Concatenation

Question:

How do I concatenate values from multiple columns into a single one?

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Example use case:

Suppose you have a table and you need to combine 2 or more columns into a single value. In this tutorial we'll show how to use the concatenation operator to create a single value from multiple columns

Code example:





Short Answer

Select the fields you want to combine and use the + operator to concatenate them into one field:

SELECT FIRST_NAME + ' ' + LAST_NAME +
       ' purchased ' +
       CAST(NUM_SHARES AS VARCHAR) +
       ' shares of ' +
       SYMBOL AS SUMMARY

Note that concatenation only works with STRING values, so you’ll have to use a CAST operation on non-string fields as demonstrated above, otherwise your query will result in an error.

Try it

1
Initialize the project

To get started, make a new directory anywhere you’d like for this project:

mkdir concatenation && cd concatenation

Then make the following directories to set up its structure:

mkdir src test

2
Get Confluent Platform

Next, create the following docker-compose.yml file to obtain Confluent Platform:

---
version: '2'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:6.0.0
    hostname: zookeeper
    container_name: zookeeper
    ports:
      - "2181:2181"
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

  broker:
    image: confluentinc/cp-kafka:6.0.0
    hostname: broker
    container_name: broker
    depends_on:
      - zookeeper
    ports:
      - "29092:29092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0

  schema-registry:
    image: confluentinc/cp-schema-registry:6.0.0
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
      - broker
    ports:
      - "8081:8081"
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: 'broker:9092'

  ksqldb-server:
    image: confluentinc/ksqldb-server:0.11.0
    hostname: ksqldb-server
    container_name: ksqldb-server
    depends_on:
      - broker
      - schema-registry
    ports:
      - "8088:8088"
    environment:
      KSQL_CONFIG_DIR: "/etc/ksqldb"
      KSQL_LOG4J_OPTS: "-Dlog4j.configuration=file:/etc/ksqldb/log4j.properties"
      KSQL_BOOTSTRAP_SERVERS: "broker:9092"
      KSQL_HOST_NAME: ksqldb-server
      KSQL_LISTENERS: "http://0.0.0.0:8088"
      KSQL_CACHE_MAX_BYTES_BUFFERING: 0
      KSQL_KSQL_SCHEMA_REGISTRY_URL: "http://schema-registry:8081"
      KSQL_KSQL_STREAMS_AUTO_OFFSET_RESET: "earliest"

  ksqldb-cli:
    image: confluentinc/ksqldb-cli:0.11.0
    container_name: ksqldb-cli
    depends_on:
      - broker
      - ksqldb-server
    entrypoint: /bin/sh
    tty: true
    environment:
      KSQL_CONFIG_DIR: "/etc/ksqldb"
    volumes:
      - ./src:/opt/app/src
      - ./test:/opt/app/test

And launch it by running:

docker-compose up -d

3
Create the ksqlDB stream interactively using the CLI

To begin developing interactively, open up the ksqlDB CLI:

docker exec -it ksqldb-cli ksql http://ksqldb-server:8088

The first thing we do is to create a stream named ACTIVITY_STREAM. This statement creates the stock_purchases topic, since it doesn’t already exist. For more details check out the ksqlDB documentation on the CREATE STREAM statement. The data contained in the topic is just plain, schemaless JSON.

CREATE STREAM ACTIVITY_STREAM (
        	      ID VARCHAR,
                      NUM_SHARES INT,
                      AMOUNT DOUBLE,
                      TXN_TS VARCHAR,
                      FIRST_NAME VARCHAR,
                      LAST_NAME  VARCHAR,
                      SYMBOL VARCHAR )

 WITH (KAFKA_TOPIC='stock_purchases',
       VALUE_FORMAT='JSON',
       PARTITIONS=1);
 

Go ahead and create the stream now by pasting this statement into the ksqlDB window you opened at the beginning of this step. After you’ve created the stream, quit the ksqlDB CLI for now by typing exit.

4
Produce events to the input topic

Now let’s produce some records for the ACTIVITY_STREAM stream

docker exec -i broker /usr/bin/kafka-console-producer --broker-list broker:9092 --topic stock_purchases

After starting the console producer it will wait for your input. To send all send all the stock transactions click on the clipboard icon on the right, then paste the following into the terminal and press enter:

{"id": "1", "num_shares": 30000, "amount": 5004.89,"txn_ts": "2020-12-04 02:35:43", "first_name": "Art","last_name": "Vandeley", "symbol": "IMEP"}
{"id": "2", "num_shares": 500, "amount": 1000.89,"txn_ts": "2020-12-04 02:35:44", "first_name": "Nick","last_name": "Fury", "symbol": "IMEP"}
{"id": "3", "num_shares": 45729, "amount": 804.89,"txn_ts": "2020-12-04 02:35:45", "first_name": "Natasha","last_name": "Romanov", "symbol": "STRK"}
{"id": "4", "num_shares": 72848, "amount": 60040.89,"txn_ts": "2020-12-04 02:35:46", "first_name": "Wanda","last_name": "Maximoff", "symbol": "STRK"}

After you’ve sent the records above, you can close the console producer by entering CTRL+C.

5
Run the streaming report interactively with the ksqldb-cli

To begin developing interactively, open up the ksqlDB CLI:

docker exec -it ksqldb-cli ksql http://ksqldb-server:8088

Set ksqlDB to process data from the beginning of each Kafka topic.

SET 'auto.offset.reset' = 'earliest';

Then let’s adjust the column width so we can easily see the results of the query

SET CLI COLUMN-WIDTH 50

Now we write a query to concatenate multiple columns. To achieve this, we will use the + operator between the fields in our SELECT statement rather than a comma.

SELECT FIRST_NAME + ' ' + LAST_NAME +
       ' purchased ' +
       CAST(NUM_SHARES AS VARCHAR) + (1)
       ' shares of ' +
       SYMBOL AS SUMMARY
FROM ACTIVITY_STREAM
EMIT CHANGES
LIMIT 4;
1 The NUM_SHARES field is an INT so we need to cast it to a VARCHAR as concatenate only works with STRING types
You can also SELECT fields you don’t want to concatenate. In that case you use a comma to separate the field from those you concatenate. For example, you can SELECT individual fields field_1 and field_2 at the same time that you concatenate field_3 with field_4. For example SELECT field_1, field_2, field_3 + field_4

This query should produce the following output:

+--------------------------------------------------+
|SUMMARY                                           |
+--------------------------------------------------+
|Art Vandeley purchased 30000 shares of IMEP       |
|Nick Fury purchased 500 shares of IMEP            |
|Natasha Romanov purchased 45729 shares of STRK    |
|Wanda Maximoff purchased 72848 shares of STRK     |
Limit Reached
Query terminated

Now that the reporting query works, let’s update it to create a continuous query for your reporting scenario

CREATE STREAM SUMMARY_RESULTS AS
  SELECT FIRST_NAME + ' ' + LAST_NAME +
       ' purchased ' +
       CAST(NUM_SHARES AS VARCHAR) +
       ' shares of ' +
       SYMBOL AS SUMMARY
FROM ACTIVITY_STREAM;

We’re done with the ksqlDB CLI for now so go ahead and type exit to quit.

6
Write your statements to a file

Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql with the following content:

CREATE STREAM ACTIVITY_STREAM (
                      ID VARCHAR,
                      NUM_SHARES INT,
                      AMOUNT DOUBLE,
                      TXN_TS VARCHAR,
                      FIRST_NAME VARCHAR,
                      LAST_NAME  VARCHAR,
                      SYMBOL VARCHAR )

 WITH (KAFKA_TOPIC='stock_purchases',
       VALUE_FORMAT='JSON',
       PARTITIONS=1);


CREATE STREAM SUMMARY_RESULTS AS
  SELECT FIRST_NAME + ' ' + LAST_NAME +
       ' purchased ' +
       CAST(NUM_SHARES AS VARCHAR) +
       ' shares of ' +
       SYMBOL AS SUMMARY
FROM ACTIVITY_STREAM;

Test it

1
Create the test data

Create a file at test/input.json with the inputs for testing:

{
  "inputs": [
    {
      "topic": "stock_purchases",
      "value": {
         "id": "1",
         "num_shares": 30000,
         "amount": 5004.89,
         "txn_ts": "2020-12-04 02:35:43",
         "first_name": "Art",
         "last_name": "Vandeley",
         "symbol": "IMEP"
      }
    },
    {
      "topic": "stock_purchases",
      "value": {
          "id": "2",
          "num_shares": 500,
          "amount": 1000.89,
          "txn_ts": "2020-12-04 02:35:44",
          "first_name": "Nick",
          "last_name": "Fury",
          "symbol": "IMEP"
      }
    },
    {
      "topic": "stock_purchases",
      "value": {
          "id": "3",
          "num_shares": 45729,
          "amount": 804.89,
          "txn_ts": "2020-12-04 02:35:45",
          "first_name": "Natasha",
          "last_name": "Romanov",
          "symbol": "STRK"
      }
    },
    {
      "topic": "stock_purchases",
      "value": {
         "id": "4",
         "num_shares": 72848,
         "amount": 60040.89,
         "txn_ts": "2020-12-04 02:35:46",
         "first_name": "Wanda",
         "last_name": "Maximoff",
         "symbol": "STRK"
      }
    }
  ]
}

Create a file at test/output.json with the expected outputs:

{
  "outputs": [
    {
      "topic": "SUMMARY_RESULTS",
      "value": {
          "SUMMARY" : "Art Vandeley purchased 30000 shares of IMEP"
      }
    },
    {
      "topic": "SUMMARY_RESULTS",
      "value": {
         "SUMMARY" : "Nick Fury purchased 500 shares of IMEP"
      }
    },
    {
      "topic": "SUMMARY_RESULTS",
      "value": {
         "SUMMARY" : "Natasha Romanov purchased 45729 shares of STRK"
      }
    },
    {
      "topic": "SUMMARY_RESULTS",
      "value": {
         "SUMMARY" : "Wanda Maximoff purchased 72848 shares of STRK"
      }
    }
  ]
}

2
Invoke the tests

Invoke the tests using the ksqlDB test runner and the statements file that you created earlier:

docker exec ksqldb-cli ksql-test-runner -i /opt/app/test/input.json -s /opt/app/src/statements.sql -o /opt/app/test/output.json

Which should pass:

	 >>> Test passed!

Take it to production

1
Send the statements to the REST API

Create a file at src/statements.sql with the following content that represents the statements we tested above without the test data.

CREATE STREAM ACTIVITY_STREAM (
                      ID VARCHAR,
                      NUM_SHARES INT,
                      AMOUNT DOUBLE,
                      TXN_TS VARCHAR,
                      FIRST_NAME VARCHAR,
                      LAST_NAME  VARCHAR,
                      SYMBOL VARCHAR )

 WITH (KAFKA_TOPIC='stock_purchases',
       VALUE_FORMAT='JSON',
       PARTITIONS=1);


CREATE STREAM SUMMARY_RESULTS AS
  SELECT FIRST_NAME + ' ' + LAST_NAME +
       ' purchased ' +
       CAST(NUM_SHARES AS VARCHAR) +
       ' shares of ' +
       SYMBOL AS SUMMARY
FROM ACTIVITY_STREAM;

Launch your statements into production by sending them to the REST API with the following command:

tr '\n' ' ' < src/statements.sql | \
sed 's/;/;\'$'\n''/g' | \
while read stmt; do
    echo '{"ksql":"'$stmt'", "streamsProperties": {}}' | \
        curl -s -X "POST" "http://localhost:8088/ksql" \
             -H "Content-Type: application/vnd.ksql.v1+json; charset=utf-8" \
             -d @- | \
        jq
done

Deploy on Confluent Cloud

1
Run your app to Confluent Cloud

Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully-managed Apache Kafka service.

First, create your Kafka cluster in Confluent Cloud. Use the promo code CC100KTS to receive an additional $100 free usage (details).

Next, from the Confluent Cloud UI, click on Tools & client config to get the cluster-specific configurations, e.g. Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application.

Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.