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Planning your installation

Consider the following when planning your installation of Event Streams.

Decide the purpose of your deployment, for example, whether you want to try a starter deployment for testing purposes, or start setting up a production deployment.

  • Use the sample deployments as a starting point if you need something to base your deployment on.
  • Size your planned deployment by considering potential throughput, the number of producers and consumers, Kafka performance tuning, and other aspects. For more details, see the performance considerations section.
  • For production use, and whenever you want your data to be saved in the event of a restart, set up persistent storage.
  • Consider the options for securing your deployment.
  • Plan for resilience by understanding Kafka high availability and how to support it, set up multiple availability zones for added resilience, and consider geo-replication to help with your disaster recovery planning.
  • Consider setting up logging for your deployment to help troubleshoot any potential issues.

Sample deployments

A number of sample configurations are provided when installing Event Streams on which you can base your deployment. These range from smaller deployments for non-production development or general experimentation to large scale clusters ready to handle a production workload.

The sample configurations can be found in the OpenShift Container Platform web console as explained in installing or on GitHub.

Development deployments

If you want to try Event Streams, use one of the development samples when configuring your instance. Installing with these settings is suitable for a starter deployment intended for testing purposes and trying out Event Streams. It is not suitable for production environments. For samples appropriate for production use, see production deployments

The following development samples are available:

Example deployment: Lightweight without security

Overview: A non-production deployment suitable for basic development, and test activities. For environments where minimum resource requirements, persistent storage, access control and encryption are not required.

This example provides a starter deployment that can be used if you simply want to try Event Streams with a minimum resource footprint. It installs an Event Streams instance with the following characteristics:

  • A small single broker Kafka cluster and a single node ZooKeeper.
  • As there is only 1 broker, no message replication takes place between brokers, and the system topics (message offset and transaction state) are configured accordingly for this.
  • There is no encryption internally between containers.
  • External connections use TLS encryption, but no authentication to keep the configuration to a minimum, making it easy to experiment with the platform.

Resource requirements for this deployment:

CPU request (cores) CPU limit (cores) Memory request (Gi) Memory limit (Gi) VPCs (see licensing)
2.9 8.8 6.3 9.1 0.5

Ensure you have sufficient CPU capacity and physical memory in your environment to service at least the resource request values. The resource limit values constrain the amount of resource the Event Streams instance is able to consume.

You can select this sample to use when installing your instance. You can also use it to start out with your configuration, and change settings as required for your purposes. For ways to customize your setup, see configuring.

Important: This deployment is not suitable for a production system even if storage configuration is applied. This is due to the number of Kafka and ZooKeeper nodes not being appropriate for data persistence and high availability. For a production system, at least three Kafka brokers and ZooKeeper nodes are required for an instance, see production sample deployments later for alternatives.

Example deployment: Development

Overview: A non-production deployment for experimenting with Event Streams configured for high availability, authentication, and no persistent storage. Suitable for basic development and testing activities.

This example provides a starter deployment that can be used if you want to try Event Streams with a minimum resource footprint. It installs an Event Streams instance with the following settings:

  • 3 Kafka brokers and 3 ZooKeeper nodes.
  • Internally, TLS encryption is used between containers.
  • External connections use TLS encryption and SCRAM SHA 512 authentication.

Resource requirements for this deployment:

CPU request (cores) CPU limit (cores) Memory request (Gi) Memory limit (Gi) VPCs (see licensing)
3.3 13.0 7.1 15.3 1.5

Ensure you have sufficient CPU capacity and physical memory in your environment to service at least the resource request values. The resource limit values constrain the amount of resource the Event Streams instance is able to consume.

You can select this sample to use when installing your instance. You can also use it to start out with your configuration, and change settings as required for your purposes. For ways to customize your setup, see configuring.

Production deployments

To start setting up a production instance, use one of the following samples.

Important: For a production setup, the sample configuration values are for guidance only, and you might need to change them. Ensure you set your resource values as required to cope with the intended usage, and also consider important configuration options for your environment and Event Streams requirements as described in the rest of this planning section.

Example deployment: Minimal production

Overview: A minimal production deployment for Event Streams.

This example provides the smallest possible production deployment that can be configured for Event Streams. It installs an Event Streams instance with the following settings:

  • 3 Kafka brokers and 3 ZooKeeper nodes.
  • Internally, TLS encryption is used between containers.
  • External connections use TLS encryption and SCRAM SHA 512 authentication.
  • Kafka tuning settings consistent with 3 brokers are applied as follows:
num.replica.fetchers: 3
num.io.threads: 24
num.network.threads: 9
log.cleaner.threads: 6

If a storage solution has been configured, the following characteristics make this a production-ready deployment:

  • Messages are replicated between brokers to ensure that no single broker is a point of failure. If a broker restarts, producers and consumers of messages will not experience any loss of service.
  • The number of threads made available for replicating messages between brokers, is increased to 3 from the default 1. This helps to prevent bottlenecks when replicating messages between brokers, which might otherwise prevent the Kafka brokers from being fully utilized.
  • The number of threads made available for processing requests is increased to 24 from the default 8, and the number of threads made available for managing network traffic is increased to 9 from the default 3. This helps prevent bottlenecks for producers or consumers, which might otherwise prevent the Kafka brokers from being fully utilized.
  • The number of threads made available for cleaning the Kafka log is increased to 6 from the default 1. This helps to ensure that records that have exceeded their retention period are removed from the log in a timely manner, and prevents them from accumulating in a heavily loaded system.

Resource requirements for this deployment:

CPU request (cores) CPU limit (cores) Memory request (Gi) Memory limit (Gi) VPCs (see licensing)
9.7 13.0 15.3 15.3 3.0

You can select this sample to use when installing your instance. You can also use it to start out with your configuration, and change settings as required for your purposes. For ways to customize your setup, see configuring.

Important: For production deployments, ensure you configure a storage solution by editing the sample as described in enabling persistent storage.

Example deployment: Production 3 brokers

Overview: A small production deployment for Event Streams.

This example installs a production-ready Event Streams instance similar to the Minimal production setup, but with added resource requirements:

  • 3 Kafka brokers and 3 ZooKeeper nodes.
  • Internally, TLS encryption is used between containers.
  • External connections use TLS encryption and SCRAM SHA 512 authentication.
  • The memory and CPU requests and limits for the Kafka brokers are increased compared to the Minimal production sample described previously to give them the bandwidth to process a larger number of messages.

Resource requirements for this deployment:

CPU request (cores) CPU limit (cores) Memory request (Gi) Memory limit (Gi) VPCs (see licensing)
15.0 22.0 30.7 33.3 12.0

Ensure you have sufficient CPU capacity and physical memory in your environment to service at least the resource request values. The resource limit values constrain the amount of resource the Event Streams instance is able to consume.

You can select this sample to use when installing your instance. You can also use it to start out with your configuration, and change settings as required for your purposes. For ways to customize your setup, see configuring.

Important: For production deployments, ensure you configure a storage solution by editing the sample as described in enabling persistent storage.

Example deployment: Production 6 brokers

Overview: A medium sized production deployment for Event Streams.

This sample configuration is similar to the Production 3 brokers sample described earlier, but with an increase in the following settings:

  • Uses 6 brokers rather than 3 to allow for additional message capacity.
  • The resource settings for the individual brokers are the same, but the number of threads made available for replicating messages between brokers is increased to 6 to cater for the additional brokers to manage.

Resource requirements for this deployment:

CPU request (cores) CPU limit (cores) Memory request (Gi) Memory limit (Gi) VPCs (see licensing)
27.1 34.3 55.1 57.7 24.0

Ensure you have sufficient CPU capacity and physical memory in your environment to service at least the resource request values. The resource limit values constrain the amount of resource the Event Streams instance is able to consume.

Important: This sample is not provided in the OpenShift Container Platform web console and can only be obtained through GitHub.

You can select this sample to use when installing your instance. You can also use it to start out with your configuration, and change settings as required for your purposes. For ways to customize your setup, see configuring.

Important: For production deployments, ensure you configure a storage solution by editing the sample as described in enabling persistent storage.

Example deployment: Production 9 brokers

Overview: A large production deployment for Event Streams.

This sample configuration is similar to the Production 6 brokers sample described earlier, but with an increase in the following settings:

  • Uses 9 Brokers rather than 6 to allow for additional message capacity.
  • The resource settings for the individual brokers are the same, but the number of threads made available for replicating messages between brokers is increased to 9 to cater for the additional brokers to manage.

Resource requirements for this deployment:

CPU request (cores) CPU limit (cores) Memory request (Gi) Memory limit (Gi) VPCs (see licensing)
39.1 46.6 79.4 82.1 36.0

Ensure you have sufficient CPU capacity and physical memory in your environment to service at least the resource request values. The resource limit values constrain the amount of resource the Event Streams instance is able to consume.

Important: This sample is not provided in the OpenShift Container Platform web console and can only be acquired through GitHub.

You can select this sample to use when installing your instance. You can also use it to start out with your configuration, and change settings as required for your purposes. For ways to customize your setup, see configuring.

Important: For production deployments, ensure you configure a storage solution by editing the sample as described in enabling persistent storage.

Planning for persistent storage

If you plan to have persistent volumes, consider the disk space required for storage.

  • Both Kafka and ZooKeeper rely on fast write access to disks. Use separate dedicated disks for storing Kafka and ZooKeeper data. For more information, see the disks and filesystems guidance in the Kafka documentation, and the deployment guidance in the ZooKeeper documentation.

    If persistence is enabled, each Kafka broker and ZooKeeper server requires one physical volume each. The number of Kafka brokers and ZooKeeper servers depends on your setup (for example, see the provided samples described in resource requirements).

  • Schema registry requires a single physical volume.

You either need to create a persistent volume for each physical volume, or specify a storage class that supports dynamic provisioning. Each component can use a different storage class to control how physical volumes are allocated.

See the OpenShift Container Platform documentation for information about creating persistent volumes and creating a storage class that supports dynamic provisioning. For both, you must have the Cluster Administrator role.

  • If these persistent volumes are to be created manually, this must be done by the system administrator before installing IBM Event Streams. These will then be claimed from a central pool when the IBM Event Streams instance is deployed. The installation will then claim the required number of persistent volumes from this pool.
  • If these persistent volumes are to be created automatically, ensure a dynamic provisioner is configured for the storage class you want to use. See data storage requirements for information about storage systems supported by Event Streams.

Important: When creating persistent volumes for each component, ensure the correct Access mode is set for the volumes as described in the following table.

Component Access mode
Kafka ReadWriteOnce
ZooKeeper ReadWriteOnce
Schema registry ReadWriteMany or ReadWriteOnce

To use persistent storage, configure the storage properties in your EventStreams custom resource.

Planning for security

Event Streams has highly configurable security options that range from the fully secured default configuration to no security for basic development and testing.

The main security vectors to consider are:

  • Kafka listeners
  • Pod-to-Pod communication
  • UI access
  • REST endpoints (REST Producer, Admin API, Schema Registry)

Secure instances of Event Streams will make use of TLS to protect network traffic. Certificates will be generated by default, or you can use custom certificates.

Note: If you want to use custom certificates, ensure you configure them before installing Event Streams.

Event Streams UI Access

As explained in the Managing access section, the IBM Cloud Platform Common Services Identity and Access Management (IAM) is used to bind a role to an identity. By default, the secure Event Streams instance will require an Administrator or higher role to authorize access. To setup LDAP (Lightweight Directory Access Protocol), assign roles to LDAP users, and create teams, see the instructions about configuring LDAP connections.

Whilst it is highly recommended to always configure Event Streams with security enabled, it is also possible to configure the Event Streams UI to not require a login, which can be useful for proof of concept (PoC) environments. For details, see configuring Event Streams UI access.

REST endpoint security

Review the security and configuration settings of your development and test environments. The REST endpoints of Event Streams have a number of configuration capabilities. See configuring access for details.

Securing communication between pods

By default, Pod-to-Pod encryption is enabled. You can configure encryption between pods when configuring your Event Streams installation.

Kafka listeners

Event Streams has both internal and external configurable Kafka listeners. Optionally, each Kafka listener can be secured with TLS or SCRAM.

Planning for resilience

If you are looking for a more resilient setup, or want plan for disaster recovery, consider setting up multiple availability zones and creating geo-replication clusters. Also, set up your environment to support Kafka’s inherent high availability design.

Kafka high availability

Kafka is designed for high availability and fault tolerance.

To reduce the impact of Event Streams Kafka broker failures, configure your installation with at least three brokers and spread them across several Red Hat OpenShift Container Platform worker nodes by ensuring you have at least as many worker nodes as brokers. For example, for 3 Kafka brokers, ensure you have at least 3 worker nodes running on separate physical servers.

Kafka ensures that topic-partition replicas are spread across available brokers up to the replication factor specified. Usually, all of the replicas will be in-sync, meaning that they are all fully up-to-date, although some replicas can temporarily be out-of-sync, for example, when a broker has just been restarted.

The replication factor controls how many replicas there are, and the minimum in-sync configuration controls how many of the replicas need to be in-sync for applications to produce and consume messages with no loss of function. For example, a typical configuration has a replication factor of 3 and minimum in-sync replicas set to 2. This configuration can tolerate 1 out-of-sync replica, or 1 worker node or broker outage with no loss of function, and 2 out-of-sync replicas, or 2 worker node or broker outages with loss of function but no loss of data.

The combination of brokers spread across nodes together with the replication feature make a single Event Streams cluster highly available.

Multiple availability zones

To add further resilience to your Event Streams cluster, you can split your servers across multiple data centers or zones, so that even if one zone experiences a failure, you still have a working system.

Multizone support provides the option to run a single Kubernetes cluster in multiple availability zones within the same region. Multizone clusters are clusters of either physical or virtual servers that are spread over different locations to achieve greater resiliency. If one location is shut down for any reason, the rest of the cluster is unaffected.

Note: For Event Streams to work effectively within a multizone cluster, the network latency between zones must not be greater than 20 ms for Kafka to replicate data to the other brokers.

Typically, high availability requires a minimum of 3 zones (sites or data centers) to ensure a quorum with high availability for components, such as Kafka and ZooKeeper. Without the third zone, you might end up with a third quorum member in a zone that already has a member of the quorum, consequently if that zone goes down, the majority of the quorum is lost and loss of function is inevitable.

OpenShift Container Platform requires a minimum of 3 zones for high availability topologies and Event Streams supports that model. This is different from the traditional primary and backup site configuration, and is a move to support the quorum-based application paradigm.

With zone awareness, Kubernetes automatically distributes pods in a replication controller across different zones. For workload-critical components, for example Kafka, ZooKeeper and REST Producer, set the number of replicas of each component to at least match the number of zones. This provides at least one replica of each component in each zone, so in the event of loss of a zone the service will continue using the other working zones.

For information about how to prepare multiple zones, see preparing for multizone clusters.

Geo-replication

Consider configuring geo-replication to aid your disaster recovery and resilience planning.

You can deploy multiple instances of IBM Event Streams and use the included geo-replication feature to synchronize data between your clusters. Geo-replication helps maintain service availability.

No additional preparation is needed on the origin cluster, IBM Event Streams as geo-replication runs on the destination cluster.

Prepare your destination cluster by setting the number of geo-replication worker nodes during installation.

Geo-replication is based on MirrorMaker 2.0, which uses Kafka Connect, enabling interoperability with other Kafka distributions.

Use geo-replication to replicate data between Event Streams clusters. Use MirrorMaker2 to move data between Event Streams clusters and other Kafka clusters.

Planning for log management

Event Streams uses the cluster logging provided by the OpenShift Container Platform to collect, store, and visualize logs. The cluster logging components are based upon Elasticsearch, Fluentd, and Kibana (EFK).

You can use this EFK stack logging capability in your environment to help resolve problems with your deployment and aid general troubleshooting.

You can use log data to investigate any problems affecting your system health.

Kafka static configuration properties

You can set Kafka broker configuration settings in your EventStreams custom resource under the property spec.strimziOverrides.kafka. These settings will override the default Kafka configuration defined by Event Streams.

You can also use this configuration property to modify read-only Kafka broker settings for an existing IBM Event Streams installation. Read-only parameters are defined by Kafka as settings that require a broker restart. Find out more about the Kafka configuration options and how to modify them for an existing installation.

Connecting clients

By default, Kafka client applications connect to cluster using the Kafka bootstrap route address. Find out more about connecting external clients to your installation.

Monitoring Kafka clusters

IBM Event Streams uses the IBM Cloud Platform Common Services monitoring service to provide you with information about the health of your Event Streams Kafka clusters. You can view data for the last 1 hour, 1 day, 1 week, or 1 month in the metrics charts.

Important: By default, the metrics data used to provide monitoring information is only stored for a day. Modify the time period for metric retention to be able to view monitoring data for longer time periods, such as 1 week or 1 month.

For more information about keeping an eye on the health of your Kafka cluster, see the monitoring Kafka topic.

Licensing

Licensing considerations

Licensing is based on a Virtual Processing Cores (VPC) metric. To use Event Streams you must have a license for all of the virtual cores that are available to all of the following Event Streams components:

  • Kafka brokers
  • Geo-Replicator nodes
  • MirrorMaker2 nodes
  • Kafka Connect nodes hosted by Event Streams

All other container types are pre-requisite components that are supported as part of Event Streams, and do not require additional licenses.

If you are using one of the samples provided, see the sample deployments section for information about the number of VPCs required. The number of VPCs indicate the licenses required.

Note: For a production installation of Event Streams, the ratio is 1 license required for every 1 VPC being used. For a non-production installation of Event Streams, the ratio is 1 license required for every 2 VPCs being used.

To flag an installation of Event Streams as production or non-production, set the spec.license.use correctly during installation. See license usage for more information about selecting the correct value.

If you add more Kafka replicas, geo-replicator nodes, MirrorMaker2 nodes, or Kafka Connect nodes, each one is an additional, separate chargeable unit. See license usage to learn how you can find out more about the number of virtual cores used by your deployment.

License usage

The license usage of IBM Event Streams is collected by the IBM Cloud Platform Common Services License Service which is automatically deployed with IBM Event Streams. This provides the service that tracks the licensed containers and their resource usage based on the product use.

When creating an instance of Event Streams, ensure that you select the correct value for spec.license.use in the custom resource. This value is used for metering purposes and could result in inaccurate charging and auditing if set incorrectly. Select one of the following values based on the purpose of your deployment:

  • CloudPakForIntegrationNonProduction for non-production deployments suitable for basic development and test activities.
  • CloudPakForIntegrationProduction for production deployments.

The sample deployments provided by Event Streams have the correct value set by default based on the sample deployment purposes.

The license usage information can be viewed by obtaining an API token that is required to make the API calls to retrieve license usage data, and then accessing provided APIs for retrieving the license usage data.

There are 3 APIs that can be viewed:

  1. Snapshot (last 30 days) This provides audit level information in a .zip file and is a superset of the other reports.
  2. Products report (last 30 days) This shows the VPC usage for all products that are deployed in IBM Cloud Pak for Integration, for example:
    [{"name":"IBM Cloud Pak for Integration","id":"c8b82d189e7545f0892db9ef2731b90d","metricPeakDate":"2020-06-10","metricQuantity":3,"metricName":"VIRTUAL_PROCESSOR_CORE"}]
    

    In this example, the metricQuantity is 3 indicating that the peak VPC usage is 3.

  3. Bundled products report (last 30 days) This shows the breakdown of bundled products that are included in IBM Cloud Paks that are deployed on a cluster with the highest VPC usage within the requested period. For Event Streams this shows the peak number of VPCs in use, the conversion ratio and the number of licenses used. For example:
    [{"productName":"IBM Event Streams for Non Production","productId":"<product_id>","cloudpakId":"<cloudpak_id>","cloudpakVersion":"2020.2.1","metricName":"VIRTUAL_PROCESSOR_CORE","metricPeakDate":"2020-06-10","metricMeasuredQuantity":6,"metricConversion":"2:1","metricConvertedQuantity":3}]
    

    In this example, the productName shows the license metrics for a IBM Event Streams for Non Production deployment. The metricMeasuredQuantity is 6 VPCs, the metricConversion is 2:1 and metricConvertedQuantity is 3 VPCs so the license usage is 3.

Note: The metricMeasuredQuantity is the peak number of VPCs used over the timeframe. If an Event Streams instance is deleted and a new instance installed, then the quantity will be the maximum used at any one time.

The following examples show the number of licenses required for specific installations:

Example 1 - Non-production 3 brokers

This example is for an Event Streams installation configured with 3 Kafka brokers, no Mirror Maker or Kafka Connect containers. In this example installation, each Kafka container requires 2 VPCs so in total 6 VPCs are being used. For non-production deployment the metrics conversion ratio is 2:1, therefore 3 licenses are required.

Example 2 - Production 3 brokers

This example is for an Event Streams installation configured with 3 Kafka brokers, no Mirror Maker or Kafka Connect containers. In this example installation, each Kafka container requires 4 VPCs so in total 12 VPCs are being used. For production deployment the metrics conversion ratio is 1:1, therefore 12 licenses are required.

Example 3 - Production 6 brokers with Geo-Replication

This example is for an Event Streams installation configured with 6 Kafka brokers, 1 Mirror Maker container and no Kafka Connect containers. In this example installation, each Kafka container requires 4 VPCs and each Mirror Maker container requires 1 VPC, so in total 25 VPCs are being used. For production deployment the metrics conversion ratio is 1:1, therefore 25 licenses are required.

Example 4 - Production 9 brokers with Geo-Replication and Kafka-Connect

This example is for an Event Streams installation configured with 9 Kafka brokers, 1 Mirror Maker and 1 Kafka Connect container. In this example installation, each Kafka container requires 4 VPCs, each Mirror Maker container requires 1 VPC and each Kafka-Connect container requires 1 VPC, so in total 38 VPCs are being used. For production deployment the metrics conversion ratio is 1:1, therefore 38 licenses are required.

If there are multiple production or non-production installations in a cluster then the API will show the total peak VPC usage for all production or non-production instances in that cluster. For example if you have 2 production instances of IBM Event Streams where each instance has 3 Kafka brokers that each use 2 VPS, then the total peak usage is 12 VPCs which converts to 12 licenses.

If there are production and non-production IBM Event Streams instances installed in the cluster, then the metricConvertedQuantity under IBM Event Streams and IBM Event Streams for Non Production will need to be added to determine the total license usage. For example:

[{"productName":"IBM Event Streams for Non Production","productId":"<product_id>","cloudpakId":"<cloudpak_id>","cloudpakVersion":"2020.2.1","metricName":"VIRTUAL_PROCESSOR_CORE","metricPeakDate":"2020-06-10","metricMeasuredQuantity":6,"metricConversion":"2:1","metricConvertedQuantity":3},{"productName":"IBM Event Streams","productId":"<product_id>","cloudpakId":"<cloudpak_id>","cloudpakVersion":"2020.2.1","metricName":"VIRTUAL_PROCESSOR_CORE","metricPeakDate":"2020-06-11","metricMeasuredQuantity":8,"metricConversion":"1:1","metricConvertedQuantity":8}]

In this example there are Event Streams installations for non-production and for production. The non-production usage is 6 VPCs which converts to 3 licenses. The production usage is 8 VPCs which converts to 8 licenses. Therefore the total license usage is 11.