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Ranges can later be merged when the load decreases. For these reasons, CockroachDB uses a divide the space approach, leveraging the S2 library. The shape of an intermediate node is dependent on many of the indexed shapes, which creates a non-localized dependency. The activities relating to range splitting/merging/rebalancing are localized to the affected ranges and do not affect other ranges, which is key to achieving horizontal scaling. This choice comes with some additional advantages: (a) bulk ingestion becomes simple, and (b) compactions in our log-structured merge tree (LSM tree) approach to organizing storage can proceed in a streaming manner across the input files, which minimizes memory consumption. In contrast, BKD trees, which are a divide the objects approach, also permit a log-structured storage organization, but, to the best of our understanding, compactions need to load all the input files to redivide the objects. The totally ordered cell-IDs are easily representable in CockroachDB’s lexicographic total order. A general multi-dimensional structure cannot be directly represented in a lexicographic totally ordered space.

To reliably capture these update events from a service’s database, we explored enabling change data capture (CDC) for Aurora/Postgres using Debezium connector, a Red Hat-developed open source project for indexing services capturing row-level changes. With Application Level CDC, we could run into consistency issues. We call this approach Application Level CDC. A distributed application has multiple instances. Therefore, we implemented save hooks in the application, which are responsible for handling data update requests, to propagate change events through Kafka whenever there is a change on the underlying data store. Two separate update calls may get served via two different instances. If we include updated values in the Kafka messages, it wouldn’t guarantee consistency and solve the issue because in certain cases multiple instances of the application will push events that are updating the same value. The initial performance testing carried out by the storage team suggested that this strategy had too much overhead and was not performant, especially when the service uses the same database for serving online traffic.

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