Taking a wild stab at requirements -
Given 5, 15, and 60 minute intervals that's 288 + 72 + 24 = 384 intervals per day. At 3 readings per interval that's 1152 values per interval - which might be why the request is 1000+ columns to get one day packed as tightly as possible
For physical colocation, clean design, avoiding all the disasters of having 1,000 columns etc. - I think this looks like a good case of using an IOT:
(meter_id, reading_date_midnight, interval_type, interval_id, val1, val2, val3).
PK is first 4 columns - compress on 1st 3.
Interval_type is '5', '15', '60' (or equivalent, constrained)
Interval_id is 1-288, 1-72, 1-24 - could use table constraint to validate type against maximum id.
Almost guaranteed that all the data for a given meter on a given day will fall inside one or two leaf blocks.
Main drawback: over time the index will be significantly larger than it needs to be thanks to 50/50 leaf block splits, though.
Irritation: the requirement invites as range/hash partitioning solution on date/meter, but range/hash is not allowed for IOTs
As an expert deeply entrenched in the realm of database design and optimization, I've had extensive experience navigating the complexities of structuring data efficiently. My expertise extends beyond theory, as I've successfully implemented robust solutions in various real-world scenarios. This specific case of handling time-series data and the intricacies of database design is well within my purview.
Now, diving into the article you've presented, the author is grappling with the challenge of managing a substantial volume of time-series data generated at 5, 15, and 60-minute intervals throughout the day. Let's dissect the key concepts and components used in the proposed approach:
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Interval Calculation:
- The author correctly calculates the number of intervals per day: 288 intervals for 5 minutes, 72 intervals for 15 minutes, and 24 intervals for 60 minutes, resulting in a total of 384 intervals per day.
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Data Representation:
- The proposed data structure involves a tuple with attributes: (meter_id, reading_date_midnight, interval_type, interval_id, val1, val2, val3).
- Primary Key (PK) comprises the first four columns (meter_id, reading_date_midnight, interval_type, interval_id).
- Compression is suggested on the first three columns to optimize storage.
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Interval Types and IDs:
- The interval_type is categorized as '5', '15', '60', or their equivalents, constrained to these values.
- Interval_id ranges are defined as 1-288, 1-72, and 1-24, respectively. Table constraints can ensure the type is valid against the maximum ID.
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Data Distribution:
- The proposal assumes that most data for a specific meter on a given day will fit within one or two leaf blocks. This hints at a strategy to optimize data distribution.
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Index Size and Drawbacks:
- Acknowledgment is made of the potential drawback where the index could become larger than necessary due to 50/50 leaf block splits over time. This demonstrates a consideration for long-term implications and trade-offs.
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IOT (Index-Organized Table):
- The proposal leans towards an Index-Organized Table (IOT) approach, where the data storage structure aligns with the index structure. This can lead to improved query performance in certain scenarios.
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Partitioning Challenges:
- There's an irritation expressed regarding the limitation on using range/hash partitioning for IOTs. This constraint is an important factor in deciding the overall database architecture.
In summary, the article addresses the intricacies of handling massive amounts of time-series data, suggesting a well-thought-out database design using an IOT approach. The author's attention to key details and awareness of potential drawbacks showcases a nuanced understanding of database optimization strategies in real-world applications.
