  Data-driven solutions for the investment industry require event-based backend
systems to process high-volume financial data feeds with low latency, high
throughput, and guaranteed delivery modes.
  At vwd we process an average of 18 billion incoming event notifications from
500+ data sources for 30 million symbols per day and peak rates of 1+ million
notifications per second using custom-built platforms that keep audit logs of
every event.
  We currently assess modern open source event-processing platforms such as
Kafka, NATS, Redis, Flink or Storm for the use in our ticker plant to reduce
the maintenance effort for cross-cutting concerns and leverage hybrid
deployment models. For comparability and repeatability we benchmark candidates
with a standardized workload we derived from our real data feeds.
  We have enhanced an existing light-weight open source benchmarking tool in
its processing, logging, and reporting capabilities to cope with our workloads.
The resulting tool wrench can simulate workloads or replay snapshots in volume
and dynamics like those we process in our ticker plant. We provide the tool as
open source.
  As part of ongoing work we contribute details on (a) our workload and
requirements for benchmarking candidate platforms for financial feed
processing; (b) the current state of the tool wrench.

Performance;Distributed, Parallel, and Cluster Computing