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#Sharding

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Sharding is a type of database partitioning that separates very large databases into smaller, faster, more easily managed parts called data shards. The word shard means a small part of a whole.

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In this article, we will run an InterSystems IRIS cluster using docker and Merge CPF files - a new feature allowing you to configure servers with ease.

On UNIX® and Linux, you can modify the default iris.cpf using a declarative CPF merge file. A merge file is a partial CPF that sets the desired values for any number of parameters upon instance startup. The CPF merge operation works only once for each instance.

Our cluster architecture is very simple, it would consist of one Node1 (master node) and two Data Nodes (check all available roles). Unfortunately, docker-compose cannot deploy to several servers (although it can deploy to remote hosts), so this is useful for local development of sharding-aware data models, tests, and such. For a productive InterSystems IRIS Cluster deployment, you should use either ICM or IKO.

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Question
· Nov 15, 2019
Question on sharding

Prior to IRIS, using ECP to share databases under heavy I/O load has known latency issues (in our environment), which pretty much restricted using shared database with relatively static or slow-changing data. In IRIS, will sharding mitigate the latency issue and allow any table to be shared?

David

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InterSystems and Intel recently conducted a series of benchmarks combining InterSystems IRIS with 2nd Generation Intel® Xeon® Scalable Processors, also known as “Cascade Lake”, and Intel® Optane™ DC Persistent Memory (DCPMM). The goals of these benchmarks are to demonstrate the performance and scalability capabilities of InterSystems IRIS with Intel’s latest server technologies in various workload settings and server configurations. Along with various benchmark results, three different use-cases of Intel DCPMM with InterSystems IRIS are provided in this report.

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