We discuss the problem of constructing efficient temporal indexes on Hyperledger Fabric, a popular Blockchain platform. The temporal nature of the data inserted by Fabric transactions can be leveraged to support various use-cases. This requires that temporal queries be processed efficiently on this data. Currently this presents significant challenges as this data is organized on file-system, is exposed via limited API and does not support temporal indexes. In a prior work [1], we presented two models for creating temporal indexes on Fabric which overcome these limitations and improve the performance of temporal queries on Fabric. The first model creates a copy of each event inserted and stores temporally close events together on Fabric. The second model keeps the event count intact but tags metadata to each event s.t. temporally close events share the same metadata. In this paper, we present variants on these two models which are better able to handle the skew present in Fabric data. We discuss the details and show that these variants significantly outperform the approaches presented in [1] when Fabric data contains skew. We also discuss the performance tradeoffs among these variants across various dimensions - data storage, query performance, event insertion time etc.
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