Set up the database

This tutorial uses the energy consumption data for over a year in a hypertable named metrics.

A service in Timescale is a cloud instance which contains your database. Each service contains a single database, named tsdb. You can connect to a service from your local system using the psql command-line utility. If you've used PostgreSQL before, you might already have psql installed. If not, check out the installing psql section.

Create a Timescale service and connect to the service

In the Timescale portal, click Create service.
Click Download the cheatsheet to download an SQL file that contains the login details for your new service. You can also copy the details directly from this page. When you have copied your password, click I stored my password, go to service overview at the bottom of the page.
When your service is ready to use, is shows a green Running label in the Service Overview. You also receive an email confirming that your service is ready to use.
On your local system, at the command prompt, connect to the service using the Service URL from the SQL file that you downloaded. When you are prompted, enter the password:
```
psql -x "<SERVICE_URL>"
Password for user tsdbadmin:
```
If your connection is successful, you'll see a message like this, followed by the psql prompt:
```
psql (13.3, server 12.8 (Ubuntu 12.8-1.pgdg21.04+1))
SSL connection (protocol: TLSv1.3, cipher: TLS_AES_256_GCM_SHA384, bits: 256, compression: off)
Type "help" for help.
tsdb=>
```

This tutorial uses the energy consumption data for over a year in a typical household. You can use this data to analyze the energy consumption pattern.

Create a hypertable

Hypertables are the core of Timescale. Hypertables enable Timescale to work efficiently with time-series data. Because Timescale is PostgreSQL, all the standard PostgreSQL tables, indexes, stored procedures and other objects can be created alongside your Timescale hypertables. This makes creating and working with Timescale tables similar to standard PostgreSQL.

Creating a hypertable

Create a standard PostgreSQL table to store the energy consumption data using CREATE TABLE:


CREATE TABLE "metrics"(
    created timestamp with time zone default now() not null,
    type_id integer                                not null,
    value   double precision                       not null
);

Convert the standard table into a hypertable partitioned on the time column using the create_hypertable() function provided by Timescale. You must provide the name of the table and the column in that table that holds the timestamp data to use for partitioning:
```
SELECT create_hypertable('metrics', 'created');
```

Load energy consumption data

When you have your database set up, you can load the energy consumption data into the metrics hypertable.

Loading energy consumption data

Important

This is a large dataset, so it might take a long time, depending on your network connection.

Download the dataset:
metrics.csv.gz
Use your file manager to decompress the downloaded dataset, and take a note of the path to the metrics.csv file.
At the psql prompt, copy the data from the metrics.csv file into your hypertable. Make sure you point to the correct path, if it is not in your current working directory:
```
\COPY metrics FROM metrics.csv CSV;
```

You can check that the data has been copied successfully with this command:


SELECT * FROM metrics LIMIT 5;

You should get five records that look like this:


created            | type_id | value 
-------------------------------+---------+-------
 2023-05-31 23:59:59.043264+00 |      13 |  1.78
 2023-05-31 23:59:59.042673+00 |       2 |   126
 2023-05-31 23:59:59.042667+00 |      11 |  1.79
 2023-05-31 23:59:59.042623+00 |      23 | 0.408
 2023-05-31 23:59:59.042603+00 |      12 |  0.96

Time-series data usually grows very quickly. And that means that aggregating the data into useful summaries can become very slow. Continuous aggregates makes aggregating data lightning fast.

If you are collecting data very frequently, you might want to aggregate your data into minutes or hours instead. For example, if you have a table of temperature readings taken every second, you can find the average temperature for each hour. Every time you run this query, the database needs to scan the entire table and recalculate the average every time.

Continuous aggregates are a kind of hypertable that is refreshed automatically in the background as new data is added, or old data is modified. Changes to your dataset are tracked, and the hypertable behind the continuous aggregate is automatically updated in the background.

You don't need to manually refresh your continuous aggregates, they are continuously and incrementally updated in the background. Continuous aggregates also have a much lower maintenance burden than regular PostgreSQL materialized views, because the whole view is not created from scratch on each refresh. This means that you can get on with working your data instead of maintaining your database.

Because continuous aggregates are based on hypertables, you can query them in exactly the same way as your other tables, and enable compression or tiered storage on your continuous aggregates. You can even create continuous aggregates on top of your continuous aggregates.

By default, querying continuous aggregates provides you with real-time data. Pre-aggregated data from the materialized view is combined with recent data that hasn't been aggregated yet. This gives you up-to-date results on every query.

Create continuous aggregates

Creating continuous aggregates for energy consumption by day and hour

Create a continuous aggregate kwh_day_by_day for energy consumption on a day to day basis:


CREATE MATERIALIZED VIEW kwh_day_by_day(time, value)
    with (timescaledb.continuous) as
SELECT time_bucket('1 day', created, 'Europe/Berlin') AS "time",
        round((last(value, created) - first(value, created)) * 100.) / 100. AS value
FROM metrics
WHERE type_id = 5
GROUP BY 1;

Add a refresh policy to keep the continuous aggregate up-to-date:


SELECT add_continuous_aggregate_policy('kwh_day_by_day',
   start_offset => NULL,
   end_offset => INTERVAL '1 hour',
   schedule_interval => INTERVAL '1 hour');

Create a continuous aggregate kwh_hour_by_hour for energy consumption on an hourly basis:


CREATE MATERIALIZED VIEW kwh_hour_by_hour(time, value)
  with (timescaledb.continuous) as
SELECT time_bucket('01:00:00', metrics.created, 'Europe/Berlin') AS "time",
       round((last(value, created) - first(value, created)) * 100.) / 100. AS value
FROM metrics
WHERE type_id = 5
GROUP BY 1;

Add a refresh policy to keep the continuous aggregate up-to-date:


SELECT add_continuous_aggregate_policy('kwh_hour_by_hour',
   start_offset => NULL,
   end_offset => INTERVAL '1 hour',
   schedule_interval => INTERVAL '1 hour');

You can confirm that the continuous aggregates were created:


SELECT view_name, format('%I.%I', materialization_hypertable_schema,materialization_hypertable_name) AS materialization_hypertable
FROM timescaledb_information.continuous_aggregates;

You should see this:


view_name     |            materialization_hypertable
------------------+--------------------------------------------------
 kwh_day_by_day   | _timescaledb_internal._materialized_hypertable_2
 kwh_hour_by_hour | _timescaledb_internal._materialized_hypertable_3

The queries in this tutorial are suitable for visualizing in Grafana. If you want to visualize the results of your queries, connect your Grafana account to the energy consumption dataset.

Add Timescale as a data source in Grafana

You can use Grafana to visualize queries directly from your Timescale database.

Before you begin, make sure you have:

Created a Timescale Cloud service.
Installed a self-managed Grafana account, or signed up for Grafana Cloud.
Found the connection details for the database you want to use as a data source. The details are contained in the cheatsheet you downloaded when you created the service.

Self-hosted Grafana

Grafana Cloud

Adding Timescale as a data source in self-hosted Grafana

In your web browser, log in to the Grafana dashboard at http://localhost:3000/. The default username is admin with a default password of admin.
In the Grafana dashboard, navigate to Configuration → Data sources. Click Add data source.
In the Add data source page, search for PostgreSQL, and select it.
Configure the data source using your connection details:
- In the Name field, type a name to use for the dataset.
- In the Host field, type the host and port for your connection, in this format: <HOST>:<PORT>. For example, example.tsdb.cloud.timescale.com:35177.
- In the Database field, type tsdb.
- In the User field, type tsdbadmin, or another privileged user.
- In the Password field, type the password.
- In the TLS/SSL Mode field, select require
- In the PostgreSQL details section, toggle TimescaleDB on.
- All other fields can be left as default values.
Click Save & test to check your details have been set correctly.

Set up the database

Create a Timescale service and connect to your service

Create a Timescale service and connect to the service

The dataset

Create a hypertable

Creating a hypertable

Load energy consumption data

Loading energy consumption data

Important

Downsampling the data

Create continuous aggregates

Creating continuous aggregates for energy consumption by day and hour

Connect to Grafana

Add Timescale as a data source in Grafana

Adding Timescale as a data source in self-hosted Grafana