ScaleDB Cache Accelerator Server (CAS): A Game Changer for Clustered Databases

ScaleDB and Oracle RAC are both clustered databases that use a shared-disk architecture. As I have mentioned previously, they both actually share data via a shared cache, so it might be more appropriate to call them shared-cache databases.

Whether it is called shared-disk or shared-cache, these databases must orchestrate the sharing of a single set of data amongst multiple nodes. This introduces two challenges: the physical sharing of the data and the logical sharing of the data.

Physical Sharing:
Raw storage is meant to work on a 1:1 basis with a single server. In order to share that data amongst multiple servers, you need either a Network File System (NFS), which shares whole files, or a Cluster File System (CFS), which shares data blocks.

Logical Sharing:
This is specific to databases. A database may request a single block of data from the storage and then it may coordinate multiple sequential changes to that block, with only the final results being written back to the storage. The database can also discriminate between reading the data and writing the data, to facilitate parallelizing these actions.

Databases must control the logical sharing of data, in order to ensure that the database doesn’t become corrupted or inconsistent, and to ensure that it provides good performance. Because logical sharing is very specific to the database, it is something that clustered databases must handle themselves. This function is addressed by a lock manager.

Physical sharing of data requires less integration with the database logic. As such, you can use a general-purpose NFS or a CFS to provide the physical file sharing capabilities. This is what Oracle RAC does, they rely upon Oracle Cluster File System 2 (OCFS2) to provide generic physical file sharing. OCFS2 then relies upon a SAN or NAS that supports multi-attach, since all of the database nodes must share the same physical files. The NAS or SAN then handles the data duplication for high availability and other services like back-up and more.

ScaleDB takes a different approach. ScaleDB not only handles the logical data sharing—with its lock manager—but it also handles the physical data sharing with its Cache Accelerator Server (CAS). CAS connects directly to the storage and handles the sharing of that data among the database nodes. Because CAS is purpose-built for the ScaleDB database it does not need services such as membership management, which create complexity and overhead in a general purpose CFS. Furthermore, ScaleDB is able to tune the CAS, in conjunction with the lock manager, to extract superior performance.

CAS also offers additional benefits. It provides a scalable shared-cache that enables the database nodes to share via the cache, which is much faster than sharing via the disk. Furthermore, since it eliminates the need for an NFS or CFS, it enables you to work with any storage. You can choose to use local storage—inside the CAS—cloud storage, or a SAN or NAS. Many in the MySQL community balk at the high cost of SAN storage with fiber channel and switches and high-cost storage. CAS supports low-cost local storage, while providing a seamless path to high-end storage as needed. Furthermore, the CAS are deployed in pairs, so the data is mirrored. Because the data is mirrored, you have redundant storage, even when using local storage inside the servers running CAS. Because it can operate on commodity hardware and because it works with any storage, CAS is ideal for cloud computing.

In summary, clustered databases like Oracle RAC and ScaleDB must implement their own lock managers to manage the logical sharing of data amongst the database nodes. Providing a purpose-built solution for the physical sharing of the data, while not required, does provide some significant advantages over using a general purpose NFS or CFS.

Cloud Insight: HP, Dell, 3PAR, VMWare & ScaleDB

The bidding war between HP and Dell for 3PAR has created great theater. The rationale is simple, both HP and Dell want a complete set of products to sell into the new cloud space and 3PAR is the only bitsized morsel among EMC, IBM and Hitachi that addresses this space. What is the compelling advantage they offer in storage? Elasticity. 3PAR provides the ability for companies to add/remove storage in thin slices (AKA thin provisioning). How does this relate to ScaleDB? We do the exact same thing for databases in the cloud and we do it for the most popular database in the cloud, MySQL.

How does VMWare play into this? Their CEO Paul Maritz was on CNBC talking about the hybrid cloud and how companies want to run core cloud capabilities on premise and then use the public cloud providers to handle compute overflow during peak usage. This means that public cloud value to corporations, assuming Maritz is correct, is based largely on their ability to provide elasticity. It will no longer be sufficient for public cloud companies to provide reserved servers, because the reserved servers will be run in the company’s data center. The public cloud will add/remove servers to handle peaks in usage. So elasticity is EVERYTHING. ScaleDB is all about elasticity for the database.

It is also interesting to note from the Maritz interview that he sees the next wave of cloud (and hence the next wave of cloud consolidation) coming from the software sector. More specifically, the ability to take existing applications and make them run on the cloud. In other words, to make them elastic. Again, this is exactly what ScaleDB does. We take existing MySQL applications and make them elastic.

It is also interesting to note that HP and Dell have decimated their own R&D and are now looking to acquire that expertise from outside, and they are willing to pay for the expertise.

Another theme playing out in the background makes this situation even more interesting. Oracle has adopted a systems approach, where they combine their hardware and software:

“The heart of the interview focused on Oracle's interest in Sun. By combining Sun's expertise in hardware with Oracle's software, Ellison suggested, the combined company can become a powerful "systems" company that sells solutions to businesses. The competitor that Ellison wants to beat: IBM.”

Summary: Cloud is the next battle ground. It all starts with the hardware/infrastructure (e.g. 3PAR) and then moves upstream to software. Oracle will be focused on selling complete systems, alienating HP & Dell, among others. This is compounded by the fact that HP and Dell have decimated their R&D, so they are forced to partner/acquire. At the same time, if Maritz’s vision of public clouds becoming effectively excess capacity for handling peaks from corporations is realized, then elasticity in the cloud will become critical as well. This obviously plays to ScaleDB’s strengths.

Comparing ScaleDB’s Shared Cache Tier vs. NFS and CFS

Prior posts addressed the performance benefits of a shared cache tier (ScaleDB CAS) and also the storage flexibility it enables.This post compares the ScaleDB CAS purpose-built file storage sharing system against off-the-shelf solutions like NFS and various cluster file systems (CFS).

When using a clustered database, like ScaleDB, each node has full access to all of the data in the database. This means that the file system (SAN, NAS, Cloud, etc.) must allow multiple nodes to share the data in the file system.

Options include:
1. Network File System (NFS)
2. Cluster File System (CFS)
3. Purpose-built file storage interface

Locking Granularity:
I won’t get deeply into the nuances of CFS (block-level ) and NFS (file-level, but you can address within the file), suffice it to say that generally speaking NFS and CFS will allow you operate on blocks of data, which are typically 8KB. Let’s say you want to operate on a record that is 200 bytes within an 8KB block. You are locking 8KB instead of 200 bytes, or 40X more than necessary.

ScaleDB’s CAS uses a purpose-built interface to storage that is optimized to leverage insight from the cluster lock manager. This enables it to lock the storage on the record level. In situations where multiple nodes are concurrently accessing data from the same block, this can be a significant performance advantage. This reduces the contention between threads/nodes enabling superior performance and nodal scalability.

Intelligent Control of RAM vs. Disk:
When writing data to storage, you can either flush it directly to disk or you can store it in cache, allowing the disk flushing to occur later, outside of the transaction. Some things, like log writing require the former, while other things work just fine (and faster) with the latter. Unfortunately, generic file systems like NFS and CFS are not privy to this insight, so they must err on the side of caution and flush everything to disk inside the transaction.

ScaleDB’s CAS is privy to the intelligence inside the database. It is therefore able to push more data into cache for improved performance. Furthermore, this optimization can be configured by users, based on their own requirements. The net result is superior performance.

Conclusion:
As general purpose solutions, NFS and CFS cannot benefit from the insight and intelligence from the internal operation of the database. Instead, NFS and CFS must act in a generalized manner. ScaleDB’s Cluster Accelerator Server (CAS), leverages insight gleaned from the cluster lock manager, and from user configurations, to optimize its interaction with storage. This makes CAS more efficient and scalable, and it improves performance.

Shared Cache Tier & Storage Flexibility

Any time you can get two for the price of one (a “2Fer”), you’re ahead of the game. By implementing our shared cache as a separate tier, you get (1) improved performance and (2) storage flexibility…a 2Fer.

What do I mean by storage flexibility? It means you can use enterprise storage, cloud storage or PC-based storage. Other shared-disk cluster databases require high-end enterprise storage like a NAS or SAN. This requirement was driven by the need for:

1. High-performance storage
2. Highly available storage
3. Multi-attach, or sharing data from a single volume of LUN across multiple nodes in the cluster.

Quite simply, you won’t see other shared-disk clustering databases using cloud storage or PC-based storage. However, the vast majority of MySQL users rely on PC-based storage, and most are not willing to pay the big bucks for high-end storage.

ScaleDB’s Cache Accelerator Server (CAS) enables users to choose the storage solution that fits their needs. See the diagram below:

Because all data is mirrored across paired CAS servers, it delivers high-availability, because if one fails the other continues running. Built-in recovery completes the HA solution. If you want further reassurance you can use a third CAS as a hot standby. This means that you can use the internal hard drives on your CAS on servers to provide highly-available storage.

The next post in this series on CAS will compare ScaleDB CAS, Network File System (NFS) and Cluster File System (CFS).

Database Architectures & Performance II

As described in the prior post, the shared-disk performance dilemma is simple:

1. If each node stores/processes data in memory, versus disk, it is much faster.
2. Each node must expose the most recent data to the other nodes, so those other nodes are not using old data.

In other words, #1 above says flush data to disk VERY INFREQUENTLY for better performance, while #2 says flush everything to disk IMMEDIATELY for data consistency.

Oracle recognized this dilemma when they built Oracle Parallel Server (OPS), the precursor to Oracle Real Application Cluster (RAC). In order to address the problem, Oracle developed Cache Fusion.

Cache fusion is a peer-based shared cache. Each node works with a certain set of data in its local cache, until another node needs that data. When one node needs data from another node, it requests it directly from the cache, bypassing the disk completely. In order to minimize this data swapping between the local caches, RAC applications are optimized for data locality. Data locality means routing certain data requests to certain nodes, thereby enjoying a higher cache hit ratio and reducing data swapping between caches. Static data locality, built into the application, severely complicates the process of adding/removing nodes to the cluster.

ScaleDB encountered the same conflict between performance and consistency (or RAM vs. disk). However, ScaleDB’s shared cache was designed with the cloud in mind. The cloud imposes certain additional design criteria:

1. The number of nodes will increase/decrease in an elastic fashion.
2. A large percentage of MySQL users will require low-cost PC-based storage

Clearly, some type of shared-cache is imperative. Memcached demonstrates the efficiency of utilizing a separate cache tier above the database, so why not do something very similar beneath the database (between the database nodes and the storage)? The local cache on each database node is the most efficient use of cache, since it avoids a network hop. The shared cache tier, in ScaleDB’s case, the Cache Accelerator Server (CAS), then serves as a fast cache for data swapping.

The Cluster Manager coordinates the interactions between nodes. This includes both database locking and data swapping via the CAS. Nodes maintain the data in their local cache until that data is required by another node. The Cluster Manager then coordinates that data swapping via the CAS. This approach is more dynamic, because it doesn’t rely on a prior knowledge about the location of that data. This enables ScaleDB to support dynamic elasticity of database nodes, which is critical for cloud computing.

The following diagram describes how ScaleDB’s Cache Accelerator Server (CAS) is implemented.

While the diagram above shows a variety of physical servers, these can be virtual servers. An entire cluster, including the lock manager, database nodes and CAS could be implemented on just two physical servers.

Both ScaleDB and Oracle rely upon a shared cache to improve performance, while maintaining data consistency. Both approaches have their relative pros and cons. ScaleDB’s tier-based approach to shared cache is optimized for cloud environments, where dynamic elasticity is important. ScaleDB’s approach also enables some very interesting advantages in the storage tier, which will be enumerated in subsequent posts.

Database Architectures & Performance

For decades the debate between shared-disk and shared-nothing databases has raged. The shared-disk camp points to the laundry list of functional benefits such as improved data consistency, high-availability, scalability and elimination of partitioning/replication/promotion. The shared-nothing camp shoots back with superior performance and reduced costs. Both sides have a point.

First, let’s look at the performance issue. RAM (average access time of 200 nanoseconds) is considerably faster than disk (average access time of 12,000,000 nanoseconds). Let me put this 200:12,000,000 ratio into perspective. A task that takes a single minute in RAM would take 41 days in disk. So why do I bring this up?

Shared-Nothing: Since the shared-nothing database has sole ownership of its data—it doesn’t share the data with other nodes—it can operate in the machine’s local RAM, only writing infrequently to disk (flushing the data to disk). This makes shared-nothing databases very fast.

Shared-Disk: Cannot rely on the machine’s local RAM, because every write by one node must be instantly available to the other nodes, to ensure that they don’t use stale data and corrupt the database. So instead of relying on local RAM, all write transactions must be written to disk. This is where the 1 minute to 41 days ratio above comes into play and kills performance of shared-disk databases.

Let’s look at some of the ways databases can utilize RAM instead of disk to improve performance:

Read Cache: Databases typically use the RAM as a fast read cache. Upon reading data from the disk, this data is stored in the read cache so that subsequent use of that data is satisfied from RAM instead of the disk. For example, upon reading a person’s name from disk, that name is stored in the cache for fast access. The database wouldn’t need to read that name from disk again until that person’s name is changed (rare), or that RAM space is reused for a piece of data that is used more frequently. Read cache can significantly improve database performance.

BOTH shared-disk and shared-nothing databases can exploit read cache. The shared-disk database just needs a system to either invalidate or update the data in read cache when one of the nodes has made a change. This is pretty standard in shared-disk databases.

Background Writing: Writing data to the disk is by far the most time consuming process in a write transaction. During the transaction, that portion of the data is locked, meaning it is unavailable for other functions. So, if you can move the writing of the data outside of the transaction—write the data in the background—you get faster transactions, which means less locking contention, which means faster throughput.

SHARED-NOTHING can exploit this performance enhancement, since each server owns the data in its RAM. However, shared-disk databases cannot do this because they need to share that updated data with the other database nodes in the cluster. Since the local node’s cache is not shared, in a shared-disk database, the only option is to use the shared disk to share that data across the nodes.

Transactional Cache: The next step in utilizing RAM instead of disk is to use it in a transactional manner. This means that the database can make multiple changes to data in RAM prior to writing the final results to disk. For example, if you have 100 widgets, you can store that inventory count in RAM, and then decrement it with each sale. If you sell 23 widgets, then instead of writing each transaction to disk, you update it in RAM. When you flush this data to disk, it results in a single disk write, writing the inventory number 77, instead of writing each of the 23 transactions individually to disk.

SHARED-NOTHING can perform transactions on data while it is in RAM. Once again, shared-disk databases cannot do this because you might have multiple nodes updating the inventory. Since they cannot look into each others local RAM, they must once again write each transaction to disk.

As you can see, shared-nothing databases have an inherent performance advantage. The next blog post will address how modern shared-disk databases address these performance challenges.

ScaleDB Introduces Clustered Database Based Upon Water Vapor

ScaleDB is proud to announce the introduction of a database that takes data storage to a new level, and a new altitude. ScaleDB’s patent pending “molecular-flipping technology” enables low energy molecular flipping that changes selected water molecules from H20 to HOH, representing positive and negative states that mimic the storage mechanism used on hard drive disks.

“Because we act at the molecular level, we achieve massive storage density with minimal energy consumption, which is critical in today’s data centers, where energy consumption is the primary cost,” said Mike Hogan, ScaleDB CEO. “A single thimble of water vapor provides the same storage capacity as a high-end SAN.”

The technology does have one small challenge: persistence. Clouds are not known for their persistence. ScaleDB relies on the Cumulus formation, since it is far beefier than some of those wimpy cirrus clouds. However, when deployed in the data center, the dry heat can be particularly damaging to cloud maintenance. One of the company’s patents centers around using heavy water, which resists evaporation and is therefore far more persistent than its lighter brethren. The company has already received approval from the IAEA to commercialize this technique.

This new technology considerably improves ScaleDB’s “green cred”. By greatly reducing energy consumption in data centers, it cuts their carbon footprint, leaving little more than a toeprint. Once the cloud storage—which has a 3-year half-life—is worn out, you can release it into the atmosphere. There is mingles with natural clouds making them denser and more reflective. Leading IPCC climate scientists have modeled the effects of this mingling and the scientific consensus is that it will reduce global temperatures by 5-6 degrees centigrade within 20 years (+/- 10 degrees centigrade). The company is in negotiations with Al Gore to promote this new technology, but they cannot comment on these negotiations because the mere fact that such negotiations are in fact happening is covered by a strict NDA and the even more legally binding pinky promise.

ScaleDB set out to become THE cloud database company and today’s announcement really takes that to a whole new level. The tentative name for this new database is VaporWare.