Showing posts with label does. Show all posts
Showing posts with label does. Show all posts
Sunday, December 4, 2016
How long does it take to detect array parameters
How long does it take to detect array parameters
RAID recovery time
RAID recovery time depends on both the time required to read a sample of data from a disk set and the time for analyzing the data sample and determining the array configuration parameters. However, read and analysis speeds depend on many other aspects and the resulted RAID recovery time will be limited by the worst of the components.
Statistical analysis time
The RAID recovery time depends directly on the data being analyzed. Based on some data the confident decision can be obtained immediately, while the other data can be absolutely useless for determining the parameters.
On top of that, it is practically beneficial to scan the disk not in a continuous pattern. Instead, the samples are picked randomly from a disk set.
These two factors together lead to the fact that the RAID recovery time can be different each time you run ReclaiMe Free RAID Recovery. In a successful scenario, the RAID can be recovered in minutes, in the worst case it will take several hours to complete recovery.
Disk throughput
RAID recovery involves reading a lot of data. In worst case, the entire disk set is to be read three times - for the RAID analysis, then for the filesystem analysis, and then when the files are copied out. The hardware setup affects the read speed significantly. Reconfiguring the storage subsystem can sometimes reduce recovery time by the factor of ten.
The following considerations apply:
- If you connect the disk to a SATA port on motherboard, you get 30 to 50 MB/sec throughput. A modern hard drive can deliver data faster, but realistic processing speed does not exceed 50 MB/sec with a rotational hard drive. The throughput is defined per SATA port, meaning that if you have two drives connected to SATA ports, data is processed twice as fast as long as CPU can handle it.
- If you use USB, make sure everything works in at least USB 2.0 mode. USB 1.1 is unacceptably slow for data recovery use; however, the USB 1.1 hubs are still produced. USB 3.0 devices are not very stable as of spring 2012. You should probably avoid USB 3.0 hubs till at least 2013. Anyway, if something goes wrong with an USB 3.0 device, it falls back to USB 2.0 mode, so you dont lose much.
- USB 2.0 provides about 15 MB/sec throughput per root port. If you connect several hard drives to an USB hub, their combined throughput is limited to 15 MB/sec.
- If you use multiple disk image files during RAID recovery, you should evenly distribute these image files across as many physical hard drives as possible. There is a singificant penalty associated with mechanical disk seeks if you put multiple images onto one hard drive.
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Monday, November 14, 2016
RAID 2 RAID 3 RAID 4 What are the characteristics and how does it work And its history
RAID 2 RAID 3 RAID 4 What are the characteristics and how does it work And its history
RAID 2, RAID 3, RAID 4 – what it is, how it works? The history lesson
After RAID 0 and RAID 1 (with RAID 1+0 and RAID 0+1) it is time for RAID 2, 3 and 4. Here we present a small description of the mentioned levels. We hope it will outline a picture of the functionality of these solutions. Although the article is a kind of history lesson – these solutions are no longer used – it is good to be aware of the origins of modern storage technologies.
RAID 2 – the bit-level striping with dedicated Hamming-code parity
In the case of RAID 2 all data are stripped (to the bit levels – not block). Each bit is written on a different drive/stripe. Such a solution requires the use of Hamming code for error correction.
Hamming code is a linear error-correcting code named after its inventor, Richard Hamming. Hamming codes can detect up to d – 1 bit errors, and correct (d – 1) / 2 bit errors, where d is the minimum hamming distance between all pairs in the code words; thus, reliable communication is possible when the Hamming distance between the transmitted and received bit patterns is less than or equal to d. By contrast, the simple parity code cannot correct errors, and can detect only an odd number of errors.
source
The number of discs in RAID 2 used to store information is equal to the logarithm of the number of discs that are protecting the mentioned data. All disks in RAID 2 work as one disk which has a capacity equal to the common capacity of all disks used to store data.
RAID 2
While RAID 2 is being used it is important to synchronize all disks. Such a solution requires that the controller, which makes disks, will spin at the same angular orientation – in other way the index will not be reached at the same time. Disintegration will lead to total uselessness of drives in array.
Such a requirement is not the only drawback. Also the need for long Hamming code generation may prove to be problematic by slowing the whole system down.
The mode of RAID 2 action may be hard to understand. The need for using Hamming code, special controllers for disks – it makes RAID 2 a not very popular solution. But if we think about it in a less pragmatic way, it may prove to be very interesting – mainly due to its modus operandi. It introduces many more complex solutions than RAID 0 and RAID 1. While everything works well, RAID 2 proves to be quite a good solution in area of data security. In case of HDD failure – no matter if it was the disk with data or the Hamming code – any part of the array may be reconstructed by the other disks used.
While it is interesting and it has its advantages, we have not heard about any commercial implementations of RAID 2. Solutions based on it were used only in the initial phase of RAID systems usage – before disks were equipped with their own correction code. Modern HDDs use various correction and optymalising algorithms. That is why the Hamming system has started to be less interesting in the area of professional usage and it is no longer implemented in modern controllers.
RAID 3 – another rare one in practice
RAID 3 works as RAID 0 does – it uses byte-level stripping – but it also uses an additional disk in the array. It is used to store checksums and it supports a special processor in parity codes calculating – so we may call it “the parity disk”.
RAID 3
In RAID 3, configuration data are divided into individual bytes and then saved on a disk. Parity byte is determined for each row of data and saved on the mentioned “parity disk”. In case of failure it allows to recover data by an appropriate calculation of the remaining bytes and parity bytes that correspond with them.
Although RAID 3 is rarely used in practice, it is worth pointing out its advantages. First of all is its resistance to damage of one disk in the arrangement. Secondly, high read speed. Unfortunately, it also has a couple of drawbacks.
The read speed is more than satisfactory but write speed is on the contrary – the reason being the necessity of checksums calculating (even RAID hardware controllers cannot solve this problem). She second disadvantage is a matter of disk failure. When it happens, the whole system will work much slower. What is more, although RAID 3 is resistant to breakdown (in case of failure of one disk in the array), replacing a damaged disk is very costly. A third problem is the disk used for calculating checksums – it is usually the bottleneck in the performance of the entire array.
As can be easily seen, RAID 3 is not a good, reliable or cheap solution. Therefore, as it was mentioned earlier, its use is rare in practice. Systems based on RAID 3 are mostly purposed for implementations where a small number of users refer to the very large files.
RAID 4 – smells like RAID 3 and 5
RAID 4 is very similar to RAID 3. The main difference is the way of sharing data. They are divided in to blocks (16, 32, 64 lub 128 kB) and written on disk s – similar to RAID 0. For each row of written data, any recorded block is written on a parity disk. In short this means that RAID 4 does not strip data at block levels but it uses byte levels for striping (block-level striping with a dedicated parity disk).
There are also similarities in relation to RAID 5, but it confines all parity data to a single drive. RAID 4 does not use distributed parity.
RAID 4
RAID 4 requires at least three disks for complete implementation and configuration. What is more, it also needs hardware support for parity calculations. This makes it possible to recover data by the appropriate mathematical operations.
If we asked: what is RAID 4 for? we would point out one particular need. Such a solution will work very well in the case of really large files – when sequential read and write data process is used. Using RAID 4 for small portions of data would be not a good idea. The reason is the need to carry out modifications of parity blocks for each I/O session. The need for continuous repeating of such an operation would cause large losses of time and slow down a whole system.
RAID 3 and RAID 4 solutions were replaced by RAID 5. You can read about it in an article by Janusz B?k: “How does RAID 5 work? The Shortest and Easiest explanation ever!”
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Wednesday, September 14, 2016
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Monday, September 12, 2016
Amazon does it all in cloud
Amazon does it all in cloud
Amazon does it all in cloud
Analytical DBMS: Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service.
In-memory DBMS: None. Third-party options on AWS include Altibase, SAP Hana, and ScaleOut.
Hadoop distributions: Amazon Elastic MapReduce. Third-party options include Cloudera and MapR.
Stream-processing technology: Amazon Kinesis.
Hardware/software systems: Not applicable.
Analytical DBMS: Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service.
In-memory DBMS: None. Third-party options on AWS include Altibase, SAP Hana, and ScaleOut.
Hadoop distributions: Amazon Elastic MapReduce. Third-party options include Cloudera and MapR.
Stream-processing technology: Amazon Kinesis.
Hardware/software systems: Not applicable.
Amazon Web Services hosts a whos who list of data-management services from third-party players -- Cloudera, Microsoft, Oracle, SAP, and many others -- but the cloud giant has its own long-term ambitions where big-data analysis is concerned. Building on its Elastic Compute Cloud (EC2) and Simple Storage Service (S3) storage infrastructure, Amazon launched its Hadoop-based Elastic MapReduce service way back in 2009. In 2013, AWS added the Redshift Data Warehousing service (based on the ParAccel DBMS), which is supported by another whos who list of independent data-integration, business intelligence, and analytics vendors. Rounding out AWSs big-data capabilities are the DynamoDB NoSQL database management service and Kinesis Stream Processing service.
Amazons biggest appeal is clearly to organizations running data-intensive applications on its cloud. That said, leading Hadoop distributor Cloudera recently partnered with AWS, apparently reasoning that many enterprises are choosing hybrid strategies in which theyre moving selected big-data workloads into the cloud while keeping sensitive data and mission-critical workloads on-premises. Look for AWS to exploit this opportunity by adding even more options to connect with enterprise data centers.
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