% of these systems. In addition, users encounter inode exhaustion in the environment where a large number of small files are generated. One user reported that even if only 30% of the disk space was used, the file system was displayed as "full" because the number of inode reached the limit. To avoid this problem, we recommend that the number of inode allocated to Metadata Servers (MDS) should be twice the number of Object Storage Targets (OST).

Configuration complexity: the setup and configuration process of Lustre is considered complicated. Users need to accurately calculate the MDS size based on the expected workload and configure inode to avoid exhaustion. In addition, you need to pay attention to the directory size and the number of files during the management process to avoid performance degradation. Some best practices recommend limiting the number of files in directories to optimize system performance.

Network dependency: Lustre is highly sensitive to network conditions. Network latency significantly affects data access speed, so the reliability of Lustre may be affected in unstable network environments.

High availability challenge: Although Lustre supports high availability configuration, improper configuration may lead to a single point of failure. Some users reported that server failures could lead to significant downtime without sufficient redundant configurations.

Client scalability: the Lustre system performs well in client support and can effectively manage 100 to 100000 clients, some of which are successfully installed to support more than 50000 clients.

GitHub activities

as an image of the official development repository, Lustre-release, the core repository of lustre has 41 forks and 0 Stars, showing a certain interest of developers, however, its visibility and community participation are still insufficient compared with other well-known open source projects. Over the past year, the warehouse has maintained a stable submission frequency, indicating that the project is actively optimizing functions and fixing bugs. In terms of community interaction, there are currently only one unsolved problem and one pull request, showing a low level of community contribution. It is worth noting that Lustre has also been mentioned in some external projects, such as 'microsoft/amlFilesystem-lustre ', which reflects the attention and contribution of large organizations to Lustre. However, Lustre's documentation resources are abundant on the Lustre Wiki platform, providing users with detailed installation, configuration, and use instructions.

Latest developments

Lustre 2.15.5 was released on June 28, 2024. This version enhances the support for RHEL 8.10 servers and clients, as well as RHEL 9.4 clients. It also supports Linux 6.1 clients and updates ZFS to 2.1.15. In addition, this version has been updated with multiple kernels to improve compatibility with different Linux distributions. The community plans to release a major version every nine months, and the next long-term support (LTS) version is 2.15.6. Future new features and improvements will be coordinated through OpenSFS and EOFS to meet user needs.

TrueNAS

background

TrueNAS is an open source storage operating system designed to provide enterprise-level file storage, block storage, and object storage solutions. Based on FreeBSD operating system and ZFS file system, it aims to realize high-performance, high-reliability and easy-to-manage storage services. The development of TrueNAS was originally created by Olivier Cochard-Labbé in 2005. It was first released in the name of FreeNAS and has achieved important development under the leadership of Volker Theile. In 2018, FreeNAS was renamed TrueNAS to demonstrate its enhanced functionality and enterprise-level support.

The CORE versions of TrueNAS include TrueNAS CORE (open source version), TrueNAS Enterprise (commercial version), and TrueNAS SCALE (Linux-based version). These versions together provide flexible storage solutions to meet users of different sizes and needs. The TrueNAS architecture supports multiple application scenarios from small home laboratories to large enterprise data centers, and can effectively manage data from TB to PB.

TrueNAS utilizes the advanced features of ZFS file systems, such as data integrity checksum and snapshot functions, to ensure a high level of data protection and storage efficiency. Its flexible hardware support and user-friendly Web management interface make configuration and management simple and efficient. TrueNAS also extends its functions through plug-ins and virtualization technologies, providing users with more application options.

Benefits

data integrity and self-repair capabilities: TrueNAS is based on the OpenZFS file system and uses its 128-bit checksum mechanism (such as CRC) to ensure data integrity during storage and transmission. OpenZFS can automatically detect and fix "silent data corruption", that is, errors that cannot be found by regular error detection tools. Through redundant copies and verification data, the system can automatically recover data when it is found to be damaged, significantly improving data reliability and stability.

High Scalability: TrueNAS supports scaling from small-scale deployment to large-scale clusters, and can process data from TB to PB. In particular, TrueNAS SCALE allows users to build storage clusters by adding more nodes through SCALE-out, achieving higher availability and performance and adapting to growing data requirements.

Efficient snapshot and clone features: TrueNAS provides efficient snapshot and clone features, allowing you to create point-in-time replicas that occupy almost no extra storage space. These features make data backup, recovery, and fast replication more efficient. Snapshots and clones not only save storage space, but also improve the flexibility and efficiency of data management.

Multi-protocol support and flexibility: TrueNAS supports multiple storage protocols, including NFS, SMB, iSCSI, and S3, to meet the needs of different application scenarios. Whether IT is file storage, block storage or object storage, TrueNAS can be seamlessly integrated into the existing IT environment to provide flexible storage solutions.

User-friendly management interface: TrueNAS provides an intuitive Web-based management interface, making configuration and management easy to use. Users can easily access and manage all functions through the graphical interface without going deep into command line operations, greatly improving management efficiency.

Powerful virtualization support: TrueNAS supports virtual machines (VMs) and containers (such as Docker and Kubernetes), which can be used not only as a storage solution, but also as an application hosting platform. This flexibility makes it suitable for a variety of workloads and application scenarios, enhancing the versatility of the platform.

Disadvantages

stability and performance issues: some users report that TrueNAS SCALE has stability issues in Web interfaces and service request forwarding, which may lead to difficulties in use. At the same time, some functions of the system (such as snapshots and data compression) require additional computing resources, which may affect performance when resources are limited.

Limitations and complexity: TrueNAS SCALE does not support apt package management. You must use the Docker container to extend the functionality. Data may be reset after the system is updated, which may cause inconvenience in some scenarios. Although the rich functions and flexibility of the system are powerful, it also increases the complexity of operation, making users with basic storage functions feel tedious.

Virtualization and container support: although Docker and Kubernetes are supported, TrueNAS SCALE lacks clear documentation and guidance when configuring and managing these containers, resulting in complex and time-consuming configuration process.

Cluster function loss: the latest version of TrueNAS SCALE deprecates the Gluster file system and cannot support Gluster-based cluster configuration. This means that users cannot use Gluster to build a multi-node Unified storage solution. However, SCALE still supports other cluster technologies, such as MinIO and Syncthing.

Hardware Compatibility: Although TrueNAS supports a wide range of hardware configurations, some old devices or hardware of specific brands may not be compatible and require additional research and verification, especially in the case of uncommon or old hardware, performance degradation or data loss may occur.

Dependency on Enterprise-level features: Although TrueNAS CORE provides many features, some advanced features (such as high availability and redundancy control) are only available in TrueNAS Enterprise versions, this limits the application of small businesses or home users in critical business environments.

User feedback

complexity and documentation: Many users are dissatisfied with the complexity of TrueNAS SCALE, especially with documentation. Existing documents are often not detailed or clear enough to effectively guide users to complete system settings and troubleshooting.

Functional limitations and configuration changes: users point out that TrueNAS has some shortcomings in implementing basic functions, such as network configuration and application management (such as Kubernetes settings), requiring temporary solutions or a large number of troubleshooting. In addition, when using applications such as TrueCharts, the configuration changes frequently and needs to be manually reconfigured, which increases the management burden.

Inconsistent performance: Some users reported performance problems when using TrueNAS SCALE, including unexpected restart and application failure. These problems lack clear error information, making troubleshooting difficult.

Error handling and comparison: users are worried about unresolved errors and the difficulty of contacting developers, which significantly affect operational efficiency. Some users also compared TrueNAS with other solutions (such as Unraid), pointing out that TrueNAS needs more attention and maintenance, while Unraid performs better in stability and ease of use.

GitHub activities

the TrueNAS repository of trueNAS shows how active its development and maintenance are. Currently, the middleware warehouse has about 2.3 Stars and 486 Forks, showing its attention in the community. Other warehouses, such as scale-build and documentation, are also active and support continuous development and document updates. Currently, the middleware repository has 12 open Pull Requests. At the same time, the project team has dealt with a large number of project problems, but there are still unresolved problems, which may affect the development progress.

Latest developments

TrueNAS released the final update 13.0-U6.1 of The 13.0 series in December 2023. This version focuses on improving stability and security and includes about 20 bug fixes and security enhancements, in particular, an occasional error occurred in OpenZFS was fixed. TrueNAS announced that version 13.1 will focus on the improvement of storage functions and plans to update key components such as FreeBSD, OpenZFS, and Samba. Users should pay special attention to the fact that with the change of version, embedded S3 service will be deprecated and must follow the official guidelines to migrate to TrueNAS SCALE. For TrueNAS CORE and Enterprise users, they can choose to migrate to TrueNAS SCALE Enterprise 23.10 to meet specific storage requirements. The migration process will be continuously optimized to improve reliability and ease of use.

Gluster

background

Gluster is an open source distributed file system, which was originally released by Gluster ued in 2005. Founded in California, USA, the company focuses on developing scalable cloud storage solutions and providing public and private cloud services for large-scale data storage. Gluster HongKong has obtained Venture capital from Nexus Venture Partners and Index Ventures.

In 2011, Red Hat acquired Gluster UED and integrated GlusterFS into its enterprise product line. In 2012, Red Hat launched a Red Hat Storage Server based on GlusterFS and renamed it Red Hat Gluster Storage in 2014. This product supports a variety of industry standard protocols with high availability and reliability, and enables fast file access by eliminating centralized metadata servers. The latest version of RHGS 3.5 will be the final version of the product and will end support on December 31, 2024.

GlusterFS is the core technology of Gluster. It adopts a metadata-free server architecture and distributes data across multiple storage nodes through elastic hash algorithms. This design avoids the bottleneck of the central metadata server and provides excellent linear scalability and high reliability. It is especially suitable for scenarios such as large-scale data storage and media streaming. The metadata-free server architecture ensures stable performance and enhances system fault tolerance.

Benefits

metadata-free server architecture: Gluster uses a metadata-free server design and uses an elastic hash algorithm to locate files. This architecture eliminates the performance bottleneck and single point of failure of the central metadata server, ensuring better performance, linear scalability, and high reliability.

Modular design: Gluster is modular and stackable, allowing users to choose different storage configurations according to their needs. Each storage node (called "brick") runs a glusterfsd process, which processes data requests and interfaces with the underlying file system.

High Scalability: Gluster supports dynamic scaling from several to hundreds of servers, and supports up to several PB of data storage. Users can add more nodes as needed to improve storage capacity and performance.

Flexible Data Management: Gluster supports multiple data management functions, including data redundancy, failover, and load balancing. You can select Replicated Volume or Dispersed Volume to protect data as needed.

High performance: Gluster can provide high-throughput and low-latency data access, which is especially suitable for large-scale applications requiring parallel processing. For example, when multiple training nodes read and write the same checkpoint file at the same time, Gluster can ensure high bandwidth.

Simplified Management: Gluster provides easy-to-use management tools to make cluster configuration and maintenance more convenient. You can easily manage storage resources through the command line interface or graphical interface.

Multi-protocol support: Gluster supports multiple access protocols, including NFS and CIFS, to make it compatible with various applications and operating systems. This flexibility enables Gluster to adapt to different scenarios.

Geographic replication: Gluster supports geographic replication and can asynchronously distribute data between different locations to improve data security and availability. This is important for data that requires cross-region backup and restoration.

User space file system: Gluster runs as a user-space file system, avoiding the complexity of module development in the Linux kernel. This makes Gluster easier to integrate with other software and provides greater flexibility.

Disadvantages

poor performance of small files: Gluster does not perform well when processing a large number of small files. Due to its design and implementation, Gluster may cause high latency in reading and writing small files, resulting in overall performance degradation. This situation is particularly evident when processing millions of small files, which may lead to slow application response.

Write latency: when using a replication volume, Gluster needs to send write operations to multiple replicas at the same time, which increases the network burden and causes write latency. For example, in a three-node cluster, write operations need to be transmitted through two networks, thus reducing write performance.

Metadata operation performance: Gluster may encounter performance bottlenecks when performing metadata operations such as directory traversal and file search. Especially when a directory contains a large number of files, the read and write performance is significantly reduced.

Complex fault recovery: Although Gluster has the ability to repair itself, the recovery process can be very complex and time-consuming after a node or disk failure. This may lead to performance degradation during recovery and affect application availability.

Memory and CPU resource consumption: Gluster requires high memory and CPU resources at runtime, especially when processing highly concurrent requests. This may lead to resource shortage and affect the overall system performance.

Network dependency: the performance of Gluster is highly dependent on network bandwidth and latency. Gluster performance will be significantly affected when network conditions are poor or bandwidth is insufficient, which is a potential problem for applications requiring high throughput.

Configuration complexity: Although Gluster provides flexible configuration options, its complexity may also become an obstacle. Users need to have certain technical knowledge to effectively configure and optimize Gluster clusters to meet the needs of specific workloads.

Lack of POSIX support: Although Gluster supports POSIX interfaces, its implementation may not fully meet all POSIX standards, which may cause compatibility problems when some applications migrate to Gluster.

User feedback

ease of use and settings: GlusterFS is generally considered easy to set up and maintain in development environments, especially in environments with low configuration requirements. However, once you migrate to a production environment, you may encounter performance bottlenecks and complexity issues.

File processing performance: users generally report that GlusterFS does not perform well when processing a large number of small files. For example, when some users execute commands such as ls -l | wc -l, it may take tens of seconds to operate thousands of small files, however, it performs well when processing large files. This phenomenon causes the response time of GlusterFS to increase significantly in scenarios where small files need to be read or listed frequently, thus affecting the overall performance.

Performance problems at the node level: after replacing a storage node (such as replacing a disk), the performance of GlusterFS may be significantly reduced. For example, a user reports that after a node is replaced, the file access latency soars from 8 to 12 milliseconds to more than 150 milliseconds, or even reaches 700 milliseconds during peak hours. This delay not only affects the performance of applications, but also leads to a decline in user experience. In addition, although GlusterFS has the function of self-repair during node fault recovery, the recovery process is complex and time-consuming, which also has a negative impact on the overall performance of the system.

Complex fault recovery: Although GlusterFS has the self-repair function, the fault recovery process may be too complex and time-consuming after a node or disk fault occurs. This complexity may affect the availability and recovery speed of the system.

Difficulty in configuration and maintenance: Some users pointed out that the configuration and maintenance of GlusterFS are complex and require high technical knowledge and experience. It is recommended to seek professional support during the system planning phase to ensure reasonable system configuration and reduce subsequent problems.

Documentation and support issues: users generally report that GlusterFS documents are outdated and lack timely updates, which brings troubles to system configuration and troubleshooting. However, the community support of IRC channel is relatively good, from which users can get practical help.

GitHub activities

gluster's main code library 'glusterfs' has about 4.6K stars and 1.1k forks on GitHub, and has handled 242 problems and 30 pull requests in the past year, shows continuous development and active community participation. The project has 107 repositories on GitHub, covering various functions such as packaging, automated configuration, and document management, showing the diversity and scalability of its functions. Although users reported that document updates were lagging behind, regular updates from multiple warehouses and positive feedback from the community on Issues showed that Gluster remained active in bug fixing, adding new features, and optimizing performance. On the whole, Gluster, as an open source project, is highly active on GitHub, showing strong vitality and development potential.

Latest developments

Gluster community released Version 11.0 on February 14, 2023, which includes a number of new features and code optimization. Major improvements include improved rmdir operation performance by about 36%, extended support for ZFS snapshots, and the implementation of namespace-based quotas. In addition, Gluster plans to adjust the release cycle of major versions from six months to once a year, and provide minor version updates within 12 months after each major version is released, to improve the stability and quality of the software. The community is also actively soliciting user feedback to ensure that future versions can better meet user needs.

BeeGFS

background

beeGFS(BeeGFS parallel file system) is a high-performance open source parallel file system designed for high-performance computing (HPC), artificial intelligence (AI) and other data-intensive applications. The project was originally developed by Fraunhofer high performance computing center in Germany in 2005 to replace the existing file systems on its new computing cluster. In 2007, the first beta version of BeeGFS was released at the ISC07 conference in Dresden, Germany, and the first stable version was released in 2008. In 2014, Fraunhofer established ThinkParQ to maintain BeeGFS and changed its name from FhGFS to BeeGFS.

The BeeGFS architecture consists of multiple components, including management services, metadata services, storage services, and client services. These components can be flexibly configured to meet users of different sizes and needs. BeeGFS supports dynamic scaling. It can scale from small clusters to enterprise-level systems with thousands of nodes, and has excellent data throughput and metadata performance. Due to its open source features, BeeGFS has been widely used worldwide, including many top supercomputers and many industries.

Benefits

high performance brought by user space architecture: BeeGFS uses User space architecture to avoid kernel-level bottlenecks in traditional file systems. This design allows independent expansion of data and metadata and supports non-interference linear expansion. As the number of nodes increases, the system performance can be significantly improved. In addition, BeeGFS distributes file content and metadata to multiple servers through distributed metadata and data stripe, supports parallel access, and optimizes the processing performance of small and large files.

High Scalability: BeeGFS can scale from small clusters to enterprise-level systems with thousands of nodes, which is suitable for application requirements of various scales. The system design allows dynamic expansion without major reconfigurations or downtime, making it easy to cope with the increasing amount of data. This flexible scalability makes BeeGFS very suitable for academic research, high-performance computing (HPC) and other data-intensive applications.

Optimized high I/O load: BeeGFS is optimized for high concurrency scenarios to effectively handle simultaneous write operations performed by multiple clients and reduce the risk of data corruption. This design is especially important for applications that require multiple processes to access shared files at the same time, ensuring data integrity and system stability.

Flexible storage options: BeeGFS supports multiple underlying file systems (such as XFS, ext4, and ZFS), and can integrate different types of storage devices in the same namespace. Users can use high-performance SSDs for key projects as needed, and use cost-effective HDD to store other data, thus achieving the best balance between cost-effectiveness and performance.

Easy to manage: the client components of BeeGFS run as patch-free kernel modules, while the server components run as daemons in the user space, which simplifies the installation and management of the system, the kernel modification requirements are avoided. In addition, BeeGFS provides comprehensive monitoring tools and graphical dashboards (such as Grafana), allowing users to easily monitor system performance and health status for effective management and optimization.

Dynamic Failover and multi-network support: BeeGFS supports multiple network connections (such as RDMA, RoCE, and TCP/IP on InfiniBand), and can automatically switch to redundant paths when the connection fails. This dynamic failover mechanism improves the reliability and performance of the system in key applications and ensures the stability of data transmission.

Wide compatibility and flexibility: BeeGFS is compatible with various Linux Distributions and Kernels and can be deployed without the need for specific enterprise-level Linux distributions. This compatibility enables organizations to efficiently utilize existing hardware, transform computing nodes into shared storage units, and flexibly adapt to different hardware environments.

Disadvantages

missing data protection and security features: BeeGFS does not support advanced data protection mechanisms (such as erasure codes or distributed RAID) and native file encryption (including static data and transmitted data). This means that users need to implement their own data redundancy and encryption solutions, which may be a significant flaw for enterprises that require high data integrity and security, especially for processing sensitive data.

Dependency on management and metadata servers: BeeGFS requires independent management and metadata servers, which increases deployment complexity and system management overhead. During high I/O operations, metadata servers may become performance bottlenecks, which may affect the performance of the entire system.

Support for modern storage technologies is limited: BeeGFS mainly supports traditional storage interfaces (such as SAS, SATA, and FC), while support for emerging storage technologies (such as NVMe-over-Fabrics) is limited. This may limit the ability to utilize cutting-edge storage solutions.

Lack of enterprise-level functions: BeeGFS lacks common enterprise-level functions in production environments, such as snapshots, backup capabilities, and hierarchical data management. This makes the management of large-scale datasets more complex and inefficient.

Performance bottlenecks of small file processing: Although BeeGFS performs well in large file processing and high throughput scenarios, it may encounter performance bottlenecks when workloads involving a large number of small files are involved, especially in AI and machine learning applications.

Insufficient protocol support: BeeGFS does not support widely used enterprise protocols such as NFS or SMB. Additional services are required to make up for these deficiencies, which increases the complexity of integration into the existing IT infrastructure.

Cost and licensing issues: BeeGFS adopts a dual license mode, in which the client software complies with the GPLv2 license, while the server components are governed by the proprietary end-user license agreement (EULA). This model may limit the flexibility of some organizations. In addition, many enterprise-level functions (such as high availability, quota management, and access control lists) are provided only through paid support contracts, which may pose obstacles to smaller organizations or users with limited budgets.

Complexity of community support and expansion: Although BeeGFS is open-source, its community contribution is relatively small, which may slow down the speed of feature development and vulnerability repair. Effective expansion of BeeGFS installation usually requires in-depth system knowledge and experience, and organizations lacking professional support may encounter difficulties in managing large-scale deployments or troubleshooting.

Platform compatibility and cost impact: BeeGFS is mainly designed for Linux operating systems, which may limit its attractiveness in organizations that use other operating systems or require cross-platform compatibility. Although Windows clients are being developed, cross-platform support is still incomplete. In addition, although the basic version is free, enterprises may face significant professional support and additional functional costs, which may make it less cost-effective than other open source alternatives.

User feedback

complexity and documentation: Many users are dissatisfied with the complexity of BeeGFS, especially during installation and configuration. Users pointed out that the lack of detailed guidance in official documents led to confusion in setup and troubleshooting. For example, some users find that the document does not cover specific configuration options or best practices when trying to configure BeeGFS, which makes them have to rely on community forums for help.

Performance problems: users generally report performance fluctuations when using BeeGFS. Some users reported that the performance did not meet expectations during large-scale data transmission. For example, some users mentioned that the speed of large file transfer was significantly lower than expected, even dropping from the original 900 MB/s to 230 MB/s, which disappointed them. Other users said that although BeeGFS performed well in reading large files, it was unable to handle small files.

Limits: Some users pointed out that BeeGFS has limited support for some advanced functions, such as high availability (HA) and storage pool management. These features usually require commercial support, which makes some users who wish to use these features feel frustrated. In addition, users want to get more features in the open source version, rather than just commercial versions.

Fault handling: users are concerned about the error handling mechanism of BeeGFS, especially when they encounter unresolved problems. Many users reported that the process of contacting the development team to solve problems was very difficult, which affected their operational efficiency. Some users therefore consider turning to other file systems to obtain better support and response speed.

GitHub activities

the BeeGFS project remains active. According to the latest data, the project has multiple public warehouses, among which the 'beegfs' repository shows continuous development activities, including functional tests and document updates. In recent months, several submissions and updates have been made, showing that developers are continuously fixing bugs and optimizing features. Community members actively participate in the discussion and contribute the code, which indicates that the project still has good development prospects.

Most new trends

the latest version of BeeGFS 7.4.4 was released on July 8, 2024. It mainly includes support for RHEL 9.4, new configuration options to optimize the performance of the native cache mode, the stability of metadata Buddy Mirroring is improved and multiple bug fixes are fixed. This version is fully compatible with 7.4.3, but it is different from 7.4.x versions earlier than 7.4.3 in compatibility. During the upgrade, all metadata nodes may need to be Updated at one time, and rolling upgrade is not supported. Previously, BeeGFS 7.2.14 was released in March 2024. This version also introduces configuration options for performance problems in the native cache mode, improves the stability of Buddy Mirroring, and fixes multiple errors.

Ceph

background

Ceph is an open-source software-defined storage platform designed to provide unified object storage, block storage, and file storage services. Based on the reliable Adaptive Distributed Object Storage (RADOS) architecture, it can efficiently manage and distribute large-scale data. Ceph was originally developed by Sage Weil in 2004 at the University of California, Santa Cruz, and released open source in 2006. The core components of Ceph include Object Storage Daemon (OSD), Monitor (Monitor), and Manager (Manager), which together achieve high data availability and fault tolerance.

The Ceph architecture supports expansion from small-scale deployment to large-scale clusters, and can process data from PB level to EB level. It uses the CRUSH(Controlled Replication Under Scalable Hashing) algorithm to distribute data, avoiding the bottleneck of centralized metadata servers and improving performance and scalability. With the continuous development of the community and contributions from organizations such as Red Hat and IBM, Ceph has been widely used in cloud computing, big data and enterprise IT environments, it has become a key component of modern storage solutions.

Benefits

data integrity and self-repair capability: Ceph ensures data integrity through checksum technology and has powerful self-repair function. Each object has a checksum that is used to detect data corruption during storage and transmission. When Ceph detects data errors, it automatically restores data from healthy replicas to ensure system reliability and data consistency. This self-repair mechanism greatly improves data stability and system fault tolerance.

High Scalability: Ceph is designed to support horizontal scaling from TB level to EB level, and can handle massive data storage requirements. Its CRUSH algorithm allows the system to efficiently allocate data and calculate data locations without central query tables, which enables Ceph to maintain performance and scalability in large-scale clusters. The uncentralized architecture of Ceph further avoids single point of failure and supports dynamically adding storage nodes without affecting the overall performance of the system.

Unified Storage Platform: Ceph integrates object storage, block storage, and file storage functions in a single system. Its core architecture supports these three storage interfaces through RADOS (reliable Adaptive Distributed Object storage), and through RGW (Object Storage Gateway), RBD (block storage device), and CephFS (file system) provides unified storage services. This unified platform supports different types of storage requirements, improving the utilization efficiency and management convenience of storage resources.

Intelligent Daemons and distributed architectures: Ceph uses intelligent Daemons (such as OSD Daemons). These Daemons communicate with each other directly in the cluster and dynamically process data replication and redistribution, the bottleneck caused by centralization is avoided. This decentralized design enables the system to process large-scale data more efficiently and supports high-performance concurrent operations and dynamic load balancing.

Disadvantages

architecture complexity: the architecture of Ceph is very complex, and the setup and management require in-depth technical knowledge and experience. This may pose challenges to organizations that lack relevant expertise, increase the difficulty of system deployment and maintenance, and may lead to performance bottlenecks and latency problems.

Performance latency: Ceph may show high latency when processing modern workloads that require consistent response time. Compared with local NVMe flash memory, Ceph performance may be significantly lower, especially in Kubernetes environments, affecting the overall response speed of the system.

Low Flash utilization: Ceph is relatively inefficient when using flash memory, usually only 15-25%. This inefficient utilization may lead to a longer reconstruction time in the fault recovery process, increasing the recovery time and network load of the system.

High network requirements: Ceph requires carefully configured networks to achieve optimal performance, including public networks and private storage networks. This complex network configuration requires additional resources and time, increasing the complexity of deployment.

Insufficient documentation and community support: Ceph documents are sometimes updated in a timely or inconsistent manner, which may affect implementation and troubleshooting. At the same time, compared with other storage solutions, Ceph may have limited community support, making it more difficult to get help when encountering problems.

User feedback

reliability and robustness: Ceph is widely believed to perform well in handling node faults and ensuring data security. Its self-repair capability and data integrity are outstanding in high availability scenarios, and can maintain data consistency and availability, even in extreme failure conditions.

Scalability: users are satisfied with the horizontal scalability of Ceph, especially in large-scale data processing. Ceph supports thousands of clients and PB-level data storage. With optimized configuration, Ceph has excellent write speed and scalability, and is suitable for cloud computing and big data environments.

Diversified application scenarios: Ceph is widely used in various environments, including OpenStack and Kubernetes. Its unified storage function supports both object storage and block storage, providing great flexibility for different application requirements.

Complexity and tuning requirements: the setup and management of Ceph are complex, and a large number of tuning requirements are required to achieve optimal performance. Users need to adjust OSD configuration and increase write cache. Some users have improved performance by 10-20% by optimizing CPU settings to reduce latency. This complexity makes it difficult for beginners to deploy and maintain Ceph.

Performance issues: users reported many performance challenges of Ceph. The write performance is lower than expected, especially when using SATA enterprise SSD, the write speed is significantly lower than that of a single mechanical hard disk. When processing small pieces of random I/O, Ceph's performance is also disappointing, IOPS is much lower than expected, and there is a significant gap compared with local NVMe storage. In addition, network latency and insufficient bandwidth have a significant impact on the overall performance of Ceph. We recommend that you use a private network to reduce latency and ensure high throughput to avoid performance bottlenecks.

Network dependency: Ceph is highly dependent on network infrastructure. An efficient Ceph cluster requires good network configuration. We recommend that you use a high-performance network architecture (such as 40 GbE) to meet the needs of modern workloads and avoid performance bottlenecks.

GitHub activities

the 'ceph' warehouse of Ceph shows its extensive influence and Community Foundation with about 13.8k Stars and 5.9K Forks. Currently, there are 943 open Pull Requests, which reflect the continuous investment of the community and core maintainers in function improvement and code optimization, and also reveal the burden of management contribution. The project has actively responded to challenges and solved 635 problems, showing an efficient maintenance and problem handling mechanism. Through milestone management, Ceph promoted the release of the version in an orderly manner, and the latest milestone was close to completion, reflecting good planning and execution. The community is composed of diversified individual developers and organizations, which promotes the integration of innovation and multiple perspectives. The stable submission frequency indicates the continuous contribution of the developer team and the continuous improvement of Ceph in terms of function expansion, bug fixes, and performance optimization.

Latest developments

ceph's main warehouse recently released v18.2.4 Reef (July 24, 2024), which includes multi-personality performance upgrades and bug fixes to further enhance the stability and reliability of the system. Then, v18.2.2 Reef (April 2024), as an emergency repair version, solved the crash problems and encoder errors related to Prometheus and ensured the stability and reliability of system operation.

OpenStack Swift

background

OpenStack Swift(Swift distributed object storage system) is an open-source distributed object storage system designed for multi-tenant and highly concurrent environments. It is especially suitable for processing backup, Web content, and, storage requirements for mobile applications and other large-scale unstructured data. The project was originally developed by Rackspace in 2009 as the core storage system of its Cloud Files service, and became part of OpenStack together with other components in 2010. With its highly scalable architecture, Swift can support small-scale deployment and expansion of storage systems to thousands of servers.

Swift architecture consists of several key components, including proxy server, Object Server, container server, and account server. Each component is responsible for specific functions to ensure reliable data distribution in the cluster and to provide high availability and final consistency. Swift's design fully considers the fault tolerance capability of dynamic expansion and hardware faults, making it an object storage solution favored by large enterprises and cloud service providers. Swift supports REST-based APIs to facilitate interaction and expansion between developers and system integrators. In addition, Swift has performed well in processing large-scale datasets and is a choice for AI and machine learning workloads.

Benefits

high-Performance Distributed Architecture: OpenStack Swift uses a distributed architecture without single point of failure to avoid performance bottlenecks in traditional centralized storage systems. Through independent deployment of proxy servers, object servers, Container servers, and account servers, Swift can flexibly process highly concurrent requests to ensure efficient storage and retrieval of data and metadata. Swift's annular data structure enables data to be automatically distributed across multiple storage nodes, enabling efficient management and retrieval of massive amounts of data.

Excellent scalability: Swift supports scaling from small-scale clusters to enterprise-level systems with thousands of servers. Scale-out allows you to increase capacity and performance by adding more storage nodes without downtime or major configuration changes. This linear expansion capability enables Swift to meet the needs of different scales from small and medium-sized enterprises to cloud service providers.

Fault tolerance and high availability: Swift has built-in data replication and self-healing functions. By automatically storing object replicas in different nodes and physical locations, it ensures that even if some nodes or hardware fail, data remains available. Its self-healing mechanism can automatically detect and fix data inconsistency, reduce manual intervention, and improve system availability and data durability.

Flexible storage policies: Swift supports multiple storage policies. Users can choose different replication policies or erasure policies based on application requirements to find the best balance between data redundancy and storage costs. This flexibility allows you to optimize system configurations based on specific application scenarios to meet diverse storage requirements.

Optimized S3 compatibility and integration: Swift provides API interfaces compatible with Amazon S3, allowing users to easily integrate existing applications into Swift storage systems. The RESTful API is designed for developers to interact with each other and is suitable for building various application scenarios, including media storage, backup, and archiving.

Integration with other open source tools: Swift can seamlessly integrate with machine learning frameworks such as PyTorch and TensorFlow, which enhances its application potential in the AI/ML field. Through this integration, developers can use the large-scale data storage capabilities provided by Swift to support AI model training, thus speeding up model development and deployment.

Disadvantages

performance limits: OpenStack Swift may not be as good as dedicated object storage systems in high IOPS scenarios. Due to its proxy server-based data access architecture, high latency may occur, especially when workloads require frequent data read and write or low latency. In addition, the final consistency model used by Swift may be delayed during data synchronization, resulting in users accessing the old version of data in a short period of time, affecting application scenarios with high real-time requirements.

Architecture complexity: Swift's distributed architecture consists of multiple components, such as proxy servers, object servers, Container servers, and account servers. The configuration and management are relatively complex. In order to correctly set up and optimize system performance, administrators need to have in-depth technical knowledge, especially in large-scale cluster environments, system upgrade and maintenance need to be carefully planned, to avoid service interruption or performance degradation due to configuration errors. Compared with other simpler storage solutions, Swift is more difficult to deploy and manage.

Heavy management tasks: although Swift is highly scalable, many management tasks still need to be manually operated, such as cluster expansion, system upgrade, and monitoring. This manual intervention may increase the complexity of operations, especially when cluster management across multiple data centers is involved, if the steps are not correctly performed, data may be unavailable or system performance problems may occur. In addition, compared with some more automated storage solutions, Swift lacks native automatic management tools, increasing O & M pressure.

Lack of native features: Swift lacks native integration with some common enterprise systems, such as LDAP or Active Directory authentication services, which increases the complexity of enterprise deployment and requires customized development to achieve complete integration. In addition, Swift does not have built-in file system gateway support (such as NFS or CIFS), which means that traditional file system applications need to adapt to its RESTful API when migrating to Swift, this increases the complexity and cost of the migration process.

User feedback

complexity of setup and management: users generally complain about the complexity of setting up OpenStack Swift. Many users encounter authorization errors during the initial configuration process and find that they lack detailed guidance on key components such as memcache, which makes troubleshooting difficult. This shows that Swift deployment has a high learning curve, which is especially challenging for beginners.

Performance limits: users report performance bottlenecks when using OpenStack Swift. Especially when processing a large number of files, such as the number of files in a single container exceeds one million, the performance is significantly reduced. In addition, the possible data access latency caused by the proxy server architecture also has a negative impact on application performance, especially in scenarios where low latency is required.

Time input: Some users mentioned that it may take 6 to 12 months from the beginning of deployment to the stable operation of the system, and requires continuous input from multiple engineers. This reflects that organizations need to invest a lot of time and resources in the effective implementation of OpenStack Swift.

Flexibility and API integration: users often praise Swift's RESTful API for its high flexibility. Integration into existing applications is usually simple. Users can use HTTP commands (PUT, GET, DELETE) to manage data without extensive modifications to the application architecture.

GitHub activities

the Swift repository of OpenStack swift shows a certain scale of activity on GitHub. Currently, it has about 2.6K Stars and 1.1k Forks, and has submitted about 1.2k times in the past year, it shows that the development team is still updating the code. Currently, there are 1.5K open Issues and 1.1K closed Issues. Although there are a large number of open Issues, the number of closed Issues reflects the project's continuous attention to problem handling. Currently, the repository does not have open Pull Requests, which may indicate a decrease in community contributions.

Latest developments

OpenStack Swift released Yoga and Zed versions in 2022, Antelope and Bobcat versions in 2023, and plans to release Caracal versions in 2024. In terms of new features, Wallaby version focuses on improving role-based access control (RBAC) and integration with other open source projects. Yoga version adds support for Keystone v3 API, the Zed version introduces support for Swift v2 API, providing more options. In terms of performance, Antelope version optimizes the processing efficiency of a large number of small files, while Bobcat version improves the replication and auditing performance. In addition, security has also been enhanced. Wallaby Introduces finer-grained access control and solves security issues related to S3 API. Caracal is expected to further improve performance and security and introduce new features that support more application scenarios.

DAOS

back scene

DAOS (distributed asynchronous object storage) is a high-performance open-source software-defined storage system designed for high-performance computing (HPC), artificial intelligence (AI), and other data-intensive applications. The project was first released by intel in 2018. It aims to use modern non-volatile storage technology to solve the performance bottleneck of traditional storage systems. DAOS architecture is based on distributed design, supports large-scale parallel access, and provides high bandwidth, low latency, and high IOPS storage capabilities. Its unique asynchronous I/O model allows parallel data access and computing tasks, thus optimizing data processing efficiency.

In recent years, DAOS has gradually expanded to the cloud computing environment and no longer relies on Intel Optane PMem, because it plans to stop the development of this product in 2025. This change has prompted the DAOS development team to explore compatibility with other storage technologies to meet the growing demand for cloud computing. The core features of DAOS include high performance, scalability, data protection and reliability, and support dynamic expansion from small clusters to large enterprise-level systems, it also uses technologies such as multiple copies and erasure codes to ensure that data is still available in the event of node failure.

Benefits

high performance: DAOS is designed to provide optimal data throughput and low latency performance, especially for large-scale data processing and high-performance computing (HPC) environments. Its efficient data access and processing capabilities enable the rapid completion of big data analysis and scientific computing tasks.

Excellent scalability: DAOS has excellent scale-out capability and can easily scale out the system by adding more storage nodes to meet the growing demand for data storage. This scalability ensures that the system can flexibly adapt to various application scenarios.

Flexible Data Model: DAOS supports multiple data access interfaces, including Object Storage, Key-value storage, and array interfaces. It is suitable for different types of data processing requirements. This flexibility enables DAOS to efficiently process structured and unstructured data to meet diverse application requirements.

Optimized Storage Architecture: DAOS optimizes the performance of small I/O operations by storing metadata in persistent memory. This architecture design improves the speed of data access and the overall performance of the system, making frequent small-scale data access more efficient.

Efficient Metadata Management: DAOS uses distributed metadata services to distribute the burden of metadata management to multiple nodes, thus improving the scalability and reliability of the system. This reduces the risk of single point of failure and improves the fault tolerance of the system.

Data consistency and integrity: DAOS uses distributed consistency protocols and data redundancy technologies to ensure data consistency and integrity. This design effectively avoids data loss or damage and ensures data reliability.

Automated O & M and self-repair: The DAOS system supports automatic fault detection and repair functions, which can monitor the health status of the system in real time and automatically repair itself. This automated o & m capability improves the reliability and stability of the system and reduces the need for manual intervention.

Optimized storage utilization: with efficient data compression and deduplication technologies, DAOS can maximize the utilization of storage resources and reduce storage costs. This optimization not only saves storage space, but also improves data access efficiency.

Disadvantages

complex management and configuration: the DAOS architecture is relatively complex, and the deployment and configuration process may require high technical knowledge. This complexity may lead to initial setup and subsequent maintenance challenges, especially for teams without relevant experience.

Limited ecosystem support: Although DAOS is gradually developing, its ecosystem and community support are still relatively limited compared with some mature storage solutions. This may result in a lack of sufficient documentation, tools, or community resources to solve problems.

Steep learning curve: it may take some time for new users to understand the architecture, functions, and best practices of DAOS. Especially when compared with traditional storage solutions, users may need to relearn relevant concepts and operation methods.

Potential data consistency issues: Although DAOS is designed to ensure data consistency, data consistency issues may still occur in extreme cases, such as network partitions or node failures. This requires users to consider these potential risks when designing applications.

Resource consumption: DAOS may require high computing and memory resources at runtime, especially when processing large-scale datasets. Such resource consumption may lead to increased operating costs, especially in cloud environments.

Lack of mature monitoring tools: Although DAOS provides some monitoring functions, its monitoring tools and visual interface may not be perfect compared with other mature storage solutions. This makes users face certain challenges when monitoring the health status and performance of the system.

User feedback

excellent performance: according to user feedback, DAOS has shown excellent performance in handling highly data-intensive tasks such as high-performance computing (HPC) and artificial intelligence (AI). In specific benchmark tests, compared with traditional storage solutions, some users reported performance improvements of up to 12%, demonstrating their advantages in processing large amounts of data.

Efficient memory management: DAOS is famous for its excellent memory efficiency. Users generally reflect that this system can save up to 91% of memory in some application scenarios. This remarkable effect benefits from DAOS's advanced data layout and memory access optimization strategy, effectively reducing resource consumption.

Initial setup and configuration challenges: Although DAOS is powerful, some users point out that its initial setup and configuration process is complex, especially when integrating with existing systems, careful planning and compatibility considerations are required, especially at the hardware level.

Extensive application adaptability: DAOS has been successfully deployed in a variety of environments, including cloud solutions and local high-performance computing systems, showing its strong adaptability and flexibility. Specific cases include the successful application in argong national laboratory (ANL) and leibniz supercomputing center (LRZ), further verifying its leading position in the industry.

Management complexity considerations: Some users have expressed concerns about the complexity of DAOS management, especially when formulating data layout policies and performing performance tuning for specific applications. This requires administrators to have high technical level and professional knowledge.

Document improvement requirements: to improve user experience and system deployment efficiency, users generally call on DAOS to provide more detailed and comprehensive document resources. These documents should cover all aspects from basic installation to advanced configuration, optimization policies, and troubleshooting to help users implement and optimize DAOS systems more effectively.

GitHub activities

DAOS open source storage system warehouse (daos-stack/daos) currently has about 740 stars and 297 forks. There are currently 456 open pull requests, which indicates that the project may have a backlog in handling contributions and improvements, A high number of open pull requests may cause delays in new features and updates. In addition, if the problem solving speed continues to slow, it may affect the user experience and development progress.

Latest developments

DAOS 2.4 was released on September 22, 2023. This update introduces a series of new features and improvements, and officially supports the Enterprise Linux 8(EL8) operating system. This version significantly extends the compatibility with ARM64 architecture and further enhances the flexible application of DAOS in diversified computing environments. In addition, DAOS now supports deployment on Google Cloud platform. Croit Platform for DAOS of Croit GmbH further simplifies the deployment and management process of DAOS clusters and reduces the user's use threshold. In the IO500 benchmark test, DAOS stood out with its excellent performance and won 13 rankings, including 5 top 10.

MinIO

background

MinIO is an open-source high-performance distributed object storage system, which was first released by MinIO ++ on March 11, 2016. It is designed for private and hybrid cloud environments. Since its release, MinIO has become an ideal storage solution for data-intensive applications such as machine learning, analysis, and cloud-native applications due to its S3 compatibility. After years of iteration, MinIO now supports PB-level storage and high concurrent access, and can run efficiently on standard hardware. Its architecture simplifies the complexity of the system, and implements efficient data storage and management through single-layer design, supporting hundreds of GB of read and write speed per second. MinIO's online erasure coding technology ensures data persistence and reliability. By dividing data into multiple data blocks and verification blocks, data integrity is ensured in case of hardware failure. At the same time, MinIO regularly checks data integrity to prevent data corruption. MinIO also has powerful security features, including data encryption, object locking, and identity access management. In addition, it supports container orchestration services such as Kubernetes and can be run as lightweight containers to meet the needs of modern cloud computing environments. This design enables each tenant to run its own MinIO cluster independently, thus improving security and manageability.

Benefits

high performance: MinIO provides extremely high data throughput and low latency, and supports reading and writing speeds up to 325 Gb/s on standard hardware. Its design optimizes high throughput and low latency, and is especially suitable for data-intensive applications such as big data analysis and machine learning.

Scalability: MinIO allows you to increase storage capacity and performance by adding nodes. The architecture supports expansion across multiple geographical regions, and each tenant can run its cluster independently to ensure efficient resource utilization and flexibility.

Efficient resource utilization: MinIO's lightweight server package (about 100MB) and symmetric architecture enable it to run efficiently on standard hardware and support hosting multiple tenants on shared resources. Its online data processing method (processing data and metadata as objects) further improves performance and consistency.

S3 compatibility: fully compatible with Amazon S3 API, allowing existing applications to be seamlessly integrated or migrated to MinIO, and supporting various S3 interactive SDKs and libraries, enhancing system flexibility and compatibility.

Data protection and security: MinIO uses erasure coding technology to provide high data durability and redundancy, maintaining data integrity even in the event of drive failure. In addition, it includes encryption (transmission and static data), object locking (compliance), and identity access management compatible with AWS IAM standards to ensure data security.

Disadvantages

S3 features: Although MinIO is fully compatible with S3 APIs, it may not be as rich as AWS S3 in some advanced features. For example, MinIO provides basic support for data management, analysis tools, and some advanced integration functions, which may limit its applicability in application scenarios that require specific functions or are highly customized.

Storage type limitation: MinIO focuses on object storage and does not support block storage or file storage. This makes it unsuitable for applications that require a unified storage platform. In addition, MinIO's erasure encoding scheme supports a maximum of 256 total slices (128 data and 128 verification), which may be limited when processing large objects.

Deployment and management complexity: Configuring a MinIO cluster with high availability and durability in a production environment can be very complex, usually requiring at least four homogeneous nodes with local additional storage. When expanding storage capacity, you need to increase the entire node pool, which increases traffic between nodes and query complexity, and may require application updates. Proper configuration of storage controllers, drives, and network settings is critical to performance. Improper configuration may lead to unpredictable performance problems.

Storage configuration limits: MinIO requires local attached storage and does not support network attached storage (NAS) or storage area network (SAN), which may limit flexibility in some environments. In addition, MinIO does not recommend RAID, pooling, or other hardware/software redundancy layers on storage controllers, which may not be compatible with some traditional storage architecture practices.

Potential performance bottleneck: MinIO performance is limited by underlying storage and network infrastructure. To achieve high performance, you need careful configuration and resource allocation. Increasing the node pool when expanding the storage capacity can increase the traffic between nodes and increase the query complexity, which may lead to performance degradation in large-scale multi-pool deployment. In addition, MinIO requires the client to use AWS Signature V4 or V2 to sign all operations. Modification of the header of intermediate components such as the load balancer may result in Signature mismatch and request failure.

User feedback

performance advantages and big data processing: MinIO performs well in processing massive amounts of data and high throughput tasks. Users praised MinIO for its fast deployment and easy configuration, which is very suitable for big data workloads, such as efficient backup of PostgreSQL databases and seamless migration of Kubernetes cluster data. In data-intensive applications, the performance of MinIO is recognized by the industry.

Deployment Challenges and configuration complexity: Although MinIO has powerful performance, users may encounter some technical challenges during actual deployment and configuration. In particular, deploying MinIO in a production environment requires at least four homogeneous nodes equipped with local storage, which may pose challenges to organizations that lack professional storage teams. In addition, users find that the relevant documents are not detailed enough, resulting in a complicated configuration process. In addition, if the upgrade fails, you may need to reinstall the MinIO, although the data can be retained through independent datasets.

Functional limitations and specific use cases: MinIO may be limited by its erasure coding scheme when processing large objects, this solution supports a maximum of 256 total shards (including 128 data shards and 128 verification shards), which may not be flexible enough when processing large files. In addition, MinIO focuses on object storage and does not provide direct support for block storage or file storage. This may not be applicable to scenarios where comprehensive storage solutions are required. In a single server or a small disk environment with limited resources, MinIO may have less obvious advantages than directly operating a file system.

Scalability: when expanding the storage capacity of MinIO, it usually needs to be carried out in units of node pools, which may increase the amount of data transmitted between nodes and the complexity of query processing. In large multi-node deployments, this expansion method may have a negative impact on system performance. Therefore, the correct configuration of storage hardware, network architecture and corresponding control logic is the key to ensure the efficient operation of the system. Any improper configuration may affect the overall performance.

GitHub activities

minIO's main repository 'minio/minio' on GitHub currently has more than 5.4K forks and 46.8k Stars, showing a broad user base and an active contributor community. The code submission frequency of this warehouse is very high. On average, new code is pushed several times a week, which shows that the development team is continuously improving the software function and stability. The latest version of MinIO was released frequently. The latest version was released on August 29, 2024. This fast release pace reflects the activity of the development team. In addition, other related warehouses also demonstrated continuous activities. For example, 'minio/minio-py '(MinIO's Python Client SDK) maintains an active development status, obtains 822 Stars and 318 Forks, and updates them regularly. 'minio/console' (a user interface that provides MinIO object storage) also has more than 270 forks and 820 Stars. In terms of problem tracking and community interaction, the number of open questions in the 'minio/minio' warehouse is relatively small (19), and the pull requests contributed by the community (11) are being actively reviewed and merged, it shows that the team responds quickly to user questions and feedback and effectively integrates community contributions.

Latest developments

on March 12th, 2024, MinIO launched the Enterprise Object Store, which enhanced the performance and scalability of Enterprise applications. On August 1, 2024, MinIO released MinIO DataPod, a reference architecture that supports ultra-large AI and data Lake workloads and optimizes the data infrastructure of AI applications. Previously, in November 2023, MinIO cooperated with VMware to launch the native integrated object storage solution of VMware Cloud Foundation, focusing on improving support for AI/ML applications and continuously optimizing products, including improving the read and write speed and processing capability of large datasets.

ZFS

background

ZFS(Zettabyte File System) is an open source File System and logical volume manager, which was first released by Sun Microsystems in 2005 to solve the limitations of traditional File systems. ZFS combines file system and volume management functions to support mass storage and provide data integrity protection. Its design goal is to provide storage solutions with high capacity, high performance and high reliability. ZFS is famous for its powerful snapshot, replication, and self-repair capabilities. These features make it widely used in enterprise-level storage solutions. By introducing a storage pool (zpool) to manage physical storage, ZFS simplifies the complexity of traditional volume management and supports a variety of advanced features, such as data integrity verification and RAID-Z fault tolerance, to ensure data security. With the increasing demand for data, ZFS has become an important part of modern storage architecture, providing reliable support for various application scenarios.

Benefits

data integrity and self-repair capability: ZFS ensures data integrity during storage and transmission by using a 128-bit checksum mechanism (such as CRC). The system can automatically detect and fix "silent errors", that is, errors that cannot be found by regular error detection tools. This function uses redundant copies and verification data to automatically recover when data is found to be damaged, thus significantly improving data reliability and stability.

High Scalability: ZFS is the first file system using a 128-bit architecture, which theoretically supports almost unlimited storage capacity, exceeding the limits of traditional 64-bit systems. It can process PB-level data and is suitable for large-scale data storage requirements. It supports dynamic expansion and flexible storage resource management.

Efficient snapshot and clone function: ZFS provides efficient snapshot and clone function, which can quickly create point-in-time copies of file systems without occupying extra storage space. This is critical for data backup, recovery, and rapid data replication. This function not only saves storage space, but also significantly improves the flexibility and efficiency of data management.

Spatial efficiency: ZFS uses the copy-on-write mechanism, which means that when modifying data, the original data is not directly overwritten, but written to a new location. This mechanism improves data security because raw data remains intact until it is updated. At the same time, write-time replication also optimizes the utilization of storage space, avoiding unnecessary data fragmentation and redundancy.

Advanced Data Services: ZFS integrates multiple advanced data services, such as deduplication, compression, and encryption. These features can significantly optimize storage efficiency, improve system performance, and increase data security. Deduplication can reduce data redundancy, compression can save storage space, and encryption provides an additional layer of protection to prevent unauthorized access.

Simplified management and operation: ZFS integrates the file system and volume manager functions into a unified system, simplifying the storage management process. Users can perform routine operations through simple command sets, reducing the complexity of learning and operations. This integrated design reduces the dependence on multiple tools and commands, making storage management more intuitive and efficient.

Flexible storage pool (zpool) and virtual device (vdev) architecture: ZFS adopts the architecture of storage pool (zpool) and virtual device (vdev), combine multiple physical disks into a logical storage pool. This architecture allows you to dynamically allocate storage resources, optimize storage usage, and improve storage scalability and flexibility.

Disadvantages

high Memory requirements: ZFS has high memory requirements. We recommend that you configure at least 1GB of RAM per TB of storage. In actual use, especially in high-load or large-scale deployment environments, recommend memory configuration may need to be higher to ensure system stability and performance. This may result in higher hardware costs, especially in deployments with large storage capacity.

Configuration and management complexity: Although ZFS provides powerful functions such as snapshots, clones, and data integrity checks, the complexity of its configuration and management may pose challenges to novice users. The function set and flexibility of ZFS require users to have certain technical knowledge and experience to give full play to their advantages. This may lead to steep learning curves and increase the complexity of management and maintenance.

Performance overhead: some advanced features of ZFS, such as data checksum and compression, require additional processing capabilities, which may lead to performance overhead. In some cases, especially in resource-constrained environments, this overhead may affect overall performance. Checksum computing operations increase the burden on the system and may affect the speed of storage operations.

Compatibility and support issues: ZFS is not as widely supported and compatible as other file systems (such as EXT4 or XFS) in some operating systems. Although Linux and other systems have good support, some operating systems may still have limited support for ZFS compared with mainstream file systems. This may limit the application scenarios and popularity of ZFS.

Single server limit: ZFS is designed to run on a single server, which limits its scalability. Compared with distributed file systems such as GPFS or Lustre, ZFS has limitations in its ability to scale out to multiple servers. This may be a problem in environments that require high availability and load balancing, because ZFS cannot directly implement data distribution and load balancing across servers.

Virtual device (VDev) is not scalable: Once created, the virtual device (VDev) is not scalable in ZFS. This means that careful planning must be made when designing the storage pool. If a VDev fails, the stability of the entire storage pool may be affected. You need to plan the number and configuration of VDev in advance to avoid future expansion problems.

License Issue: ZFS uses CDDL(Common Development and Distribution License), which raises some controversies about the compatibility with GPL(General Public License) code. Some Linux distributions, such as Red Hat, are concerned about the compatibility between CDDL and GPL, which may limit the use and integration of ZFS in some environments.

User feedback

user feedback on ZFS covers its significant advantages and challenges. In terms of advantages, ZFS's self-repair function has been highly praised by users, especially the case of successful data recovery under RAID-Z2 configuration, which demonstrates its powerful data protection capability. At the same time, the snapshot and clone functions of ZFS show high efficiency, allowing users to quickly create snapshots of large datasets with minimal impact on performance. In terms of storage pool management, ZFS provides flexible scalability, allowing users to easily add disks to expand storage space.

However, ZFS also faces some significant problems. First, high memory requirements become an important consideration for user configuration, especially when handling large-capacity zpool. Secondly, when using traditional HDD, the performance of ZFS may be significantly reduced, affecting the user experience. In addition, the configuration is complex, so you need to adjust multiple parameters to optimize system performance. The risk of data loss is also mentioned in user feedback, especially when resources are insufficient. Finally, fragmentation, long backup time, and compatibility with some operating systems and kernel versions are also challenges that users may encounter when using ZFS.

GitHub activities

the main repository 'openzfs/zfs' of ZFS currently has more than 10.4K Stars and 1.7K forks, showing a wide user base and developer participation. The latest version 2.2.6 was released in September 2024 and supports Linux kernel 4.18 to 6.10 and FreeBSD 12.2 and later. Currently, there are 1.3K open questions and 159 open pull requests in the repository.

Most new trends

ZFS 2.2.6 was released on September 4, 2024. Major updates include fixing the zfs receive function to solve data corruption problems. This version supports Linux kernel 4.18 to 6.10 and FreeBSD 12.2-RELEASE and later. The previous version 2.1.15 was released in November 2023 and was mainly fixed for compatibility issues. The latest updates focus on improving performance, enhancing data integrity checks, and expanding support for different operating systems. New features include improvements to RAIDZ configuration and optimization of metadata management in ARC.

HDFS

background

HDFS(Hadoop Distributed File System) is an open source distributed file system designed for the storage and processing of large-scale datasets. As part of the Apache Hadoop project, HDFS is designed to run on general-purpose servers, providing high fault tolerance and scalability. The system adopts a master-slave architecture, in which NameNode manages the metadata of the file system, and DataNode is responsible for the actual data storage. HDFS divides files into data blocks and stores them on multiple datanodes. This optimizes the performance of large file processing and batch processing tasks. Although the write and read mode is suitable for batch processing, it is not suitable for real-time data processing.

Benefits

high fault tolerance: HDFS replicates data blocks to multiple nodes (the default replication factor is 3) to achieve high fault tolerance. This means that even if some nodes fail, the system can automatically detect and restore data to ensure continuous availability and integrity of the data.

Scalability: HDFS supports horizontal scaling. You can simply add more nodes to expand storage and computing capabilities. The system can support datasets ranging from hundreds of TB to PB without downtime or complex configuration of the system.

High throughput: HDFS is designed to optimize the high throughput of data read and write, especially suitable for processing large-scale datasets. Its distributed architecture supports efficient data transmission and provides fast data access in batch tasks.

Data Locality: HDFS uses the principle of data locality to schedule computing tasks near the node where data is located, reducing network transmission latency and bandwidth consumption, thus improving overall performance and processing efficiency.

Supports multiple data formats: HDFS can store and process structured, semi-structured and unstructured data, providing great flexibility for data analysis and processing. You can flexibly select data formats and processing methods as needed.

Simplified Data Management: The master-slave architecture of HDFS (including NameNode and DataNode) makes data management more efficient. NameNode is responsible for metadata management, while DataNode processes actual data storage and Operation. This separation improves the management efficiency and reliability of the system.

Suitable for large-scale applications: HDFS is suitable for large data sets ranging from GB to PB. It is suitable for big data analysis, data warehouse, machine learning, and other application scenarios. It can support large-scale data processing tasks.

Disadvantages

single point of failure: NameNode in HDFS is the source of single point of failure. If the NameNode fails, the entire file system cannot operate until the NameNode is restored. Although high availability configuration can alleviate this problem through redundant NameNode, this increases the complexity and maintenance costs of the system.

Lack of full POSIX compatibility: HDFS optimizes high throughput and batch processing, but relaxes some POSIX requirements, so it does not fully comply with POSIX standards. This may cause compatibility issues when some applications that require strict POSIX compatibility are running on HDFS.

Low storage efficiency of small files: HDFS performs well in processing large files, but the storage efficiency is low. Each file block requires NameNode to manage metadata, which increases the memory usage of NameNode and may lead to performance degradation.

Limited concurrent writing support: HDFS supports single-write semantics by default and allows appending to existing files, but does not support concurrent writing to a single file. This limits application scenarios that require more writes.

Lack of heterogeneous storage support: HDFS does not support different types of storage (such as SSD and NVMe) in the same cluster. All datanodes must use the same underlying storage technology, which limits the flexibility of optimizing storage for different workloads.

Potential performance bottleneck: NameNode may become a performance bottleneck for metadata-intensive operations, handling all namespace operations. The centralized NameNode architecture may limit the overall throughput and affect system performance.

Portability restrictions: Java implementations of HDFS may limit their performance. Compared with native file systems, they cannot make full use of platform-specific optimizations and functions, which may affect cross-platform performance.

User feedback

metadata bottleneck: HDFS encounters a serious metadata bottleneck when processing a large number of small files. About 1GB of heap memory is required for each 1 million additional objects, which causes JVM garbage collection problems and seriously affects cluster performance.

Performance problems: when you use HDFS and Hive to query small XML files, the query performance is poor. A single query takes more than 30 seconds, an internal Hive error occurred, causing the query to fail.

Not suitable for small datasets: HDFS is not ideal for small datasets because its overhead and complexity are higher than those of simple file systems or databases. Especially when processing small files, the performance is significantly reduced.

Maintenance workload: it is generally believed that HDFS has a large maintenance workload and needs to regularly monitor the cluster status. For example, if the number of data blocks to be copied exceeds 100, the system performance will be affected.

GitHub activities

the open source activity of HDFS on GitHub shows a dynamic ecosystem. The main HDFS projects include Apache Hadoop, which is the core library of HDFS. It has 14.6K stars and 1.1k unmerged pull requests, showing continuous development and community attention. In addition, other important projects include 'gowfs'(Go client), 'HDFS-DU (visualization tool), 'hdfs_fdw' (external data wrapper for PostgreSQL), and 'fs-hdfs '(Rust Library). And 'HdfsUtils'(HDFS metadata analysis tool) are actively expanding the application scope and functions of HDFS. The activity and community participation of these projects demonstrate the importance of HDFS in the open source community and its potential for future development. Overall, the open source ecosystem of HDFS is not only continuously supported, but also continuously innovated and improved.

Latest developments

Apache Hadoop 3.3.6 was released in June 2023. This version introduces HDFS Router-Router Federation, which supports more powerful namespace management through Federation between routers. In addition, the new version also adds the function of storing Delegation tokens in MySQL, significantly improving the throughput of Token operations. At the same time, multiple HDFS-specific APIs are migrated to Hadoop Common, making it easier for applications that rely on HDFS semantics to run on other compatible file systems. In Apache Hadoop 3.4.0 released in March 2024, Router-based Federation was further strengthened, especially in terms of data balance and transparent data movement capabilities. These updates are designed to improve the efficiency and reliability of HDFS in processing large-scale data, making Hadoop clusters more efficient and stable in handling complex data processing tasks.

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source: Andy730