eventhub-analyzer 0.6.1

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Event Hub Analyzer

Event Hub Analyzer is a small command line tool that can be used to analyze certain aspects of Event Hubs.

Installation

pip install eventhub-analyzer

Checkpoints per partition

For every event hub, consumer group, the number of events/sequence numbers between two invocations is retrieved and the throughput per partition is calculated. This can be used to determine if the load is correctly distributed among partitions.

When there are some partition that get little to no throughput while others have large throughput, it is a sign that the partition key is not chosen optimally and that you should try to choose a property with a higher cardinality as the partition key.

Usage

eventhub-analyzer checkpoints -n CONTAINER_NAME -c CONNECTION_STRING

You can also specify the settings via environment variables:

export STORAGE_ACCOUNT_CONNECTION_STRING='DefaultEndpointsProtocol=https;AccountName=x;AccountKey=y;EndpointSuffix=core.windows.net'
export CONTAINER_NAME='event-hub-offsets'
eventhub-analyzer checkpoints

Example output

Event Hub: telemetry, Consumer Group: my-consumer
Event Hub   Consumer Group   Partition   Events per second
telemetry   my-consumer              0             158.034
telemetry   my-consumer              1             203.257
telemetry   my-consumer              2             148.103
telemetry   my-consumer              3               0.000
telemetry   my-consumer              4             201.780
telemetry   my-consumer              5             106.081
telemetry   my-consumer              6              72.307
telemetry   my-consumer              7             160.783
telemetry   my-consumer              8             118.351

As you can see, partition 3 is not getting any events and the number of events is not well distributed overall. There might be some gains possible by choosing a different partition key (or by partitioning manually on the client).

Clearing checkpoints

Example:

eventhub-analyzer clear-checkpoints --consumer-group redis-timeseries

Publishing

poetry build
poetry publish