Problems

Apache MetaModel use by clients to abstract their DB access. No doubt in this that it works great and efficient for SQL DB (JDBC DB’s). But the problem that was faced by many clients is that it was not that much suitable for Cassandra, HBase and other NoSQL DBs. There was a need to create an abstraction that can allow some implementation specific choices that based on DB type.

There was another problem, and that is there was a need to create such code that at the end give such results that look similar as metamodel result set structure.

The project can only be considered as successful only if we use CRUD functions for the demonstration for Postgres, Oracle, SQL Server, Cassandra and HBase with performance information indicated.

Need

The need is Cassandra and HBase need to be tested and populated with at least 100k rows to validate Read/Select performance. For having the solution of above problems, there was need of expert Java development team that can handle this problem very quickly.

Design

Metadata abstraction function (beans) develop by us have CRUD functions along with any data context elements and table creation/dropping/modification. The design of coding recently drawn up by us having the same results (MM Dataset) matched with the MM supports.

Reads

This is the coding that we implemented within these functions. It helped us to avail the facility to get tenant_id from the text.

Solution

For the better solution of 2 databases that including the increased performance and the results according to the application was needed. We have applied R&D on the existing code for getting the performance credibility and efficiency. This is one of the main reason of changing in metamodel application. However, the SQL contexts other than tenant-id is much straight MM. In simple words, there is no need of NoSQL DBs for implementations.

Also, the clients cannot use Phoenix driver for HBase and custom driver for Cassandra for filtering. So it is important to prove our performance. For this, we use 1 SQL DB (Postgres or My SQL) and HBase and Cassandra with 100k rows of data at least in it. With the help of these changes now we can perform stats with the help of CRUD quires against these functions.

The composite data context is another challenge. So we handled this as well up to almost 2 connections. The Cassandra and HBase custom DB extensions are developed by using plug-in mentality.

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