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Concepts → SQL interface (SQLi)

About the SQL interface (SQLi)

The Analytics Service exposes all tenants in an Incorta cluster as individual databases over the SQL interface (SQLi) using the PostgreSQL protocol.

The PostgreSQL protocol supports a JDBC connection using the appropriate PostgreSQL JDBC driver. With the SQLi, an external application such as Microsoft Power BI or Tableau can query a tenant database using Structured Query Language (SQL).

Internally, the SQLi manages how to process an external SQL query. Based off the query shape, the SQLi determines if the in-memory query engine of the Analytics Service processes the query or if Apache Spark processes the query using the SQL App and Spark SQL.

About SQL query shapes

To learn more about the typical query shapes, data models, scale sizes, and benchmark queries, visit tpc.org.

Here are few that are relevant to data warehousing and analytics related benchmarks that most professional in the industry find relevant:

Name Link
TPC-DS http://tpc.org/tpcds/default5.asp
TPC-H http://tpc.org/tpch/default5.asp

Analytics Service native support for Left Outer Joins

By default, the Loader Service builds a Direct Data Map using LEFT OUTER JOINS for a table relationships in a physical schema. For this reason, the Analytics Service in-memory engine supports SQL query shapes that feature LEFT OUTER JOINS in the SQL statement.

Spark SQL

Apache Sparks supports numerous query shapes including various JOIN types natively:

  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN
  • CROSS JOIN
  • SEMI JOIN
  • ANTI JOIN

To learn more, please review Apache Spark SQL Join Syntax

External integrations often require robust JOIN support for various query shapes.

SQli ports

There are two default ports for the SQLi.

  • 5436 directs external queries to the SQLi interface
  • 5442 directs external queries to the Apache Spark

The SQLi directs a query that conform to a LEFT OUTER JOIN shape to the Analytics Service. For all other query shapes, the SQLi directs the query to Apache Spark using Spark SQL.


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