- Choose a PTI that will identify each row to result in the most even distribution of time series data among the AMPs in the map used by the table. Use the table below to help determine what to include in your PTI.
- Choose a PTI that includes columns that are frequently queried, to speed data access for those queries.
All PTIs use the the timestamp data from the time series data, and require the timecode_data_type in the PTI definition. The following general guidelines can help you determine what else to specify in your PTI definition, based on the general nature of the time series data. In all cases, you can optionally include a timezero_date in your PTI definition.
Time Series Characteristics | Include in PTI | Basis for Data Distribution to AMPs |
---|---|---|
Single continuous long time series Example: A building monitoring system collects sensor data for a single building on a 7/24 continuous basis. |
timebucket_duration | Timebucket value |
Multiple continuous, long time series Example: Ongoing oceanic and atmospheric monitoring, such as ocean buoy sensor data, where there are thousands of buoys, and data is received from each buoy over a long period of time. |
timebucket_duration and column_list
Choose columns likely to be in common queries of the PTI table. |
Timebucket value, and values of columns included in PTI |
Short, static time series Example: Airline flight information, where each flight has an known departure time and arrival time within a short and static time frame. Other examples include scanning data from ultrasound and CAT scan imaging, and electron microscope crystal or cell structure data. |
column_list
Choose columns likely to be in common queries of the PTI table. |
Values of columns included in PTI |