Time Series

The Ultimate Guide to Time Series Data with Apache Cassandra - Part 1: Fundamentals

• 6 min read

Time series data represents one of the most common and challenging workloads for modern data platforms. From IoT device metrics to financial market data, the ability to efficiently store and query chronological measurements at scale has become a critical requirement for many organizations. After years of helping clients implement time series solutions with Apache Cassandra, I’ve developed a set of best practices that can make the difference between a system that struggles and one that scales effortlessly.

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Cassandra Time Series Data Modeling For Massive Scale

One of the big challenges people face when starting out working with Cassandra and time series data is understanding the impact of how your write workload will affect your cluster. Writing too quickly to a single partition can create hot spots that limit your ability to scale out. Partitions that get too large can lead to issues with repair, streaming, and read performance. Reading from the middle of a large partition carries a lot of overhead, and results in increased GC pressure. Cassandra 4.0 should improve the performance of large partitions, but it won’t fully solve the other issues I’ve already mentioned. For the foreseeable future, we will need to consider their performance impact and plan for them accordingly.

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