您也会得到关于分片和数据包顺序的信息。
You will also get information about the fragmentation and packet sequence.
您可能听说过IP分片。
现在我们定义应用程序中要使用的分片策略。
Now we define the shard strategies to be used in the application.
支持自动地基于hash函数的数据分片策略。
Automatic, hash-function based data partitioning and distribution.
原子单元中的所有记录都会存储在相同的分片上。
上面描述的分片引擎使用应用程序提供的分片策略。
The sharding engine described above USES the sharding strategy provided by the application.
到目前为止，分片框架不能用于HQL，它只能用于SQL。
As of now, sharding framework cannot be used with HQL. It can only be used with SQL.
数据库分片是指对数据库进行水平分区，其中每个分区称为一个碎片。
Database sharding refers to the horizontal partitioning of a database with each individual partition called a shard.
当然你可以说出许多技巧来降低数据库的负载，比如利用缓存，数据分片等等。
There's a lot of things I can say about using caching, sharding, and other techniques to take load off the database.
与匀质型的体系结构相反，在分层型或分片型体系结构中，玩家连接到一个服务器层。
In contrast to the homogeneous-type architecture, players within the tiered - or shard-type architecture connect to a layer of servers.
幸运的是，OStcp层可以负责将协议数据划分为避免IP分片的tcp报文段。
The OS TCP layer fortunately takes care of splitting the protocol data into TCP segments that avoid IP fragmentation.
此外，调整分片的过程应该是个离线的过程，因为对物理设施进行修改时还需要对键进行调整。
Additionally, the process of rebalancing shards should be an offline process due to key rebalancing as the physical infrastructure is modified.
无论如何，集群、复制和分片都具有优缺点，在开始采用一种具体战略时应该认真权衡它们。
Regardless, clustering, replication, and sharding each have pros and cons that should be weighed heavily before embarking on an individual strategy.
当然，Scala支持局部套用，如清单8展示的 Scala文档的部分片段所示
Of course, Scala supports currying, as illustrated by the snippet from the Scala documentation shown in Listing 8
分片和重组的代价非常高，并且尽管仅由中间路由器执行这项任务，但是它对吞吐量有很大的影响。
Fragmentation and reassembly are a costly affair and, though this is performed only by intermediate routers, it has severe impact on throughput.
Jan提醒说这个技巧还可以用来实现其他的数据分布策略，例如分片(Sharding)。
Jan notes that the technique can also be used to implement other data distribution strategies, such as sharding.
在微软的分片模式中，“分片键”用于将数据映射到特定的分片上，后者就是一个数据实体的主键。
In the Microsoft sharding pattern a "sharding key" is used to map data to specific shards which is the primary key in one of the data entities.
MongoDB会自动将查询引导到合适的分片上，因此应用程序并不需要了解哪个分片持有什么数据元素。
Directing a query to the appropriate shards is handled automatically, so applications do not need to keep track of which shard holds what data elements.
微软将在2011年发布SQLAzureFederations，它将支持数据库级别的分片。
Microsoft will release SQL Azure Federations which will support sharding at the database level in 2011.
使用这种方法，您至少可以了解(或者猜测)在不对ip层进行分片的情况下可能通过的tcp报文段的大小。
Using this method, you can at least know (or guess, rather) what TCP segment sizes are likely to be passed through without getting fragmented at the IP level.
数据库分片提供许多优势，比如对数据库的更快读写、改进的搜索响应、更小的表格尺寸以及对表格的按需分配。
Database sharding provides many advantages, such as faster reads and writes to the database, improved search response, smaller table sizes, and distribution of tables based on need.
每个分片都是一组配置成Replica集的机器，这意味着分片里的每台机器都拥有分片数据的完整拷贝。
Each shard is a group of machines set up as a replica set, meaning that each machine in the shard has a full copy of the shard's data.
例如，虚拟化是其中一种方式，采用此方式时，计算机会被分片，以得到最佳的性能，尽管这些方式也有其各自的问题和局限性。
For example, virtualization is one way in which computers can be sliced to get the best performance, although they have their own issues and limitations.
合并排序本身并非并行算法，因为它可以顺序执行。当数据集太大，内存无法容纳，必须分片保存的时候，经常使用合并排序。
Merge-sort is not an inherently parallel algorithm, as it can be done sequentially, and is popular when the data set is too large to fit in memory and must be sorted in pieces.
允许使用Hibernate、但尚不需要分片的SaaS应用程序采用解决方案，而无需重大重构，前提是它们确实达到了这个阶段。
Allows existing SaaS applications which use Hibernate, but do not yet need sharding, to adopt the solution without major refactoring if and when they do reach this stage.
另外，Domino7中的各种跨平台的改进还可以节省 10% 到 20% 的处理器资源（例如快速池的实现和内存分片）。
In addition, various cross-platform improvements in Domino 7 contributed another 10 to 20 percent of processor savings (such as quick pool implementation and memory fragmentation).
下面的代码并没有先请求所有的记录，然后对所需要的记录进行分片，而是在实际的查询中使用了5作为OFFSET、10作为LIMIT，这可以极大地提高性能。
Rather than request all the records and then slice the records needed, the code below USES an OFFSET of 5 and a LIMIT of 10 in the actual SQL query, greatly improving performance.
我们将使用HibernateShards (Hibernate的一个扩展)解决SaaS应用程序的数据库扩展问题，并向Hibernate用户提供分片的功能。
We will address the database scaling issues of SaaS applications using Hibernate Shards, which is an extension of Hibernate, and makes available the features of sharding to Hibernate users.
我们将使用HibernateShards (Hibernate的一个扩展)解决SaaS应用程序的数据库扩展问题，并向Hibernate用户提供分片的功能。
We will address the database scaling issues of SaaS applications using Hibernate Shards, which is an extension of Hibernate, and makes available the features of sharding to Hibernate users.
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