======== Run demo ======== **Demo validated 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 - git - jq .. note:: If you prefer a non-Docker version and have Elasticsearch and Kibana running on your local machine, please follow `these instructions `__. 1. Clone the `cp-demo GitHub repository `__: .. sourcecode:: bash git clone https://github.com/confluentinc/cp-demo 2. In Docker's advanced `settings `__, increase the memory dedicated to Docker to at least 8GB (default is 2GB). 3. From the ``cp-demo`` directory, start the entire demo by running a single command that generates the keys and certificates, brings up the Docker containers, and configures and validates the environment. This will take less than 5 minutes to complete. .. sourcecode:: bash ./scripts/start.sh 4. Use Google Chrome to view the |c3| 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``. 5. View the data in the Kibana dashboard at http://localhost:5601/app/kibana#/dashboard/Wikipedia ======== Playbook ======== |c3| Tour -------------------------------- Follow along with the `Demo 2: Tour `_ video. .. raw:: html

1. **MONITORING –> System Health**: |c3| 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 .. figure:: images/landing_page.png 2. **MANAGEMENT –> Kafka Connect**: |c3| 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. .. figure:: images/connect_source.png :alt: image - Kafka Connect **Sinks** tab shows the connector ``elasticsearch-ksql``. Click ``Edit`` to see the details of the connector configuration and custom transforms. .. figure:: images/connect_sink.png :alt: image 3. **MONITORING –> Data Streams –> Message Delivery**: hover over any chart to see number of messages and average latency within a minute time interval. .. figure:: images/message_delivery.png :alt: image 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. .. figure:: images/connect_consumer_group.png :alt: image 4. **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. .. figure:: images/request_latencies.png :alt: image Topic Management -------------------------------- |c3| has a useful interface to manage topics in a Kafka cluster. 1. **MANAGEMENT –> Topics**: Scroll down to and click on the topic `wikipedia.parsed`. .. figure:: images/topic_actions.png :alt: image 2. **MANAGEMENT –> Topics -> Status**: View which brokers are leaders for which partitions and where all partitions reside. .. figure:: images/topic_info.png :alt: image 3. **MANAGEMENT –> Topics -> Schema**: View the schema for this topic. For `wikipedia.parsed`, the topic value is using a Schema registered with |sr| (the topic key is just a string). .. figure:: images/topic_schema.png :alt: image 4. **MANAGEMENT –> Topics -> Inspect**: View messages for this topic, in real-time. .. figure:: images/topic_inspect.png :alt: image 5. **MANAGEMENT –> Topics -> Settings**: View configuration settings for this topic. .. figure:: images/topic_settings.png :alt: image 6. **MANAGEMENT -> Topics**: click the ``+ Create`` button on the top right to create a new topic in your Kafka cluster. You can also view and edit settings of Kafka topics in the cluster. Read more on |c3| `topic management `__. .. figure:: images/create_topic.png :alt: image 7. When Confluent Monitoring Interceptors are configured on Kafka clients, they write metadata to a topic called ``_confluent-monitoring``. Kafka clients include any application that uses the Apache Kafka client API to connect to Kafka brokers, such as custom client code or any service that has embedded producers or consumers, such as Kafka Connect, KSQL, or a Kafka Streams application. |c3| uses that topic to ensure that all messages are delivered and to provide statistics on throughput and latency performance. From that same topic, you can also derive which producers are writing to which topics and which consumers are reading from which topics, and an example script is provided with the repo (note: this is for demo purposes only, not suitable for production). .. sourcecode:: bash $ ./scripts/app/map_topics_clients.py Reading topic _confluent-monitoring for 60 seconds...please wait EN_WIKIPEDIA_GT_1 producers _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-84e85189-4f37-460c-991f-bb7bbb4b5a58-StreamThread-12-producer _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-84e85189-4f37-460c-991f-bb7bbb4b5a58-StreamThread-9-producer consumers _confluent-ksql-default_query_CSAS_EN_WIKIPEDIA_GT_1_COUNTS_3 EN_WIKIPEDIA_GT_1_COUNTS producers _confluent-ksql-default_query_CSAS_EN_WIKIPEDIA_GT_1_COUNTS_3-df19ff7e-4d42-4b40-8133-a3632c86e42d-StreamThread-13-producer _confluent-ksql-default_query_CSAS_EN_WIKIPEDIA_GT_1_COUNTS_3-df19ff7e-4d42-4b40-8133-a3632c86e42d-StreamThread-14-producer consumers EN_WIKIPEDIA_GT_1_COUNTS-consumer WIKIPEDIABOT producers _confluent-ksql-default_query_CSAS_WIKIPEDIABOT_1-7d47ae21-e734-43da-9782-bae3191fc85a-StreamThread-7-producer _confluent-ksql-default_query_CSAS_WIKIPEDIABOT_1-7d47ae21-e734-43da-9782-bae3191fc85a-StreamThread-8-producer consumers connect-elasticsearch-ksql WIKIPEDIANOBOT producers _confluent-ksql-default_query_CSAS_WIKIPEDIANOBOT_0-6f29b3fb-abf8-4c3e-bb8d-266cb5aa65c6-StreamThread-2-producer _confluent-ksql-default_query_CSAS_WIKIPEDIANOBOT_0-6f29b3fb-abf8-4c3e-bb8d-266cb5aa65c6-StreamThread-3-producer consumers WIKIPEDIANOBOT-consumer _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-KSTREAM-AGGREGATE-STATE-STORE-0000000007-changelog producers _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-84e85189-4f37-460c-991f-bb7bbb4b5a58-StreamThread-12-producer _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-84e85189-4f37-460c-991f-bb7bbb4b5a58-StreamThread-9-producer _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-KSTREAM-AGGREGATE-STATE-STORE-0000000007-repartition producers _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2-84e85189-4f37-460c-991f-bb7bbb4b5a58-StreamThread-11-producer consumers _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2 wikipedia.parsed producers connect-worker-producer consumers _confluent-ksql-default_query_CSAS_WIKIPEDIABOT_1 _confluent-ksql-default_query_CSAS_WIKIPEDIANOBOT_0 _confluent-ksql-default_query_CTAS_EN_WIKIPEDIA_GT_1_2 connect-replicator wikipedia.parsed.replica producers connect-worker-producer .. _ksql-demo-3: KSQL ---- Follow along with the `Demo 3: KSQL `_ video. .. raw:: html

In this demo, KSQL is authenticated and authorized to connect to the secured Kafka cluster, and it is already running queries as defined in the `KSQL command file `__. 1. The KSQL server is listening on port 8088. You have two options for interfacing with KSQL: (a) Use Control Center's integrated `KSQL UI `__. From the |c3| UI, click **DEVELOPMENT –> KSQL**: .. figure:: images/development_ksql.png :alt: image (b) Run KSQL CLI to get to the KSQL CLI prompt. .. sourcecode:: bash docker-compose exec ksql-cli ksql http://ksql-server:8088 2. **DEVELOPMENT -> KSQL -> STREAMS**: View the existing KSQL streams. (If you are using the KSQL CLI, at the ``ksql>`` prompt type ``SHOW STREAMS;``). .. figure:: images/ksql_streams_list.png :alt: image 3. **DEVELOPMENT -> KSQL -> STREAMS**: Describe the schema (fields or columns) and source and sink of an existing KSQL stream. Click on ``WIKIPEDIA``. .. figure:: images/wikipedia_describe.png :alt: image 4. **DEVELOPMENT -> KSQL -> TABLES**: View the existing KSQL tables. (If you are using the KSQL CLI, at the ``ksql>`` prompt type ``SHOW TABLES;``). .. figure:: images/ksql_tables_list.png :alt: image 5. **DEVELOPMENT -> KSQL -> PERSISTENT QUERIES**: View the existing KSQL queries, which are continuously running. (If you are using the KSQL CLI, at the ``ksql>`` prompt type ``SHOW QUERIES;``). .. figure:: images/ksql_queries_list.png :alt: image 6. **DEVELOPMENT -> KSQL -> STREAMS**: View messages from different KSQL streams and tables. Right click on your stream of choice, select ``Query`` which takes you to the Query Editor with a pre-populated query such as ``select * from WIKIPEDIA limit 5;``. Click on the ``Run query`` button to run. .. figure:: images/ksql_query_topic.png :alt: image 7. **DEVELOPMENT -> KSQL -> STREAMS**: Create a new stream from an existing topic. Click on the button ``Create Stream`` and follow the prompts. 8. **DEVELOPMENT -> KSQL -> QUERY EDITOR**: View the configured KSQL server properties set in the docker-compose.yml file. In the query editor, type ``SHOW PROPERTIES;`` and then click on the ``Run query`` button. .. figure:: images/ksql_properties.png :alt: image 9. In this demo, KSQL is run with Confluent Monitoring Interceptors configured which enables |c3| Data Streams to monitor KSQL queries. The consumer group names ``ksql_query_`` correlate to the KSQL query names above, and |c3| is showing the records that are incoming to each query. * View throughput and latency of the incoming records for the persistent KSQL "Create Stream As Select" query ``CSAS_WIKIPEDIABOT``, which is displayed as ``ksql_query_CSAS_WIKIPEDIABOT`` in |c3|. .. figure:: images/ksql_query_CSAS_WIKIPEDIABOT.png :alt: image * View throughput and latency of the incoming records for the persistent KSQL "Create Table As Select" query ``CTAS_EN_WIKIPEDIA_GT_1``, which is displayed as ``ksql_query_CTAS_EN_WIKIPEDIA_GT_1`` in |c3|. .. figure:: images/ksql_query_CTAS_EN_WIKIPEDIA_GT_1.png :alt: image * View throughput and latency of the incoming records for the persistent KSQL "Create Stream As Select" query ``CTAS_EN_WIKIPEDIA_GT_1_COUNTS``, which is displayed as ``ksql_query_CSAS_EN_WIKIPEDIA_GT_1_COUNTS`` in |c3|. .. figure:: images/tumbling_window.png :alt: image .. note:: In |c3| the stream monitoring graphs for consumer groups ``ksql_query_CSAS_EN_WIKIPEDIA_GT_1_COUNTS`` and ``EN_WIKIPEDIA_GT_1_COUNTS-consumer`` are displaying data at 5-minute intervals instead of smoothly like the other consumer groups. This is because |c3| displays data based on message timestamps, and the incoming stream for these consumer groups is a tumbling window with a window size of 5 minutes. Thus all its messages are timestamped to the beginning of each 5-minute window. This is also why the latency for these streams appears to be high. Kafka streaming tumbling windows are working as designed, and |c3| is reporting them accurately. 10. This demo creates two streams ``EN_WIKIPEDIA_GT_1`` and ``EN_WIKIPEDIA_GT_1_COUNTS``, and the reason is to demonstrate how KSQL windows work. ``EN_WIKIPEDIA_GT_1`` counts occurences with a tumbling window, and for a given key it writes a `null` into the table on the first seen message. The underlying Kafka topic for ``EN_WIKIPEDIA_GT_1`` does not filter out those nulls, but since we want to send downstream just the counts greater than one, there is a separate Kafka topic for ````EN_WIKIPEDIA_GT_1_COUNTS`` which does filter out those nulls (e.g., the query has a clause ``where ROWTIME is not null``). From the bash prompt, view those underlying Kafka topics. .. sourcecode:: bash docker exec connect kafka-avro-console-consumer --bootstrap-server kafka1:9091 --topic EN_WIKIPEDIA_GT_1 \ --property schema.registry.url=https://schemaregistry:8085 \ --consumer.config /etc/kafka/secrets/client_without_interceptors.config --max-messages 10 null {"USERNAME":"Atsme","WIKIPAGE":"Wikipedia:Articles for deletion/Metallurg Bratsk","COUNT":2} null null null {"USERNAME":"7.61.29.178","WIKIPAGE":"Tandem language learning","COUNT":2} {"USERNAME":"Attar-Aram syria","WIKIPAGE":"Antiochus X Eusebes","COUNT":2} ... docker exec connect kafka-avro-console-consumer --bootstrap-server kafka1:9091 --topic EN_WIKIPEDIA_GT_1_COUNTS \ --property schema.registry.url=https://schemaregistry:8085 \ --consumer.config /etc/kafka/secrets/client_without_interceptors.config --max-messages 10 {"USERNAME":"Atsme","COUNT":2,"WIKIPAGE":"Wikipedia:Articles for deletion/Metallurg Bratsk"} {"USERNAME":"7.61.29.178","COUNT":2,"WIKIPAGE":"Tandem language learning"} {"USERNAME":"Attar-Aram syria","COUNT":2,"WIKIPAGE":"Antiochus X Eusebes"} {"USERNAME":"RonaldB","COUNT":2,"WIKIPAGE":"Wikipedia:Open proxy detection"} {"USERNAME":"Dormskirk","COUNT":2,"WIKIPAGE":"Swindon Designer Outlet"} {"USERNAME":"B.Bhargava Teja","COUNT":3,"WIKIPAGE":"Niluvu Dopidi"} ... Consumer rebalances ------------------- Follow along with the `Demo 4: Consumer Rebalances `_ video. .. raw:: html

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. .. sourcecode:: bash ./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. .. figure:: images/consumer_start_one.png :alt: image 3. Add a second consumer ``consumer_app_2`` to the existing consumer group ``app``. .. sourcecode:: bash ./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. .. figure:: images/consumer_start_two.png :alt: image Slow consumers -------------- Follow along with the `Demo 5: Slow Consumers `_ video. .. raw:: html

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 <#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``. .. sourcecode:: bash ./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. .. figure:: images/slow_consumer.png :alt: image 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. .. figure:: images/slow_consumer_fetch_latency.png :alt: image 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. .. figure:: images/slow_consumer_fetch_latency_breakdown.png :alt: image 7. **MONITORING -> Consumer lag**: consumer lag is the topic's high water mark (latest offset for the topic that has been written) minus the current consumer offset (latest offset read for that topic by that consumer group). Keep in mind topic write rate and consumer group read rate when considering what the significance of how big is the consumer lag. In the demo, view the consumer lag for the ``app`` consumer group: expect consumer 1 to be have much more lag than consumer 2 because of the throttle you added in an earlier step. .. figure:: images/consumer_lag_app.png :alt: image 8. Remove the consumption quota for the consumer. Latency for ``consumer_app_1`` recovers to steady state values. .. sourcecode:: bash ./scripts/app/throttle_consumer.sh 1 delete Over consumption ---------------- Follow along with the `Demo 6: Over Consumption `_ video. .. raw:: html

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 <#consumer-rebalances>`__. .. figure:: images/verify_two_consumers.png :alt: image 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. .. sourcecode:: bash ./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. .. figure:: images/over_consumption_before_2.png :alt: image 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. .. sourcecode:: bash docker-compose exec kafka1 kafka-consumer-groups \ --reset-offsets --group app --shift-by -200 --bootstrap-server kafka1:10091 \ --all-topics --execute Your output should resemble: .. sourcecode:: bash 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. .. sourcecode:: bash ./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. .. figure:: images/over_consumption_after_2.png :alt: image Under consumption ----------------- Follow along with the `Demo 7: Under Consumption `_ video. .. raw:: html

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 <#consumer-rebalances>`__. .. figure:: images/verify_two_consumers.png :alt: image 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. .. sourcecode:: bash ./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. .. figure:: images/under_consumption_before.png :alt: image 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. .. figure:: images/under_consumption_before_herringbone.png :alt: image 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. .. sourcecode:: bash docker-compose exec kafka1 kafka-consumer-groups \ --reset-offsets --group app --to-latest --bootstrap-server kafka1:10091 \ --all-topics --execute Your output should resemble: .. sourcecode:: bash 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. .. sourcecode:: bash ./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. .. figure:: images/under_consumption_after.png :alt: image Failed broker ------------- Follow along with the `Demo 8: Failed Broker `_ video. .. raw:: html

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. .. sourcecode:: bash 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. .. figure:: images/broker_down_failed.png :alt: image 3. View topic details to see that there are out of sync replicas on broker 2. .. figure:: images/broker_down_replicas.png :alt: image 4. Restart the Docker container running Kafka broker 2. .. sourcecode:: bash 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. .. figure:: images/broker_down_steady.png :alt: image 6. Click on the broker count ``2`` inside the circle to view when the broker counts changed. .. figure:: images/broker_down_times.png :alt: image Alerting -------- Follow along with the `Demo 9: Alerting `_ video. .. raw:: html

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``. .. figure:: images/alerts_triggers.png :alt: image 2. If you followed the steps in the `failed broker <#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. .. figure:: images/pause_connector.png :alt: image 5. View the Alert history to see that this trigger happened and caused an alert. .. figure:: images/trigger_history.png :alt: image 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``. .. figure:: images/replicator_consumer_group.png :alt: image 2. **MANAGEMENT –> Topics**: scroll down to view the topics called ``wikipedia.parsed`` (Replicator is consuming data from this topic) and ``wikipedia.parsed.replica`` (Replicator automatically created this topic and is copying data to it). Click on ``Consumer Groups`` for the topic ``wikipedia.parsed`` and observe that one of the consumer groups is called ``connect-replicator``. .. figure:: images/replicator_topic_info.png 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. .. figure:: images/pause_connector.png :alt: image 4. Observe that the ``connect-replicator`` consumer group has stopped consumption. .. figure:: images/replicator_streams_stopped.png 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 -------- Follow along with the `Security `_ video. 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 which is configured for `SASL/DIGEST-MD5 `__ - `Authorization `__. If a resource has no associated ACLs, then users are not allowed to access the resource, except super users - `HTTPS for Control Center `__ - `HTTPS for Schema Registry `__ - `HTTPS for Connect `__ .. note:: This demo showcases a secure |CP| 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 services authenticate as their respective names. 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``, ``schemaregistry``, ``client``, ``restproxy``, ``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: .. sourcecode:: bash 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: #. Communicate with brokers via the PLAINTEXT port .. sourcecode:: bash # PLAINTEXT port docker-compose exec kafka1 kafka-consumer-groups --list --bootstrap-server kafka1:10091 #. 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. .. sourcecode:: bash # 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 #. If you try to communicate with brokers via the SASL_SSL port but don’t specify the SASL_SSL parameters, it will fail .. sourcecode:: bash # SASL_SSL port without SASL_SSL parameters docker-compose exec kafka1 kafka-consumer-groups --list --bootstrap-server kafka1:9091 Your output should resemble: .. sourcecode:: bash Error: Executing consumer group command failed due to Request METADATA failed on brokers List(kafka1:9091 (id: -1 rack: null)) 3. Verify which authenticated users are configured to be super users. .. sourcecode:: bash docker-compose logs kafka1 | grep SUPER_USERS Your output should resemble the following. Notice this authorizes each service name which authenticates as itself, as well as the unauthenticated ``PLAINTEXT`` which authenticates as ``ANONYMOUS`` (for demo purposes only): .. sourcecode:: bash KAFKA_SUPER_USERS=User:client;User:schemaregistry;User:restproxy;User:broker;User:connect;User:ANONYMOUS 4. Verify that a user ``client`` which authenticates via SASL can consume messages from topic ``wikipedia.parsed``: .. sourcecode:: bash ./scripts/consumers/listen_wikipedia.parsed.sh SASL 5. Verify that a user which authenticates via SSL cannot consume messages from topic ``wikipedia.parsed``. It should fail with an exception. .. sourcecode:: bash ./scripts/consumers/listen_wikipedia.parsed.sh SSL Your output should resemble: .. sourcecode:: bash [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``. .. sourcecode:: bash # Authorizer logger logs the denied operation docker-compose logs kafka1 | grep kafka.authorizer.logger Your output should resemble: .. sourcecode:: bash [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. .. sourcecode:: bash docker-compose exec kafka1 /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 kafka1 /usr/bin/kafka-acls \ --authorizer-properties zookeeper.connect=zookeeper:2181 \ --list --topic wikipedia.parsed --group test Your output should resemble: .. sourcecode:: bash 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``. .. sourcecode:: bash ./scripts/consumers/listen_wikipedia.parsed.sh SSL 9. Because ZooKeeper is configured for `SASL/DIGEST-MD5 `__, any commands that communicate with ZooKeeper need properties set for ZooKeeper authentication. This authentication configuration is provided by the ``KAFKA_OPTS`` setting on the brokers. For example, notice that the `throttle script `__ runs on the Docker container ``kafka1`` which has the appropriate `KAFKA_OPTS` setting. The command would otherwise fail if run on any other container aside from ``kafka1`` or ``kafka2``. |sr| and REST Proxy ------------------- The connectors used in this demo are configured to automatically read and write Avro-formatted data, leveraging the `Confluent Schema Registry `__ . The `Confluent REST Proxy `__ is running for optional client access. 1. View the |sr| subjects for topics that have registered schemas for their keys and/or values. Notice the security arguments passed into the ``curl`` command which are required to interact with |sr|, which is listening for HTTPS on port 8085. .. sourcecode:: bash docker-compose exec schemaregistry curl -X GET --cert /etc/kafka/secrets/schemaregistry.certificate.pem --key /etc/kafka/secrets/schemaregistry.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt https://schemaregistry:8085/subjects | jq . [ "ksql_query_CTAS_EN_WIKIPEDIA_GT_1-KSQL_Agg_Query_1526914100640-changelog-value", "ksql_query_CTAS_EN_WIKIPEDIA_GT_1-KSQL_Agg_Query_1526914100640-repartition-value", "EN_WIKIPEDIA_GT_1_COUNTS-value", "WIKIPEDIABOT-value", "EN_WIKIPEDIA_GT_1-value", "WIKIPEDIANOBOT-value", "wikipedia.parsed-value" ] 2. Register a new Avro schema (a record with two fields ``username`` and ``userid``) into |sr| for the value of a new topic ``users``. Note the schema id that it returns, e.g. below schema id is ``6``. .. sourcecode:: bash docker-compose exec schemaregistry curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" --cert /etc/kafka/secrets/schemaregistry.certificate.pem --key /etc/kafka/secrets/schemaregistry.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt --data '{ "schema": "[ { \"type\":\"record\", \"name\":\"user\", \"fields\": [ {\"name\":\"userid\",\"type\":\"long\"}, {\"name\":\"username\",\"type\":\"string\"} ]} ]" }' https://schemaregistry:8085/subjects/users-value/versions | jq . { "id": 6 } 3. View the new schema for the subject ``users-value``. From |c3|, click **MANAGEMENT -> Topics**. Scroll down to and click on the topic `users` and select "SCHEMA". .. figure:: images/schema1.png :alt: image You may alternatively request the schema via the command line: .. sourcecode:: bash docker-compose exec schemaregistry curl -X GET --cert /etc/kafka/secrets/schemaregistry.certificate.pem --key /etc/kafka/secrets/schemaregistry.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt https://schemaregistry:8085/subjects/users-value/versions/1 | jq . { "subject": "users-value", "version": 1, "id": 6, "schema": "{\"type\":\"record\",\"name\":\"user\",\"fields\":[{\"name\":\"username\",\"type\":\"string\"},{\"name\":\"userid\",\"type\":\"long\"}]}" } 4. Use the REST Proxy, which is listening for HTTPS on port 8086, to produce a message to the topic ``users``, referencing schema id ``6``. .. sourcecode:: bash docker-compose exec restproxy curl -X POST -H "Content-Type: application/vnd.kafka.avro.v2+json" -H "Accept: application/vnd.kafka.v2+json" --cert /etc/kafka/secrets/restproxy.certificate.pem --key /etc/kafka/secrets/restproxy.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt --data '{"value_schema_id": 6, "records": [{"value": {"user":{"userid": 1, "username": "Bunny Smith"}}}]}' https://restproxy:8086/topics/users {"offsets":[{"partition":1,"offset":0,"error_code":null,"error":null}],"key_schema_id":null,"value_schema_id":6} 5. Use the REST Proxy to consume the above message from the topic ``users``. This is a series of steps. .. sourcecode:: bash # 5.1 Create consumer instance my_avro_consumer docker-compose exec restproxy curl -X POST -H "Content-Type: application/vnd.kafka.v2+json" --cert /etc/kafka/secrets/restproxy.certificate.pem --key /etc/kafka/secrets/restproxy.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt --data '{"name": "my_consumer_instance", "format": "avro", "auto.offset.reset": "earliest"}' https://restproxy:8086/consumers/my_avro_consumer # 5.2 Subscribe my_avro_consumer to the `users` topic docker-compose exec restproxy curl -X POST -H "Content-Type: application/vnd.kafka.v2+json" --cert /etc/kafka/secrets/restproxy.certificate.pem --key /etc/kafka/secrets/restproxy.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt --data '{"topics":["users"]}' https://restproxy:8086/consumers/my_avro_consumer/instances/my_consumer_instance/subscription # 5.3 Get messages for my_avro_consumer subscriptions # Note: Issue this command twice due to https://github.com/confluentinc/kafka-rest/issues/432 docker-compose exec restproxy curl -X GET -H "Accept: application/vnd.kafka.avro.v2+json" --cert /etc/kafka/secrets/restproxy.certificate.pem --key /etc/kafka/secrets/restproxy.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt https://restproxy:8086/consumers/my_avro_consumer/instances/my_consumer_instance/records # 5.4 Delete the consumer instance my_avro_consumer docker-compose exec restproxy curl -X DELETE -H "Content-Type: application/vnd.kafka.v2+json" --cert /etc/kafka/secrets/restproxy.certificate.pem --key /etc/kafka/secrets/restproxy.key --tlsv1.2 --cacert /etc/kafka/secrets/snakeoil-ca-1.crt https://restproxy:8086/consumers/my_avro_consumer/instances/my_consumer_instance ======================== Troubleshooting the demo ======================== 1. 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 8 GB (default is 2 GB). .. sourcecode:: bash docker-compose ps Your output should resemble: .. sourcecode:: bash Name Command State Ports --------------------------------------------------------------------------------------------------------------------------------- connect /etc/confluent/docker/run Up 0.0.0.0:8083->8083/tcp, 9092/tcp control-center /etc/confluent/docker/run Up 0.0.0.0:9021->9021/tcp, 0.0.0.0:9022->9022/tcp elasticsearch /bin/bash bin/es-docker Up 0.0.0.0:9200->9200/tcp, 0.0.0.0:9300->9300/tcp kafka-client bash -c -a echo Waiting fo ... Up 0.0.0.0:7073->7073/tcp, 9092/tcp kafka1 /etc/confluent/docker/run Up 0.0.0.0:29091->29091/tcp, 0.0.0.0:9091->9091/tcp, 9092/tcp kafka2 /etc/confluent/docker/run Up 0.0.0.0:29092->29092/tcp, 0.0.0.0:9092->9092/tcp kibana /bin/sh -c /usr/local/bin/ ... Up 0.0.0.0:5601->5601/tcp ksql-cli /bin/sh Up ksql-server /etc/confluent/docker/run Up 0.0.0.0:8088->8088/tcp replicator-for-jar-transfer sleep infinity Up 8083/tcp, 9092/tcp restproxy /etc/confluent/docker/run Up 8082/tcp, 0.0.0.0:8086->8086/tcp schemaregistry /etc/confluent/docker/run Up 8081/tcp, 0.0.0.0:8085->8085/tcp zookeeper /etc/confluent/docker/run Up 0.0.0.0:2181->2181/tcp, 2888/tcp, 3888/tcp 2. To view sample messages for each topic, including ``wikipedia.parsed``: .. sourcecode:: bash ./scripts/consumers/listen.sh 3. If the data streams monitoring appears to stop for the Kafka source connector, restart the connect container. .. sourcecode:: bash docker-compose restart connect 4. If a command that communicates with ZooKeeper appears to be failing with the error ``org.apache.zookeeper.KeeperException$NoAuthException``, change the container you are running the command from to be either ``kafka1`` or ``kafka2``. This is because ZooKeeper is configured for `SASL/DIGEST-MD5 `__, and any commands that communicate with ZooKeeper need properties set for ZooKeeper authentication. ======== 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. .. code:: bash ./scripts/app/stop_consumer_app_group_graceful.sh 2. Stop the Docker demo, destroy all components and clear all Docker volumes. .. sourcecode:: bash ./scripts/stop.sh