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Exasol

Decision Support Benchmark TPC-H won by #Exasol

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Using DbVisualizer to work with #Oracle, #PostgreSQL and #Exasol

As a Database Developer or Database Administrator, it becomes increasingly unlikely that you will work with only one platform.

It’s quite useful to have one single tool to handle multiple different database platforms. And that’s exactly the ambition of DbVisualizer.

As a hypothetical scenario, let’s assume you are a database admin who works on a project to migrate from Oracle to EDB Postgres and Exasol.

The goal might be to replace the corporate Oracle database landscape, moving the OLTP part to EDB Postgres and the DWH / Analytics part to Exasol.

Instead of having to switch constantly between say SQL Developer, psql and EXAplus, a more efficient approach would be using DbVisualizer for all three.

Why you cannot use #Oracle’s SQL Developer to connect to #Exasol

Many of our customers are using Oracle together with SQL Developer, so this question comes up regularly: Can we use SQL Developer also for Exasol?

Short answer is: Unfortunately not.

I tried myself to make that work with no success. Then I found this on Stackoverflow:

Jeff Smith: “No, that’s not supported. SQL Developer’s 3rd party JDBC connectivity is provided for one use case – migrations to Oracle Database.
There’s no support on that for Exasol DB, so there’s no connectivity support provided.
If you want a generic jdbc db client, that’s not Oracle SQL Developer.” [Highlighted by me]

Free online courses to learn about #Exasol

Why should you bother? Because Exasol is the fastest analytical database in the world, outperforming any competitor. Therefore, expertise about Exasol might soon be very valuable also in your company.
Free training helps us to spread the knowledge in a scalable way, empowering customers across the globe to get the best out of Exasol and supporting our rapid growth.

You can register here. The free online courses are branded as “Exacademy”:

Automatic Indexes in #Exasol

An Exasol database will automatically create, maintain and drop indexes, following the core idea to deliver great performance without requiring much administrative efforts. Like our tables, our indexes are always compressed and you don’t need to configure anything for that.

Joins between two or more tables are processed like this in Exasol: One table is full scanned (this is called the root table) and the other tables are joined using an index on their join columns.

If these indexes on the join columns are not already existing, they are automatically created during the join operation. Taking two tables t1 and t2 as an example, and a statement like

Recover dropped tables with Virtual Access Restore in #Exasol

The technique to recover only certain objects from an ordinary backup is called Virtual Access Restore. Means you create a database from backup that contains only the minimum elements needed to access the objects you request. This database is then removed afterwards.

Let’s see an example. This is my initial setup:

Understanding Partitioning in #Exasol

Exasol introduced Partitioning in version 6.1. This feature helps to improve the performance of statements accessing large tables. As an example, let’s take these two tables:

 

Accelerate your #BI Performance with #Exasol

Your BI users complain about slow performance of their analytical queries? Is this your Status Quo?

Installing an #Exasol 6.1 Cluster on VirtualBox

After having installed the latest VirtualBox version, an ISO file with the latest Exasol version has to be downloaded. The machine hosting VirtualBox should have at least 16 GB RAM and 80 GB free disk space in order to run a 2+1 Cluster with 3 data nodes and one license server. I’m doing it on my Windows 10 notebook.

Understanding Distribution in #Exasol

Exasol doesn’t need much administration but getting distribution right matters

Exasol uses a clustered shared-nothing architecture with many sophisticated internal mechanisms to deliver outstanding performance without requiring much administration. Getting the distribution of rows between cluster nodes right is one of the few critical tasks left, though. To explain this, let’s say we have two tables t1 and t2: