Hortonworks Cybersecurity Platform
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Storm Tuning

There are several Storm properties you can adjust to tune your Storm topologies. Achieving the desired performance can be iterative and will take some trial and error.

Hortonworks recommends you start your tuning with the Storm topology defaults and smaller numbers in terms of parallelism. Then you can iteratively increase the values until you achieve your desired level of performance. Use the offset lag tool to verify your settings.

The following sections assume log type messages. However, if your data consists of emails which are much larger in size, then you should adjust your values accordingly. For more detailed information on Storm's architecture, see References.

Storm Topology Parallelism

To provide a uniform distribution to each machine and jvm process, you can modify the values for the number of tasks, executors, and workers properties. Start with small values and iteratively increase the values so you don't overwhelm you CPU with too many processes.

Usually your number of tasks is equal to the number of executors, which is the default in Storm. Flux does not provide a method to independently set the number of tasks, so for enrichments and indexing, which use Flux, num tasks are always equal to num executors.

You can change the number of workers in the Storm property topology.workers.

The following table lists the variables you can set to adjust the parallelism in a Storm topology and provides recommendations for their values:

Storm Topology Variables Description Value
num tasks Tasks are instances of a given spout or bolt. Executors are threads in a process. Set the number of tasks as a multiple of the number of executors.
num executors Executors are threads in a process. Set the number of executors as a multiple of the number of workers.
num workers Workers are jvm processes. Set the number of workers as a multiple of the number of machines

Maximum Number of Tuples

The topology.max.spout.pending setting sets the maximum number of tuples that can be in a field (for example, not yet acked) at any given time within your topology. You set this property as a form of back pressure to ensure that you don't flood your topology.

topology.max.spout.pending

Topology Acker Executors

The topology.ackers.executors setting specifies how many threads are dedicated to tuple acking. Set this setting to equal the number of partitions in your inbound Kafka topic.

topology.ackers.executors

Spout Recommended Defaults

As a general rule, it is optimal to set spout parallelism equal to the number of partitions used in your Kafka topic. Any greater parallelism will leave you with idle consumers because Kafka limits the maximum number of consumers to the number of partitions. This is important because Kafka has certain ordering guarantees for message delivery per partition that would not be possible if more than one consumer in a given consumer group is able to read from that partition.

You can modify the following spout settings in the spout-config.json file. However, if the spout default settings work for your system, you can omit these settings from the file. These default settings are based on recommendations from Storm and are provided in the Kafka spout itself.

{
    ...
    "spout.pollTimeoutMs" : 200,
    "spout.maxUncommittedOffsets" : 10000000,
    "spout.offsetCommitPeriodMs" : 30000
}