Hive – Horizontal scaling for Alfresco systems.

Welcome to Hive! A platform that allows horizontal scaling for Alfresco systems.

It’s a known fact that managing multiple smaller environments is more performant than having one large environment. Forget about those sleepless nights trying to rebuild your statistics or defragment your indexes.

By replacing one big repository with several smaller ones, the HIVE has the ability to make Large Environments more responsive while spending less money. The HIVE allows the handling of zillions of documents, reducing some known problems on large Alfresco deployments.

And the best?! All processes happen in the backend. So, the user will always view it as a Single Big Repository. Compatible with all existing Alfresco clients including Share, ACA, ADW and REST API. The Hive head is the single point of entry so it can be seen as any standalone Alfresco server.

1) Reducing maintenance costs of handling a big environment

Using a Big Deployment, user will face performance issues on two key components: Database and Search Engine. The bigger the environment, the higher the maintenance’s need for those components. By using more smaller environments, you don’t need to spend so much time maintaining the infrastructure. Taking Kubernetes’ view on Pods, stop treating those components has world-prized, high-maintenance pets: treat them as pets, gaining free time for yourself.

2) Distribution of content load by using Load Balancing algorithms

When uploading new content to HIVE, it will be saved to a specific repository. By default, HIVE will use Round-Robin based load balancing algorithm to determine which repository should store the new content. It’s possible to define new algorithms, according to the customer’s needs (by user, by department/site, etc).

3) Migration of an existing big environment to a pre-balanced HIVE

Big environments have special needs for content migration, rendering file-by-file migration solution impossible to execute. By using Texport, it will coordinate with HIVE to map which files are going to which repository. By load balancing before migration, it’s possible to adopt better migration techniques (such as in-place migration).

4) Faster searches

By using independent repositories, HIVE will parallelize search requests to several repositories. Each repository will have smaller datasets, therefore will be faster. HIVE aggregates all responses into a single response. Also, SpeedySearch is compatible with HIVE, bringing search performance to a higher level, by using no-sql solutions.

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