13 KiB
Using our Sample Datasets
Available samples
We have created several sample datasets (using pg_dump) to help you get
started using TimescaleDB. These datasets vary in database size, number
of time intervals, and number of values for the partition field.
(Note that these dataset backups already include our time-series database, so you won't need to manually install our extension, nor run the setup scripts, etc.)
Device ops: These datasets are designed to represent metrics (e.g. CPU, memory, network) collected from mobile devices. (Click on the name to download.)
devices_small- 1,000 devices recorded over 1,000 time intervalsdevices_med- 5,000 devices recorded over 2,000 time intervalsdevices_big- 3,000 devices recorded over 10,000 time intervals
For more details and example usage, see In-depth: Device ops datasets.
Weather: These datasets are designed to represent temperature and humidity data from a variety of locations. (Click on the name to download.)
weather_small- 1,000 locations over 1,000 two-minute intervalsweather_med- 1,000 locations over 15,000 two-minute intervalsweather_big- 2,000 locations over 20,000 two-minute intervals
For more details and example usage, see In-depth: Weather datasets.
Importing
Data is easily imported using the standard way of restoring pg_dump backups.
Briefly the steps are:
- Unzip the archive,
- Create a database for the data (using the same name as the dataset)
- Import the data via
psql
Each of our archives is named [dataset_name].bak.tar.gz, so if you are using
dataset devices_small, the commands are:
# (1) unzip the archive
tar -xvzf devices_small.bak.tar.gz
# (2) create a database with the same name
psql -U postgres -h localhost -c 'CREATE DATABASE devices_small;'
# (3) import data
psql -U postgres -d devices_small -h localhost < devices_small.bak
The data is now ready for you to use.
# To access your database (e.g., devices_small)
psql -U postgres -h localhost -d devices_small
In-depth: Device ops datasets
After importing one of these datasets (devices_small, devices_med,
devices_big), you will find a plain Postgres table called device_info
and a hypertable called readings. The device_info table has (static)
metadata about each device, such as the OS name and manufacturer. The
readings hypertable tracks data sent from each device, e.g. CPU activity,
memory levels, etc. Because hypertables are exposed as a single table, you
can query them and join them with the metadata as you would normal SQL
tables (see Example Queries below).
Schemas
Table "public.device_info"
Column | Type | Modifiers
-------------+------+-----------
device_id | text |
api_version | text |
manufacturer | text |
model | text |
os_name | text |
Table "public.readings"
Column | Type | Modifiers
--------------------+------------------+-----------
time | bigint |
device_id | text |
battery_level | double precision |
battery_status | text |
battery_temperature | double precision |
bssid | text |
cpu_avg_1min | double precision |
cpu_avg_5min | double precision |
cpu_avg_15min | double precision |
mem_free | double precision |
mem_used | double precision |
rssi | double precision |
ssid | text |
Indexes:
"readings_device_id_time_idx" btree (device_id, "time" DESC)
"readings_time_idx" btree ("time" DESC)
Example Queries
Note: Uses dataset devices_med
10 most recent battery temperature readings for charging devices
SELECT time, device_id, battery_temperature
FROM readings
WHERE battery_status = 'charging'
ORDER BY time DESC LIMIT 10;
time | device_id | battery_temperature
-----------------------+------------+---------------------
2016-11-15 23:39:30-05 | demo000000 | 101
2016-11-15 23:39:30-05 | demo000016 | 97
2016-11-15 23:39:30-05 | demo000019 | 96.3
2016-11-15 23:39:30-05 | demo000045 | 95.9
2016-11-15 23:39:30-05 | demo000048 | 97.1
2016-11-15 23:39:30-05 | demo000049 | 101.7
2016-11-15 23:39:30-05 | demo000070 | 95.9
2016-11-15 23:39:30-05 | demo000074 | 95.8
2016-11-15 23:39:30-05 | demo000089 | 100.5
2016-11-15 23:39:30-05 | demo000094 | 100.6
(10 rows)
Busiest devices (1 min avg) whose battery level is below 33% and is not charging
SELECT time, readings.device_id, cpu_avg_1min,
battery_level, battery_status, device_info.model
FROM readings
JOIN device_info ON readings.device_id = device_info.device_id
WHERE battery_level < 33 AND battery_status = 'discharging'
ORDER BY cpu_avg_1min DESC, time DESC LIMIT 5;
time | device_id | cpu_avg_1min | battery_level | battery_status | model
-----------------------+------------+--------------+---------------+----------------+---------
2016-11-15 22:51:00-05 | demo004320 | 98.99 | 32 | discharging | pinto
2016-11-15 21:16:30-05 | demo001647 | 98.99 | 30 | discharging | pinto
2016-11-15 19:13:00-05 | demo003758 | 98.99 | 30 | discharging | focus
2016-11-15 17:09:00-05 | demo004924 | 98.99 | 15 | discharging | mustang
2016-11-15 12:35:00-05 | demo002196 | 98.99 | 27 | discharging | pinto
(5 rows)
SELECT date_trunc('hour', time) "hour",
min(battery_level) min_battery_level,
max(battery_level) max_battery_level
FROM readings r
WHERE r.device_id IN (
SELECT DISTINCT device_id FROM device_info
WHERE model = 'pinto' OR model = 'focus'
) GROUP BY "hour" ORDER BY "hour" ASC LIMIT 12;
hour | min_battery_level | max_battery_level
-----------------------+-------------------+-------------------
2016-11-15 07:00:00-05 | 19 | 100
2016-11-15 08:00:00-05 | 10 | 100
2016-11-15 09:00:00-05 | 6 | 100
2016-11-15 10:00:00-05 | 6 | 100
2016-11-15 11:00:00-05 | 6 | 100
2016-11-15 12:00:00-05 | 6 | 100
2016-11-15 13:00:00-05 | 6 | 100
2016-11-15 14:00:00-05 | 6 | 100
2016-11-15 15:00:00-05 | 6 | 100
2016-11-15 16:00:00-05 | 6 | 100
2016-11-15 17:00:00-05 | 6 | 100
2016-11-15 18:00:00-05 | 6 | 100
(12 rows)
**
In-depth: Weather datasets
After importing one of these datasets (weather_small, weather_med,
weather_big), you will find a plain Postgres table called locations and
a hypertable called conditions. The locations table has metadata about
each of the locations, such as its name and environmental type. The
conditions hypertable tracks readings of temperature and humidity from
those locations. Because hypertables are exposed as a single table, you can
query them and join them with the metadata as you would normal SQL tables (see Example Queries below).
Schemas
Table "public.locations"
Column | Type | Modifiers
------------+------+-----------
device_id | text |
location | text |
environment | text |
Table "public.conditions"
Column | Type | Modifiers
------------+--------------------------+-----------
time | timestamp with time zone | not null
device_id | text |
temperature | double precision |
humidity | double precision |
Indexes:
"conditions_device_id_time_idx" btree (device_id, "time" DESC)
"conditions_time_idx" btree ("time" DESC)
Example Queries
Note: Uses dataset weather_med
Last 10 readings
SELECT * FROM conditions c ORDER BY time DESC LIMIT 10;
time | device_id | temperature | humidity
-----------------------+--------------------+--------------------+--------------------
2016-12-06 02:58:00-05 | weather-pro-000000 | 33.899999999999885 | 52.799999999999955
2016-12-06 02:58:00-05 | weather-pro-000001 | 61.50000000000002 | 48.400000000000006
2016-12-06 02:58:00-05 | weather-pro-000002 | 34.599999999999895 | 49.399999999999906
2016-12-06 02:58:00-05 | weather-pro-000003 | 82.80000000000041 | 82.30000000000061
2016-12-06 02:58:00-05 | weather-pro-000004 | 85.00000000000034 | 76.00000000000013
2016-12-06 02:58:00-05 | weather-pro-000005 | 60.2 | 48.29999999999989
2016-12-06 02:58:00-05 | weather-pro-000006 | 64 | 52.899999999999956
2016-12-06 02:58:00-05 | weather-pro-000007 | 83.10000000000039 | 83.00000000000057
2016-12-06 02:58:00-05 | weather-pro-000008 | 85.00000000000034 | 95.89999999999998
2016-12-06 02:58:00-05 | weather-pro-000009 | 83.30000000000044 | 92.19999999999995
(10 rows)
Last 10 readings from 'outside' locations
SELECT time, c.device_id, location,
trunc(temperature, 2) temperature, trunc(humidity, 2) humidity
FROM conditions c
INNER JOIN locations l ON c.device_id = l.device_id
WHERE l.environment = 'outside'
ORDER BY time DESC LIMIT 10;
time | device_id | location | temperature | humidity
-----------------------+--------------------+---------------+-------------+----------
2016-12-06 02:58:00-05 | weather-pro-000000 | arctic-000000 | 33.89 | 52.79
2016-12-06 02:58:00-05 | weather-pro-000002 | arctic-000001 | 34.59 | 49.39
2016-12-06 02:58:00-05 | weather-pro-000003 | field-000000 | 82.80 | 82.30
2016-12-06 02:58:00-05 | weather-pro-000004 | swamp-000000 | 85.00 | 76.00
2016-12-06 02:58:00-05 | weather-pro-000007 | field-000001 | 83.10 | 83.00
2016-12-06 02:58:00-05 | weather-pro-000008 | swamp-000001 | 85.00 | 95.89
2016-12-06 02:58:00-05 | weather-pro-000009 | swamp-000002 | 83.30 | 92.19
2016-12-06 02:58:00-05 | weather-pro-000011 | swamp-000003 | 85.20 | 82.79
2016-12-06 02:58:00-05 | weather-pro-000013 | arctic-000002 | 35.49 | 50.69
2016-12-06 02:58:00-05 | weather-pro-000018 | swamp-000004 | 83.40 | 60.60
(10 rows)
Hourly average, min, and max temperatures for "field" locations
SELECT date_trunc('hour', time) "hour",
trunc(avg(temperature), 2) avg_temp,
trunc(min(temperature), 2) min_temp,
trunc(max(temperature), 2) max_temp
FROM conditions c
WHERE c.device_id IN (
SELECT device_id FROM locations
WHERE location LIKE 'field-%'
) GROUP BY "hour" ORDER BY "hour" ASC LIMIT 24;
hour | avg_temp | min_temp | max_temp
-----------------------+----------+----------+----------
2016-11-15 07:00:00-05 | 73.24 | 68.00 | 78.89
2016-11-15 08:00:00-05 | 74.25 | 69.09 | 80.09
2016-11-15 09:00:00-05 | 75.24 | 69.59 | 81.09
2016-11-15 10:00:00-05 | 76.25 | 70.69 | 82.39
2016-11-15 11:00:00-05 | 77.26 | 71.29 | 83.59
2016-11-15 12:00:00-05 | 78.26 | 72.39 | 84.79
2016-11-15 13:00:00-05 | 79.24 | 73.19 | 85.79
2016-11-15 14:00:00-05 | 80.22 | 73.69 | 86.79
2016-11-15 15:00:00-05 | 81.22 | 74.49 | 87.59
2016-11-15 16:00:00-05 | 82.24 | 75.29 | 88.59
2016-11-15 17:00:00-05 | 83.22 | 76.59 | 89.39
2016-11-15 18:00:00-05 | 84.23 | 77.59 | 90.00
2016-11-15 19:00:00-05 | 85.22 | 78.39 | 90.00
2016-11-15 20:00:00-05 | 85.16 | 78.29 | 90.00
2016-11-15 21:00:00-05 | 84.16 | 77.49 | 89.59
2016-11-15 22:00:00-05 | 83.16 | 76.19 | 88.59
2016-11-15 23:00:00-05 | 82.15 | 75.09 | 87.70
2016-11-16 00:00:00-05 | 81.15 | 74.09 | 86.70
2016-11-16 01:00:00-05 | 80.15 | 72.99 | 85.90
2016-11-16 02:00:00-05 | 79.14 | 71.89 | 84.80
2016-11-16 03:00:00-05 | 78.13 | 70.69 | 83.69
2016-11-16 04:00:00-05 | 77.15 | 69.39 | 82.80
2016-11-16 05:00:00-05 | 77.16 | 69.39 | 83.60
2016-11-16 06:00:00-05 | 78.13 | 70.59 | 84.90
(24 rows)