Astra allows both bulk and real time operations through AstraDB and Astra Streaming. For each service there are multiple interfaces available and integration with Apache Beam/Google Dataflow is possible in different ways. Some of the design choices for this integration are below:

Data Bulk Operations

The Astra service which handles massive amount of data is Astra DB. It provides multiples ways to load data but some methods are preferred over others.

• Cassandra and CQL: This is the way to go. It is the most mature and provides an efficient way to execute queries. With the native drivers you can run reactive queries and token range queries to distribute the load across the nodes. This is the approach that was taken with the original CassandraIO connector. The existing CassandraIO connector does not support Astra but we leveraged it to create a new AstraIO connector.

• CQL over REST: This interface can be use with any HTTP client. While the Astra SDKs provides a built-in client, this interface is not the best for bulk loading as it introduces an extra layer of serialization.

• CQL over GraphQL: This interface can be used with any HTTP Client. While the Astra SDKs provides a built-in client, this interface is not the best for bulk loading as it introduces an extra layer of serialization.

• CQL over GRPC: This interface is stateless, with an optimized serialization component (grpc), and reactive interfaces so it is a viable option. Currently, the operations exposed are CQL and the token metadata information is not available to perform range queries.

Data Streaming Operations

The Astra service to handle streaming data is Astra Streaming. It provides multiple interfaces like JMS, RabbitMQ, Kafka, and built-in Apache Beam support is available in standard connectors.

To leverage the split capabilities of Pulsar, a PulsarIO connector was released in 2022. To learn more about its development you can follow this video from the Beam Summit 2022.

Objectives

Apache Beam is an open-source, unified programming model for batch and streaming data processing pipelines that simplifies large-scale data processing dynamics. Thousands of organizations around the world choose Apache Beam due to its unique data processing features, proven scale, and powerful yet extensible capabilities.

Main Concepts

• Pipeline: A Pipeline encapsulates your entire data processing task, from start to finish. This includes reading input data, transforming that data, and writing output data. All Beam driver programs must create a Pipeline. When you create the Pipeline, you must also specify the execution options that tell the Pipeline where and how to run.

• PCollection: A PCollection represents a distributed data set that your Beam pipeline operates on. The data set can be bounded, meaning it comes from a fixed source like a file, or unbounded, meaning it comes from a continuously updating source via a subscription or other mechanisms. Your pipeline typically creates an initial PCollection by reading data from an external data source, but you can also create a PCollection from in-memory data within your driver program. From there, PCollections are the inputs and outputs for each step in your pipeline.

• PTransform: A PTransform represents a data processing operation, or a step, in your pipeline. Every PTransform takes one or more PCollection objects as input, performs a processing function that you provide on the elements of that PCollection, and produces zero or more output PCollection objects.

• Input and Output so called I/O transforms: Beam comes with a number of “IOs” - library PTransforms that read or write data to various external storage systems.

I/O Connectors

Apache Beam I/O connectors provide read and write transforms for the most popular data storage systems so that Beam users can benefit from natively optimised connectivity. With the available I/Os, Apache Beam pipelines can read and write data to and from an external storage type in a unified and distributed way.

Integration with DataStax Astra is inspired by the built-in CassandraIO and PulsarIO connectors. This integration leverages a new AstraIO connector.

Runners

A runner in Apache Beam is responsible for executing pipelines on a particular processing engine or framework, such as Apache Flink or Google Cloud Dataflow. The runner translates the Beam pipeline into the appropriate format for the underlying engine, manages job execution, and provides feedback on job progress and status.

• Install Java Development Kit (JDK) 8+

Use java reference documentation targetting your operating system to install a Java Development Kit. You can then validate your installation with the following command.

java--version

• Install Apache Maven (3.8+)

Samples and tutorials have been designed with Apache Maven. Use the reference documentation top install maven validate your installation with

mvn-version

• Create your DataStax Astra account:

Sign Up

• Create an Astra Token

An astra token acts as your credentials, it holds the different permissions. The scope of a token is the whole organization (tenant) but permissions can be edited to limit usage to a single database.

To create a token, please follow this guide

The Token is in fact three separate strings: a Client ID, a Client Secret and the token proper. You will need some of these strings to access the database, depending on the type of access you plan. Although the Client ID, strictly speaking, is not a secret, you should regard this whole object as a secret and make sure not to share it inadvertently (e.g. committing it to a Git repository) as it grants access to your databases.

{
"ClientId":"ROkiiDZdvPOvHRSgoZtyAapp",
"ClientSecret":"fakedfaked",
"Token":"AstraCS:fake"
}

It is handy to have your token declare as an environment variable (replace with proper value):

export ASTRA_TOKEN="AstraCS:replace_me"

• Create a Database and a keyspace

With your account you can run multiple databases, a Databases is an Apache Cassandra cluster. It can live in one or multiple regions (dc). In each Database you can have multiple keyspaces. In the page we will use the database name db_demo and the keyspace keyspace_demo.

You can create the DB using the user interface and here is a tutorial. You can also use Astra command line interface. To install and setup the CLI run the following:

curl -Ls "https://dtsx.io/get-astra-cli" | bash
source ~/.astra/cli/astra-init.sh
astra setup --token ${ASTRA_TOKEN}

To create DB and keyspace with the CLI:

astra db create db_demo -k keyspace_demo --if-not-exists

• Download the Secure Connect Bundle for current database

A Secure Connect Bundle contains the certificates and endpoints informations to open a mTLS connection. Often mentionned as scb its scope is a database AND a region. If your database is deployed on multiple regions you will have to download the bundle for each one and initiate the connection accordingly. Instructions to download Secure Connect Bundle are here

You can download the secure connect bundle from the user interface and here is a tutorial. You can also use Astra command line interface.

astra db download-scb db_demo -f /tmp/secure-connect-bundle-db-demo.zip

• Clone the Repository with AstraIO and sample flows

git clone https://github.com/DataStax-Examples/astra-dataflow-starter.git

• Build the project with maven

cd astra-dataflow-starter
mvn clean install -Dmaven.test.skip=true

• In this page multiple flows will be described and this is how the project is defined:

In this pipeline, 100 records are generated randomly to populate a table simpledata in AstraDB. The simpledata table looks like the following:

CREATETABLEsimpledata(
idintPRIMARYKEY,
datatext
);

• The pipeline requires 3 arguments:

• Parameters are defined in a specialized interface LoadDataPipelineOptions inheriting from PipelineOptions

/**
 * Interface definition of parameters needed for this pipeline
 */
publicinterface LoadDataPipelineOptionsextendsPipelineOptions{
@Description("The Zip file to secure the transport (secure connect bundle)")
@Validation.Required
StringgetSecureConnectBundle();
voidsetSecureConnectBundle(Stringpath);
@Description("The token used as credentials (Astra Token)")
@Validation.Required
StringgetToken();
voidsetToken(Stringtoken);
@Description("Target Keyspace in the database")
@Validation.Required
StringgetKeyspace();
voidsetKeyspace(Stringkeyspace);
}

• Parameters are marshalled all the time with a PipelineOptionsFactory.fromArgs(args).

LoadDataPipelineOptionsastraOptions=PipelineOptionsFactory
.fromArgs(args)
.withValidation()
.as(LoadDataPipelineOptions.class);
FileSystems.setDefaultPipelineOptions(astraOptions);

• Run the pipeline

// Create a pipeline with the options
PipelinepipelineWrite=Pipeline.create(astraOptions);
pipelineWrite
// Create 100 records randomly
.apply(Create.of(AstraIOTestUtils.generateTestData(100)))
// Create the target table
.apply(newCreateTableTransform<SimpleDataEntity>(astraOptions))
// Write data in tables
.apply(AstraIO.<SimpleDataEntity>write()
.withToken(astraOptions.getToken())
.withKeyspace(astraOptions.getKeyspace())
.withSecureConnectBundle(newFile(astraOptions.getSecureConnectBundle()))
.withEntity(SimpleDataEntity.class));
// Pipeline Execution
pipelineWrite.run().waitUntilFinish();

• Setup parameters

cd samples-astra-beam-pipelines
export ASTRA_KEYSPACE=demo
export ASTRA_SCB_PATH=/tmp/scb-demo.zip
export ASTRA_TOKEN=AstraCS:uZclXTY....

• Run the pipeline

mvn -Pdirect-runner compile \
  exec:java \
  -Dexec.mainClass=com.dtx.astra.pipelines.beam.BulkDataLoadWithBeam \
  -Dexec.args="\
      --keyspace=${ASTRA_KEYSPACE} \
      --secureConnectBundle=${ASTRA_SCB_PATH} \
      --token=${ASTRA_TOKEN}"

• Validate table simpledata has been populated astra db cqlsh demo -k demo \ -e "select * from simpledata" \ --connect-timeout 20 \ --request-timeout 20

In this pipeline, the contents of an Astra table are exported as set of CSV files. The read is split in token ranges for maximum performance (reads are distributed accross the nodes). Multiple files are produced in the output directory.

• The pipeline requires 5 arguments:

• Those parameters are parsed using a specialized PipelineOptions interface:

publicinterface ExportTablePipelineOptionsextendsPipelineOptions{
@Description("AstraToken Value")
@Validation.Required
ValueProvider<String>getAstraToken();
voidsetAstraToken(ValueProvider<String>token);
@Description("Location of fie on disk")
@Validation.Required
ValueProvider<String>getSecureConnectBundle();
voidsetSecureConnectBundle(ValueProvider<String>path);
@Description("Source Keyspace")
@Validation.Required
StringgetKeyspace();
voidsetKeyspace(Stringkeyspace);
@Description("Source Table")
StringgetTable();
voidsetTable(Stringtable);
@Description("Destination folder")
@Validation.Required
StringgetTargetFolder();
voidsetTargetFolder(Stringfolder);
}

• Items are read with a AstraIO.read() as an entity, then serialized as a String

@ProcessElement
publicvoidprocessElement(ProcessContextc){
StringcsvLine=c.element().getId()+";"+c.element().getData();
LOGGER.info("CSV Line: {}",csvLine);
c.output(csvLine);
}

• Run the pipeline

// Build Read
PipelineexportCsvPipeline=Pipeline.create(options);
exportCsvPipeline
.apply("Read Table",AstraIO
.<SimpleDataEntity>read()
.withToken(options.getAstraToken().get())
.withSecureConnectBundle(newFile(options.getSecureConnectBundle().get()))
.withKeyspace(options.getKeyspace())
.withTable(options.getTable())
.withCoder(SerializableCoder.of(SimpleDataEntity.class))
.withEntity(SimpleDataEntity.class))
.apply("MapCsv",ParDo.of(newMapRecordAsCsvLine()))
.apply("WriteCsvInLocally",TextIO.write().to(options.getTargetFolder()));
exportCsvPipeline
.run()
.waitUntilFinish(Duration.standardSeconds(30));

• Setup parameters

cd samples-astra-beam-pipelines
export ASTRA_KEYSPACE=demo
export ASTRA_SCB_PATH=/tmp/scb-demo.zip
export ASTRA_TABLE=simpledata
export DESTINATION=/tmp
export ASTRA_TOKEN=AstraCS:uZclXTY....

• Run the pipeline

mvn -Pdirect-runner compile exec:java \
  -Dexec.mainClass=com.dtx.astra.pipelines.beam.BulkDataExportWithBeam \
  -Dexec.args="\
    --astraToken=${ASTRA_TOKEN} \
    --secureConnectBundle=${ASTRA_SCB_PATH} \
    --keyspace=${ASTRA_KEYSPACE} \
    --table=${ASTRA_TABLE} \
    --targetFolder=${DESTINATION}"

Google Dataflow is an hosted version of Apache Beam running in the Google Cloud Platform, it is also called an Apache Beam Runner It allows users to build and execute data pipelines. It enables the processing of large amounts of data in a parallel and distributed manner, making it scalable and efficient. Dataflow supports both batch and streaming processing, allowing for real-time data analysis. Users can write data processing pipelines using a variety of programming languages such as Java, Python, and SQL. Dataflow provides native integration with main Google Cloud services, such as BigQuery and Pub/Sub.

Dataflow provides built-in integrations with most in use Google Cloud Platform products suchh as Cloud Storage, Pub/Sub, Datastore or Big Query. The plaform can be extended and run any java code and I/O connectors deployed form the CLI.

Integration with DataStax comes with the integration of proper runners but also some best practice on how to handle the credentials.

• Install Java Development Kit (JDK) 8+

Use java reference documentation targetting your operating system to install a Java Development Kit. You can then validate your installation with the following command.

java--version

• Install Apache Maven (3.8+)

Samples and tutorials have been designed with Apache Maven. Use the reference documentation top install maven validate your installation with

mvn-version

• Create your DataStax Astra account:

Sign Up

• Create an Astra Token

An astra token acts as your credentials, it holds the different permissions. The scope of a token is the whole organization (tenant) but permissions can be edited to limit usage to a single database.

To create a token, please follow this guide

The Token is in fact three separate strings: a Client ID, a Client Secret and the token proper. You will need some of these strings to access the database, depending on the type of access you plan. Although the Client ID, strictly speaking, is not a secret, you should regard this whole object as a secret and make sure not to share it inadvertently (e.g. committing it to a Git repository) as it grants access to your databases.

{
"ClientId":"ROkiiDZdvPOvHRSgoZtyAapp",
"ClientSecret":"fakedfaked",
"Token":"AstraCS:fake"
}

It is handy to have your token declare as an environment variable (replace with proper value):

export ASTRA_TOKEN="AstraCS:replace_me"

• Create a Database and a keyspace

With your account you can run multiple databases, a Databases is an Apache Cassandra cluster. It can live in one or multiple regions (dc). In each Database you can have multiple keyspaces. In the page we will use the database name db_demo and the keyspace keyspace_demo.

You can create the DB using the user interface and here is a tutorial. You can also use Astra command line interface. To install and setup the CLI run the following:

curl -Ls "https://dtsx.io/get-astra-cli" | bash
source ~/.astra/cli/astra-init.sh
astra setup --token ${ASTRA_TOKEN}

To create DB and keyspace with the CLI:

astra db create db_demo -k keyspace_demo --if-not-exists

• Download the Secure Connect Bundle for current database

A Secure Connect Bundle contains the certificates and endpoints informations to open a mTLS connection. Often mentionned as scb its scope is a database AND a region. If your database is deployed on multiple regions you will have to download the bundle for each one and initiate the connection accordingly. Instructions to download Secure Connect Bundle are here

You can download the secure connect bundle from the user interface and here is a tutorial. You can also use Astra command line interface.

astra db download-scb db_demo -f /tmp/secure-connect-bundle-db-demo.zip

• 1. Create project

In the Google Cloud console, on the project selector page, select or create a Google Cloud project

Note: If you don't plan to keep the resources that you create in this guide, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project. Create a new Project in Google Cloud Console or select an existing one.

• 2. Enable Billing: Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project

• 3. Save project ID: The project identifier is available in the column ID. We will need it so let's save it as an environment variable

export GCP_PROJECT_ID=integrations-379317
export GCP_PROJECT_CODE=747469159044
export GCP_USER=cedrick.lunven@datastax.com
export GCP_COMPUTE_ENGINE=747469159044-compute@developer.gserviceaccount.com

• 4. Download and install gCoud CLI

curl https://sdk.cloud.google.com | bash

• 5. Associated CLI with project in GCP

gcloud init

• 6. Describe the project

gcloud projects describe ${GCP_PROJECT_ID}

• 7. Enable expected API

gcloud services enable dataflow compute_component \
    logging storage_component storage_api \
    bigquery pubsub datastore.googleapis.com \
    cloudresourcemanager.googleapis.com

• 8. Add Roles to dataflow users: To complete the steps, your user account must have the Dataflow Admin role and the Service Account User role. The Compute Engine default service account must have the Dataflow Worker role. To add the required roles in the Google Cloud console:

gcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \
    --member="user:${GCP_USER}" \
    --role=roles/iam.serviceAccountUser
gcloud projects add-iam-policy-binding ${GCP_PROJECT_ID}  \
    --member="serviceAccount:${GCP_COMPUTE_ENGINE}" \
    --role=roles/dataflow.admin
gcloud projects add-iam-policy-binding ${GCP_PROJECT_ID}  \
    --member="serviceAccount:${GCP_COMPUTE_ENGINE}" \
    --role=roles/dataflow.worker
gcloud projects add-iam-policy-binding ${GCP_PROJECT_ID}  \
    --member="serviceAccount:${GCP_COMPUTE_ENGINE}" \
    --role=roles/storage.objectAdmin

• 9. Create buckets for the project in cloud storage: Flows will load and export CSV files. In GCP we will create dedicated folder in Google Cloud Storage.

gsutil mb -c STANDARD -l US gs://astra_dataflow_inputs
gsutil mb -c STANDARD -l US gs://astra_dataflow_outputs
gsutil ls

• 10. Create secrets for the project in secret manager. To connect to AstraDB you need a token (credentials) and a zip used to secure the transport. Those two inputs should be defined as secrets.

gcloud secrets create astra-token \
   --data-file <(echo -n "${ASTRA_TOKEN}") \
   --replication-policy="automatic"
gcloud secrets create cedrick-demo-scb \
   --data-file ${ASTRA_SCB_PATH} \
   --replication-policy="automatic"
gcloud secrets add-iam-policy-binding cedrick-demo-scb \
    --member="serviceAccount:${GCP_COMPUTE_ENGINE}" \
    --role='roles/secretmanager.secretAccessor'
gcloud secrets add-iam-policy-binding astra-token \
    --member="serviceAccount:${GCP_COMPUTE_ENGINE}" \
    --role='roles/secretmanager.secretAccessor'
gcloud secrets list

• 11. Check that your secrets can be read

mvn -Pdataflow-runner compile exec:java \
  -Dexec.mainClass=com.dtx.astra.pipelines.test.ReadSecretAndConnectDataFlow \
  -Dexec.args="\
  --astraToken=projects/${GCP_PROJECT_CODE}/secrets/astra-token/versions/1 \
  --secureConnectBundle=projects/${GCP_PROJECT_CODE}/secrets/cedrick-demo-scb/versions/1 \
  --runner=DataflowRunner \
  --project=${GCP_PROJECT_ID} \
  --region=us-central1"

In this pipeline, 100 records are generated randomly to populate a table simpledata in Cassandra in AstraDB.The simpledata table looks like:

CREATETABLEsimpledata(
idintPRIMARYKEY,
datatext
);

• We create a pipeline with 3 arguments:

• Those parameters are parsed using a specialized PipelineOptions interface:

/**
 * Flow Interface
 */
publicinterface LoadDataPipelineOptionsextendsPipelineOptions{
@Description("Location of Astra Token secret")
@Validation.Required
StringgetAstraToken();
voidsetAstraToken(Stringtoken);
@Description("Location of secret for secure connect bundle")
@Validation.Required
StringgetSecureConnectBundle();
voidsetSecureConnectBundle(Stringpath);
@Description("Destination Keyspace")
@Validation.Required
StringgetKeyspace();
voidsetKeyspace(Stringkeyspace);
}

• Secrets are extracted from Secret Manager:

SecretManagerServiceClientclient=SecretManagerServiceClient.create();
StringastraToken=client
.accessSecretVersion(astraOptions.getAstraToken())
.getPayload().getData()
.toStringUtf8();
LOGGER.info("+ Token retrieved");
byte[]astraSecureBundle=client
.accessSecretVersion(astraOptions.getSecureConnectBundle())
.getPayload().getData()
.toByteArray();
LOGGER.info("+ Secure connect bundle retrieved");

• Pipeline uses the parameters:

PipelinepipelineWrite=Pipeline.create(astraOptions);
pipelineWrite.apply("Create 100 random items",Create.of(AstraIOTestUtils.generateTestData(100)))
.apply("Write into Astra",AstraIO.<SimpleDataEntity>write()
.withToken(astraToken)
.withKeyspace(astraOptions.getKeyspace())
.withSecureConnectBundleData(astraSecureBundle)
.withEntity(SimpleDataEntity.class));
pipelineWrite.run().waitUntilFinish();

• Run the pipeline. As you see, the runner is set to DataflowRunner and the parameters are provided with the option exec.args in the command. The token and cloud secure bundle are read from secrets.

mvn -Pdataflow-runner compile exec:java \
  -Dexec.mainClass=com.dtx.astra.pipelines.beam.dataflow.BulkDataLoadWithDataflow \
  -Dexec.args="\
    --astraToken=projects/${GCP_PROJECT_CODE}/secrets/astra-token/versions/1 \
    --secureConnectBundle=projects/${GCP_PROJECT_CODE}/secrets/cedrick-demo-scb/versions/1 \
    --keyspace=${ASTRA_KEYSPACE} \
    --runner=DataflowRunner \
    --project=${GCP_PROJECT_ID} \
    --region=us-central1"

In this pipeline, the content of an Astra Table is exported as a set of CSV Files. The read is split in token ranges for maximum performance (read are distributed accross the nodes). Multiple files are produced in the output directory. The files are created in Google CLoud Storage.

• The pipeline requires 5 arguments:

• Those parameters are parsed using a specialized PipelineOptions interface:

publicinterface ExportTablePipelineOptionsextendsPipelineOptions{
@Description("AstraToken Value")
@Validation.Required
ValueProvider<String>getAstraToken();
voidsetAstraToken(ValueProvider<String>token);
@Description("Location of fie on disk")
@Validation.Required
ValueProvider<String>getSecureConnectBundle();
voidsetSecureConnectBundle(ValueProvider<String>path);
@Description("Source Keyspace")
@Validation.Required
StringgetKeyspace();
voidsetKeyspace(Stringkeyspace);
@Description("Source Table")
StringgetTable();
voidsetTable(Stringtable);
@Description("Destination folder")
@Validation.Required
StringgetTargetFolder();
voidsetTargetFolder(Stringfolder);
}

• Items are read with a AstraIO.read() as entity, then serialized as a String

@ProcessElement
publicvoidprocessElement(ProcessContextc){
StringcsvLine=c.element().getId()+";"+c.element().getData();
LOGGER.info("CSV Line: {}",csvLine);
c.output(csvLine);
}

• Secrets are extracted from the Secret Manager and used for the READ

• Run the pipeline. As you see, the runner is set to DataflowRunner and the parameters are provided with the option exec.args in the command. The token and cloud secure bundle are read from the secrets.

mvn -Pdataflow-runner compile exec:java \
  -Dexec.mainClass=com.dtx.astra.pipelines.dataflow.BulkDataExportWithDataflow \
  -Dexec.args="\
      --astraToken=projects/${GCP_PROJECT_CODE}/secrets/astra-token/versions/1 \
      --secureConnectBundle=projects/${GCP_PROJECT_CODE}/secrets/cedrick-demo-scb/versions/1 \
      --keyspace=demo \
      --table=simpledata \
      --targetFolder=gs://astra_dataflow_ouput
      --runner=DataflowRunner \
      --project=integrations-379317 \
      --region=us-central1"