A Lastpoint index enables fast retrieval of the latest data point for each time series in a time series table.
Overview
Lastpoint indexes are designed to quickly retrieve the latest status of each time series in a time series table, enabling fast access to the most recent data point of monitored objects.
After you create a Lastpoint index for a time series table, Tablestore automatically synchronizes the latest data point of each time series to the Lastpoint index by using local indexing. Once the initial synchronization is complete, the index enters an incremental synchronization state with no synchronization latency. A Lastpoint index includes the partition key (the _#h field) generated by Tablestore for the time series, the time series identifiers, and the latest data point of each time series.
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The Lastpoint index feature is supported in the following regions: China (Hangzhou), China (Shanghai), China (Beijing), and China (Zhangjiakou).
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If you want to view the sample data in a Lastpoint index, see Appendix: Sample data in a Lastpoint index.
Scenarios
The following table describes the typical scenarios of Lastpoint indexes.
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Scenario |
Description |
|
IoT platform |
In Internet of vehicles (IoV) scenarios, vehicles regularly report data such as location coordinates, in-vehicle temperature, and remaining power. Lastpoint indexes enable real-time vehicle location tracking by quickly querying the latest location of each vehicle, helping you monitor vehicle status in real time. |
|
Monitoring system |
In device monitoring scenarios, you often need the latest readings of metrics such as device operating status, temperature, and humidity. Lastpoint indexes efficiently retrieve the latest status of each device, supporting quick response and adjustment. |
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Financial transactions |
In real-time financial transactions, accessing the latest price data is crucial for decision-making. Lastpoint indexes help you quickly obtain the latest market quotations for trend analysis. |
Lastpoint index operations
The following table describes the operations and call methods available for Lastpoint indexes.
|
Feature |
Description |
Call method |
|
Create a Lastpoint index |
Creates a Lastpoint index for a time series table. |
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Query Lastpoint index data |
Queries Lastpoint index data by executing SQL statements or using the same method as reading data from a data table. |
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Delete a Lastpoint index |
Deletes a Lastpoint index from a time series table. |
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Retrieve Lastpoint index |
Creates a search index for a Lastpoint index to leverage search index capabilities for efficient data retrieval. |
Billing
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Building a Lastpoint index does not incur fees. However, data storage in the Lastpoint index and reading data from the index incur fees. For more information, see Billable items of the TimeSeries model.
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If you query or retrieve Lastpoint index data by executing SQL statements or using a search index, additional fees are incurred based on the billing rules of the respective features. For more information, see Billable items of SQL query and Billable items of search indexes.
Appendix: Sample data in a Lastpoint index
The following table shows the data in a time series table that contains two time series.
The _m_name, _data_source, and _tags fields are time series identifiers. The _time field is the time when the data is recorded. The cpu_usage and cpu_sys fields are metering metrics.
|
_m_name |
_data_source |
_tags |
_time |
cpu_usage |
cpu_sys |
|
cpu |
host_1 |
["region=hangzhou"] |
1712476524000000 |
10.0 |
5.0 |
|
cpu |
host_1 |
["region=hangzhou"] |
1712476525000000 |
12.0 |
5.0 |
|
cpu |
host_1 |
["region=hangzhou"] |
1712476526000000 |
14.0 |
5.0 |
|
cpu |
host_2 |
["region=hangzhou"] |
1712476524000000 |
10.0 |
5.0 |
|
cpu |
host_2 |
["region=hangzhou"] |
1712476525000000 |
20.0 |
5.0 |
|
cpu |
host_2 |
["region=hangzhou"] |
1712476526000000 |
40.0 |
5.0 |
After you create a Lastpoint index, Tablestore automatically synchronizes the latest data point of each time series to the index. The following table shows sample data in the Lastpoint index.
The _#h field is the partition key generated by Tablestore for the time series.
|
_#h |
_m_name |
_data_source |
_tags |
_time |
cpu_usage |
cpu_sys |
|
03#cpu#a3 |
cpu |
host_1 |
["region=hangzhou"] |
1712476526000000 |
14.0 |
5.0 |
|
48#cpu#a5 |
cpu |
host_2 |
["region=hangzhou"] |
1712476526000000 |
40.0 |
5.0 |