Data Storage Application – 🎧 Prefer to listen? Check out this episode on the Google Library podcast. Where should your application store data? Of course, the choice depends on the use case. This post will discuss the different storage options available in Google for three types of storage: object storage, block storage, and file storage. It also covers the best use cases for each storage option.
Object Storage – Storage Storage is object storage for binary data and objects, blobs and unstructured data. You usually use it for any application and any kind of data that you need to store for any period of time. You can add or retrieve data as often as you like. Stored objects have identifiers, metadata, attributes and actual data. Metadata can include anything about a file’s security classification, which programs have access to it, and similar information. Use cases for object storage include applications that require highly accessible and highly durable data, such as: B. streaming videos, images, and rendered documents and web pages. It is also used to store large amounts of data for use cases such as genomics and data analysis. You can also use it to store backups and archives for regulatory compliance. Or use it to replace old physical tape recordings and transfer them to storage. It is also commonly used for disaster recovery because it takes almost no time to switch to a backup to recover from a disaster. There are 4 levels of storage based on budget, availability and frequency of access. 1. Standard suits for high performance, frequent access and highest availability: – Regional/dual-regional location for frequently used data/high performance – Multi-regional for global content migration 2. Proximity for data access with access less than once a month 3 Coldline for data viewed less frequently than once per quarter. 4. Save the data you want to keep for years. Using standard storage is slightly more expensive because it allows automatic redundancy and frequent access options. Nearline, cold line and document storage offer 99% availability and significantly lower costs. Block Storage – Persistent HDD and Local SSD Persistent HDD and Local SSD are block storage options. It is integrated with Compute Engine virtual machines and Kubernetes Engine. Block storage involves dividing a file into equal-sized data blocks, each with its own address, but without additional information (metadata) to provide more context for each data block. The operating system can access block storage directly as a mounted volume. Persistent disks are block storage for VMs that offer a range of latency and performance. In this article, I have discussed persistent disks in detail. Persistent disk use cases include disks for VMs and read-only data shared across multiple VMs. It is also used for fast and continuous backups of running VMs. Because of the high performance options available, Persistent Disk is also a good storage option for databases. Local SSDs are also block storage, but are temporary and therefore typically used for stateless workloads that require as low latency as possible. Use cases include flash-optimized databases, hosting cache layers for analytics or memory for any application, as well as advanced analytics and media migration. File Storage – File Store Now File Store! As a fully managed Network Attached Storage (NAS), Filestore provides a shared file system for unstructured data. It provides truly low latency, enabling simultaneous access to tens of thousands of clients with predictable scalability and performance up to hundreds of thousands of IOPS, tens of GB/s of throughput and hundreds of TBs. You can increase or decrease the capacity as needed. Common File Store use cases include High Performance Computing (HPC), Media Processing, Electronic Design Automation (EDA), Application Migration, Web Content Management, Life Science Data Analysis and more! Conclusion This is a brief overview of the different storage options in Google. For a closer look at each of these storage options, check out this storage options page or this video 👇
Data Storage Application
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The software consists of two parts – the storage server (target) and the storage server (driver, initiator) that is installed on each physical server (host, node). Each host can be a storage server, a storage server, or both (ie, a combined facility/infrastructure). For storage clients, volumes appear as local block devices under /dev//*. Data on a volume can be read and written by all clients simultaneously, and consistency is ensured by a synchronous replication protocol. Clients communicate with servers in parallel. including support for other operating systems/hypervisors as storage system clients via scalable and highly available iSCSI targets designed for this purpose.
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Offers a standard block tool. One or more volumes can be created using the JSON API or Volume Manager CLI. Multiple copies (copies) of data, written simultaneously across the cluster, ensure redundancy. Users can set the desired number of duplicate copies, we recommend 3 data copies.
Offers very high flexibility in volume control. Each disk added to the cluster increases the cluster’s capacity not only for new data but also for existing data. does not enforce a strict storage hierarchy related to and mirrors the underlying disk. It creates only one datastore (global namespace) that uses the full capacity and performance of the standard drive set.
In, redundancy is ensured by a synchronous replication algorithm. This can be considered a very advanced software RAID between server and rack. Consistency is ensured through consistent monitoring of data integrity. Typically, the data needed by the client resides on drives located on all servers in the cluster. This layout provides high-performance and real-time load balancing. Data location and replication can be chosen independently for each volume.
Supports various other hypervisors including VMware vSphere/ESX/ESXi, Windows Server, Hyper-V and others. This view accesses shared storage services using the iSCSI protocol. This approach provides high compatibility with any system that supports iSCSI. As this is an early build, it cannot yet run in a hyper-consistent configuration with this operating system/hypervisor.
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Currently provides native support for Linux with KVM, LXC, LVM, Docker and any other technology compatible with the Linux storage stack and emerging as a standard SAN for other hypervisors/operating systems. integrated with OpenStack, CloudStack, OpenNebula and supports OnApp, libvirt and Proxmox as well as custom cloud management solutions. It is compatible with many file systems such as B. ext4 and XFS file systems, and with any system designed to work with block devices, e.g. B. Databases and cluster file systems such as OCFS and GFS. In the case of VMware/Windows, it appears as one large block and is formatted with VMFS, NTFS or FAT.
Replacing traditional SAN storage boxes, all-flash arrays and other storage software. It offers highly reliable, scalable and high-performance block storage based on standard x86 servers. Data is “sliced” and copies are distributed across a selected number of servers or racks. This ensures high reliability, speed and quick recovery time.
In addition, it enables the implementation of integrated/integrated infrastructure solutions, referred to as hyper-convergence. Users can run computing power (virtual machines, applications, databases, etc.) on the same server as storage (). This is because it is extremely efficient as it uses only 5-10% of each server’s resources, leaving the majority of resources to run the application. This convergence of storage and compute workloads enables customers to increase utilization, significantly reduce total cost of ownership (TCO) and therefore increase return on investment (ROI). The biggest challenge for the QA department is automating functional and GUI tests, reviewing large amounts of statistical data and exporting Excel files. In addition, automated testing covers critical test cases of the application. To test stability under heavy loads
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