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Answer: Construct a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second.
The optimal solution involves creating a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second. This approach ensures efficient data retrieval, supports large data volumes without degrading query performance, and is cost-effective by avoiding per-query charges. BigQuery is less suited for time series data, and wide table models in both BigQuery and Bigtable compromise on query efficiency and scalability. Reference: [Google Cloud Bigtable Schema Design for Time Series](https://cloud.google.com/bigtable/docs/schema-design-time-series)
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What is the optimal database and data model for storing one-second interval samples of time series CPU and memory usage for millions of computers, enabling real-time, ad hoc analytics, ensuring scalability, and avoiding per-query charges?
A
Design a wide table in Bigtable with a row key that merges the computer identifier with the sample time at each minute, and aggregate the values for each second as column data.
B
Construct a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second.
C
Develop a wide table in BigQuery with a column for each sample value at each second and update the row with the interval for each second.
D
Establish a table in BigQuery, and append the new samples for CPU and memory to the table.
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