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Add examples to Sample readme
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Rob Kiefer
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@ -12,10 +12,25 @@ intervals, and number of values for the partition field.
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**Device ops**: These datasets are designed to represent metrics (e.g. CPU,
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memory, network) collected from mobile devices.
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1. [`devices_small`](https://iobeamdata.blob.core.windows.net/datasets/devices_small.bak.tar.gz) - 1,000 devices recorded over 1,000 time intervals
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1. [`devices_med`](https://iobeamdata.blob.core.windows.net/datasets/devices_med.bak.tar.gz) - 10,000 devices recorded over 1,000 time intervals
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1. [`devices_med`](https://iobeamdata.blob.core.windows.net/datasets/devices_med.bak.tar.gz) - 5,000 devices recorded over 2,000 time intervals
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1. [`devices_big`](https://iobeamdata.blob.core.windows.net/datasets/devices_big.bak.tar.gz) - 3,000 devices recorded over 10,000 time intervals
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For more details and example usage, see
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[In-depth: Device ops datasets](#in-depth-devices).
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**Weather**: These datasets are designed to represent temperature and
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humidity data from a variety of locations.
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1. [`weather_small`](https://iobeamdata.blob.core.windows.net/datasets/weather_small.bak.tar.gz) - 1,000 locations over 1,000 two-minute intervals
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1. [`weather_med`](https://iobeamdata.blob.core.windows.net/datasets/weather_med.bak.tar.gz) - 1,000 locations over 15,000 two-minute intervals
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1. [`weather_big`](https://iobeamdata.blob.core.windows.net/datasets/weather_big.bak.tar.gz) - 2,000 locations over 20,000 two-minute intervals
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For more details and example usage, see
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[In-depth: Weather datasets](#in-depth-weather).
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### Importing
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Data is easily imported using the standard way of restoring `pg_dump` backups.
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@ -41,3 +56,237 @@ The data is now ready for you to use.
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# To access your database (e.g., devices_small)
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psql -U postgres -h localhost -d devices_small
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```
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### In-depth: Device ops datasets <a name="in-depth-devices"></a>
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After importing one of these datasets (`devices_small`, `devices_med`,
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`devices_big`), you will find a plain Postgres table called `device_info`
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and a hypertable called `readings`. The `device_info` table has (static)
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metadata about each device, such as the OS name and manufacturer. The
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`readings` hypertable tracks data sent from each device, e.g. CPU activity,
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memory levels, etc. Because hypertables are exposed as a single table, you
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can query them and join them with the metadata as you would normal SQL
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tables (see Example Queries below).
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#### Schemas
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```sql
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Table "public.device_info"
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Column | Type | Modifiers
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-------------+------+-----------
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device_id | text |
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api_version | text |
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manufacturer | text |
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model | text |
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os_name | text |
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```
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```sql
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Table "public.readings"
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Column | Type | Modifiers
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--------------------+------------------+-----------
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time | bigint |
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device_id | text |
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battery_level | double precision |
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battery_status | text |
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battery_temperature | double precision |
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bssid | text |
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cpu_avg_1min | double precision |
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cpu_avg_5min | double precision |
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cpu_avg_15min | double precision |
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mem_free | double precision |
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mem_used | double precision |
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rssi | double precision |
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ssid | text |
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Indexes:
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"readings_device_id_time_idx" btree (device_id, "time" DESC)
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"readings_time_idx" btree ("time" DESC)
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```
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#### Example Queries
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_Note: Uses dataset_ `devices_med`
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**10 most recent battery temperature readings for charging devices**
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```sql
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SELECT time, device_id, battery_temperature
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FROM readings
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WHERE battery_status = 'charging'
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ORDER BY time DESC LIMIT 10;
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time | device_id | battery_temperature
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-----------------------+------------+---------------------
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2016-11-15 23:39:30-05 | demo004887 | 99.3
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2016-11-15 23:39:30-05 | demo004882 | 100.8
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2016-11-15 23:39:30-05 | demo004862 | 95.7
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2016-11-15 23:39:30-05 | demo004844 | 95.5
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2016-11-15 23:39:30-05 | demo004841 | 95.4
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2016-11-15 23:39:30-05 | demo004804 | 101.6
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2016-11-15 23:39:30-05 | demo004784 | 100.6
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2016-11-15 23:39:30-05 | demo004760 | 99.1
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2016-11-15 23:39:30-05 | demo004731 | 97.9
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2016-11-15 23:39:30-05 | demo004729 | 99.6
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(10 rows)```
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**Busiest devices (1 min avg) whose battery level is below 33% and
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is not charging**
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```sql
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SELECT time, readings.device_id, cpu_avg_1min,
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battery_level, battery_status, device_info.model
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FROM readings
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JOIN device_info ON readings.device_id = device_info.device_id
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WHERE battery_level < 33 AND battery_status = 'discharging'
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ORDER BY cpu_avg_1min DESC, time DESC LIMIT 5;
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time | device_id | cpu_avg_1min | battery_level | battery_status | model
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-----------------------+------------+--------------+---------------+----------------+---------
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2016-11-15 23:30:00-05 | demo003764 | 98.99 | 32 | discharging | focus
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2016-11-15 22:54:30-05 | demo001935 | 98.99 | 30 | discharging | pinto
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2016-11-15 19:10:30-05 | demo000695 | 98.99 | 23 | discharging | focus
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2016-11-15 16:46:00-05 | demo002784 | 98.99 | 18 | discharging | pinto
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2016-11-15 14:58:30-05 | demo004978 | 98.99 | 22 | discharging | mustang
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(5 rows)
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```
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```sql
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SELECT date_trunc('hour', time) "hour",
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min(battery_level) min_battery_level,
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max(battery_level) max_battery_level
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FROM readings r
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WHERE r.device_id IN (
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SELECT DISTINCT device_id FROM device_info
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WHERE model = 'pinto' OR model = 'focus'
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) GROUP BY "hour" ORDER BY "hour" ASC LIMIT 12;
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hour | min_battery_level | max_battery_level
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-----------------------+-------------------+-------------------
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2016-11-15 07:00:00-05 | 17 | 99
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2016-11-15 08:00:00-05 | 11 | 98
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2016-11-15 09:00:00-05 | 6 | 97
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2016-11-15 10:00:00-05 | 6 | 97
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2016-11-15 11:00:00-05 | 6 | 97
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2016-11-15 12:00:00-05 | 6 | 97
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2016-11-15 13:00:00-05 | 6 | 97
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2016-11-15 14:00:00-05 | 6 | 98
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2016-11-15 15:00:00-05 | 6 | 100
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2016-11-15 16:00:00-05 | 6 | 100
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2016-11-15 17:00:00-05 | 6 | 100
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2016-11-15 18:00:00-05 | 6 | 100
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(12 rows)
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```
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**
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### In-depth: Weather datasets <a name="in-depth-weather"></a>
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After importing one of these datasets (`weather_small`, `weather_med`,
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`weather_big`), you will find a plain Postgres table called `locations` and
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a hypertable called `conditions`. The `locations` table has metadata about
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each of the locations, such as its name and environmental type. The
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`conditions` hypertable tracks readings of temperature and humidity from
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those locations. Because hypertables are exposed as a single table, you can
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query them and join them with the metadata as you would normal SQL tables (see Example Queries below).
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#### Schemas
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```sql
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Table "public.locations"
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Column | Type | Modifiers
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------------+------+-----------
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device_id | text |
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location | text |
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environment | text |
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```
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```sql
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Table "public.conditions"
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Column | Type | Modifiers
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------------+--------------------------+-----------
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time | timestamp with time zone | not null
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device_id | text |
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temperature | double precision |
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humidity | double precision |
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Indexes:
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"conditions_device_id_time_idx" btree (device_id, "time" DESC)
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"conditions_time_idx" btree ("time" DESC)
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```
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#### Example Queries
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_Note: Uses dataset_ `weather_med`
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**Last 10 readings**
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```sql
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SELECT * FROM conditions c ORDER BY time DESC LIMIT 10;
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time | device_id | temperature | humidity
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-----------------------+--------------------+--------------------+--------------------
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2016-12-06 02:58:00-05 | weather-pro-000000 | 84.10000000000034 | 83.70000000000053
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2016-12-06 02:58:00-05 | weather-pro-000001 | 35.999999999999915 | 51.79999999999994
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2016-12-06 02:58:00-05 | weather-pro-000002 | 68.90000000000006 | 63.09999999999999
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2016-12-06 02:58:00-05 | weather-pro-000003 | 83.70000000000041 | 84.69999999999989
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2016-12-06 02:58:00-05 | weather-pro-000004 | 83.10000000000039 | 84.00000000000051
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2016-12-06 02:58:00-05 | weather-pro-000005 | 85.10000000000034 | 81.70000000000017
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2016-12-06 02:58:00-05 | weather-pro-000006 | 61.09999999999999 | 49.800000000000026
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2016-12-06 02:58:00-05 | weather-pro-000007 | 82.9000000000004 | 84.80000000000047
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2016-12-06 02:58:00-05 | weather-pro-000008 | 58.599999999999966 | 40.2
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2016-12-06 02:58:00-05 | weather-pro-000009 | 61.000000000000014 | 49.399999999999906
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(10 rows)
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```
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**Last 10 readings from 'outside' locations**
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```sql
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SELECT time, c.device_id, location,
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trunc(temperature, 2) temperature, trunc(humidity, 2) humidity
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FROM conditions c
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INNER JOIN locations l ON c.device_id = l.device_id
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WHERE l.environment = 'outside'
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ORDER BY time DESC LIMIT 10;
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time | device_id | location | temperature | humidity
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-----------------------+--------------------+---------------+-------------+----------
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2016-12-06 02:58:00-05 | weather-pro-000000 | field-000000 | 84.10 | 83.70
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2016-12-06 02:58:00-05 | weather-pro-000001 | arctic-000000 | 35.99 | 51.79
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2016-12-06 02:58:00-05 | weather-pro-000003 | swamp-000000 | 83.70 | 84.69
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2016-12-06 02:58:00-05 | weather-pro-000004 | field-000001 | 83.10 | 84.00
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2016-12-06 02:58:00-05 | weather-pro-000005 | swamp-000001 | 85.10 | 81.70
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2016-12-06 02:58:00-05 | weather-pro-000007 | field-000002 | 82.90 | 84.80
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2016-12-06 02:58:00-05 | weather-pro-000014 | field-000003 | 84.50 | 83.90
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2016-12-06 02:58:00-05 | weather-pro-000015 | swamp-000002 | 85.50 | 66.00
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2016-12-06 02:58:00-05 | weather-pro-000017 | arctic-000001 | 35.29 | 50.59
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2016-12-06 02:58:00-05 | weather-pro-000019 | arctic-000002 | 36.09 | 48.80
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(10 rows)
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```
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**Hourly average, min, and max temperatures for "field" locations**
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```sql
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SELECT date_trunc('hour', time) "hour",
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trunc(avg(temperature), 2) avg_temp,
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trunc(min(temperature), 2) min_temp,
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trunc(max(temperature), 2) max_temp
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FROM conditions c
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WHERE c.device_id IN (
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SELECT device_id FROM locations
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WHERE location LIKE 'field-%'
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) GROUP BY "hour" ORDER BY "hour" ASC LIMIT 24;
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hour | avg_temp | min_temp | max_temp
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-----------------------+----------+----------+----------
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2016-11-15 07:00:00-05 | 73.80 | 68.00 | 79.09
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2016-11-15 08:00:00-05 | 74.80 | 68.69 | 80.29
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2016-11-15 09:00:00-05 | 75.75 | 69.39 | 81.19
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2016-11-15 10:00:00-05 | 76.75 | 70.09 | 82.29
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2016-11-15 11:00:00-05 | 77.77 | 70.79 | 83.39
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2016-11-15 12:00:00-05 | 78.76 | 71.69 | 84.49
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2016-11-15 13:00:00-05 | 79.73 | 72.69 | 85.29
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2016-11-15 14:00:00-05 | 80.72 | 73.49 | 86.99
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2016-11-15 15:00:00-05 | 81.73 | 74.29 | 88.39
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2016-11-15 16:00:00-05 | 82.70 | 75.09 | 88.89
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2016-11-15 17:00:00-05 | 83.70 | 76.19 | 89.99
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2016-11-15 18:00:00-05 | 84.67 | 77.09 | 90.00
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2016-11-15 19:00:00-05 | 85.64 | 78.19 | 90.00
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2016-11-15 20:00:00-05 | 86.53 | 78.59 | 90.00
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2016-11-15 21:00:00-05 | 86.40 | 78.49 | 90.00
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2016-11-15 22:00:00-05 | 85.39 | 77.29 | 89.30
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2016-11-15 23:00:00-05 | 84.40 | 76.19 | 88.70
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2016-11-16 00:00:00-05 | 83.39 | 75.39 | 87.90
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2016-11-16 01:00:00-05 | 82.40 | 74.39 | 87.10
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2016-11-16 02:00:00-05 | 81.40 | 73.29 | 86.29
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2016-11-16 03:00:00-05 | 80.38 | 71.89 | 85.40
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2016-11-16 04:00:00-05 | 79.41 | 70.59 | 84.40
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2016-11-16 05:00:00-05 | 78.39 | 69.49 | 83.60
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2016-11-16 06:00:00-05 | 78.42 | 69.49 | 84.40
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(24 rows)
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```
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