ScaleOut StateServer is an in-memory distributed cache across a server farm or compute grid. Unlike middleware vendors, StateServer is aims at being a very good data cache, it doesn't try to handle job scheduling as well.
StateServer is what you might get when you take Memcached and merge in all the value added distributed caching features you've ever dreamed of. True, Memcached is free and ScaleOut StateServer is very far from free, but for those looking a for a satisfying out-of-the-box experience, StateServer may be just the caching solution you are looking for. Yes, "solution" is one of those "oh my God I'm going to pay through the nose" indicator words, but it really applies here. Memcached is a framework whereas StateServer has already prepackaged most features you would need to add through your own programming efforts.
Why use a distributed cache? Because it combines the holly quadrinity of computing: better performance, linear scalability, high availability, and fast application development. Performance is better because data is accessed from memory instead of through a database to a disk. Scalability is linear because as more servers are added data is transparently load balanced across the servers so there is an automated in-memory sharding. Availability is higher because multiple copies of data are kept in memory and the entire system reroutes on failure. Application development is faster because there's only one layer of software to deal with, the cache, and its API is simple. All the complexity is hidden from the programmer which means all a developer has to do is get and put data.
StateServer follows the RAM is the new disk credo. Memory is assumed to be the system of record, not the database. If you want data to be stored in a database and have the two kept in sync, then you'll have to add that layer yourself. All the standard memcached techniques should work as well for StateServer. Consider however that a database layer may not be needed. Reliability is handled by StateServer because it keeps multiple data copies, reroutes on failure, and has an option for geographical distribution for another layer of added safety. Storing to disk wouldn't make you any safer.
Via email I asked them a few questions. The key question was how they stacked up against Memcached? As that is surely one of the more popular challenges they would get in any sales cycle, I was very curious about their answer. And they did a great job differentiation themselves. What did they say?
First, for an in-depth discussion of their technology take a look ScaleOut Software Technology, but here a few of the highlights:
Platforms: .Net, Linux, Solaris
Languages: .Net, Java and C/C++
Transparent Services: server farm membership, object placement, scaling, recovery, creating and managing replicas, and handling synchronization on object access.
Performance: Scales with measured linear throughput gain to farms with 64 servers. StateServer was subjected to maximum access load in tests that ramped from 2 to 64 servers, with more than 2.5 gigabytes of cached data and a sustained throughput of over 92,000 accesses per second using a 20 Mbits/second Infiniband network. StateServer provided linear throughput increases at each stage of the test as servers and load were added.
Data cache only. Doesn't try to become middleware layer for executing jobs. Also will not sync to your database.
Local Cache View. Objects are cached on the servers where they were most recently accessed. Application developers can view the distributed cache as if it were a local cache which is accessed by the customary add, retrieve, update, and remove operations on cached objects. Object locking for synchronization across threads and servers is built into these operations and occurs automatically.
Automatic Sharding and Load Balancing. Automatically partitions all of distributed cache's stored objects across the farm and simultaneously processes access requests on all servers. As servers are added to the farm, StateServer automatically repartitions and rebalances the storage workload to scale throughput. Likewise, if servers are removed, ScaleOut StateServer coalesces stored objects on the surviving servers and rebalances the storage workload as necessary.
High Availability. All cached objects are replication on up to two additional servers. If a server goes offline or loses network connectivity, ScaleOut StateServer retrieves its objects from replicas stored on other servers in the farm, and it creates new replicas to maintain redundant storage as part of its "self-healing" process. Uses a quorum-based updating scheme.
Flexible Expiration Policies. Optional object expiration after sliding or fixed timeouts, LRU memory reclamation, or object dependency changes. Asynchronous events are also available to signal object expiration.
Geographical Scaleout. Has the ability to automatically replicate to a remote cache using the ScaleOut GeoServer option.
Parallel Query. Perform fully parallel queries on cached objects. Developers can attach metadata or "tags" to cached objects and query the cache for all matching objects. ScaleOut StateServer performs queries in parallel across all caching servers and employs patent-pending technology to ensure that query operations are both highly available and scalable. This is really cool technology that really leverages the advantage of in-memory databases. Sharding means you have a scalable system then can execute complex queries in parallel without you doing all the work you would normally do in a sharded system. And you don't have to resort to the complicated logics need for SimpleDB and BigTable type systems. Very nice.
Pricing:
- Development Edition: No Charge
- Professional Edition: $1,895 for 2 servers
- Data Center Edition: $71,995 for 64 servers
- GeoServer Option First two data centers $14,995, Each add'l data center $7,495.
- Support: 25% of software license fee
Some potential negatives about ScaleOut StateServer:
I couldn't find a developer forum. There may be one, but it eluded me. One thing I always look for is a vibrant developer community and I didn't see one. So if you have problems or want to talk about different ways of doing things, you are on your own.
The sales group wasn't responsive. I sent them an email with a question and they never responded. That always makes me wonder how I'll be treated once I've put money down.
The lack of developer talk made it hard for me to find negatives about the product itself, so I can't evaluate its quality in production.
In the next section the headings are my questions and the responses are from ScaleOut Software.
Why use ScaleOut StateServer instead of Memcached?
I've [Dan McMillan, VP Sales] included some data points below based on our current understanding of the Memcached product. We don't use and haven't tested Memcached internally, so this comparison is based in part upon our own investigations and in part what we are hearing from our own customers during their evaluation and comparisons. We are aware that Memcached is successfully being used on many large, high volume sites. We believe strong demand for ScaleOut is being driven by companies that need a ready-to-deploy solution that provides advanced features and just works. We also hear that Memcached is often seen as a low cost solution in the beginning, but development and ongoing management costs sometimes far exceed our licensing fees.
What sets ScaleOut apart from Memcached (and other competing solutions) is that ScaleOut was architected from the ground up to be a fully integrated and automated caching solution. ScaleOut offers both scalability and high availability, where our competitors typically provide only one or the other. ScaleOut is considered a full-featured, plug-n-play caching solution at a very reasonable price point, whereas we view Memcached as a framework in which to build your own caching solution. Much of the cost in choosing Memcached will be in development and ongoing management. ScaleOut works right out of the box.
I asked ScaleOut Software founder and chief architect, Bill Bain for his thoughts on this. He is a long-time distributed caching and parallel computing expert and is the architect of ScaleOut StateServer. He had several interesting points to share about creating a distributed cache by using an open source (i.e. build it yourself) solution versus ScaleOut StateServer.
First, he estimates that it would take considerable time and effort for engineers to create a distributed cache that has ScaleOut StateServer's fundamental capabilities. The primary reason is that the open source method only gives you a starting point, but it does not include most capabilities that are needed in a distributed cache. In fact, there is no built-in scalability or availability, the two principal benefits of a distributed cache. Here is some of the functionality that you would have to build:
Scalable storage and throughput. You need to create a means of storing objects across the servers in the farm in a way that will scale as servers are added, such as creating and managing partitions. Dynamic load balancing of objects is needed to avoid hot spots, and to our knowledge this is not provided in memcached.
High availability. To ensure that objects are available in the case of a server failure, you need to create replicas and have a means of automatically retrieving them in case a server fails. Also, just knowing that a server has failed requires you to develop a scalable heart-beating mechanism that spans all servers and maintains a global membership. Replicas have to be atomically updated to maintain the coherency of the stored data.
Global object naming. The storage, load-balancing, and high availability mechanisms need to make use of efficient, global object naming and lookup so that any client can access any object in the distributed cache, even after load-balancing or recovery actions.
Distributed locking. You need distributed locking to coordinate accesses by different clients so that there are not conflicts or synchronization issues as objects are read, updated and deleted. Distributed locks have to automatically recover in case of server failures.
Object timeouts. You also will need to build the capability for the cache to handle object timeouts (absolute and sliding) and to make these timeouts highly available.
Eventing. If you want your application to be able to catch asynchronous events such as timeouts, you will need a mechanism to deliver events to clients, and this mechanism should be both scalable and highly available.
Local caching. You need the ability to internally cache deserialized data on the clients to keep response times fast and avoid deserialization overhead on repeated reads. These local caches need to be kept coherent with the distributed cache.
Management. You need a means to manage all of the servers in the distributed cache and to collect performance data. There is no built-in management capability in memcached, and this requires a major development effort.
Remote client support. ScaleOut currently offers both a standard configuration (installed as a Windows service on each web server) and a remote client configuration (Installed on a dedicated cache farm).
ASP.Net/Java interoperability. Our Java/Linux release will offer true ASP.Net/Java interop, allowing you to share objects and manage sessions across platforms. Note: we just posted our "preview" release last week.
Indexed query functionality. Our forthcoming ScaleOut 4.0 release will contain this feature, which allows you to query the store to return objects based on metadata.
Multiple data center support. With our GeoServer product, you can automatically replicate cached information to up to 8 remote data centers. This provides a powerful solution for disaster recovery, or even "active-active" configurations. GeoServer's replication is both scalable and high available.
In addition to the above, we hope that the fact ScaleOut Software provides a commercial solution that is reasonably priced, supported and constantly improved would be viewed as an important plus for our customers. In many cases, in-house and open source solutions are not supported or improved once the original developer is gone or is assigned to other priorities.
Do you find yourself in competition with the likes of Terracotta, GridGain, GridSpaces, and Coherence type products?
Our ScaleOut technology has previously been targeted to the ASP.Net space. Now that we are entering the Java/Linux space, we will be competing with companies like the ones you mentioned above, which are mainly Java/Linux focused as well.
We initially got our start with distributed caching for ecommerce applications, but grid computing seems to be a strong growth area for us as well. We are now working with some large Wall Street firms on grid computing projects that involve some (very large) grid operations.
I would like to reiterate that we are very focused on data caching only. We don't try to do job scheduling or other grid computing tasks, but we do improve performance and availability for those tasks via our distributed data cache.
What architectures your customers are using with your GeoServer product?
A. GeoServer is a newer, add-on product that is designed to replicate the contents of two or more geographically separated ScaleOut object stores (caches). Typically a customer might use GeoServer to replicate object data between a primary data center site and a DR site. GeoServer facilitates continuous (async.) replication between sites, so if site A goes offline, the other site B is immediately available to handle the workload.
Our ScaleOut technology offers 3 primary benefits: Scalability, performance & high availability. From a single web farm perspective, ScaleOut provides high availability by making either 1 or 2 (this is configurable) replica copies of each master object and storing the replica on an alternate host server in the farm. ScaleOut provides uniform access to the object from any server, and protects the object in the case of a server failure. With GeoServer, these benefits are extended across multiple sites.
It is true that distributed caches typically hold temporary, fast-changing data, but that data can still be very critical to ecommerce, or grid computing applications. Loss of this data during a server failure, worker process recycle or even a grid computation process is unacceptable. We improve performance by keeping the data in-memory, while still maintaining high availability.
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Friday, September 5, 2008 at 12:52AM