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Remove sample data instructions and point to docs site
This commit directs users to our docs site for using sample data so we don't have to keep both places in sync.
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Rob Kiefer
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README.md
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README.md
@ -127,14 +127,14 @@ shared_preload_libraries = 'timescaledb'
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### Setting up your initial database
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Now, we'll install our extension and create an initial database.
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Now, we'll install our extension and create an initial database. Below
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you'll find instructions for creating a new, empty database.
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You again have two options for setting up your initial database:
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1. *Empty Database* - To set up a new, empty database, please follow the instructions below.
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2. *Database with pre-loaded sample data* - To help you quickly get started, we have also created some sample datasets. See
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[Using our Sample Datasets](docs/UsingSamples.md) for further instructions. (Includes installing our extension.)
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To help you quickly get started, we have also created some sample
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datasets. Once you complete the initial setup below you can then
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easily import this data to play around with TimescaleDB functionality.
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See [our Sample Datasets](https://docs.timescale.com/other-sample-datasets)
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for further instructions.
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#### Setting up an empty database
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@ -1,296 +0,0 @@
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## Using our Sample Datasets
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### Available samples
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We have created several sample datasets (using `pg_dump`) to help you get
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started using TimescaleDB. These datasets vary in database size, number
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of time intervals, and number of values for the partition field.
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(Note that these dataset backups already include our time-series
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database, so you won't need to manually install our extension,
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nor run the setup scripts, etc.)
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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. (Click on the name to
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download.)
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1. [`devices_small`](https://timescaledata.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://timescaledata.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://timescaledata.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. (Click on the name to download.)
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1. [`weather_small`](https://timescaledata.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://timescaledata.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://timescaledata.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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Briefly the steps are:
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1. Unzip the archive,
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1. Create a database for the data (using the same name as the dataset)
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1. Import the data via `psql`
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Each of our archives is named `[dataset_name].bak.tar.gz`, so if you are using
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dataset `devices_small`, the commands are:
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```bash
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# (1) unzip the archive
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tar -xvzf devices_small.bak.tar.gz
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# (2) create a database with the same name
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psql -U postgres -h localhost -c 'CREATE DATABASE devices_small;'
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# (3) import data
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psql -U postgres -d devices_small -h localhost < devices_small.bak
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```
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The data is now ready for you to use.
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```bash
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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 | demo004866 | 101.1
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2016-11-15 23:39:30-05 | demo004848 | 96
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2016-11-15 23:39:30-05 | demo004836 | 97.2
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2016-11-15 23:39:30-05 | demo004827 | 99.5
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2016-11-15 23:39:30-05 | demo004792 | 101.7
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2016-11-15 23:39:30-05 | demo004761 | 95.7
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2016-11-15 23:39:30-05 | demo004740 | 100.2
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2016-11-15 23:39:30-05 | demo004729 | 96.1
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2016-11-15 23:39:30-05 | demo004723 | 95.4
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2016-11-15 23:39:30-05 | demo004711 | 98.8
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(10 rows)
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```
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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 21:26:00-05 | demo000559 | 98.99 | 32 | discharging | mustang
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2016-11-15 20:31:00-05 | demo000312 | 98.99 | 29 | discharging | pinto
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2016-11-15 18:00:00-05 | demo002979 | 98.99 | 26 | discharging | pinto
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2016-11-15 17:40:00-05 | demo003978 | 98.99 | 25 | discharging | pinto
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2016-11-15 13:55:30-05 | demo004548 | 98.99 | 12 | discharging | pinto
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(5 rows)
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```
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**Minimum and maximum battery levels per hour for the first 12 hours**
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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 | 20 | 99
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2016-11-15 08:00:00-05 | 12 | 98
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2016-11-15 09:00:00-05 | 8 | 97
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2016-11-15 10:00:00-05 | 6 | 100
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2016-11-15 11:00:00-05 | 6 | 100
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2016-11-15 12:00:00-05 | 6 | 100
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2016-11-15 13:00:00-05 | 6 | 100
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2016-11-15 14:00:00-05 | 6 | 100
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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-000999 | 35.49999999999991 | 50.60000000000004
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2016-12-06 02:58:00-05 | weather-pro-000998 | 34.7999999999999 | 48.70000000000001
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2016-12-06 02:58:00-05 | weather-pro-000997 | 84.90000000000035 | 86.30000000000001
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2016-12-06 02:58:00-05 | weather-pro-000996 | 83.40000000000038 | 83.40000000000055
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2016-12-06 02:58:00-05 | weather-pro-000995 | 83.30000000000038 | 82.4000000000006
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2016-12-06 02:58:00-05 | weather-pro-000994 | 83.9000000000004 | 70.70000000000024
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2016-12-06 02:58:00-05 | weather-pro-000993 | 35.79999999999991 | 51.30000000000005
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2016-12-06 02:58:00-05 | weather-pro-000992 | 81.90000000000046 | 82.4000000000006
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2016-12-06 02:58:00-05 | weather-pro-000991 | 62.1 | 48.2
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2016-12-06 02:58:00-05 | weather-pro-000990 | 83.60000000000042 | 76.70000000000014
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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-000999 | arctic-000204 | 35.49 | 50.60
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2016-12-06 02:58:00-05 | weather-pro-000998 | arctic-000203 | 34.79 | 48.70
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2016-12-06 02:58:00-05 | weather-pro-000997 | swamp-000213 | 84.90 | 86.30
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2016-12-06 02:58:00-05 | weather-pro-000996 | field-000199 | 83.40 | 83.40
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2016-12-06 02:58:00-05 | weather-pro-000995 | field-000198 | 83.30 | 82.40
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2016-12-06 02:58:00-05 | weather-pro-000994 | swamp-000212 | 83.90 | 70.70
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2016-12-06 02:58:00-05 | weather-pro-000993 | arctic-000202 | 35.79 | 51.30
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2016-12-06 02:58:00-05 | weather-pro-000992 | field-000197 | 81.90 | 82.40
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2016-12-06 02:58:00-05 | weather-pro-000990 | swamp-000211 | 83.60 | 76.70
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2016-12-06 02:58:00-05 | weather-pro-000989 | field-000196 | 83.30 | 81.70
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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 | 73.59 | 68.10 | 79.09
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2016-11-15 08:00:00-05 | 74.59 | 68.89 | 80.19
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2016-11-15 09:00:00-05 | 75.60 | 69.79 | 81.69
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2016-11-15 10:00:00-05 | 76.59 | 70.49 | 82.79
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2016-11-15 11:00:00-05 | 77.59 | 71.69 | 84.19
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2016-11-15 12:00:00-05 | 78.59 | 72.79 | 85.49
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2016-11-15 13:00:00-05 | 79.62 | 73.69 | 86.49
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2016-11-15 14:00:00-05 | 80.62 | 74.29 | 87.49
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2016-11-15 15:00:00-05 | 81.62 | 75.09 | 88.59
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2016-11-15 16:00:00-05 | 82.63 | 76.09 | 89.89
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2016-11-15 17:00:00-05 | 83.64 | 77.19 | 90.00
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2016-11-15 18:00:00-05 | 84.63 | 77.99 | 90.00
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2016-11-15 19:00:00-05 | 85.58 | 79.09 | 90.00
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2016-11-15 20:00:00-05 | 85.48 | 79.19 | 90.00
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2016-11-15 21:00:00-05 | 84.47 | 78.29 | 89.50
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2016-11-15 22:00:00-05 | 83.48 | 77.09 | 88.49
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2016-11-15 23:00:00-05 | 82.46 | 75.79 | 87.70
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2016-11-16 00:00:00-05 | 81.45 | 75.09 | 86.80
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2016-11-16 01:00:00-05 | 80.45 | 73.89 | 85.70
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2016-11-16 02:00:00-05 | 79.45 | 72.79 | 84.90
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2016-11-16 03:00:00-05 | 78.46 | 71.99 | 84.00
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2016-11-16 04:00:00-05 | 77.48 | 71.09 | 83.00
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2016-11-16 05:00:00-05 | 77.50 | 71.09 | 82.90
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2016-11-16 06:00:00-05 | 78.49 | 71.69 | 84.20
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(24 rows)
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```
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