Skip to content

Cassandra

Info

Writing data to Cassandra is a paid feature. Try the free trial here.

Creating a data generator for Cassandra. You will build a Docker image that will be able to populate data in Cassandra for the tables you configure.

Requirements

  • 20 minutes
  • Git
  • Gradle
  • Docker
  • Cassandra

Get Started

First, we will clone the data-caterer-example repo which will already have the base project setup required.

git clone git@github.com:pflooky/data-caterer-example.git

If you already have a Cassandra instance running, you can skip to this step.

Cassandra Setup

Next, let's make sure you have an instance of Cassandra up and running in your local environment. This will make it easy for us to iterate and check our changes.

cd docker
docker-compose up -d cassandra

Permissions

Let's make a new user that has the required permissions needed to push data into the Cassandra tables we want.

CQL Permission Statements 
GRANT INSERT ON <schema>.<table> TO data_caterer_user;

Following permissions are required when enabling configuration.enableGeneratePlanAndTasks(true) as it will gather metadata information about tables and columns from the below tables.

CQL Permission Statements 
GRANT SELECT ON system_schema.tables TO data_caterer_user;
GRANT SELECT ON system_schema.columns TO data_caterer_user;

Plan Setup

Create a new Java or Scala class.

  • Java: src/main/java/com/github/pflooky/plan/MyAdvancedCassandraJavaPlan.java
  • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedCassandraPlan.scala

Make sure your class extends PlanRun.

import com.github.pflooky.datacaterer.java.api.PlanRun;

public class MyAdvancedCassandraJavaPlan extends PlanRun {
}

import com.github.pflooky.datacaterer.api.PlanRun

class MyAdvancedCassandraPlan extends PlanRun {
}

This class defines where we need to define all of our configurations for generating data. There are helper variables and methods defined to make it simple and easy to use.

Connection Configuration

Within our class, we can start by defining the connection properties to connect to Cassandra.

var accountTask = cassandra(
    "customer_cassandra",   //name
    "localhost:9042",       //url
    "cassandra",            //username
    "cassandra",            //password
    Map.of()                //optional additional connection options
)

Additional options such as SSL configuration, etc can be found here.

val accountTask = cassandra(
    "customer_cassandra",   //name
    "localhost:9042",       //url
    "cassandra",            //username
    "cassandra",            //password
    Map()                   //optional additional connection options
)

Additional options such as SSL configuration, etc can be found here.

Schema

Let's create a task for inserting data into the account.accounts and account.account_status_history tables as defined underdocker/data/cql/customer.cql. This table should already be setup for you if you followed this step. We can check if the table is setup already via the following command:

docker exec host.docker.internal cqlsh -e 'describe account.accounts; describe account.account_status_history;'

Here we should see some output that looks like the below. This tells us what schema we need to follow when generating data. We need to define that alongside any metadata that is useful to add constraints on what are possible values the generated data should contain.

CREATE TABLE account.accounts (
    account_id text PRIMARY KEY,
    amount double,
    created_by text,
    name text,
    open_time timestamp,
    status text
)...

CREATE TABLE account.account_status_history (
    account_id text,
    eod_date date,
    status text,
    updated_by text,
    updated_time timestamp,
    PRIMARY KEY (account_id, eod_date)
)...

Trimming the connection details to work with the docker-compose Cassandra, we have a base Cassandra connection to define the table and schema required. Let's define each field along with their corresponding data type. You will notice that the text fields do not have a data type defined. This is because the default data type is StringType which corresponds to text in Cassandra.

{
    var accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
            .table("account", "accounts")
            .schema(
                    field().name("account_id"),
                    field().name("amount").type(DoubleType.instance()),
                    field().name("created_by"),
                    field().name("name"),
                    field().name("open_time").type(TimestampType.instance()),
                    field().name("status")
            );
}

val accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
  .table("account", "accounts")
  .schema(
    field.name("account_id"),
    field.name("amount").`type`(DoubleType),
    field.name("created_by"),
    field.name("name"),
    field.name("open_time").`type`(TimestampType),
    field.name("status")
  )

Field Metadata

We could stop here and generate random data for the accounts table. But wouldn't it be more useful if we produced data that is closer to the structure of the data that would come in production? We can do this by defining various metadata that add guidelines that the data generator will understand when generating data.

account_id

account_id follows a particular pattern that where it starts with ACC and has 8 digits after it. This can be defined via a regex like below. Alongside, we also mention that it is the primary key to prompt ensure that unique values are generated.

field().name("account_id").regex("ACC[0-9]{8}").primaryKey(true),

field.name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
amount

amount the numbers shouldn't be too large, so we can define a min and max for the generated numbers to be between 1 and 1000.

field().name("amount").type(DoubleType.instance()).min(1).max(1000),

field.name("amount").`type`(DoubleType).min(1).max(1000),
name

name is a string that also follows a certain pattern, so we could also define a regex but here we will choose to leverage the DataFaker library and create an expression to generate real looking name. All possible faker expressions can be found here

field().name("name").expression("#{Name.name}"),

field.name("name").expression("#{Name.name}"),
open_time

open_time is a timestamp that we want to have a value greater than a specific date. We can define a min date by using java.sql.Date like below.

field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),

field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
status

status is a field that can only obtain one of four values, open, closed, suspended or pending.

field().name("status").oneOf("open", "closed", "suspended", "pending")

field.name("status").oneOf("open", "closed", "suspended", "pending")
created_by

created_by is a field that is based on the status field where it follows the logic: if status is open or closed, then it is created_by eod else created_by event. This can be achieved by defining a SQL expression like below.

field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),

field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),

Putting it all the fields together, our class should now look like this.

var accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
        .table("account", "accounts")
        .schema(
                field().name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
                field().name("amount").type(DoubleType.instance()).min(1).max(1000),
                field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
                field().name("name").expression("#{Name.name}"),
                field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
                field().name("status").oneOf("open", "closed", "suspended", "pending")
        );

val accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
  .table("account", "accounts")
  .schema(
    field.name("account_id").primaryKey(true),
    field.name("amount").`type`(DoubleType).min(1).max(1000),
    field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
    field.name("name").expression("#{Name.name}"),
    field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
    field.name("status").oneOf("open", "closed", "suspended", "pending")
  )

Additional Configurations

At the end of data generation, a report gets generated that summarises the actions it performed. We can control the output folder of that report via configurations. We will also enable the unique check to ensure any unique fields will have unique values generated.

var config = configuration()
        .generatedReportsFolderPath("/opt/app/data/report")
        .enableUniqueCheck(true);

val config = configuration
  .generatedReportsFolderPath("/opt/app/data/report")
  .enableUniqueCheck(true)

Execute

To tell Data Caterer that we want to run with the configurations along with the accountTask, we have to call execute . So our full plan run will look like this.

public class MyAdvancedCassandraJavaPlan extends PlanRun {
    {
        var accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
                .table("account", "accounts")
                .schema(
                        field().name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
                        field().name("amount").type(DoubleType.instance()).min(1).max(1000),
                        field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
                        field().name("name").expression("#{Name.name}"),
                        field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
                        field().name("status").oneOf("open", "closed", "suspended", "pending")
                );

        var config = configuration()
                .generatedReportsFolderPath("/opt/app/data/report")
                .enableUniqueCheck(true);

        execute(config, accountTask);
    }
}

class MyAdvancedCassandraPlan extends PlanRun {
  val accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
    .table("account", "accounts")
    .schema(
      field.name("account_id").primaryKey(true),
      field.name("amount").`type`(DoubleType).min(1).max(1000),
      field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
      field.name("name").expression("#{Name.name}"),
      field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
      field.name("status").oneOf("open", "closed", "suspended", "pending")
    )

  val config = configuration
    .generatedReportsFolderPath("/opt/app/data/report")
    .enableUniqueCheck(true)

  execute(config, accountTask)
}

Run

Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just created.

./run.sh
#input class MyAdvancedCassandraJavaPlan or MyAdvancedCassandraPlan
#after completing
docker exec docker-cassandraserver-1 cqlsh -e 'select count(1) from account.accounts;select * from account.accounts limit 10;'

Your output should look like this.

 count
-------
  1000

(1 rows)

Warnings :
Aggregation query used without partition key


 account_id  | amount    | created_by         | name                   | open_time                       | status
-------------+-----------+--------------------+------------------------+---------------------------------+-----------
 ACC13554145 | 917.00418 | zb CVvbBTTzitjo5fK |          Jan Sanford I | 2023-06-21 21:50:10.463000+0000 | suspended
 ACC19154140 |  46.99177 |             VH88H9 |       Clyde Bailey PhD | 2023-07-18 11:33:03.675000+0000 |      open
 ACC50587836 |  774.9872 |         GENANwPm t |           Sang Monahan | 2023-03-21 00:16:53.308000+0000 |    closed
 ACC67619387 | 452.86706 |       5msTpcBLStTH |         Jewell Gerlach | 2022-10-18 19:13:07.606000+0000 | suspended
 ACC69889784 |  14.69298 |           WDmOh7NT |          Dale Schulist | 2022-10-25 12:10:52.239000+0000 | suspended
 ACC41977254 |  51.26492 |          J8jAKzvj2 |           Norma Nienow | 2023-08-19 18:54:39.195000+0000 | suspended
 ACC40932912 | 349.68067 |   SLcJgKZdLp5ALMyg | Vincenzo Considine III | 2023-05-16 00:22:45.991000+0000 |    closed
 ACC20642011 | 658.40713 |          clyZRD4fI |  Lannie McLaughlin DDS | 2023-05-11 23:14:30.249000+0000 |      open
 ACC74962085 | 970.98218 |       ZLETTSnj4NpD |          Ima Jerde DVM | 2023-05-07 10:01:56.218000+0000 |   pending
 ACC72848439 | 481.64267 |                 cc |        Kyla Deckow DDS | 2023-08-16 13:28:23.362000+0000 | suspended

(10 rows)

Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what was executed.

Sample report