Import data from Amazon RDS into Amazon S3 using Amazon MSK, Apache Kafka Connect, Debezium, Apicurio Registry, and Amazon EKS
Introduction
In the last post, Hydrating a Data Lake using Query-based CDC with Apache Kafka Connect and Kubernetes on AWS, we utilized Kafka Connect to export data from an Amazon RDS for PostgreSQL relational database and import the data into a data lake built on Amazon Simple Storage Service (Amazon S3). The data imported into S3 was converted to Apache Parquet columnar storage file format, compressed, and partitioned for optimal analytics performance, all using Kafka Connect. To improve data freshness, as data was added or updated in the PostgreSQL database, Kafka Connect automatically detected those changes and streamed them into the data lake using query-based Change Data Capture (CDC).
This follow-up post will examine log-based CDC as a marked improvement over query-based CDC to continuously stream changes from the PostgreSQL database to the data lake. We will perform log-based CDC using Debezium’s Kafka Connect Source Connector for PostgreSQL rather than Confluent’s Kafka Connect JDBC Source connector, which was used in the previous post for query-based CDC. We will store messages as Apache Avro in Kafka running on Amazon Managed Streaming for Apache Kafka (Amazon MSK). Avro message schemas will be stored in Apicurio Registry. The schema registry will run alongside Kafka Connect on Amazon Elastic Kubernetes Service (Amazon EKS).
Change Data Capture
According to Gunnar Morling, Principal Software Engineer at Red Hat, who works on the Debezium and Hibernate projects, and well-known industry speaker, there are two types of Change Data Capture — Query-based and Log-based CDC. Gunnar detailed the differences between the two types of CDC in his talk at the Joker International Java Conference in February 2021, Change data capture pipelines with Debezium and Kafka Streams.
You can find another excellent explanation of CDC in the recent post by Lewis Gavin of Rockset, Change Data Capture: What It Is and How to Use It.
Query-based vs. Log-based CDC
To demonstrate the high-level differences between query-based and log-based CDC, let’s examine the results of a simple SQL UPDATE statement captured with both CDC methods.
UPDATE public.address
SET address2 = 'Apartment #1234'
WHERE address_id = 105;
Here is how that change is represented as a JSON message payload using the query-based CDC method described in the previous post.
{
"address_id": 105,
"address": "733 Mandaluyong Place",
"address2": "Apartment #1234",
"district": "Asir",
"city_id": 2,
"postal_code": "77459",
"phone": "196568435814",
"last_update": "2021-08-13T00:43:38.508Z"
}
Here is how the same change is represented as a JSON message payload using log-based CDC with Debezium. Note the metadata-rich structure of the log-based CDC message as compared to the query-based message.
{
"after": {
"address": "733 Mandaluyong Place",
"address2": "Apartment #1234",
"phone": "196568435814",
"district": "Asir",
"last_update": "2021-08-13T00:43:38.508453Z",
"address_id": 105,
"postal_code": "77459",
"city_id": 2
},
"source": {
"schema": "public",
"sequence": "[\"1090317720392\",\"1090317720392\"]",
"xmin": null,
"connector": "postgresql",
"lsn": 1090317720624,
"name": "pagila",
"txId": 16973,
"version": "1.6.1.Final",
"ts_ms": 1628815418508,
"snapshot": "false",
"db": "pagila",
"table": "address"
},
"op": "u",
"ts_ms": 1628815418815
}
Avro and Schema Registry
Apache Avro is a compact, fast, binary data format, according to the documentation. Avro relies on schemas. When Avro data is read, the schema used when writing it is always present. This permits each datum to be written with no per-value overheads, making serialization both fast and small. This also facilitates use with dynamic scripting languages since data, together with its schema, is fully self-describing.
We can decouple the data from its schema by using schema registries like the Confluent Schema Registry or Apicurio Registry. According to Apicurio, in a messaging and event streaming architecture, data published to topics and queues must often be serialized or validated using a schema (e.g., Apache Avro, JSON Schema, or Google Protocol Buffers). Of course, schemas can be packaged in each application. Still, it is often a better architectural pattern to register schemas in an external system [schema registry] and then reference them from each application.
It is often a better architectural pattern to register schemas in an external system and then reference them from each application.
Using Debezium’s PostgreSQL source connector, we will store changes from the PostgreSQL database’s write-ahead log (WAL) as Avro in Kafka, running on Amazon MSK. The message’s schema will be stored separately in Apicurio Registry as opposed to with the message, thus reducing the size of the messages in Kafka and allowing for schema validation and schema evolution.
pagila.public.film schemaDebezium
Debezium, according to their website, continuously monitors your databases and lets any of your applications stream every row-level change in the same order they were committed to the database. Event streams can be used to purge caches, update search indexes, generate derived views and data, and keep other data sources in sync. Debezium is a set of distributed services that capture row-level changes in your databases. Debezium records all row-level changes committed to each database table in a transaction log. Then, each application reads the transaction logs they are interested in, and they see all of the events in the same order in which they occurred. Debezium is built on top of Apache Kafka and integrates with Kafka Connect.
The latest version of Debezium includes support for monitoring MySQL database servers, MongoDB replica sets or sharded clusters, PostgreSQL servers, and SQL Server databases. We will be using Debezium’s PostgreSQL connector to capture row-level changes in the Pagila PostgreSQL database. According to Debezium’s documentation, the first time it connects to a PostgreSQL server or cluster, the connector takes a consistent snapshot of all schemas. After that snapshot is complete, the connector continuously captures row-level changes that insert, update, and delete database content committed to the database. The connector generates data change event records and streams them to Kafka topics. For each table, the default behavior is that the connector streams all generated events to a separate Kafka topic for that table. Applications and services consume data change event records from that topic.
Prerequisites
Similar to the previous post, this post will focus on data movement, not how to deploy the required AWS resources. To follow along with the post, you will need the following resources already deployed and configured on AWS:
- Amazon RDS for PostgreSQL instance (data source);
- Amazon S3 bucket (data sink);
- Amazon MSK cluster;
- Amazon EKS cluster;
- Connectivity between the Amazon RDS instance and Amazon MSK cluster;
- Connectivity between the Amazon EKS cluster and Amazon MSK cluster;
- Ensure the Amazon MSK Configuration has
auto.create.topics.enable=true. This setting isfalseby default; - IAM Role associated with Kubernetes service account (known as IRSA) that will allow access from EKS to MSK and S3 (see details below);
As shown in the architectural diagram above, I am using three separate VPCs within the same AWS account and AWS Region, us-east-1, for Amazon RDS, Amazon EKS, and Amazon MSK. The three VPCs are connected using VPC Peering. Ensure you expose the correct ingress ports, and the corresponding CIDR ranges on your Amazon RDS, Amazon EKS, and Amazon MSK Security Groups. For additional security and cost savings, use a VPC endpoint to ensure private communications between Amazon EKS and Amazon S3.
Source Code
All source code for this post and the previous post, including the Kafka Connect and connector configuration files and the Helm charts, is open-sourced and located on GitHub.GitHub — garystafford/kafka-connect-msk-demo: For the post, Hydrating a Data Lake using Change Data…
For the post, Hydrating a Data Lake using Change Data Capture (CDC), Apache Kafka, and Kubernetes on AWS — GitHub …github.com
Authentication and Authorization
Amazon MSK provides multiple authentication and authorization methods to interact with the Apache Kafka APIs. For example, you can use IAM to authenticate clients and to allow or deny Apache Kafka actions. Alternatively, you can use TLS or SASL/SCRAM to authenticate clients and Apache Kafka ACLs to allow or deny actions. In my last post, I demonstrated the use of SASL/SCRAM and Kafka ACLs with Amazon MSK:Securely Decoupling Applications on Amazon EKS using Kafka with SASL/SCRAM
Securely decoupling Go-based microservices on Amazon EKS using Amazon MSK with IRSA, SASL/SCRAM, and data encryptionitnext.io
Any MSK authentication and authorization should work with Kafka Connect, assuming you correctly configure Amazon MSK, Amazon EKS, and Kafka Connect. For this post, we are using IAM Access Control. An IAM Role associated with a Kubernetes service account (known as IRSA) allows EKS to access MSK and S3 using IAM (see more details below).
Sample PostgreSQL Database
For this post, we will continue to use PostgreSQL’s Pagila database. The database contains simulated movie rental data. The dataset is fairly small, making it less ideal for ‘big data’ use cases but small enough to quickly install and minimize data storage and analytical query costs.
Before continuing, create a new database on the Amazon RDS PostgreSQL instance and populate it with the Pagila sample data. A few people have posted updated versions of this database with easy-to-install SQL scripts. Check out the Pagila scripts provided by Devrim Gündüz on GitHub and also by Robert Treat on GitHub.
Last Updated Trigger
Each table in the Pagila database has a last_update field. A simplistic way to detect changes in the Pagila database is to use the last_update field. This is a common technique to determine if and when changes were made to data using query-based CDC, as demonstrated in the previous post. As changes are made to records in these tables, an existing database function and a trigger to each table will ensure the last_update field is automatically updated to the current date and time. You can find further information on how the database function and triggers work with Kafka Connect in this post, kafka connect in action, part 3, by Dominick Lombardo.
CREATE OR REPLACE FUNCTION update_last_update_column()
RETURNS TRIGGER AS
$$
BEGIN
NEW.last_update = now();
RETURN NEW;
END;
$$ language 'plpgsql';
CREATE TRIGGER update_last_update_column_address
BEFORE UPDATE
ON address
FOR EACH ROW
EXECUTE PROCEDURE update_last_update_column();
Kafka Connect and Schema Registry
There are several options for deploying and managing Kafka Connect, the Kafka management APIs and command-line tools, and the Apicurio Registry. I prefer deploying a containerized solution to Kubernetes on Amazon EKS. Some popular containerized Kafka options include Strimzi, Confluent for Kubernetes (CFK), and Debezium. Another option is building your own Docker Image using the official Apache Kafka binaries. I chose to build my own Kafka Connect Docker Image using the latest Kafka binaries for this post. I then installed the necessary Confluent and Debezium connectors and their associated Java dependencies into the Kafka installation. Although not as efficient as using an off-the-shelf container, building your own image will teach you how Kafka, Kafka Connect, and Debezium work, in my opinion.
In regards to the schema registry, both Confluent and Apicurio offer containerized solutions. Apicurio has three versions of their registry, each with a different storage mechanism: in-memory, SQL, and Kafka. Since we already have an existing Amazon RDS PostgreSQL instance as part of the demonstration, I chose the Apicurio SQL-based registry Docker Image for this post, apicurio/apicurio-registry-sql:2.0.1.Final.
If you choose to use the same Kafka Connect and Apicurio solution I used in this post, a Helm Chart is included in the post’s GitHub repository, kafka-connect-msk-v2. The Helm chart will deploy a single Kubernetes pod to the kafka Namespace on Amazon EKS. The pod comprises both the Kafka Connect and Apicurio Registry containers. The deployment is intended for demonstration purposes and is not designed for use in Production.
apiVersion: v1
kind: Service
metadata:
name: kafka-connect-msk
spec:
type: NodePort
selector:
app: kafka-connect-msk
ports:
- port: 8080
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: kafka-connect-msk
labels:
app: kafka-connect-msk
component: service
spec:
replicas: 1
strategy:
type: Recreate
selector:
matchLabels:
app: kafka-connect-msk
component: service
template:
metadata:
labels:
app: kafka-connect-msk
component: service
spec:
serviceAccountName: kafka-connect-msk-iam-serviceaccount
containers:
- image: garystafford/kafka-connect-msk:1.1.0
name: kafka-connect-msk
imagePullPolicy: IfNotPresent
- image: apicurio/apicurio-registry-sql:2.0.1.Final
name: apicurio-registry-mem
imagePullPolicy: IfNotPresent
env:
- name: REGISTRY_DATASOURCE_URL
value: jdbc:postgresql://your-pagila-database-url.us-east-1.rds.amazonaws.com:5432/apicurio-registry
- name: REGISTRY_DATASOURCE_USERNAME
value: apicurio_registry
- name: REGISTRY_DATASOURCE_PASSWORD
value: 1L0v3Kafka!
Before deploying the chart, create a new PostgreSQL database, user, and grants on your RDS instance for the Apicurio Registry to use for storage:
CREATE DATABASE "apicurio-registry";
CREATE USER apicurio_registry WITH PASSWORD '1L0v3KafKa!';
GRANT CONNECT, CREATE ON DATABASE "apicurio-registry" to apicurio_registry;
Update the Helm chart’s value.yaml file with the name of your Kubernetes Service Account associated with the Kafka Connect pod (serviceAccountName) and your RDS URL (registryDatasourceUrl). The IAM Policy attached to the IAM Role associated with the pod’s Service Account should provide sufficient access to Kafka running on the Amazon MSK cluster from EKS. The policy should also provide access to your S3 bucket, as detailed here by Confluent. Below is an example of an (overly broad) IAM Policy that would allow full access to any Kafka clusters running on Amazon MSK and to your S3 bucket from Kafka Connect running on Amazon EKS.
Once the variables are updated, use the following command to deploy the Helm chart:
helm install kafka-connect-msk-v2 ./kafka-connect-msk-v2 \
--namespace $NAMESPACE --create-namespace
Confirm the chart was installed successfully by checking the pod’s status:
kubectl get pods -n kafka -l app=kafka-connect-msk
If you have any issues with either container while deploying, review the individual container’s logs:
export KAFKA_CONTAINER=$(
kubectl get pods -n kafka -l app=kafka-connect-msk | \
awk 'FNR == 2 {print $1}')
kubectl logs $KAFKA_CONTAINER -n kafka kafka-connect-msk
kubectl logs $KAFKA_CONTAINER -n kafka apicurio-registry-mem
Kafka Connect
Get a shell to the running Kafka Connect container using the kubectl exec command:
export KAFKA_CONTAINER=$(
kubectl get pods -n kafka -l app=kafka-connect-msk | \
awk 'FNR == 2 {print $1}')
kubectl exec -it $KAFKA_CONTAINER -n kafka -c kafka-connect-msk -- bash
Confirm Access to Registry from Kafka Connect
If the Helm Chart was deployed successfully, you should now observe 11 new tables in the public schema of the new apicurio-registry database. Below, we see the new database and tables, as shown in pgAdmin.
Confirm the registry is running and accessible from the Kafka Connect container by calling the registry’s system/info REST API endpoint:
curl -s http://localhost:8080/apis/registry/v2/system/info | jq
The Apicurio Registry’s Service targets TCP port 8080. The Service is exposed on the Kubernetes worker node’s external IP address at a static port, the NodePort. To get the NodePort of the service, use the following command:
kubectl describe services kafka-client-msk -n kafka
To access the Apicurio Registry’s web-based UI, add the NodePort to the Security Group of the EKS nodes with the source being your IP address, a /32 CIDR block.
To get the external IP address (EXTERNAL-IP) of any Amazon EKS worker nodes, use the following command:
kubectl get nodes -o wide
Use the <NodeIP>:<NodePort> combination to access the UI from your web browser, for example, http://54.237.41.128:30433. The registry will be empty at this point in the demonstration.
Configure Bootstrap Brokers
Before starting Kafka Connect, you will need to modify Kafka Connect’s configuration file. Kafka Connect is capable of running workers in standalone or distributed modes. Since we will be using Kafka Connect’s distributed mode, modify the config/connect-distributed.properties file. A complete sample of the configuration file I used in this post is shown below.
Kafka Connect and the schema registry will run on Amazon EKS, while Kafka and Apache ZooKeeper run on Amazon MSK. Update the bootstrap.servers property to reflect your own comma-delimited list of Amazon MSK Kafka Bootstrap Brokers. To get the list of the Bootstrap Brokers for your Amazon MSK cluster, use the AWS Management Console, or the following AWS CLI commands:
# get the msk cluster's arn
aws kafka list-clusters --query 'ClusterInfoList[*].ClusterArn'
# use msk arn to get the brokers
aws kafka get-bootstrap-brokers --cluster-arn your-msk-cluster-arn
# alternately, if you only have one cluster, then
aws kafka get-bootstrap-brokers --cluster-arn $(
aws kafka list-clusters | jq -r '.ClusterInfoList[0].ClusterArn')
Update the config/connect-distributed.properties file.
# ***** CHANGE ME! *****
bootstrap.servers=b-1.your-cluster.123abc.c2.kafka.us-east-1.amazonaws.com:9098,b-2.your-cluster.123abc.c2.kafka.us-east-1.amazonaws.com:9098, b-3.your-cluster.123abc.c2.kafka.us-east-1.amazonaws.com:9098
group.id=connect-cluster
key.converter.schemas.enable=true
value.converter.schemas.enable=true
offset.storage.topic=connect-offsets
offset.storage.replication.factor=2
#offset.storage.partitions=25
config.storage.topic=connect-configs
config.storage.replication.factor=2
status.storage.topic=connect-status
status.storage.replication.factor=2
#status.storage.partitions=5
offset.flush.interval.ms=10000
plugin.path=/usr/local/share/kafka/plugins
# kafka connect auth using iam
ssl.truststore.location=/tmp/kafka.client.truststore.jks
security.protocol=SASL_SSL
sasl.mechanism=AWS_MSK_IAM
sasl.jaas.config=software.amazon.msk.auth.iam.IAMLoginModule required;
sasl.client.callback.handler.class=software.amazon.msk.auth.iam.IAMClientCallbackHandler
# kafka connect producer auth using iam
producer.ssl.truststore.location=/tmp/kafka.client.truststore.jks
producer.security.protocol=SASL_SSL
producer.sasl.mechanism=AWS_MSK_IAM
producer.sasl.jaas.config=software.amazon.msk.auth.iam.IAMLoginModule required;
producer.sasl.client.callback.handler.class=software.amazon.msk.auth.iam.IAMClientCallbackHandler
# kafka connect consumer auth using iam
consumer.ssl.truststore.location=/tmp/kafka.client.truststore.jks
consumer.security.protocol=SASL_SSL
consumer.sasl.mechanism=AWS_MSK_IAM
consumer.sasl.jaas.config=software.amazon.msk.auth.iam.IAMLoginModule required;
consumer.sasl.client.callback.handler.class=software.amazon.msk.auth.iam.IAMClientCallbackHandler
For convenience when executing Kafka commands, set the BBROKERS environment variable to the same comma-delimited list of Kafka Bootstrap Brokers, for example:
export BBROKERS="b-1.your-cluster.123abc.c2.kafka.us-east-1.amazonaws.com:9098,b-2.your-cluster.123abc.c2.kafka.us-east-1.amazonaws.com:9098, b-3.your-cluster.123abc.c2.kafka.us-east-1.amazonaws.com:9098"
Confirm Access to Amazon MSK from Kafka Connect
To confirm you have access to Kafka running on Amazon MSK, from the Kafka Connect container running on Amazon EKS, try listing the exiting Kafka topics:
bin/kafka-topics.sh --list \
--bootstrap-server $BBROKERS \
--command-config config/client-iam.properties
You can also try listing the existing Kafka consumer groups:
bin/kafka-consumer-groups.sh --list \
--bootstrap-server $BBROKERS \
--command-config config/client-iam.properties
If either of these fails, you likely have networking or security issues blocking access from Amazon EKS to Amazon MSK. Check your VPC Peering, Route Tables, IAM/IRSA, and Security Group ingress settings. Any one of these items can cause communications issues between the container and Kafka running on Amazon MSK.
Once configured, start Kafka Connect as a background process.
Kafka Connect
bin/connect-distributed.sh \
config/connect-distributed.properties > /dev/null 2>&1 &
To confirm Kafka Connect starts properly, immediately tail the connect.log file. The log will capture any startup errors for troubleshooting.
tail -f logs/connect.log
You can also examine the background process with the ps command to confirm Kafka Connect is running. Note the process with PID 4915, shown below. Use the kill command along with the PID to stop Kafka Connect if necessary.
If configured properly, Kafka Connect will create three new topics, referred to as Kafka Connect internal topics, when Kafka Connect starts up. The topics are defined in the config/connect-distributed.properties file: connect-configs, connect-offsets, and connect-status. According to Confluent, Connect stores connector and task configurations, offsets, and status in these topics. The Internal topics must have a high replication factor, a compaction cleanup policy, and an appropriate number of partitions. These new topics can be confirmed using the following command.
bin/kafka-topics.sh --list \
--bootstrap-server $BBROKERS \
--command-config config/client-iam.properties \
| grep connect-
Kafka Connect Connectors
This post demonstrates the use of a set of Kafka Connect source and sink connectors. The source connector is based on the Debezium Source Connector for PostgreSQL and the Apicurio Registry. The sink connector is based on the Confluent Amazon S3 Sink connector and the Apicurio Registry.
Connector Source
Create or modify the file, config/debezium_avro_source_connector_postgresql_05.json. Update lines 3–6, as shown below, to reflect your RDS instance connection details.
The source connector exports existing data and ongoing changes from six related tables within the Pagila database’s public schema: actor , film, film_actor , category, film_category, and language. Data will be imported into a corresponding set of six new Kafka topics: pagila.public.actor, pagila.public.film, and so forth. (see line 9, above).
Data from the tables is stored in Apache Avro format in Kafka, and the schemas are stored separately in the Apicurio Registry (lines 11–18, above).
Connector Sink
Create or modify the file, config/s3_sink_connector_05_debezium_avro.json. Update line 7, as shown below to reflect your Amazon S3 bucket’s name.
The sink connector flushes new data to S3 every 300 records or 60 seconds from the six Kafka topics (lines 4–5, 9–10, above). The schema for the data being written to S3 is extracted from the Apicurio Registry (lines 17–24, above).
The sink connector optimizes the raw data imported into S3 for downstream processing by writing GZIP-compressed Apache Parquet files to Amazon S3. Using Parquet’s columnar file format and file compression should help optimize ELT against the raw data once in S3 (lines 12–13, above).
Deploy Connectors
Deploy the source and sink connectors using the Kafka Connect REST Interface:
curl -s -d @"config/debezium_avro_source_connector_postgresql_05.json" \
-H "Content-Type: application/json" \
-X PUT http://localhost:8083/connectors/debezium_avro_source_connector_postgresql_05/config | jq
curl -s -d @"config/s3_sink_connector_05_debezium_avro.json" \
-H "Content-Type: application/json" \
-X PUT http://localhost:8083/connectors/s3_sink_connector_05_debezium_avro/config | jq
Confirming the Deployment
Use the following commands to confirm the new set of connectors are deployed and running correctly.
curl -s -X GET http://localhost:8083/connectors | jq
curl -s -H "Content-Type: application/json" \
-X GET http://localhost:8083/connectors/debezium_avro_source_connector_postgresql_05/status | jq
curl -s -H "Content-Type: application/json" \
-X GET http://localhost:8083/connectors/s3_sink_connector_05_debezium_avro/status | jq
The items stored in Apicurio Registry, such as event schemas and API designs, are known as registry artifacts. If we re-visit the Apicurio Registry’s UI, we should observe 12 artifacts — a ‘key’ and ‘value’ artifact for each of the six tables we exported from the Pagila database.
Examing the Amazon S3, you should note six sets of S3 objects within the /topics/ object key prefix organized by topic name.
Within each topic name key, there should be a set of GZIP-compressed Parquet files.
Use the Amazon S3 console’s ‘Query with S3 Select’ again to view the data contained in the Parquet-format files. Alternately, you can use the AWS CLI with the s3 API:
export SINK_BUCKET="your-s3-bucket"
export KEY="topics/pagila.public.film/partition=0/pagila.public.film+0+0000000000.gz.parquet"
aws s3api select-object-content \
--bucket $SINK_BUCKET \
--key $KEY \
--expression "select * from s3object limit 5" \
--expression-type "SQL" \
--input-serialization '{"Parquet": {}}' \
--output-serialization '{"JSON": {}}' "output.json" \
&& cat output.json | jq \
&& rm output.json
In the sample data below, note the metadata-rich structure of the log-based CDC messages as compared to the query-based messages we observed in the previous post:
{
"after": {
"special_features": [
"Deleted Scenes",
"Behind the Scenes"
],
"rental_duration": 6,
"rental_rate": 0.99,
"release_year": 2006,
"length": 86,
"replacement_cost": 20.99,
"rating": "PG",
"description": "A Epic Drama of a Feminist And a Mad Scientist who must Battle a Teacher in The Canadian Rockies",
"language_id": 1,
"title": "ACADEMY DINOSAUR",
"original_language_id": null,
"last_update": "2017-09-10T17:46:03.905795Z",
"film_id": 1
},
"source": {
"schema": "public",
"sequence": "[null,\"1177089474560\"]",
"xmin": null,
"connector": "postgresql",
"lsn": 1177089474560,
"name": "pagila",
"txId": 18422,
"version": "1.6.1.Final",
"ts_ms": 1629340334432,
"snapshot": "true",
"db": "pagila",
"table": "film"
},
"op": "r",
"ts_ms": 1629340334434
}
Database Changes with Log-based CDC
What happens when we change data within the tables that Debezium and Kafka Connect are monitoring? To answer this question, let’s make a few DML changes to the Pagila database: inserts, updates, and deletes:
INSERT INTO public.category (name)
VALUES ('Techno Thriller');
UPDATE public.film
SET release_year = 2021,
rental_rate = 2.99
WHERE film_id = 1;
UPDATE public.film
SET rental_duration = 3
WHERE film_id = 2;
UPDATE public.film_category
SET category_id = (
SELECT DISTINCT category_id
FROM public.category
WHERE name = 'Techno Thriller')
WHERE film_id = 3;
UPDATE public.actor
SET first_name = upper('Kate'),
last_name = upper('Winslet')
WHERE actor_id = 6;
DELETE
FROM public.film_actor
WHERE film_id = 375;
To see how these changes propagate, first, examine the Kafka Connect logs. Below, we see example log events corresponding to some of the database changes shown above. The Kafka Connect source connector detects changes, which are then exported from PostgreSQL to Kafka. The sink connector then writes these changes to Amazon S3.
We can view the S3 bucket, which should now have new Parquet files corresponding to our changes. For example, the two updates we made to the film record with film_id of 1. Note the operation is an update ("op": "u") and the presence of the data in after block.
{
"after": {
"special_features": [
"Deleted Scenes",
"Behind the Scenes"
],
"rental_duration": 6,
"rental_rate": 2.99,
"release_year": 2021,
"length": 86,
"replacement_cost": 20.99,
"rating": "PG",
"description": "A Epic Drama of a Feminist And a Mad Scientist who must Battle a Teacher in The Canadian Rockies",
"language_id": 1,
"title": "ACADEMY DINOSAUR",
"original_language_id": null,
"last_update": "2021-08-19T03:19:57.073053Z",
"film_id": 1
},
"source": {
"schema": "public",
"sequence": "[\"1177693455424\",\"1177693455424\"]",
"xmin": null,
"connector": "postgresql",
"lsn": 1177693471392,
"name": "pagila",
"txId": 18445,
"version": "1.6.1.Final",
"ts_ms": 1629343197100,
"snapshot": "false",
"db": "pagila",
"table": "film"
},
"op": "u",
"ts_ms": 1629343197389
}
In another example, we see the delete made in the film_actor table, to the record with the film_id of 375. Note the operation is a delete ("op": "d") and the presence of the before block but no after block.
{
"before": {
"last_update": "1970-01-01T00:00:00Z",
"actor_id": 5,
"film_id": 375
},
"source": {
"schema": "public",
"sequence": "[\"1177693516520\",\"1177693516520\"]",
"xmin": null,
"connector": "postgresql",
"lsn": 1177693516520,
"name": "pagila",
"txId": 18449,
"version": "1.6.1.Final",
"ts_ms": 1629343198400,
"snapshot": "false",
"db": "pagila",
"table": "film_actor"
},
"op": "d",
"ts_ms": 1629343198426
}
Debezium Event Flattening SMT
The challenge with the Debezium message structure shown above in S3 is the verbosity of the payload and the nested nature of the data. As a result, developing SQL queries against such records would be difficult. For example, given the message structure shown above, even the simplest query in Amazon Athena becomes significantly more complex:
SELECT after.actor_id, after.first_name, after.last_name, after.last_update
FROM
(SELECT *,
ROW_NUMBER()
OVER ( PARTITION BY after.actor_id
ORDER BY after.last_UPDATE DESC) AS row_num
FROM "pagila_kafka_connect"."pagila_public_actor") AS x
WHERE x.row_num = 1
ORDER BY after.actor_id;
To specifically address the needs of different consumers, Debezium offers the event flattening single message transformation (SMT). The event flattening transformation is a Kafka Connect SMT. We covered Kafka Connect SMTs in the previous post. Using the event flattening SMT, we can shape the message received by Kafka to be more attuned to the specific consumers of our data lake. To implement the event flattening SMT, modify and redeploy the source connector, adding additional configuration (lines 19–23, below).
We will include the op, db, schema, lsn, and source.ts_ms metadata fields, along with the actual record data (table) in the transformed message. This means we have chosen to exclude all other fields from the messages. The transform will flatten the message’s nested structure.
Making this change to the message structure by adding the transformation results in new versions of the message’s schemas automatically being added to the Apicurio Registry by the source connector:
pagila.public.film schemaAs a result of the event flattening SMT by the source connector, our message structure is significantly simplified:
{
"actor_id": 7,
"first_name": "BOB",
"last_name": "MOSTEL",
"last_update": "2021-08-19T21:01:55.090858Z",
"__op": "u",
"__db": "pagila",
"__schema": "public",
"__table": "actor",
"__lsn": 1191920555344,
"__source_ts_ms": 1629406915091,
"__deleted": "false"
}
Note the new __deleted field, which results from lines 21–22 of the source connector configuration, shown above. Debezium keeps tombstone records for DELETE operations in the event stream and adds __deleted , set to true or false. Below, we see an example of two DELETE operations on the film_actor table.
{
"actor_id": 52,
"film_id": 376,
"last_update": "1970-01-01T00:00:00Z",
"__op": "d",
"__db": "pagila",
"__schema": "public",
"__table": "film_actor",
"__lsn": 1192390296016,
"__source_ts_ms": 1629408869556,
"__deleted": "true"
}
{
"actor_id": 60,
"film_id": 376,
"last_update": "1970-01-01T00:00:00Z",
"__op": "d",
"__db": "pagila",
"__schema": "public",
"__table": "film_actor",
"__lsn": 1192390298976,
"__source_ts_ms": 1629408869556,
"__deleted": "true"
}
Viewing Data in the Data Lake
A convenient way to examine both the existing data and ongoing data changes in our data lake is to crawl and catalog the S3 bucket’s contents with AWS Glue, then query the results with Amazon Athena. AWS Glue’s Data Catalog is an Apache Hive-compatible, fully-managed, persistent metadata store. AWS Glue can store the schema, metadata, and location of our data in S3. Amazon Athena is a serverless Presto-based (PrestoDB) ad-hoc analytics engine, which can query AWS Glue Data Catalog tables and the underlying S3-based data.
With the data crawled and cataloged in Glue, let’s perform some additional changes to the Pagila database’s film table.
UPDATE public.film
SET release_year = 2019,
rental_rate = 3.99
WHERE film_id = 1;
UPDATE public.film
SET rental_duration = 4
WHERE film_id = 2;
UPDATE public.film
SET rental_duration = 7
WHERE film_id = 2;
INSERT INTO public.category (name)
VALUES ('Steampunk');
UPDATE public.film_category
SET category_id = (
SELECT DISTINCT category_id
FROM public.category
WHERE name = 'Steampunk')
WHERE film_id = 3;
UPDATE public.film
SET release_year = 2017,
rental_rate = 3.99
WHERE film_id = 4;
UPDATE public.film_actor
SET film_id = 100
WHERE film_id = 5;
UPDATE public.film_category
SET film_id = 100
WHERE film_id = 5;
UPDATE public.inventory
SET film_id = 100
WHERE film_id = 5;
DELETE
FROM public.film
WHERE film_id = 5;
We should be able to almost immediately observe these database changes by executing a query with Amazon Athena. The changes are propagated from PostgreSQL to Kafka to S3 within seconds or less by Kafka Connect based on the connector configurations. Performing a typical query in Athena will return all of the original records as well as any updates or deletes we made as duplicate records (records identical film_id primary keys).
SELECT film_id, title, release_year, rental_rate, rental_duration,
date_format(from_unixtime(__source_ts_ms/1000), '%Y-%m-%d %h:%i:%s') AS timestamp
FROM "pagila_kafka_connect"."pagila_public_film"
ORDER BY film_id, timestamp
Note the original records as well as each change we made earlier. The timestamp field, derived from the __source_ts_ms metadata field represents the server time at which the transaction was committed, according to Debezium. Also, note the records with their film_id of 5 in the query results — the record we deleted from the film table. The field values are (mostly) null in the latest record, except for any fields with default values in the Pagila table definition. If there are default values (e.g., rental_duration smallint default 3 not null or rental_rate numeric(4,2) default 4.99 not null) set on a field, those values end up in the deleted record when using the event flattening SMT. It doesn’t negatively impact anything except adding additional size to a tombstone record (unclear if this is expected behavior with Debezium or an artifact of the WAL entry).
To view only the most current data and ignore deleted records, we can use the ROW_NUMBER() function and add a predicate to check the value of the __deleted field:
SELECT film_id, title, release_year, rental_rate, rental_duration,
date_format(from_unixtime(__source_ts_ms/1000), '%Y-%m-%d %h:%i:%s') AS timestamp
FROM
(SELECT *,
ROW_NUMBER()
OVER ( PARTITION BY film_id
ORDER BY __source_ts_ms DESC) AS row_num
FROM "pagila_kafka_connect"."pagila_public_film") AS x
WHERE x.row_num = 1
AND __deleted != 'true'
ORDER BY film_id
Now we only see the latest records, including the removal of any deleted records. Although this method is effective for a single set of records, the query is far too intricate to apply to complex joins and aggregations, in my opinion.
Data Movement
Using Amazon Athena, we can easily write the results of our ROW_NUMBER() query back to the data lake for further enrichment or analysis. Athena’s CREATE TABLE AS SELECT (CTAS) SQL statement creates a new table in Athena (an external table in AWS Glue Data Catalog) from the results of a SELECT statement in the subquery. Athena stores data files created by the CTAS statement in a specified location in Amazon S3 and created a new AWS Glue Data Catalog table to store the result set’s schema and metadata information. CTAS supports several file formats and storage options.
Wrapping the last query in Athena’s CTAS statement, as shown below, we can write the query results as SNAPPY-compressed Parquet-format files, partitioned by the movie rating, to a new location in the Amazon S3 bucket. Using common data lake terminology, I will refer to the resulting filtered and cleaned dataset as refined or silver instead of the raw ingestion or bronze data originating from our data source, PostgreSQL, via Kafka.
CREATE TABLE pagila_kafka_connect.pagila_public_film_refined
WITH (
format='PARQUET',
parquet_compression='SNAPPY',
partitioned_by=ARRAY['rating'],
external_location='s3://my-s3-table/refined/film/'
) AS
SELECT film_id, title, release_year, rental_rate, rental_duration,
date_format(from_unixtime(__source_ts_ms/1000), '%Y-%m-%d %h:%i:%s') AS timestamp, rating
FROM
(SELECT *,
ROW_NUMBER()
OVER ( PARTITION BY film_id
ORDER BY __source_ts_ms DESC) AS row_num
FROM "pagila_kafka_connect"."pagila_public_film") AS x
WHERE x.row_num = 1
AND __deleted = 'false'
ORDER BY film_id
Examing the Amazon S3 bucket, again, you should observe a new set of S3 objects within the /refined/film/ key path, partitioned by rating.
We should also see a new table in the same AWS Glue Data Catalog containing metadata, location, and schema information about the data we wrote to S3 using the CTAS statement. We can perform additional queries on the refined dataset.
SELECT *
FROM "pagila_kafka_connect"."pagila_public_film_refined"
ORDER BY film_id
CRUD Operations in the Data Lake
To fully take advantage of CDC and maximize the freshness of data in the data lake, we would need to also adopt modern data lake file formats like Apache Hudi, Apache Iceberg, or Delta Lake, along with analytics engines such as Apache Spark with Spark Structured Streaming to process the data changes. Using these technologies, it is possible to perform record-level upserts and deletes of data in an object store like Amazon S3. Hudi, Iceberg, and Delta Lake offer features including ACID transactions, schema evolution, upserts, deletes, time travel, and incremental data consumption in a data lake. ELT engines like Spark can read streaming Debezium-generated CDC messages from Kafka and process those changes using Hudi, Iceberg, or Delta Lake.
Conclusion
This post explored how log-based CDC could help us hydrate data from an Amazon RDS database into an Amazon S3-based data lake. We leveraged the capabilities of Amazon MSK, Amazon EKS, Apache Kafka Connect, Debezium, Apache Avro, and Apicurio Registry. In a subsequent post, we will learn how data lake file formats like Apache Hudi, Apache Iceberg, and Delta Lake, along with Apache Spark Structured Streaming, can help us actively manage the data in our data lake.
This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.
Programmatic Ponderings 