This page describes a tutorial to aggregate data Kafka topic data into a stream using Lenses SQL Processors
In this tutorial we will see how data in a stream can be aggregated continuously using GROUP BY and how the aggregated results are emitted downstream.
In Lenses SQL you can read your data as a STREAM and quickly aggregate over it using the GROUP BY clause and SELECT STREAM
Let’s assume that we have a topic (game-sessions) that contains data regarding remote gaming sessions by users.
Each gaming session will contain:
the points the user achieved throughout the session
Metadata information regarding the session:
The country where the game took place
The language the user played the game in
The above structure represents the value of each record in our game-sessions topic.
Additionally, each record will be keyed by user information, including the following:
A pid, or player id, representing this user uniquely
Some additional denormalised user details:
a name
a surname
an age
Keep in mind this is just an example in the context of this tutorial. Putting denormalised data in keys is not something that should be done in a production environment.
In light of the above, a record might look like the following (in json for simplicity):
We can replicate such structure using SQL Studio and the following query:
We can then use SQL Studio again to insert the data we will use in the rest of the tutorial:
Now we can start processing the data we have inserted above.
One requirement could be to count how many games each user has played. Additionally, we want to ensure that, should new data come in, it will update the calculations and return the up to date numbers.
We can achieve the above with the following query:
The content of the output topic, groupby-key, can now be inspected in the Lenses Explore screen and it will look similar to this:
As you can see, the keys of the records did not change, but their value is the result of the specified aggregation.
You might have noticed that groupby-key has been created as a compacted topic, and that is by design.
All aggregations result in a Table because they maintain a running, fault-tolerant, state of the aggregation and when the result of an aggregation is written to a topic, then the topic will need to reflect these semantics (which is what a compacted topic does).
We can expand on the example from the previous section. We now want to know, for each user, the following:
count how many games the user has played
what are the user’s best 3 results
what is the user’s average of points
All the above can be achieved with the following query:
The content of the output topic, groupby-key-multi-aggs, can now be inspected in the Lenses Explore screen, and it will look similar to this:
Our analytics skills are so good that we are now asked for more. We now want to calculate the same statistics as before, but grouping together players that played from the same country and used the same language.
Here is the query for that:
The content of the output topic, groupby-country-and-language, can now be inspected in the Lenses Explore screen and it will look similar to this:
Notice how we projected sessionMetadata.language as sessionLanguage in the query.
We could do that because sessionMetadata.language is part of the GROUP BY clause.
Lenses SQL only supportsas Full Group By mode, so if the projected field is not part of the GROUP BY clause, the query will be invalid.
One final scenario we will cover in this tutorial is when we want to filter some data within our aggregation.
There are two possible types of filtering we might want to do, when it comes to aggregations:
Pre-aggregation: we want some rows to be ignored by the grouping, so they will not be part of the calculation done by aggregation functions. In these scenarios we will use the WHERE clause.
Post-aggregation: we want to filter the aggregation results themselves, so that those aggregated records which meet some specified condition are not emitted at all. In these scenarios we will use the HAVING clause.
Let’s see an example.
We want calculate the usual statistics from the previous scenarios, but grouping by the session language only.
However, we are interested only in languages that are used a small amount of times (we might want to focus our marketing team’s effort there); additionally, we are aware that some users have been using VPNs to access our platform, so we want to exclude some records from our calculations, if a given user appeared to have played from a given country.
For the sake of this example, we will:
Show statistics for languages that are used less than 9 times
Ignore sessions that Dave made from Spain (because we know he was not there)
The query for all the above is:
The content of the output topic, groupby-language-filtered, can now be inspected in the Lenses Explore screen and it will look similar to this:
Notice that IT (which is the only language that has 9 sessions in total) appears in the output but without any data in the value section.
This is because aggregations are Tables, and the key IT used to be present (while it was lower than 9), but then it was removed. Deletion is expressed, in Tables, by setting the value section of a record to null, which is what we are seeing here.
In this tutorial you learned how to use aggregation over Streams to:
group by the current key of a record
calculate multiple results in a single processor
group by a combination of different fields of the input record
filtering both the data that is to be aggregated, and the one that will be emitted by the aggregation itself
You achieved all the above using Lenses SQL engine.
You can now proceed to learn about more complex scenarios like aggregation over Tables and windowed aggregations.
Good luck and happy streaming!
