When you use the TimeSeries model, storage space is required to store data in time series and metadata of time series. In addition, read and write operations on data in time series and metadata of time series consume read and write throughput. This topic describes the billable items of the TimeSeries model and provides billing examples.
Usage notes
- Since May 26, 2022, the TimeSeries model is no longer provided free of charge.
- Data in time series consists of points in time at which data is generated and specific data values. The storage volume of data in time series and the read and write throughput that are consumed by the read and write operations on data in time series are the major billable items of the TimeSeries model. You can select high-performance storage or capacity storage based on the instance type that you specified. The pricing for the throughput that is consumed by read and write operations on data in time series is the same as the pricing for the throughput that is consumed by read and write operations on common data in capacity instances.
- Metadata of time series contains the identifiers and properties of time series. The pricing for the storage of metadata of time series is the same as the pricing for the storage of common data in high-performance instances. The pricing for the throughput that is consumed by a read or write operation on metadata of time series is the same as the pricing for the throughput that is consumed by a read or write operation on common data in high-performance instances.
- When you access Tablestore over the Internet, you are charged for outbound traffic over the Internet. You are not charged for traffic over the internal network and inbound traffic.
Billable items
| Time series data | Billable item | Billing method | Description |
|---|---|---|---|
| Data in time series | Data storage |
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High-performance storage is supported when you select high-performance instance as the instance type. Capacity storage is supported when you select capacity instance as the instance type. Data in time series supports a higher data compression ratio than common data. |
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The pricing for the throughput that is consumed by read and write operations on data
in time series is the same as the pricing for the throughput that is consumed by read
and write operations on common data in capacity instances. Unit: CU.
The number of CUs that are consumed by a read or write operation is calculated based
on the amount of data in time series on which the read or write operation is performed.
The number of write CUs that are consumed for a write operation on data in time series
is the same as the amount of data that is written to Tablestore in KB. The number
of read CUs that are consumed for a read operation on data in time series is one quarter
of the amount of data that is read from Tablestore in KB.
Notice If the amount of data that is written to Tablestore is not an integer in KB, the amount
is rounded up. If the amount of data that is read from Tablestore is not a multiple
of 4 in KB, the amount is rounded up to the nearest multiple of 4.
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| Metadata of time series | Data storage |
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The pricing for the storage of metadata of time series is the same as the pricing
for the storage of common data in high-performance instances.
When you store metadata of time series in Tablestore, storage space is required for indexes. The size of the metadata of each time series is 4 KB for calculation. |
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The pricing for the throughput that is consumed by read and write operations on metadata
of time series is the same as the pricing for the throughput that is consumed by read
and write operations on common data in high-performance instances. Unit: CU.
When you call the operation to update the metadata of time series or the operation
to retrieve metadata of time series, the number of CUs that are consumed for each
row on which the operation is performed is calculated.
Notice If the amount of data on which the operation is performed is less than 4 KB in size,
the amount is rounded up to 4 KB.
You are not charged when the system automatically updates metadata of time series. |