hamroune / cp-demo

Confluent Platform Demo including Control Center, KSQL, Replicator, Schema Registry, Security

Table of Contents

• Overview
• Installation
• Start demo
• Playbook
  • Tour of Confluent Control Center
  • KSQL
  • Consumer rebalances
  • Slow consumers
  • Over consumption
  • Under consumption
  • Failed broker
  • Alerting
  • Replicator
  • Security
• Troubleshooting the demo
• Teardown

Overview

This demo shows users how to monitor Kafka streaming ETL deployments using Confluent Control Center. All the components in the Confluent platform have security enabled end-to-end. Follow along with the playbook in this README and watch the video tutorials.

The use case is a stream processing on live edits to real Wikipedia pages. Wikimedia Foundation has IRC channels that publish edits happening to real wiki pages (e.g. #en.wikipedia, #en.wiktionary) in real time. Using Kafka Connect, a Kafka source connector kafka-connect-irc streams raw messages from these IRC channels, and a custom Kafka Connect transform kafka-connect-transform-wikiedit transforms these messages and then the messages are written to a Kafka cluster. This demo uses KSQL for data enrichment, or you can optionally develop and run your own Kafka Streams application. Then a Kafka sink connector kafka-connect-elasticsearch streams the data out of Kafka, applying another custom Kafka Connect transform called NullFilter. The data is materialized into Elasticsearch for analysis by Kibana.

---

Note: this is a Docker environment and has all services running on one host. Do not use this demo in production. It is meant exclusively to easily demo the Confluent Platform. In production, Confluent Control Center should be deployed with a valid license and with its own dedicated metrics cluster, separate from the cluster with production traffic. Using a dedicated metrics cluster is more resilient because it continues to provide system health monitoring even if the production traffic cluster experiences issues.

---

Installation

Follow along with the video

This demo has been verified with:

• Docker version 17.06.1-ce
• Docker Compose version 1.14.0 with Docker Compose file format 2.1
• Java version 1.8.0_92
• MacOS 10.12

1. Download the repository with git clone:

   $ git clone https://github.com/confluentinc/cp-demo
   

2. In the advanced Docker preferences settings, increase the memory available to Docker to at least 8GB (default is 2GB).

Run demo

1. From the cp-demo directory, generate certs used for security.

   $ (cd scripts/security && ./certs-create.sh)

2. Start the demo. It will take about 2 minutes for all containers to start and for Confluent Control Center GUI to be ready.

   $ docker-compose up -d

3. Verify the status of the Docker containers show Up state, except for the kafka-client container which is expected to have Exit 0 state. If any containers are not up, verify in the advanced Docker preferences settings that the memory available to Docker is at least 8GB (default is 2GB).

   $ docker-compose ps
            Name                        Command               State                              Ports                            
   ------------------------------------------------------------------------------------------------------------------------------
   cpdemo_connect_1          /etc/confluent/docker/run        Up       0.0.0.0:8083->8083/tcp, 9092/tcp
   cpdemo_control-center_1   /etc/confluent/docker/run        Up       0.0.0.0:9021->9021/tcp
   cpdemo_elasticsearch_1    /bin/bash bin/es-docker          Up       0.0.0.0:9200->9200/tcp, 0.0.0.0:9300->9300/tcp
   cpdemo_kafka-client_1     bash -c -a echo Waiting fo ...   Exit 0
   cpdemo_kafka1_1           /etc/confluent/docker/run        Up       0.0.0.0:29091->29091/tcp, 0.0.0.0:9091->9091/tcp, 9092/tcp
   cpdemo_kafka2_1           /etc/confluent/docker/run        Up       0.0.0.0:29092->29092/tcp, 0.0.0.0:9092->9092/tcp
   cpdemo_kibana_1           /bin/sh -c /usr/local/bin/ ...   Up       0.0.0.0:5601->5601/tcp
   cpdemo_ksql-cli_1         perl -e while(1){ sleep 99 ...   Up       0.0.0.0:9098->9098/tcp
   cpdemo_schemaregistry_1   /etc/confluent/docker/run        Up       8081/tcp, 0.0.0.0:8082->8082/tcp
   cpdemo_zookeeper_1        /etc/confluent/docker/run        Up       0.0.0.0:2181->2181/tcp, 2888/tcp, 3888/tcp
   

4. Wait till Confluent Control Center is running fully. Verify it is ready when the logs show the following event:

   $ docker-compose logs -f control-center | grep -e "Started NetworkTrafficServerConnector"
   control-center_1       | [2017-09-06 16:37:33,133] INFO Started NetworkTrafficServerConnector@26a529dc{HTTP/1.1}{0.0.0.0:9021} (org.eclipse.jetty.server.NetworkTrafficServerConnector)

5. Run the setup script which customizes the Kafka cluster, Kafka source and sink connectors, Elasticsearch, and Kibana dashboard.

   $ ./scripts/setup.sh

6. Use Google Chrome to view the Confluent Control Center GUI at http://localhost:9021.

   Click on the top right button that shows the current date, and change Last 4 hours to Last 30 minutes.

7. View the data in the Kibana dashboard at http://localhost:5601/app/kibana#/dashboard/Wikipedia

Playbook

Tour of Confluent Control Center

Follow along with the video

1. Monitoring --> System Health: Confluent Control Center landing page shows the overall system health of a given Kafka cluster. For capacity planning activities, view cluster utilization:

   • CPU: look at network and thread pool usage, produce and fetch request latencies
   • Network utilization: look at throughput per broker or per cluster
   • Disk utilization: look at disk space used by all log segments, per broker

   

2. Management --> Kafka Connect: Confluent Control Center uses the Kafka Connect API to manage Kafka connectors.

   • Kafka Connect Sources tab shows the connectors wikipedia-irc and replicate-topic. Click Edit to see the details of the connector configuration and custom transforms.

     

   • Kafka Connect Sinks tab shows the connector elasticsearch-ksql. Click Edit to see the details of the connector configuration and custom transforms.

     

3. Monitoring --> Data Streams --> Message Delivery: hover over any chart to see number of messages and average latency within a minute time interval.

   

   The Kafka Connect sink connectors have corresponding consumer groups connect-elasticsearch-ksql and connect-replicator. These consumer groups will be in the consumer group statistics in the stream monitoring charts.

   

4. Management --> Topics --> Topic Information: For a given topic, click on the three dots ... next to the topic name and click on View details. View which brokers are leaders for which partitions and the number of consumer groups currently consuming from this topic. Click on the boxed consumer group count to select a consumer group for which to monitor its data streams and jump to it.

   

5. Monitoring --> System Health: to identify bottlenecks, you can see a breakdown of produce and fetch latencies through the entire request lifecycle. Click on the line graph in the Request latency chart. The request latency values can be shown at the median, 95th, 99th, or 99.9th percentile. Depending on where the bottlenecks are, you can tune your brokers and clients appropriately.

   

KSQL

Follow along with the video

In this demo, KSQL is configured with properties to connect to the secured Kafka cluster and is already running queries.

1. Run the KSQL CLI to get more information on the queries, streams, and tables.

   $ docker-compose exec ksql-cli ksql-cli remote http://localhost:8080
   ...
   ksql> SHOW QUERIES;
   ksql> DESCRIBE WIKIPEDIABOT;
   ksql> SELECT * FROM WIKIPEDIABOT LIMIT 3;
   ksql> DESCRIBE EN_WIKIPEDIA_GT_1;
   ksql> SELECT * FROM EN_WIKIPEDIA_GT_1 LIMIT 3;

   By default when you run a SELECT in KSQL it will return new data that is added. If you want to view data already existing in the topic run this first (once per session):

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

2. Monitoring --> Data Streams --> Message Delivery: KSQL queries are materialized in Confluent Control Center as consumer groups with names ksql_query_. To correlate these consumer groups to the actual KSQL query, note the query id in the output of:

   $ docker-compose exec ksql-cli ksql-cli remote http://localhost:8080 --exec "show queries;"

3. Monitoring --> Data Streams --> Message Delivery: graphs for consumer groups EN_WIKIPEDIA_GT_1_COUNTS-consumer and ksql_query_CSAS_EN_WIKIPEDIA_GT_1_COUNTS are displaying data at intervals instead of smoothly like the other consumer groups. This is because Confluent Control Center displays data based on message timestamps, and this particular stream of a data is a tumbling window with a window size of 5 minutes. Thus all its message timestamps are marked to the beginning of each 5-minute window and this is why the latency for these streams appears to be high. Kafka streaming tumbling windows are working as designed and Confluent Control Center is reporting them accurately.

   

Consumer rebalances

Follow along with the video

Control Center shows which consumers in a consumer group are consuming from which partitions and on which brokers those partitions reside. Control Center updates as consumer rebalances occur in a consumer group.

1. Start consuming from topic wikipedia.parsed with a new consumer group app with one consumer consumer_app_1. It will run in the background.

   $ ./scripts/app/start_consumer_app.sh 1

2. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption. Click on the box View Details above the bar graph to drill down into consumer group details. This consumer group app has a single consumer consumer_app_1 consuming all of the partitions in the topic wikipedia.parsed. The first bar may be red because the consumer started in the middle of a time window and did not receive all messages produced during that window. This does not mean messages were lost.

   

3. Add a second consumer consumer_app_2 to the existing consumer group app.

   $ ./scripts/app/start_consumer_app.sh 2

4. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption. Notice that the consumers consumer_app_1 and consumer_app_2 now share consumption of the partitions in the topic wikipedia.parsed. When the second consumer was added, that bar may be red for both consumers because a consumer rebalance occurred during that time window. This does not mean messages were lost, as you can confirm at the consumer group level.

   

Slow consumers

Follow along with the video

Streams monitoring in Control Center can highlight consumers that are slow to keep up with the producers. This is critial to monitor for real-time applications where consumers should consume produced messages with as low latency as possible. To simulate a slow consumer, we will use Kafka's quota feature to rate-limit consumption from the broker side, for just one of two consumers in a consumer group.

1. Click on Data streams, and View Details for the consumer group app. Click on the left-hand blue circle on the consumption line to verify there are two consumers consumer_app_1 and consumer_app_2, that were created in an earlier section. If these two consumers are not running, start them as described in the section consumer rebalances.

   

2. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption.

3. Add a consumption quota for one of the consumers in the consumer group app.

   $ ./scripts/app/throttle_consumer.sh 1 add

   Note: you are running a Docker demo environment with all services running on one host, which you would never do in production. Depending on your system resource availability, sometimes applying the quota may stall the consumer (KAFKA-5871), thus you may need to adjust the quota rate. See the ./scripts/app/throttle_consumer.sh script for syntax on modifying the quota rate.

   • If consumer group app does not increase latency, decrease the quota rate
   • If consumer group app seems to stall, increase the quota rate

4. View the details of the consumer group app again, consumer_app_1 now shows high latency, and consumer_app_2 shows normal latency.

   

5. In the System Health dashboard, you see that the fetch request latency has likewise increased. This is the because the broker that has the partition that consumer_app_1 is consuming from is taking longer to service requests.

   

6. Click on the fetch request latency line graph to see a breakdown of produce and fetch latencies through the entire request lifecycle. The middle number does not necessarily equal the sum of the percentiles of individual segments because it is the total percentile latency.

   

7. Remove the consumption quota for the consumer. Latency for consumer_app_1 recovers to steady state values.

   $ ./scripts/app/throttle_consumer.sh 1 delete

Over consumption

Follow along with the video

Streams monitoring in Control Center can highlight consumers that are over consuming some messages, which is an indication that consumers are processing a set of messages more than once. This may happen intentionally, for example an application with a software bug consumed and processed Kafka messages incorrectly, got a fix, and then reprocesses previous messages correctly. This may also happen unintentionally if an application crashes before committing processed messages. To simulate over consumption, we will use Kafka's consumer offset reset tool to set the offset of the consumer group app to an earlier offset, thereby forcing the consumer group to reconsume messages it has previously read.

1. Click on Data streams, and View Details for the consumer group app. Click on the blue circle on the consumption line on the left to verify there are two consumers consumer_app_1 and consumer_app_2, that were created in an earlier section. If these two consumers are not running and were never started, start them as described in the section consumer rebalances.

   

2. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption.

3. Stop the consumer group app to stop consuming from topic wikipedia.parsed. Note that the command below stops the consumers gracefully with kill -15, so the consumers follow the shutdown sequence.

   $ ./scripts/app/stop_consumer_app_group_graceful.sh

4. Wait for 2 minutes to let messages continue to be written to the topics for a while, without being consumed by the consumer group app. Notice the red bar which highlights that during the time window when the consumer group was stopped, there were some messages produced but not consumed. These messages are not missing, they are just not consumed because the consumer group stopped.

   

5. Reset the offset of the consumer group app by shifting 200 offsets backwards. The offset reset tool must be run when the consumer is completely stopped. Offset values in output shown below will vary.

   $ docker-compose exec kafka1 kafka-consumer-groups --reset-offsets --group app --shift-by -200 --bootstrap-server kafka1:10091 --all-topics --execute
   
   TOPIC                          PARTITION  NEW-OFFSET     
   wikipedia.parsed               1          4071           
   wikipedia.parsed               0          7944 

6. Restart consuming from topic wikipedia.parsed with the consumer group app with two consumers.

   $ ./scripts/app/start_consumer_app.sh 1
   $ ./scripts/app/start_consumer_app.sh 2

7. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption. Notice several things:

   • Even though the consumer group app was not running for some of this time, all messages are shown as delivered. This is because all bars are time windows relative to produce timestamp.
   • For some time intervals, the the bars are red and consumption line is above expected consumption because some messages were consumed twice due to rewinding offsets.
   • The latency peaks and then gradually decreases, because this is also relative to the produce timestamp.
   

Under consumption

Follow along with the video

Streams monitoring in Control Center can highlight consumers that are under consuming some messages. This may happen intentionally when consumers stop and restart and operators change the consumer offsets to the latest offset. This avoids delay processing messages that were produced while the consumers were stopped, especially when they care about real-time. This may also happen unintentionally if a consumer is offline for longer than the log retention period, or if a producer is configured for acks=0 and a broker suddenly fails before having a chance to replicate data to other brokers. To simulate under consumption, we will use Kafka's consumer offset reset tool to set the offset of the consumer group app to the latest offset, thereby skipping messages that will never be read.

1. Click on Data Streams, and View Details for the consumer group app. Click on the blue circle on the consumption line on the left to verify there are two consumers consumer_app_1 and consumer_app_2, that were created in an earlier section. If these two consumers are not running and were never started, start them as described in the section consumer rebalances.

   

2. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption.

3. Stop the consumer group app to stop consuming from topic wikipedia.parsed. Note that the command below stops the consumers ungracefully with kill -9, so the consumers did not follow the shutdown sequence.

   $ ./scripts/app/stop_consumer_app_group_ungraceful.sh

4. Wait for 2 minutes to let messages continue to be written to the topics for a while, without being consumed by the consumer group app. Notice the red bar which highlights that during the time window when the consumer group was stopped, there were some messages produced but not consumed. These messages are not missing, they are just not consumed because the consumer group stopped.

   

5. Wait for another few minutes and notice that the bar graph changes and there is a herringbone pattern to indicate that perhaps the consumer group stopped ungracefully.

   

6. Reset the offset of the consumer group app by setting it to latest offset. The offset reset tool must be run when the consumer is completely stopped. Offset values in output shown below will vary.

   $ docker-compose exec kafka1 kafka-consumer-groups --reset-offsets --group app --to-latest --bootstrap-server kafka1:10091 --all-topics --execute
   
   TOPIC                          PARTITION  NEW-OFFSET     
   wikipedia.parsed               1          8601           
   wikipedia.parsed               0          15135

7. Restart consuming from topic wikipedia.parsed with the consumer group app with two consumers.

   $ ./scripts/app/start_consumer_app.sh 1
   $ ./scripts/app/start_consumer_app.sh 2

8. Let this consumer group run for 2 minutes until Control Center stream monitoring shows the consumer group app with steady consumption. Notice that during the time period that the consumer group app was not running, no produced messages are shown as delivered.

   

Failed broker

Follow along with the video

To simulate a failed broker, stop the Docker container running one of the two Kafka brokers.

1. Stop the Docker container running Kafka broker 2.

   $ docker-compose stop kafka2

2. After a few minutes, observe the System Health shows the broker count has gone down from 2 to 1, and there are many under replicated partitions.

   

3. View topic details to see that there are out of sync replicas on broker 2.

   

4. Restart the Docker container running Kafka broker 2.

   $ docker-compose start kafka2

5. After about a minute, observe the System Health view in Confluent Control Center. The broker count has recovered to 2, and the topic partitions are back to reporting no under replicated partitions.

   

6. Click on the broker count 2 inside the circle to view when the broker counts changed.

   

Alerting

Follow along with the video

There are many types of Control Center alerts and many ways to configure them. Use the Alerts management page to define triggers and actions, or click on a streams monitoring graph for consumer groups or topics to setup alerts from there.

1. This demo already has pre-configured triggers and actions. View the Alerts Triggers screen, and click Edit against each trigger to see configuration details.

   • The trigger Under Replicated Partitions happens when a broker reports non-zero under replicated partitions, and it causes an action Email Administrator.
   • The trigger Consumption Difference happens when consumption difference for the Elasticsearch connector consumer group is greater than 0, and it causes an action Email Administrator.
   

2. If you followed the steps in the failed broker section, view the Alert history to see that the trigger Under Replicated Partitions happened and caused an alert when you stopped broker 2.

3. You can also trigger the Consumption Difference trigger. In the Kafka Connect -> Sinks screen, edit the running Elasticsearch sink connector.

4. In the Kafka Connect view, pause the Elasticsearch sink connector by pressing the pause icon in the top right. This will stop consumption for the related consumer group.

   

5. View the Alert history to see that this trigger happened and caused an alert.

   

Replicator

Confluent Replicator copies data from a source Kafka cluster to a destination Kafka cluster. The source and destination clusters are typically different clusters, but in this demo, Replicator is doing intra-cluster replication, i.e., the source and destination Kafka clusters are the same. As with the rest of the components in the solution, Confluent Replicator is also configured with security.

1. Monitoring --> Data Streams --> Message Delivery: monitor throughput and latency of Confluent Replicator in the Data streams monitoring view. Replicator is a Kafka Connect source connector and has a corresponding consumer group connect-replicator.

   

2. Management --> Topics: scroll down to view the topics called wikipedia.parsed (Replicator is consuming data from this topic) and wikipedia.parsed.replica (Replicator is copying data to this topic). Click on Consumer Groups for the topic wikipedia.parsed and observe that one of the consumer groups is called connect-replicator.

   

3. Management --> Kafka Connect: pause the Replicator connector by pressing the pause icon in the top right. This will stop consumption for the related consumer group.

   

4. Observe that the connect-replicator consumer group has stopped consumption.

   

5. Restart the Replicator connector.

6. Observe that the connect-replicator consumer group has resumed consumption. Notice several things:

    * Even though the consumer group `connect-replicator` was not running for some of this time, all messages are shown as delivered. This is because all bars are time windows relative to produce timestamp.
    * The latency peaks and then gradually decreases, because this is also relative to the produce timestamp.
   

Security

All the components in this demo are enabled with many security features:

• SSL for encryption, except for ZooKeeper which does not support SSL
• SASL/PLAIN for authentication, except for ZooKeeper
• Authorization. If a resource has no associated ACLs, then users are not allowed to access the resource, except super users
• HTTPS for Schema Registry

---

Note: this demo showcases a secure Confluent Platform for educational purposes and is not meant to be complete best practices. There are certain differences between what is shown in the demo and what you should do in production:

• Each component should have its own username, instead of authenticating all users as client
• Authorize users only for operations that they need, instead of making all of them super users
• If the PLAINTEXT security protocol is used, these ANONYMOUS usernames should not be configured as super users
• Consider not even opening the PLAINTEXT port if SSL or SASL_SSL are configured

---

Encryption & Authentication:

Each broker has four listener ports:

• PLAINTEXT port called PLAINTEXT for users with no security enabled
• SSL port port called SSL for users with just SSL without SASL
• SASL_SSL port called SASL_SSL for communication between services inside Docker containers
• SASL_SSL port called SASL_SSL_HOST for communication between any potential services outside of Docker that communicate to the Docker containers

port	kafka1	kafka2
PLAINTEXT	10091	10092
SSL	11091	11092
SASL_SSL	9091	9092
SASL_SSL_HOST	29091	29092

Authorization:

All the brokers in this demo authenticate as broker, and all other components authenticate as client. Per the broker configuration parameter super.users, as it is set in this demo, the only users that can communicate with the cluster are those that authenticate as broker or client, or users that connect via the PLAINTEXT port (their username is ANONYMOUS). All other users are not authorized to communicate with the cluster.

1. Verify the ports on which the Kafka brokers are listening with the following command, and they should match the table shown below:

   $ docker-compose logs kafka1 | grep "Registered broker 1"
   $ docker-compose logs kafka2 | grep "Registered broker 2"

2. This demo automatically generates simple SSL certificates and creates keystores, truststores, and secures them with a password. To communicate with the brokers, Kafka clients may use any of the ports on which the brokers are listening. To use a security-enabled port, they must specify security parameters for keystores, truststores, password, or authentication so the Kafka command line client tools pass the security configuration file with interceptors or without interceptors with these security parameters. As an example, to communicate with the Kafka cluster to view all the active consumer groups:

a. Communicate with brokers via the PLAINTEXT port

# PLAINTEXT port
$ docker-compose exec kafka1 kafka-consumer-groups --list --bootstrap-server kafka1:10091

b. Communicate with brokers via the SASL_SSL port, and SASL_SSL parameters configured via the --command-config argument for command line tools or --consumer.config for kafka-console-consumer.

# SASL_SSL port with SASL_SSL parameters
$ docker-compose exec kafka1 kafka-consumer-groups --list --bootstrap-server kafka1:9091 --command-config /etc/kafka/secrets/client_without_interceptors.config

c. If you try to communicate with brokers via the SASL_SSL port but don't specify the SASL_SSL parameters, it will fail

```bash
# SASL_SSL port without SASL_SSL parameters
$ docker-compose exec kafka1 kafka-consumer-groups --list --bootstrap-server kafka1:9091

Error: Executing consumer group command failed due to Request METADATA failed on brokers List(kafka1:9091 (id: -1 rack: null))
```

3. Verify the super users are configured for the authenticated users broker, client, and unauthenticated PLAINTEXT.

   $ docker-compose logs kafka1 | grep SUPER_USERS
   
   KAFKA_SUPER_USERS=User:client;User:broker;User:ANONYMOUS

4. Verify that a user client which authenticates via SASL can consume messages from topic wikipedia.parsed:

   $ ./scripts/consumers/listen_wikipedia.parsed.sh SASL

5. Verify that a user which authenticates via SSL cannot consume messages from topic wikipedia.parsed. It fails with an exception org.apache.kafka.common.errors.TopicAuthorizationException: Not authorized to access topics: [wikipedia.parsed].

   $ ./scripts/consumers/listen_wikipedia.parsed.sh SSL
   
   [2018-01-12 21:13:18,481] ERROR Unknown error when running consumer:  (kafka.tools.ConsoleConsumer$)
   org.apache.kafka.common.errors.TopicAuthorizationException: Not authorized to access topics: [wikipedia.parsed]

6. Verify that the broker's Authorizer logger logs the denial event. As shown in the log message, the user which authenticates via SSL has a username CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US, not just client.

       # Authorizer logger logs the denied operation
       $ docker-compose logs kafka1 | grep kafka.authorizer.logger
   
   [2018-01-12 21:13:18,454] INFO Principal = User:CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US is Denied Operation = Describe from host = 172.23.0.7 on resource = Topic:wikipedia.parsed (kafka.authorizer.logger)
   [2018-01-12 21:13:18,464] INFO Principal = User:CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US is Denied Operation = Describe from host = 172.23.0.7 on resource = Group:test (kafka.authorizer.logger)

7. Add an ACL that authorizes user CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US, and then view the updated ACL configuration.

   $ docker-compose exec connect /usr/bin/kafka-acls --authorizer-properties zookeeper.connect=zookeeper:2181 \
       --add --topic wikipedia.parsed --allow-principal User:CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US --operation Read --group test
   
   $ docker-compose exec connect /usr/bin/kafka-acls --authorizer-properties zookeeper.connect=zookeeper:2181 \
       --list --topic wikipedia.parsed --group test
   
   Current ACLs for resource `Topic:wikipedia.parsed`: 
   	User:CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US has Allow permission for operations: Read from hosts: * 
   
   Current ACLs for resource `Group:test`: 
   	User:CN=client,OU=TEST,O=CONFLUENT,L=PaloAlto,ST=Ca,C=US has Allow permission for operations: Read from hosts: * 

8. Verify that the user which authenticates via SSL is now authorized and can successfully consume some messages from topic wikipedia.parsed.

   $ ./scripts/consumers/listen_wikipedia.parsed.sh SSL

Troubleshooting the demo

1. To view sample messages for each topic, including wikipedia.parsed:

   $ ./scripts/consumers/listen.sh

2. If the data streams monitoring appears to stop for the Kafka source connector, restart the connect container.

   $ docker-compose restart connect

Teardown

1. Stop the consumer group app to stop consuming from topic wikipedia.parsed. Note that the command below stops the consumers gracefully with kill -15, so the consumers follow the shutdown sequence.

   $ ./scripts/app/stop_consumer_app_group_graceful.sh

2. Stop the Docker demo, destroy all components and clear all Docker volumes.

   $ ./scripts/reset_demo.sh