However, the system might need to limit the operations that can be performed during the reconfiguration. If shards are replicated, it might be possible to keep some of the replicas online while others are split, merged, or reconfigured.
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Oracle database partitioning Offline#
These operations can be very time consuming, and might require taking one or more shards offline while they are performed. Instead, use a hash of a customer identifier to distribute data more evenly across partitions.Ĭhoose a sharding key that minimizes any future requirements to split large shards, coalesce small shards into larger partitions, or change the schema.
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For example, using the first letter of a customer's name causes an unbalanced distribution, because some letters are more common. It's also important to ensure that a single shard does not exceed the scale limits (in terms of capacity and processing resources) of the data store.Īvoid creating "hot" partitions that can affect performance and availability. Other shards might be smaller, but each item is accessed much more frequently. Some shards might be very large, but each item has a low number of access operations. It's more important to balance the number of requests. The shards don't have to be the same size. The key must ensure that data is partitioned to spread the workload as evenly as possible across the shards. It can be difficult to change the key after the system is in operation. The most important factor is the choice of a sharding key.
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Sharding spreads the load over more computers, which reduces contention and improves performance.įigure 1 - Horizontally partitioning (sharding) data based on a partition key. Each shard holds the data for a contiguous range of shard keys (A-G and H-Z), organized alphabetically. In this example, product inventory data is divided into shards based on the product key. Horizontal partitioning (sharding)įigure 1 shows horizontal partitioning or sharding. For example, you might divide data into shards and then use vertical partitioning to further subdivide the data in each shard. These strategies can be combined, and we recommend that you consider them all when you design a partitioning scheme. For example, an e-commerce system might store invoice data in one partition and product inventory data in another. In this strategy, data is aggregated according to how it is used by each bounded context in the system. For example, frequently accessed fields might be placed in one vertical partition and less frequently accessed fields in another.įunctional partitioning. The fields are divided according to their pattern of use. In this strategy, each partition holds a subset of the fields for items in the data store. Each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers. In this strategy, each partition is a separate data store, but all partitions have the same schema. Horizontal partitioning (often called sharding). There are three typical strategies for partitioning data: For managed PaaS data stores, this consideration is less relevant, because these services are designed with built-in redundancy. Operations on other partitions can continue. If one instance fails, only the data in that partition is unavailable.
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Separating data across multiple servers avoids a single point of failure. For example, large binary data can be stored in blob storage, while more structured data can be held in a document database. Partitioning allows each partition to be deployed on a different type of data store, based on cost and the built-in features that data store offers. Match the data store to the pattern of use. For example, you can define different strategies for management, monitoring, backup and restore, and other administrative tasks based on the importance of the data in each partition. Partitioning offers many opportunities for fine-tuning operations, maximizing administrative efficiency, and minimizing cost. In some cases, you can separate sensitive and nonsensitive data into different partitions and apply different security controls to the sensitive data. Operations that affect more than one partition can run in parallel. Correctly done, partitioning can make your system more efficient. Data access operations on each partition take place over a smaller volume of data. If you divide data across multiple partitions, each hosted on a separate server, you can scale out the system almost indefinitely. When you scale up a single database system, it will eventually reach a physical hardware limit. It is not the same as SQL Server table partitioning. In this article, the term partitioning means the process of physically dividing data into separate data stores.