Common SQL recipes and best practises
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README.md

sql-cookbook

Common SQL recipes and best practises

Purpose

This repository aims to provide a number of best practises and ideas on how to construct queries that provide interesting data in a way that works well with our platform.

Use these queries as you please for educational purposes and to adjust them to suit your specific needs.

Also everyone is welcome to contribute, just fork the project and file a pull request.

Reference

These queries can be used to run within the deltaDNA platform in the data mining section. Alternatively when adding either _live or _dev to the table selection you can use these queries in your favourite DB tool via direct access as well.

Data mining reference.

Direct SQL Access reference.

The queries are executed by an HP vertica DBMS and the SQL dialect used is defined by them and described in the following documentation: HP Vertica documentation A HP Vertica documentation.

Prerequisites

We assume some basic knowledge of SQL select statements and a basic knowledge of the data structure within the DeltaDNA platform, what events you are sending in and what they denote.

In this cookbook we aim to show how SQL queries can be best used to query the data with the DeltaDNA platform. The theory applies to both the data mining feature as well as direct access.

Data Mining

Our data mining feature enables you to write an SQL query that retrieves data from the tables exposed and shows your the result on screen. These results can then be shown as a graph, a pie chart or as plain data. The queries can be stored and used in custom dashboards and email reports. You can even use data mining queries to create a list of userID's that you can then use within the platform as a target list.

Direct Access

Direct Access is the name of our direct database connection. We provide an endpoint where you can directly connect to the data warehouse as if it was a simple PostgreSQL (Postgres) database. This way you can connect via tools like Tableau, dBeaver or directly within Python or R to the data warehouse. This provides for an infinitely flexible use of the data but it does require some more work on your part. What direct access is not intended for is to mirror the data somewhere else, if you'd like to do this then please download the raw data exports from our S3 archive and use these.

Data mining and Direct Access, the differences

Data mining is part of the platform and is linked to the game and the environment. Within data mining you'll have the events table which is the table for the environment you're in (dev or live) for your game. Direct access connects to the game, this means here you don't have an events table but both the events_dev and the events_live

The data warehouse

The data warehouse consists of a Verica database, we currently expose this via direct access as a Postgres database since the SQL dialect is similar but Postgres is supported by more clients than Vertica.

Vertica is a column store database that is especially good at analytics. Obviously this is a good thing but there are some things to keep in mind.

There are a lot of columns, hence why it's also called a wide table, for the way it is stored this is not an issue since only rows that have a value in that column are stored. Naturally there will be quite a few empty values in this table. Due to all events being stored in this one table there will be a lot of rows when represented as a table.

When querying the data it makes a big difference how many columns are within scope. The columns you are not interested in should not be queried. This makes select count(*) from events usually faster than select * from events limit 1 When you select a row with all columns anyway you'll quickly find that it is hard to find the values you are actually interested since you will keep having to scroll horizontally.

Tips

Use analytic functions instead of joins

  • use case = select clientVersion (clientVersionLast)

Get the last clientVersion known for a user from the user_metrics table and join it back on to the events table by joining the events

select events.EventName, user_metrics.fieldClientVersionLast, count(distinct userId) as users
from events, user_metrics
where user_metrics.user_id = events.userId
and eventDate > current_date-14
group by events.EventName, user_metrics.fieldClientVersionLast

Instead you can use an analytic function to find the last value of the field clientVersion for that user.

with dataCTE as (select eventName, 
last_value(ClientVersion ignore nulls) over (partition by userId order by eventId) as lastClientVersion, userId
from events
where eventDate > current_date-14
)
select eventName, lastClientVersion, count(distinct userId) as users
from dataCTE
group by eventName, lastClientVersion