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Plan Data Nodes

Plan Data Nodes

Depending on the anticipated working set of the sharded data you intend to store on the cluster and the nature of the queries you will run against it, as few as four data nodes may be appropriate for your cluster. You can always add data nodes to an existing cluster and rebalance the sharded data (see Add Data Nodes and Rebalance Data), so erring on the conservative side is reasonable.

A good basic method for an initial estimate of the ideal number of data nodes needed for a production configuration (subject to resource limitations) is to calculate the total amount of database cache needed for the cluster and then determine which combination of server count and memory per server is optimal in achieving that, given your circumstances and resource availability. This is not unlike the usual sizing process, except that it involves dividing the resources required across multiple systems. (For an important discussion of performance planning, including memory management and scaling, CPU sizing and scaling, and other considerations, see System Resource Planning and Management.)

The size of the database cache required starts with your estimation of the total amount of sharded data you anticipate storing on the cluster, and of the amount of nonsharded data on the cluster that will be frequently joined with sharded data. You can then use these totals to estimate the working sets for both sharded data and frequently joined nonsharded data, which added together represent the total database caching capacity needed for all the data nodes in the cluster. This calculation is detailed in Planning an InterSystems IRIS Sharded Cluster.

Considering all your options regarding both number of nodes and memory per node, you can then configure enough data nodes so that the database cache (global buffer pool) on each data node equals, or comes close to equalling, its share of that capacity. Under many scenarios, you will be able to roughly determine the number of data nodes to start with simply by dividing the total cache size required by the memory capacity of the systems you available to deploy as cluster nodes.

All data nodes in a sharded cluster should have identical or at least closely comparable specifications and resources; parallel query processing is only as fast as the slowest data node. In addition, the configuration of all InterSystems IRIS instances in the cluster should be consistent; database settings such as collation and those SQL settings configured at instance level (default date format, for example) should be the same on all nodes to ensure correct SQL query results. Standardized procedures and the available automated deployment methods for sharded clusters can help ensure this consistency.

Because applications can connect to any data node's cluster namespace and experience the full dataset as if it were local, the general recommended best practice is to load balance application connections across all of the data nodes in a cluster. ICM and the IKO can automatically provision and configure a load balancer for the data nodes as needed under typical scenarios; if deploying a sharded cluster by other means, a load balancing mechanism is required.

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