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Tutorial: Word Counts

We will build a simple word counter, which is a great introduction to Quix Streams and Kafka!

You'll learn how to:

  • Create a topic
  • Do simple event alterations
  • Generate multiple events from a single event
  • Filter any undesired events
  • Produce events, with new Kafka keys, to a topic

Outline of the Problem

Imagine we are a company with various products.

We want to process text reviews for our various products in real time and see what words are the most popular across all of them.

We need an application that will split reviews into individual words, and then send the counts of each individually downstream for further processing.

Our Example

We will use a simple producer to generate text to be processed by our new Word Counter application.

NOTE: our example uses JSON formatting for Kafka message values.

Event Expansion

The most important concept we want to explore here is how you can "expand" a single event into multiple new ones.

More than that: each new event generated via expansion is processed individually through the remainder of your pipeline, allowing you to write ALL your operations like they are handling a single event...because they are!

NOTE: Expanding often includes adjusting outgoing Kafka keys as well, so we additionally showcase that.

Before Getting Started

  • You will see links scattered throughout this tutorial.
    • Tutorial code links are marked >>> LIKE THIS <<< .
    • All other links provided are completely optional.
    • They are great ways to learn more about various concepts if you need it!

Generating Text Data

We have a >>> Review Producer <<< that generates a static set of "reviews", which are simply strings, where the key is the product name.

The Kafka message looks like:

# ...
{kafka_key: 'product_one', kafka_value: "WOW I love product_one. It is the best."}
{kafka_key: 'product_two', kafka_value: "Fire the creator of product_two please."}
# etc...

Word Counter Application

Now let's go over our >>> Word Counter Application <<< line-by-line!

Create Application

app = Application(
    broker_address=os.environ.get("BROKER_ADDRESS", "localhost:9092"),

First, create the Quix Streams Application, which is our constructor for everything! We provide it our connection settings, consumer group (ideally unique per Application), and where the consumer group should start from on our topic.

NOTE: Once you are more familiar with Kafka, we recommend learning more about auto_offset_reset.

Define Topics

product_reviews_topic = app.topic(name="product_reviews")
word_counts_topic = app.topic(name="product_review_word_counts")

Next we define our input/output topics, named product_reviews and product_review_word_counts, respectively.

They each return Topic objects, used later on.

NOTE: the topics will automatically be created for you in Kafka when you run the application should they not exist.

The StreamingDataFrame (SDF)

sdf = app.dataframe(topic=product_reviews_topic)

Now for the fun part: building our StreamingDataFrame, often shorthanded to "SDF".

SDF allows manipulating the message value in a dataframe-like fashion using various operations.

After initializing, we continue re-assigning to the same sdf variable as we add operations.

(Also: notice that we pass our input Topic from the previous step to it.)

Tokenizing Text

def tokenize_and_count(text):
    words = Counter(text.lower().replace(".", " ").split()).items()
    return words

sdf = sdf.apply(tokenize_and_count, expand=True)

This is where most of the magic happens!

We alter our text data with SDF.apply(F) (F should take your current message value as an argument, and return your new message value): our F here is tokenize_and_count.

Basically we do some fairly typical string normalization and count the words, resulting in (word, count) pairs.

This effectively turns this:

>>> "Bob likes bananas and Frank likes apples."

to this:

>>> [('bob', 1), ('likes', 2), ('bananas', 1), ('and', 1), ('frank', 1), ('apples', 1)]

NOTE: two VERY important and related points around the expand=True argument:

  1. it tells SDF "hey, this .apply() returns multiple independent events!"

  2. Our F returns a list (or a non-dict iterable of some kind), hence the "expand"!

Filtering Expanded Results

def should_skip(word_count_pair):
    word, count = word_count_pair
    return word not in ['i', 'a', 'we', 'it', 'is', 'and', 'or', 'the']

sdf = sdf.filter(should_skip)

Now we filter out some "filler" words using SDF.filter(F), where F is our should_skip function.

For SDF.filter(F), if the (boolean-ed) return value of F is:

  • True -> continue processing this event

  • False -> stop ALL further processing of this event (including produces!)

Remember that each word is now an independent event now due to our previous expand, so our F expects only a single word count pair.

With this filter applied, our "and" event is removed:

>>> [('bob', 1), ('likes', 2), ('bananas', 1), ('frank', 1), ('apples', 1)]

Producing Events With New Keys

sdf = sdf.to_topic(word_counts_topic, key=lambda word_count_pair: word_count_pair[0])

Finally, we produce each event downstream (they will be independent messages) via SDF.to_topic(T), where T is our previously defined Topic (not the topic name!).

Notice here the optional key argument, which allows you to provide a custom key generator.

While it's fairly common to maintain the input event's key (SDF's default behavior), there are many reasons why you might adjust here (NOTE: advanced concept below)!

We are changing the message key to the word; this data structure enables calculating total word counts over time from this topic (with a new application, of course!).

In the end we would produce 5 messages in total, like so:

# two shown here...
{kafka_key: "bob", kafka_value: ["bob", 1]}
{kafka_key: "likes", kafka_value: ["likes", 2]}
# etc...

Try it yourself!

1. Run Kafka

First, have a running Kafka cluster.

To conveniently follow along with this tutorial, just run this simple one-liner.

2. Install Quix Streams

In your python environment, run pip install quixstreams

3. Run the Producer and Application

Just call python and python in separate windows.

4. Check out the results!

Look at all those counted works, beautiful!

If you were interested in learning how to aggregate across events as we hinted at with our key changes, check out how easy it is with either SDF's stateful functions or windowing, depending on your use case!