Back to top The interface of traGtor The window of traGtor is build in six sections appearing in the order you will step through a common conversion task. All sections have some short explanation and icons about their function and are made mostly self explaining.
Blog This In my first post in this three part series I talked about the need for distributed transactional databases that scale-out horizontally across commodity machines, as compared to traditional transactional databases that employ a "scale-up" design.
Simply adding more machines is a quicker, cheaper and more flexible way of increasing database capacity than forklift upgrades to giant steam-belching servers. It also brings the promise of continuous availability and of geo-distributed operation. The second post in this series provided an overview of the three historical approaches to designing distributed transactional database systems, namely: Shared Disk Designs e.
Shared Nothing Designs e. All of them have some advantages over traditional client-server database systems, but they each have serious limitations in relation to cost, complexity, dependencies on specialized infrastructure, and workload-specific performance trade-offs.
I noted that we are very excited about a recent innovation in distributed database design, introduced by NuoDB's technical founder Jim Starkey. We call the concept Durable Distributed Cache DDCand I want to spend a little write api transaction failed brightcove voll in this third and final post talking about what it is, with a high-level overview of how it works.
Storage-Centric The first insight Jim had was that all general-purpose relational databases to-date have been architected around a storage-centric assumption, and that this is a fundamental problem when it comes to scaling out.
The Durable Distributed Cache architecture inverts that idea, imagining the database as a set of in-memory container objects that can overflow to disk if necessary, and can be retained in backing stores for durability purposes. Storage-Centric may sound like splitting hairs, but it turns out that it is really significant.
The reasons are best described by example. And suppose that at some moment server 23 needs object In this case, server 23 might determine that object 17 is instantiated in memory on seven other servers.
It simply requests the object from the most responsive server. Note that as the object was in memory, the operation involved no disk IO - it was a remote memory fetch, which is orders of magnitude faster than going to disk.
You might ask about the case in which object 17 does not exist in memory elsewhere. In the Durable Distributed Cache architecture this is handled by certain servers "faking" that they have all the objects in memory. As it relates to supplying objects, these "backing store servers" behave exactly like the other servers except they can't guarantee the same response times.
So all servers in the DDC architecture can request objects and supply objects. They are peers in that sense and in all other senses. Some servers have a subset of the objects at any given time, and can therefore only supply a subset of the database to other servers.
Other servers have all the objects and can supply any of them, but will be slower to supply objects that are not resident in memory. TEs are pure in memory servers that do not need to use disks. They are autonomous and can unilaterally load and eject objects from memory according to their needs.
Unlike TEs, SMs can't just drop objects on the floor when they are finished with them; instead they must ensure they are safely placed in durable storage. For readers familiar with caching architectures, you might have already recognized that these TEs are in effect a distributed DRAM cache, and the SMs are specialized TEs that ensure durability.
Hence the name Durable Distributed Cache. Resilience to Failure It turns out that any object state that is present on a TE is either already committed to the disk i. This means that the database has the interesting property of being resilient to the loss of TEs.
You can shut a TE down or just unplug it and the system does not lose data. It will lose throughput capacity of course, and any partial transactions on the TE will be reported to the application as failed transactions.
But transactional applications are designed to handle transaction failure. If you reissue the transaction at the application level it will be assigned to a different TE and will proceed to completion. Recall that you can have as many SMs as you like.
They are effectively just TEs that secretly stash away the objects in some durable store. And, unless you configure it not to, each SM might as well store all the objects. Disks are cheap, which means that you have as many redundant copies of the whole database as you want.
In fact, as long as you have at least one TE and one SM running, you still have a running database. Resilience to failure is one of the longstanding but unfulfilled promises of distributed transactional databases. The DDC architecture addresses this directly.
Think of the TE layer as a cache.D - Empfehlung der CCITT/ITU von Special tariff principles for short transaction transmissions on the international packet switched public data networks using the fast select facility with restriction.
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