Utilizing Kafka Join Securely within the Cloudera Knowledge Platform







On this submit I’ll show how Kafka Join is built-in within the Cloudera Knowledge Platform (CDP), permitting customers to handle and monitor their connectors in Streams Messaging Supervisor whereas additionally pertaining to safety features reminiscent of role-based entry management and delicate data dealing with. If you’re a developer transferring information in or out of Kafka, an administrator, or a safety knowledgeable this submit is for you. However earlier than I introduce the nitty-gritty first let’s begin with the fundamentals.

Kafka Join

For the aim of this text it’s ample to know that Kafka Join is a strong framework to stream information out and in of Kafka at scale whereas requiring a minimal quantity of code as a result of the Join framework handles many of the life cycle administration of connectors already. As a matter of truth, for the most well-liked supply and goal programs there are connectors already developed that can be utilized and thus require no code, solely configuration. 

The core constructing blocks are: connectors, which orchestrate the information motion between a single supply and a single goal (one in every of them being Kafka); duties which are chargeable for the precise information motion; and staff that handle the life cycle of all of the connectors.

Kafka permits native assist for deploying and managing connectors, which signifies that after beginning a Join cluster submitting a connector configuration and/or managing the deployed connector may be completed by a REST API that’s uncovered by Kafka. Streams Messaging Supervisor (SMM) builds on prime of this and gives a user-friendly interface to switch the REST API calls.

Streams Messaging Supervisor

Disclaimer: descriptions and screenshots on this article are made with CDP 7.2.15 as SMM is beneath energetic improvement; supported options may change from model to model (like what number of varieties of connectors can be found). 

SMM is Cloudera’s resolution to observe and work together with Kafka and associated providers. The SMM UI is made up of a number of tabs, every of which include totally different instruments, capabilities, graphs, and so forth, that you should use to handle and acquire clear insights about your Kafka clusters. This text focuses on the Join tab, which is used to work together with and monitor Kafka Join.

Creating and configuring connectors

Earlier than any monitoring can occur step one is to create a connector utilizing the New Connector button on the highest proper, which navigates to the next view:

On the highest left two varieties of connector templates are displayed:                     supply to ingest information into, and sink to tug information out of Kafka. By default the Supply Templates tab is chosen so the supply connector templates are displayed which are accessible in our cluster. Notice that the playing cards on this web page don’t characterize the connector cases which are deployed on the cluster, relatively they characterize the kind of connectors which are accessible for deployment on the cluster. For instance, there’s a JDBC Supply connector template, however that doesn’t imply that there’s a JDBC Supply connector at present transferring information into Kafka, it simply signifies that the required libraries are in place to assist deploying JDBC Supply connectors. 

After a connector is chosen the Connector Kind is offered.

The Connector Kind is used to configure your connector. Most connectors included by default in CDP are shipped with a pattern configuration to ease configuration. The properties and values included within the templates depend upon the chosen connector. Typically, every pattern configuration consists of the properties which are probably wanted for the connector to work, with some wise defaults already current. If a template is accessible for a particular connector, it’s robotically loaded into the Connector Kind when you choose the connector. The instance above is the prefilled type of the Debezium Oracle Supply connector.

Let’s take a look on the variety of options the Connector Kind gives when configuring a connector.

Including, eradicating, and configuring properties

Every line within the type represents a configuration property and its worth. Properties may be configured by populating the accessible entries with a property identify and its configuration worth. New properties may be added and eliminated utilizing the plus/trash bin icons. 

Viewing and modifying giant configuration values

The values you configure for sure properties is probably not a brief string or integer; some values can get fairly giant. For instance, Stateless NiFi connectors require the stream.snapshot property, the worth of which is the total contents of a JSON file (assume tons of of strains). Properties like these may be edited in a modal window by clicking the Edit button.

Hiding delicate values

By default properties are saved in plaintext so they’re seen to anybody who has entry to SMM with applicable authorization rights.

There is perhaps properties within the configurations like passwords and entry keys that customers wouldn’t wish to leak from the system; to safe delicate information from the system these may be marked as secrets and techniques with the Lock icon, which achieves two issues:

  • The property’s worth will probably be hidden on the UI.
  • The worth will probably be encrypted and saved in a safe method on the backend.

Notice: Properties marked as secrets and techniques can’t be edited utilizing the Edit button.

To enter the technical particulars for a bit, not solely is the worth merely encrypted, however the encryption key used to encrypt the worth can also be wrapped with a worldwide encryption key for an added layer of safety. Even when the worldwide encryption secret is leaked, the encrypted configurations may be simply re-encrypted, changing the previous international key with a Cloudera offered software. For extra data, see Kafka Join Secrets and techniques Storage.

Importing and enhancing configurations

In case you have already ready native Kafka Join configurations you should use the Import Connector Configuration button to repeat and paste it or browse it from the file system utilizing a modal window.

This characteristic can show particularly helpful for migrating Kafka Join workloads into CDP as present connector configurations may be imported with a click on of a button. 

Whereas importing there may be even an possibility to reinforce the configuration utilizing the Import and Improve button. Enhancing will add the properties which are probably wanted, for instance: 

  • Properties which are lacking in comparison with the pattern configuration.
  • Properties from the stream.snapshot of StatelessNiFi connectors.

Validating configurations

On the highest proper you’ll be able to see the Validate button. Validating a configuration is obligatory earlier than deploying a connector. In case your configuration is legitimate, you’ll see a “Configuration is legitimate” message and the Subsequent button will probably be enabled to proceed with the connector deployment. If not, the errors will probably be highlighted throughout the Connector Kind. Typically, you’ll encounter 4 varieties of errors:

  • Basic configuration errors
    Errors that aren’t associated to a particular property seem above the shape within the Errors part.
  • Lacking properties
    Errors concerning lacking configurations additionally seem within the Errors part with the utility button Add Lacking Configurations, which does precisely that: provides the lacking configurations to the beginning of the shape.
  • Property particular errors
    Errors which are particular to properties (displayed beneath the suitable property).
  • Multiline errors
    If a single property has a number of errors, a multiline error will probably be displayed beneath the property.


To show SMM’s monitoring capabilities for Kafka Join I’ve arrange two MySql connectors: “gross sales.product_purchases” and “monitoring.raw_metrics”. Now the aim of this text is to indicate off how Kafka Join is built-in into the Cloudera ecosystem, so I cannot go in depth on how one can arrange these connectors, however if you wish to comply with alongside you could find detailed steerage in these articles: 

MySQL CDC with Kafka Join/Debezium in CDP Public Cloud

The utilization of safe Debezium connectors in Cloudera environments 

Now let’s dig extra into the Join web page, the place I beforehand began creating connectors. On the Connector web page there’s a abstract of the connectors with some general statistics, like what number of connectors are operating and/or failed; this may also help decide if there are any errors at a look.

Beneath the general statistics part there are three columns, one for Supply Connectors, one for Matters, and one for Sink Connectors. The primary and the final characterize the deployed connectors, whereas the center one shows the matters that these connectors work together with. 

To see which connector is linked to which matter simply click on on the connector and a graph will seem.

Aside from filtering based mostly on connector standing/identify and viewing the kind of the connectors some customers may even do fast actions on the connectors by hovering over their respective tiles.

The sharp eyed have already seen that there’s a Connectors/Cluster Profile navigation button between the general statistics part and the connectors part.

By clicking on the Cluster Profile button, worker-level data may be considered reminiscent of what number of connectors are deployed on a employee, success/failure charges on a connector/process stage, and extra.


On the Connector tab there may be an icon with a cogwheel, urgent that may navigate to the Connector Profile web page, the place detailed data may be considered for that particular connector.

On the prime data wanted to guage the connector’s standing may be considered at a look, reminiscent of standing, operating/failed/paused duties, and which host the employee is situated on. If the connector is in a failed state the inflicting exception message can also be displayed.

Managing the connector or creating a brand new one can also be potential from this web page (for sure customers) with the buttons situated on the highest proper nook.

Within the duties part task-level metrics are seen, for instance: what number of bytes have been written by the duty, metrics associated to information, and the way a lot a process has been in operating or paused state, and in case of an error the stack hint of the error.

The Connector Profile web page has one other tab referred to as Connector Settings the place customers can view the configuration of the chosen connector, and a few customers may even edit it.

Securing Kafka Join

Securing Connector administration

As I’ve been hinting beforehand there are some actions that aren’t accessible to all customers. Let’s think about that there’s a firm promoting some form of items by a web site. Most likely there’s a workforce monitoring the server the place the web site is deployed, a workforce who displays the transactions and will increase the value of a product based mostly on rising demand or set coupons in case of declining demand. These two groups have very totally different specialised talent units, so it’s cheap to anticipate that they can not tinker with one another’s connectors. That is the place Apache Ranger comes into play.

Apache Ranger permits authorization and audit over varied sources (providers, recordsdata, databases, tables, and columns) by a graphical person interface and ensures that authorization is constant throughout CDP stack elements. In Kafka Join’s case it permits finegrained management over which person or group can execute which operation for a particular connector (these particular connectors may be decided with common expressions, so no have to record them one after the other).

The permission mannequin for Kafka Join is described within the following desk:

Useful resource Permission Permits the person to…
Cluster View Retrieve details about the server, and the kind of connector that may be deployed to the cluster
Handle Work together with the runtime loggers
Validate Validate connector configurations
Connector View Retrieve details about connectors and duties
Handle Pause/resume/restart connectors and duties or reset energetic matters (that is what’s displayed within the center column of the Join overview web page)
Edit Change the configuration of a deployed connector
Create Deploy connectors
Delete Delete connectors


Each permission in Ranger implies the Cluster-view permission, so that doesn’t have to be set explicitly.

Within the earlier examples I used to be logged in with an admin person who had permissions to do every little thing with each connector, so now let’s create a person with person ID mmichelle who’s a part of the monitoring group, and in Ranger configure the monitoring group to have each permission for the connectors with identify matching common expression monitoring.*.

Now after logging in as mmichelle and navigating to the Connector web page I can see that the connectors named gross sales.* have disappeared, and if I attempt to deploy a connector with the identify beginning with one thing aside from monitoring. the deploy step will fail, and an error message will probably be displayed. 

Let’s go a step additional: the gross sales workforce is rising and now there’s a requirement to distinguish between analysts who analyze the information in Kafka, assist individuals who monitor the gross sales connectors and assist analysts with technical queries, backend assist who can handle the connectors, and admins who can deploy and delete gross sales connectors based mostly on the wants of the analysts.

To assist this mannequin I’ve created the next customers:

Group Consumer Connector matching regex Permissions
gross sales+analyst ssamuel * None
gross sales+assist ssarah gross sales.* Connector – View
gross sales+backend ssebastian gross sales.* Connector – View/ Handle
gross sales+admin sscarlett gross sales.* Connector – View/ Handle/ Edit/ Create/ Delete

Cluster – Validate

If I have been to log in with sscarlett I might see an analogous image as mmichelle; the one distinction can be that she will be able to work together with connectors which have a reputation beginning with “gross sales.”. 

So let’s log in as ssebastian as a substitute and observe that the next buttons have been eliminated:

  1. New Connector button from the Connector overview and Connector profile web page.
  2. Delete button from the Connector profile web page.
  3. Edit button on the Connector settings web page.

That is additionally true for ssarah, however on prime of this she additionally doesn’t see:

  1. Pause/Resume/Restart buttons on the Connector overview web page’s connector hover popup or on the Connector profile web page.
  2. Restart button is completely disabled on the Connector profile’s duties part.

To not point out ssamuel who can login however can’t even see a single connector. 

And this isn’t solely true for the UI; if a person from gross sales would go across the SMM UI and take a look at manipulating a connector of the monitoring group (or another that’s not permitted) immediately by Kafka Join REST API, that particular person would obtain authorization errors from the backend.

Securing Kafka matters

At this level not one of the customers have entry on to Kafka matter sources if a Sink connector stops transferring messages from Kafka backend assist and admins can’t verify if it’s as a result of there are not any extra messages produced into the subject or one thing else. Ranger has the facility to grant entry rights over Kafka sources as nicely. 

Let’s go into the Kafka service on the Ranger UI and set the suitable permissions for the gross sales admins and gross sales backend teams beforehand used for the Kafka Join service. I might give entry rights to the matters matching the * regex, however in that case sscarlet and ssebastian might additionally by accident work together with the matters of the monitoring group, so let’s simply give them entry over the production_database.gross sales.* and gross sales.* matters.

Now the matters that the gross sales connectors work together with seem on the matters tab of the SMM UI and so they can view the content material of them with the Knowledge Explorer.

Securing Connector entry to Kafka

SMM (and Join) makes use of authorization to limit the group of customers who can handle the Connectors. Nevertheless, the Connectors run within the Join Employee course of and use credentials totally different from the customers’ credentials to entry matters in Kafka.

By default connectors use the Join employee’s Kerberos principal and JAAS configuration to entry Kafka, which has each permission for each Kafka useful resource. Due to this fact with default configuration a person with a permission to create a Connector can configure that connector to learn from or write to any matter within the cluster.

To control this Cloudera has launched the kafka.join.jaas.coverage.prohibit.connector.jaas property, which if set to “true” forbids the connectors to make use of the join employee’s principal.

After enabling this within the Cloudera Supervisor, the beforehand working connectors have stopped working, forcing connector directors to override the connector employee principal utilizing the sasl.jaas.config property:

To repair this exception I created a shared person for the connectors (sconnector) and enabled PAM authentication on the Kafka cluster utilizing the next article: 

configure shoppers to hook up with Apache Kafka Clusters securely – Half 3: PAM authentication.

In case of sink connectors, the shopper configurations are prefixed with shopper.override; in case of supply connectors, the shopper configurations are prefixed with producer.override (in some instances admin.override. is also wanted).

So for my MySqlConnector I set  producer.override.sasl.jaas.config=org.apache.kafka.widespread.safety.plain.PlainLoginModule required username=”sconnector” password=”<secret>”;

This might trigger the connector to entry the Kafka matter utilizing the PLAIN credentials as a substitute of utilizing the default Kafka Join employee principal’s identification.

To keep away from disclosure of delicate data, I additionally set the producer.override.sasl.jaas.config as a secret utilizing the lock icon.

Utilizing a secret saved on the file system of the Kafka Join Staff (reminiscent of a Kerberos keytab file) for authentication is discouraged as a result of the file entry of the connectors can’t be set individually, solely on a employee stage. In different phrases, connectors can entry one another’s recordsdata and thus use one another’s secrets and techniques for authentication. 


On this article I’ve launched how Kafka Join is built-in with Cloudera Knowledge Platform, how connectors may be created and managed by the Streams Messaging Supervisor, and the way customers can make the most of safety features offered in CDP 7.2.15. If you’re and wish check out CDP you should use the CDP Public Cloud with a 60 days free trial utilizing the hyperlink https://www.cloudera.com/marketing campaign/try-cdp-public-cloud.html


Securing JAAS override

Kafka Join Secret Storage

configure shoppers to hook up with Apache Kafka Clusters securely – Half 3: PAM authentication

MySQL CDC with Kafka Join/Debezium in CDP Public Cloud

The utilization of safe Debezium connectors in Cloudera environments


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