  Permissioned blockchain systems promise to provide both decentralized trust
and privacy. Hyperledger Fabric is currently one of the most wide-spread
permissioned blockchain systems and is heavily promoted both in industry and
academia. Due to its optimistic concurrency model, the transaction failure
rates in Fabric can become a bottleneck. While there is active research to
reduce failures, there is a lack of understanding on their root cause and,
consequently, a lack of guidelines on how to configure Fabric optimally for
different scenarios. To close this gap, in this paper, we first introduce a
formal definition of the different types of transaction failures in Fabric.
Then, we develop a comprehensive testbed and benchmarking system,
HyperLedgerLab, along with four different chaincodes that represent realistic
use cases and a chaincode/workload generator. Using HyperLedgerLab, we conduct
exhaustive experiments to analyze the impact of different parameters of Fabric
such as block size, endorsement policies, and others, on transaction failures.
We further analyze three recently proposed optimizations from the literature,
Fabric++, Streamchain and FabricSharp, and evaluate under which conditions they
reduce the failure rates. Finally, based on our results, we provide
recommendations for Fabric practitioners on how to configure the system and
also propose new research directions.

Distributed, Parallel, and Cluster Computing;Databases