Automatically Copying Data From Kafka

Vertica offers a scheduler that loads streamed messages from one or more Kafka topics. Automatically loading streaming data has a number of advantages over manually using COPY:

• The streamed data automatically appears in your database. The frequency with which new data appears in your database is governed by the scheduler's frame duration.
• The scheduler provides an exactly-once consumption process. The schedulers manage offsets for you so that each message sent by Kafka is consumed once.
• You can configure backup schedulers to provide high-availability. Should the primary scheduler fail for some reason, the backup scheduler automatically takes over loading data.
• The scheduler manages resources for the data load. You control its resource usage through the settings on the resource pool you assign to it . When loading manually, you must take into account the resources your load consumes.

There are a few drawbacks to using a scheduler which may make it unsuitable for your needs. You may find that schedulers do not offer the flexibility you need for your load process. For example, schedulers cannot perform business logic during the load transaction. If you need to perform this sort of processing, you are better off creating your own load process. This process would periodically run COPY statements to load data from Kafka. Then it would perform the business logic processing you need before committing the transaction.

For information on job scheduler requirements, refer to Vertica Integration for Apache Kafka.

What the Job Scheduler Does

The scheduler is responsible for scheduling loads of data from Kafka. The scheduler's basic unit of processing is a frame, which is a period of time. Within each frame, the scheduler assigns a slice of time for each active microbatch to run. Each microbatch is responsible for loading data from a single source. Once the frame ends, the scheduler starts the next frame. The scheduler continues this process until you stop it.

The Anatomy of a Scheduler

Each scheduler has several groups of settings, each of which control an aspect of the data load. These groups are:

• The scheduler itself, which defines the configuration schema, frame duration, and resource pool.
• Clusters, which define the hosts in the Kafka cluster that the scheduler contacts to load data. Each scheduler can contain multiple clusters, allowing you to load data from multiple Kafka clusters with a single scheduler.
• Sources, which define the Kafka topics and partitions in those topics to read data from.
• Targets, which define the tables in Vertica that will receive the data. These tables can be traditional Vertica database tables, or they can be flex tables.
• Load specs, which define setting Vertica uses while loading the data. These settings include the parsers and filters Vertica needs to use to load the data. For example, if you are reading a Kafka topic that is in Avro format, your load spec needs to specify the Avro parser and schema.
• Microbatches, which represent an individual segment of a data load from a Kafka stream. They combine the definitions for your cluster, source, target, and load spec that you create using the other vkconfig tools. The scheduler uses all of the information in the microbatch to execute COPY statements using the KafkaSource UDL function to transfer data from Kafka to Vertica. The statistics on each microbatch's load is stored in the stream_microbatch_history table.

The vkconfig Script

You use a Linux command-line script named vkconfig to create, configure, and run schedulers. This script is installed on your Vertica hosts along with the Vertica server in the following path:

/opt/vertica/packages/kafka/bin/vkconfig

The vkconfig script contains multiple tools. The first argument to the vkconfig script is always the tool you want to use. Each tool performs one function, such as changing one group of settings (such as clusters or sources) or starting and stopping the scheduler. For example, to create or configure a scheduler, you use the command: 

$ /opt/vertica/packages/kafka/bin/vkconfig scheduler other options...

What Happens When You Create a Scheduler

When you create a new scheduler, the vkconfig script takes the following steps:

• Creates a new Vertica schema using the name you specified for the scheduler. You use this name to identify the scheduler during configuration.
• Creates the tables needed to manage the Kafka data load in the newly-created schema. See Data Streaming Schema Tables for more information.

Validating Schedulers

When you create or configure a scheduler, it validates the following settings:

• Confirms that all brokers in the specified cluster exist.
• Connects to the specified host or hosts and retrieves the list of all brokers in the Kafka cluster. Getting this list always ensures that the scheduler has an up-to-date list of all the brokers. If the host is part of a cluster that has already been defined, the scheduler cancels the configuration.
• Confirms that the specified source exists.
• Retrieves the number of partitions in the source. If no number of partitions is specified, Vertica sets the value to the number of partitions the source has in the cluster.

You can disable validation using the --validation-type option in the vkconfig script's scheduler tool. See Scheduler Tool Options for more information.

Synchronizing Schedulers

By default, the scheduler automatically synchronizes its configuration and source information with Kafka host clusters. You can configure the synchronization interval using the --config-refresh scheduler utility option. Each interval, the scheduler:

• Checks for updates to the scheduler's configuration by querying its settings in its Vertica configuration schema.
• Performs all of the checks listed in Validating Schedulers.

You can configure synchronization settings using the --auto-sync option using the vkconfig script's scheduler tool. Scheduler Tool Options for details.

Launching a Scheduler

You use the vkconfig script's launch tool to launch a scheduler.

When you launch a scheduler, it collects data from your sources, starting at the specified offset. You can view the stream_microbatch_history table to see what the scheduler is doing at any given time.

To learn how to create, configure, and launch a scheduler, see Setting up a Scheduler in this guide.

You can also choose to bypass the scheduler. For example, you might want to do a single load with a specific range of offsets. For more information, see Manually Copying Data From Kafka in this guide.

Managing a Running Scheduler

When you launch a scheduler from the command line, it runs in the foreground. It will run until you kill it (or the host shuts down). Usually, you want to start the scheduler as a daemon process that starts it when the host operating system starts, or after the Vertica database has started.

You shut down a running scheduler using the vkconfig script's shutdown tool. See Shutdown Tool Options for details.

You can change most of a scheduler's settings (adding or altering clusters, sources, targets, and microbatches for example) while it is running. The scheduler automatically acts on the configuration updates.

Launching Multiple Job Schedulers for High Availability

For high availability, you can launch two or more identical schedulers that target the same configuration schema. You differentiate the schedulers using the launch tool's --instance-name option (see Launch Tool Options). The scheduler not in use remains in stand-by mode until the active scheduler fails or is disabled. If the active scheduler fails, the backup scheduler takes over loading data from Kafka where the primary left off.

Passing Options to the Scheduler's JVM

The scheduler uses a Java Virtual Machine to connect to Vertica via JDBC. You can pass command-line options to the JVM through a Linux environment variable named VKCONFIG_JVM_OPTS. This option is useful when configuring a scheduler to use TLS/SSL encryption when connecting to Vertica. See Configuring Your Scheduler for TLS/SSL Connections for more information.

Viewing Schedulers from the MC

You can view the status of Kafka jobs from the MC. For more information, refer to Viewing Load History.