Storage Considerations¶
Storage considerations are a complex matter as the various options provide or restrict one’s ability to adjust the necessary parameters as the need arises. It is foremost a challenge to clearly articulate and prioritize the criteria for storage, and map the theory on to a practical implementation design.
This article intends to provide information and outline details, and sometimes opinions and recommendations, but it is not a guide to providing you with the storage solution that you want or require.
Generally, the most important considerations for storage include;
because nothing is as humiliating as losing all your data.
because nothing is more stressful than none of your data being available.
because nothing is as annoying as waiting, followed by some more waiting.
because-ENOSPCis good only when it applies to your stomach.
because your data must be available, backed up and archived.
Cost,
because you can’t buy a beer or feed a family with an empty wallet.
Storage is not a part of Kolab Groupware, in that Kolab does not ship a particular storage solution as part of the product, and it has no particular requirements for storage either.
As such, your SAN, NAS, local disk, local array of disks or network share or even the flash drive of a Raspberry Pi could be used, although the following considerations are important:
- The Kolab Groupware IMAP spool is I/O intensive (large volumes of data are read and get written).
- The IMAP spool consists of many small files.
As such, we recommend you take into account;
- The available bandwidth between the IMAP server and the storage provider, if at all on the network,
- The (network) protocol overhead, if any, should file-level read and/or write locking be required or implied.
- Atomic file operations (TODO: Explain this).
- Parallel access (such as shared mailboxes or multi-client attendance) – TODO: Explain this, too.
General Notes on Storage¶
The aforementioned considerations Redundancy, Availability, Performance, Scalability, Capacity and Cost are not all of them equally important – not to all organizations, and not to all requirements when the priorities are set out against the implied cost of the ideal solution.
They are also not mutually exclusive in that, for example, redundancy may partly address some of the availability concerns – depending on the exact nature of the final deployment of course, and backup/recovery capabilities in turn may parly address redundancy requirements.
What is deemed acceptable is another culprit – often operational cost or familiarity of staff with a particular storage solution may get in the way of an otherwise appropriate storage solution.
We believe that provided a sufficient amount of accurate information, however, you are able to make an informed choice, and that an informed choice is always better than an ill-informed one.
Redundancy¶
Storage redundancy is achieved through replication of data. It is important to understand that, as a matter of design principle, redundancy does not in and by itself provide increased availability.
How redundancy could increase availability depends on the exact implementation, and the various options for practical implementation each have their own set of implications for cases of failure and the need to, under certain circumstances, failover and/or recover.
How redundancy is achieved in an “acceptable” manner is another subject open to interpretation; it is sometimes deemed acceptable to create backups daily, and therefore potentially accept the loss of up to one day’s worth of information from live spools – which may or may not be recoverable through different means. More commonly however is to not settle for less than real-time replication of data.
While storage ultimately amounts to disks, it is important to understand that a number of (virtual) devices, channels, links and interfaces exist between an application operating data on disk [1], and the physical sectors and blocks of storage on that disk. In a way, this number of layers can be compared with the OSI model for networking – but it is not the same at all.
This section addresses the most commonly used levels at which replication can be applied.
Storage Volume Level Replication¶
When using the term storage volume level replication we mean to indicate the replication of disk volumes as a whole. A simplistic replication scenario of a data disk between two nodes could look as follows:
For a fully detailed picture of the internal structures, please see the DRBD website, the canonical experts on this level of replication.
At the time of this writing, the DRBD website has an excellent diagram on its front page, explaining why one master is active, and slaves are not:
As illustrated, normally, storage-level replication occurs in such fashion, that it can be compared with a distributed version of a RAID-1 array. This incurs limitations that need to be evaluated carefully.
In a hardware RAID-1 array, storage is physically constrained to a single node, and pairs of replicated disks are treated as one. In a software RAID-1 array, it is the operating system’s software RAID driver that can (must) address the individual disks, but makes the array appear as a single disk to all higher-level software. Here too, the disks are physically constrained to one physical node.
In both cases, a single point of control exists with full and exclusive access to the physical disk device(s), namely the interface for all higher-level software to interact with the storage.
This is the underlying cause of the storage-level replication conundrum.
To illustrate the conundrum, we use a software RAID-1 array. The
individual disk volumes that make up the RAID-1 array are not hidden
from the rest of the operating system, but more importantly, direct
access to the underlying device is not prohibited. With an example pair
sda2 and sdb2, nothing prevents you from executing mkfs.ext4
on /dev/sdb2 thereby corrupting the array – other than perhaps not
having the necessary administrative privileges.
To further illustrate, position one disk in the RAID-1 array on the other side of a network (such as is a DRBD topology, as illustrated). Since now two nodes participate in nurturing the mirrored volume, two points of control exist – each node controls the access to its local disk device(s).
Participating nodes are required to successfully coordinate their I/O with one another, which on the level of entire storage volumes is a very impractical effort with high latency and enormous overhead, should more than one node be allowed, to access the replicated device [2].
It is therefore understood that, using storage level replication;
- Only one side of the mirrored volume can be active (master), and the other side must remain passive (slave),
- The active and passive nodes therefore have a cluster solution implemented to manage application’s failover and management of the change in replication topology (a slave becomes the I/O master, the former master becomes the replication slave, and other slaves, if any, learn about the new master to replicate from),
- Failover implementations include the STONITH principle, ensuring no two nodes in parallel perform I/O on the same volume at any given time.
Warning
Storage-level replication does not protect against filesystem or payload corruption – the replication happily mirrors the “faulty” bits as it is completely agnostic to the bits’ relevance.
Warning
For the reasons outlined in this section, storage volume level replication has only a limited number of Kolab Groupware deployment scenarios for which it would be recommended – such as Disaster Recovery Failover.
Integrated Storage Protocol Level Replication¶
Integrated storage protocol level replication is a different approach to making storage volumes redundant, applying the replication on a different level.
Integrated storage protocol level replication isn’t necessarily limited to replication for the purposes of redundancy only, as it may already include parallel access controls, distribution across multiple storage nodes (each providing a part of the total storage volume available), enabling the use of cheap commodity hardware to provide the individual parts (called “bricks”) that make up the larger volume.
Additional features may include the use of a geographically oriented set of parameters for the calculation and assignment of replicated chunks of data (ie. “brick replication topology”).
Current implementations of this type of technology include GlusterFS and Ceph. Put way too simplistically, both technologies apply very smart ways of storing individual objects, sometimes with additional facilities for certain object types. How they work exactly is far beyond the scope of this document.
Both technologies however are considered more efficient for fewer larger objects, than they are for more smaller objects. Both storage solutions also tend to be more efficient at addressing individual objects directly, rather than hierarchies of objects (for listing).
This is meant to indicate that while both solutions scale up to millions of objects, they facilitate a particular I/O pattern better than the I/O pattern typically associated with a large volume of messages in IMAP spools. More frequent and very short-lived I/O against individual objects in a filesystem mounted directly causes a significant amount of overhead in negotiating the access to the objects across the storage cluster.
Both technologies are perfectly suitable for large clusters with relatively small filesystems (see Filesystems: Smaller is Better) if they are mounted directly from the storage clients. They are particularly feasible if not too many parallel write operations to individual objects (files) are likely to occur (think, for example, of web application servers and (asset-)caching proxies).
Alternatively, fewer larger objects could be stored – such as disk images for a virtualization environment. The I/O patterns internal to the virtual machine would remain the same, but the I/O pattern of the storage client (the hypervisor) is the equivalent of a single lock-and-open when the virtual machine starts.
It is therefore understood that, especially in deployments of a larger scale, one should not mount a GlusterFS or CephFS filesystem directly from within an IMAP server, as an individual IMAP mail spool consists of many very small objects each individually addressed frequently, and in short-lived I/O operations, and consider the use of these distributed filesystems for a different level of object storage, such as disk images for a virtualization environment:
In this illustration, Hypervisor #1 and Hypervisor #2 are storage clients, and replicated bricks hold the disk images of each guest.
Each hypervisor can, in parallel, perform I/O against each individual disk image, allowing (for example) both Hypervisor #1 and Hypervisor #2 to run guests with disk images for which Brick #3 has been selected as the authoritative copy.
Application Level Replication¶
Yet another means to provide redundancy of data is to use application- level replication where available.
Famous examples include database server replication, where one or more MySQL masters are used for write operations, and one or more MySQL slaves are used for read operations, and LDAP replication.
Cyrus IMAP can also replicate its mail spools to other systems, such that multiple backends hold the payload served to your users.
Availability¶
Availability of storage too can be achieved via multiple routes. In one of the aforementioned technologies, replicated bricks both available real-time and online, in a parallel read-write capacity, provided high- availability through redundancy (see Integrated Storage Protocol Level Replication).
An existing chunk of storage you might have is likely backed by a level of RAID, with redundancy through mirroring individual disk volumes and/or the inline calculation of parity, and perhaps also some spare disks to replace those that are kicked or fall out of line.
Further features might include battery-backed I/O controllers, redundant power supplies connected to different power groups, a further UPS and a diesel generator (you start up once a month, right?).
The availability features of a data center are beyond the scope of this document, but when we speak of availability with regards to storage, we intend to speak of immediate, instant, online availability with automated failover (such as the RAID array) – and more prominently, without interruption.
Multipath¶
Multipath is an enhancement technique in which multiple paths that are available to the storage can be balanced, shaped and failed over automatically. Imagine the following networking diagram:
The null situation is depicted in the previous wiring diagram. When multipath kicks in, primary vs. secondary paths will be chosen for each individual target (that is unique). However, the system maintains a list of potential paths, and continuously monitors all paths for their viability.
In the example, for Node attaching to iSCSI Target #1 results in up to 4 paths to iSCSI Target #1 – 4 paths, not 8, because the networking of Switch #1 and Switch #2 is not considered a path with iSCSI – two nodes and two send targets each.
Multipath chooses one path to the available storage:
Should one port, bridge, controller, switch or cable fail, then the I/O can fall back on to any of the remaining available paths.
As per the example, this might mean the following (with Canister #2 failing):
Performance¶
Storage Tiering¶
Storage tiering includes the combination of different types of storage or storage volumes with different performance expectations within the infrastructure, so that a larger volume of slower, cheaper storage can be used for items that are not used that much, and/or are not that important for day-to-day operations, while a smaller volume of faster, more expensive storage can be used for items that are frequently accessed and have significant importance to everyday use.
The Kolab Groupware administrator guide has a section on using admin-tweaking-cyrus-imapd-storage-tiering to tweak Cyrus IMAP performance, to illustrate various opportunities to make optimal use of your storage.
As a general rule of thumb, you could divide
operating system disks and payload disks; the operating
system disk could hold your base installation of a node, including
everything in the root (/) filesystem, while your payload disk(s)
hold the files and directories that contain the system’s service(s)
payload (such as /var/lib/mysql/, /var/spool/imap/,
/var/lib/imap/, /var/lib/dirsrv/, etc.).
Distributing what is and what is not frequently used may be a cumbersome task for administrators. Some storage vendor’s appliances offer automated storage tiering, where some disks in the appliance are SSDs, while others are SATA or SAS HDDs, and the appliance itself tiers the storage.
A similar solution is available to Linux nodes, through dm-cache, provided they run a recent kernel.
Disk Buffering¶
Reading from a disk is considered very, very slow when compared to accessing a node’s (real) memory. While dependent on the particular I/O pattern of an application, it is not uncommon at all for an application to read the same part of a disk volume several times during a relatively short period of time.
In Kolab Groupware, for example, while a user is logged in, a mail
folder’s cyrus.index is read more frequently than it is
written to – such as when refreshing the folder view, when opening a
message in the folder, when replying to a message, etc.
It is important to appreciate the impact of memory-based buffer cache for this type of I/O on the overall performance of the environment.
Should no (local) memory-based buffer cache be available, because for example you are using a network filesystem (NFS, GlusterFS, etc.), then it is extremely important to appreciate the consequences in terms of the performance expectations.
Readahead¶
Reading ahead is a feature in which – in a future-predicting, anticipatory fashion – a chunk of storage is read in addition to the chunk of storage actually being requested.
A common application of read-ahead is to record all files accessed during the boot process of a node, such that later boot sequences can read files from disk, and insert them in to the memory-based buffer cache ahead of software actually issuing the call to read the file. The file’s contents can now be reproduced from the faster (real) memory rather then from the slow disk.
Readahead generally does not matter for small files, unless read operations work on a collective of aggregate message files. It does however matter for attached devices on infrastructural components such as hypervisors, where entire block devices (for the guest) are the files or block devices being read.
The ideal setting for readahead depends on a variety of factors and can usually only be established by monitoring an environment and tweaking the setting (followed by some more monitoring).
Scalability¶
When originally planning for storage capacity, a few things are to be taken in to account. We’ll point these out and address them later in this section.
Generically speaking, when storage capacity is planned for initially, a certain period of time is used to establish how much storage might be required (for that duration).
However, let’s suppose regulatory provisions dictate a period of 10 years of business communications need to be preserved. How does one accurately predict the volume of communications over the next 10 years?
Let’s suppose your organization is in flux, expanding or contracting as businesses do at times, or budget cuts and unexpected extra tasks to your organization might incur. Or when the organization takes over or otherwise incorporates another.
Today’s storage coming with a certain price-tag, and tomorrow’s with a different one, it can be an interesting exercise to plan for storage to grow organicly as needed, rather than make large investments to provide capacity that may only be used years from today, or not be used at all, or turn out to still not be sufficient.
One may also consider planning for the future expansion of the storage solution chosen today, possibly including significant changes in requirements (larger mailboxes).
Data Retention¶
Kolab Groupware by default does not delete IMAP spool contents from the filesystem for a period of 69 days.
This means that when a 100 users each have 1 GB of quota, the actual data footprint might grow way beyond 100 GB on disk.
Depending on the nature of how you use Kolab Groupware, a reasonable expectation can be formulated and used for calculating the likely amount of disk space used in addition to the content that continues to count towards quota.
For example, if a large amount of message traffic ends up in a shared folder that many users read messages from and respond to (such as might be the case for an info@example.org email address), then around triple the amount of traffic per month will continue to be stored on disk, plus what you decide is still current and not deleted by users (the “live size”).
User’s Groupware Folders¶
Users tend not to clean up their calendars, removing old appointments that have no bearing on today’s views/operations any longer. Kolab Groupware does not (yet) provide means to purge these items. They do however count towards a user’s quota.
Capacity¶
Regardless of the volume of storage in total, this section relates to the volume of storage allocated to any one singular specific purpose in Kolab Groupware, and capacity planning in light of that (not the layer providing the storage).
Archiving and e-Discovery notwithstanding, the largest chunks of data volume you are going to find in Kolab Groupware are the live IMAP spools.
Let each individual IMAP spool be considered a volume, or part of a volume if you feel inclined to share volumes across Cyrus IMAP backend instances. Regardless, you need a filesystem somewhere (even if the bricks building the volume are distributed) – the recommended restrictions you should put forth to the individual chunks of storage lay therein.
Saturating the argument to make a point, imagine, if you will, a million users with one gigabyte of data each. Just the original, minimal data footprint is now around and about one petabyte.
Performing a filesystem check (fsck.ext4 comes to mind) on a single one petabyte volume will be prohibitively expensive simply considering the duration of the command to complete execution, let alone successful execution, for your million users will not have access to their data while the command has not finished – again, let alone it finished successfully.
Solely therefore would you require a second copy of the groupware payload, now establishing a minimal data footprint to two petabyte.
Note
Also note that the two replicas of one petabyte would, if the replication occurs at the storage volume level, run the risk of corrupting both replicas’ filesystems.
Your requirements for data redundancy aside, filesystem checks needing to be performed at least regularly [5], if not for the level of likelihood they need to happen because actual recovery is required, should be restricted to a volume of data that enables the check to complete in a timely fashion, and possibly (when no data redundancy is implemented) even within a timeframe you feel comfortable you can hold off your users/customers while they have no access to their data [6].
For filesystem checks to need to happen regularly, is not to say that such filesystem checks require the node to be taken offline. Should you use Logical Volume Management (LVM) for example, and not allocate 100% of the volume group to the logical volume that holds the IMAP spool, than intermediate filesystem checks can be executed on a snapshot of said logical volume instead, and while the node remains online, to give you a generic impression of the filesystem’s health. Given this information, you can schedule a service window should you feel the need to check the actual filesystem.
A good article on filesystems, the corruption of data and their causes and mitigation strategies has been written up by LWN, The 2006 Linux Filesystem Workshop. This article also explains what it is a filesystem check actually does, and why it is usually configured to be ran after either a certain amount of delay or number of mounts.
Cost¶
Footnotes
| [1] | Applications may also operate on data not stored on disk at all, which is another common avenue potentially resulting in loss of data – or corruption, which is merely a type of data-loss that one may potentially be able to recover from. |
| [2] | With read operations, the other node(s) must be blocked from writing, and with write operations, the other node(s) must be blocked from reading and writing. |
| [3] | When using ClusterLVM, you would use logical volumes as disks for your guests. |
| [4] | When using GFS, you would mount the GFS filesystem partition on each hypervisor and use disk image files. |
| [5] | NFS is a set of protocols operating on the layers 5, 6 and 7 of the OSI model for networking. |
| [6] | Execute filesystem checks regularly to increase your level of confidence, that should emergency repairs need to be performed, you are able to recover swiftly. The MTBF of a stable filesystem has most often been subject to the failure of the underlying disk, with the filesystem unable to recover (in time) from the underlying disk failing (partly). |
| [7] | Online filesystem checks |