Materialized Views in SQL Stream Builder

Materialized Views in SQL Stream Builder

Cloudera SQL Stream Builder (SSB) offers the facility of a unified stream processing engine to non-technical customers to allow them to combine, mixture, question, and analyze each streaming and batch information sources in a single SQL interface. This permits enterprise customers to outline occasions of curiosity for which they should constantly monitor and reply rapidly.  

There are a lot of methods to distribute the outcomes of SSB’s steady queries to embed actionable insights into enterprise processes. On this weblog we are going to cowl materialized viewsa particular kind of sink that makes the output out there through REST API. 

In SSB we will use SQL to question stream or batch information, carry out some type of aggregation or information manipulation, then output the outcome right into a sink. A sink might be one other information stream or we might use a particular kind of information sink we name a materialized view (MV). An MV is a particular kind of sink that enables us to output information from our question right into a tabular format persevered in a PostgreSQL database. We are able to additionally question this information later, optionally with filters utilizing SSBs REST API. 

If we wish to simply use the outcomes of our SQL job from an exterior software, MVs are the very best and simplest way to take action. All we have to do is outline the MV on the UI interface and functions will be capable of retrieve information through REST API.

Think about, as an illustration, that we have now a real-time Kafka stream containing airplane information and we’re engaged on an software that should obtain all planes in a sure space, above some altitude at any given time through REST. This isn’t a easy job to do, since planes are consistently shifting and altering their altitudes, and we have to learn this information from an unbounded stream. If we add a materialized view to our SSB job, that can create a REST endpoint from which we can retrieve the newest outcome from our job. We are able to additionally add filters to this request, so for instance, our software can use the MV to indicate all of the planes which are flying larger than some user-specified altitude.

Creating a brand new job

An MV all the time belongs to a single job, so to create an MV we should first create a job in SSB. To create a job we can even have to create a mission first which is able to present us a Software program Growth Lifecycle (SDLC) for our functions and permits us to gather all our job and desk definitions or information sources in a central place.

Getting the information

For instance we are going to use the identical Computerized Dependent Surveillance Broadcast (ADS-B) information we utilized in different posts and examples. For reference, ADS-B information is generated and broadcast by planes whereas flying. The info consists of a airplane ID, altitude, latitude and longitude, velocity, and so forth.

To higher illustrate how MVs work, let’s execute a easy SQL question to retrieve the entire information from our stream. 

SELECT * FROM airplanes;

The creation of the “airplanes” desk has been omitted, however suffice it to say airplanes is a digital desk we have now created, which is fed by a stream of ADS-B information flowing via a Kafka matter. Please test our documentation to see how that’s achieved. The question above will generate output like the next:

As you possibly can see from the output, there are all types of attention-grabbing information factors. In our instance let’s concentrate on altitude.

Flying excessive

From the SSB Console, click on on the “Materialized View” button on the highest proper:

An MV configuration panel will open that can look much like the next:



SSB permits us to configure the brand new MV extensively, so we are going to undergo them right here.

Allow MV

For the MV to be out there as soon as we have now completed configuring it, “Allow MV” have to be enabled. This configuration additionally permits us to simply disable this function sooner or later with out eradicating all the opposite settings.

Major key

Each MV requires a main key, as this shall be our main key within the underlying relational database as properly. The important thing is among the fields returned by the SSB SQL question, and it’s out there from the dropdown. In our case we are going to select icao, as a result of we all know that icao is the identification quantity for every airplane, so it’s a good match for the first key. 


Retention and min row retention depend

This worth tells SSB how lengthy it ought to preserve the information round earlier than eradicating it from the MV database. It’s set to 5 minutes by default. Every row within the MV is tagged with an insertion time, so if the row has been round longer than the “Retention (Seconds)” time then the row is eliminated. Word, there may be additionally another methodology for managing retention, and that’s the discipline beneath the retention time, known as “Min Row Retention Depend,” which is used to point the minimal variety of rows we want to preserve within the MV, no matter how outdated the information is perhaps. For instance lets say, “We wish to preserve the final 1,000 rows regardless of how outdated that information is.” In that case we might set “Retention (Seconds)” to 0, and set “Min Row Retention Depend” to 1,000.

For this instance we won’t change the default values.

API key

As talked about earlier, each MV is related to a REST API. The REST API endpoint have to be protected by an API Key. If none has been added but, one may be created right here as properly.


Lastly we get to essentially the most attention-grabbing half, choosing tips on how to question our information within the MV database.

API endpoint

Clicking on the “Add New Question” button opens a pop-up that enables us to configure the REST API endpoint, in addition to choosing the information we want to question.

As we stated earlier, we have an interest within the airplane’s altitude, however let’s additionally add the power to filter the sphere altitude when calling the REST API. Our MV will be capable of solely present planes which are flying larger than some consumer specified altitude (i.e., present planes flying larger than 10,000 toes). In that case within the “URL Sample” field we might enter:


Word the {param} worth. The URL sample can take parameters which are specified inside curly brackets. Once we retrieve information for the MV, the REST API will map these parameters in our filters, so the consumer calling the endpoint can set the worth. See beneath. 

Select the information

Now it’s time to choose what information to gather as a part of our MV. The info fields we will select come from the preliminary SSB SQL question we wrote, so if we stated SELECT * FROM airplanes; the “Choose Columns” dropdown may have issues like fmild, icao, lat, counter, altitude, and so forth. For our instance let’s select icao, lat, lon and altitude.


We’ve got an issue. The info fields within the stream, together with the altitude, are all of VARCHAR kind, making it infeasible to filter for numeric information. We have to make a easy change to our SQL and convert the altitude into an INT, and name it peak, to distinguish it from the unique altitude discipline. Let’s change the SQL to the next: 

SELECT *, CAST(altitude AS INT) AS peak FROM airplanes;

Now we will substitute altitude with peak, and use that to filter.


Now to filter by peak we have to map the parameter we beforehand created ({param})  to the peak discipline. By clicking on the “Filters” tab, after which the “+ Rule” button, we will add our filter.


For the “Discipline” we select peak, for the “Operator” we would like “greater_or_equal,” and for the “Worth” we use the {param} we used within the REST API endpoint. Now the MV question will filter the rows by the worth of peak being higher than the worth that the consumer would give to {param} when issuing the REST request, for instance:

That may output one thing much like the next:


Materialized views are a really helpful out-of-the-box information sink, which give for the gathering of information in a tabular format, in addition to a configurable REST API question layer on high of that that can be utilized by third get together functions.

Anyone can check out SSB utilizing the Stream Processing Community Edition (CSP-CE). CE makes growing stream processors straightforward, as it may be achieved proper out of your desktop or some other growth node. Analysts, information scientists, and builders can now consider new options, develop SQL-based stream processors regionally utilizing SQL Stream Builder powered by Flink, and develop Kafka Customers/Producers and Kafka Join Connectors, all regionally earlier than shifting to manufacturing in CDP.

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