I joined Timescale as a developer advocate. One of my creative tasks there was live streaming. As I’ve been working with Ruby since 2007, I created a live streaming series about how to use Timescale with Ruby.

I’d like to teach you how to use the gem with Ruby. Not Rails. Only plain Ruby and some ActiveRecord flavor that turns it more interesting.

In this post, I will cover the basics of inserting, querying, looking at some timescale metadata, and the compression system. Soon, I’ll cover more features like compression policies, continuous aggregates, and more advanced features.

If you want to run the complete example yourself, it’s alive and belongs to the official docs. If you find any bugs or want to improve the example, feel free to submit a PR.

If you’re curious about the live streaming, you can also check the links from all episodes here.


The Timescale gem depends on PG and ActiveRecord. I chose ActiveRecord as it was the main wrapper for ORM in the Ruby ecosystem. In addition, the concepts are pretty simple and can be easily migrated to another framework.

Let’s start requiring bundler inline to avoid the creation of the Gemfile. I’m adding the timescaledb gem, and the mentioned libraries will be fetched as dependencies. Also, pry is here because it’s the best REPL to debug any Ruby code.

# all_in_one.rb file
require 'bundler/inline

gemfile(true) do
  gem 'timescaledb'
  gem 'pry'

A good part of using the bundler inline is that you don’t have to require the dependencies.

Now, let’s start by establishing the connection. Let’s use the URI as the last parameter from the command line. For example, to run from my localhost, I can refer to the database URI like this:

ruby all_in_one.RB postgres://jonatasdp@localhost:5432/playground

Make sure you change the jonatasdp to your username and the playground to your database name. Check how to install Timescale first, or you can try a free instance on the Timescale cloud.

ActiveRecord::Base.establish_connection( ARGV.last)

With this line, you’re making the connection to the database, and you can already start making queries or whatever you want.

Creating the first hypertable with Ruby

The concept is that Hypertables are PostgreSQL tables designed to handle time-series data. So anything you can do with a regular PostgreSQL table, you can do with a hypertable. The advantage is that you have an outstanding performance and user experience for time-series data.

It improves performance by partitioning time-series data on its time column. While you can still perform all your operations using the table name, Timescale is smart to maintain the hypertable’s partitions while you can deal with a single, regular PostgreSQL table.

It’s also able to compress the data of the child tables and fold the partitions in multiple dimensions.

As we’re not going to have the Rails migrations system in this small example, we can start defining what our hypertable looks like.

Let’s call it Event, starting from the model, and the objective is to store events that happen at a specific time with a payload that can allow us to give the event more details.

class Event < ActiveRecord::Base
  self.primary_key = nil

Note that Timescale hypertables are all about time-series data, and you generally don’t need to have primary keys.

ActiveRecord default returns the object’s id when you insert it into the database. Setting a primary_key to another column helps us to avoid this behavior as it would say the column id was not found.

The following line is about saying it’s hypertable. The acts_as_hypertable macro allows you to override several options. By default, it uses the created_at as the time column, and the rest of the hypertable configurations are using the defaults just to allow us to make some progress here.

Creating the minimum migration system

Now, let’s create the minimum migration setup and use the hypertable.

Let’s start moving the logs to the standard output as ActiveRecord is configured to send the information to a file. The feedback will help check how every step is executed as we test.

ActiveRecord::Base.logger = Logger.new(STDOUT)

The next step is to call the create_table method from ActiveRecord. And here is where the timescaledb gem acts. Then, finally, we can inject the desired behavior with the hypertable option.

ActiveRecord::Base.connection.instance_exec do

  drop_table(:events) if Event.table_exists?

  hypertable_options = {
    time_column: 'created_at',
    chunk_time_interval: '1 day',
    compress_segmentby: 'identifier',
    compression_interval: '7 days'

  create_table(:events, id: false, hypertable: hypertable_options) do |t|
    t.string :identifier, null: false
    t.jsonb :payload

I love using instance_exec as it allows me to execute several commands from a different perspective. Instead of having the connection as a variable, I can jump into the scope and declare some code similar to the rails migration files. Easier to move to an actual app later if I’m only prototyping for a while ;)

Now, let’s break down what we have in the hypertable options:

  hypertable_options = {
    time_column: 'created_at',
    chunk_time_interval: '1 day',
    compress_segmentby: 'identifier',
    compression_interval: '7 days'

The time_column option calls the create_hypertable function in the command line. So, converting to SQL, after creating the table, it will have an extra line like calling the create_hypertable function:

select create_hypertable('events', 'created_at' )

The hypertable contains several optional arguments that can also be used in this migration helper. In our case, we’re overriding only the chunk_time_interval that the default is 7 days, and we’re going with 1 day.

The following two options are related to the compression concept. First, it reduces the amount of space used by your data. Some queries also speed up query time because fewer bytes must be read from the disk.

    compress_segmentby: 'identifier',
    compression_interval: '7 days'

The segmentby option works like a group by clause for compression. In our case, we have an identifier that will be used to group types of events.

Check the segmentation guide to better understand this feature’s power.

Behind the scenes, the timescale gem is just inserting some SQL queries that interface with Timescale functions. For this example, it’s running the following queries:

  timescaledb.compress_segmentby = 'identifier'

And the compression_interval is responsible for establishing a policy on when to compress the data. So in our example, after the data is 7 days old, we can already compress it to save space.

SELECT add_compression_policy('events', INTERVAL '7 days');

Inserting some data

Now, let’s insert some random data into the hypertable to see how it works:

Event.insert_all [
  { identifier: "sign_up", payload: {"name" => "Eon"} },
  { identifier: "login", payload: {"email" => "eon@timescale.com"} },
  { identifier: "click", payload: {"user" => "eon", "path" => "/install/timescaledb"} },
  { identifier: "scroll", payload: {"user" => "eon", "path" => "/install/timescaledb"} },
  { identifier: "logout", payload: {"email" => "eon@timescale.com"} }

As we’re using the created_at column as the time column, we don’t need to specify it because ActiveRecord will automatically set it.

If you want to assign it, you’ll need to override this method to allow it to pass in the payload:

class Event < ActiveRecord::Base
  def self.timestamp_attributes_for_create_in_model

Also, note that I’m using insert_all because it’s faster than trying to go record by record.

Generate data with faker

Now, let’s use the faker gem to create some massive fake data for the events table:

# all_in_one.rb file
require 'bundler/inline

gemfile(true) do
  gem 'timescaledb'
  gem 'faker'
  gem 'pry'

Let’s create a method to generate a random payload using different emails and names.

def generate_fake_data(total: 100_000)
  time = 1.month.ago
  total.times.flat_map do
    identifier = %w[sign_up login click scroll logout view]
    time = time + rand(60).seconds
      created_at: time,
      updated_at: time,
      identifier: identifier.sample,
      payload: {
        "name" => Faker::Name.name,
        "email" => Faker::Internet.email

The fake data generator will start creating records from 1 year ago, and the period between the records is from 1 to 60 seconds. Now, inserting a few thousand records will be easy:

batch = generate_fake_data total: 10_000
Event.insert_all(batch, returning: false)

Now, it’s time to query the data and learn how to use the helpers that are available for querying data.

Querying data

A few scopes are available to help filter the data and make it easier to work with the time column and filter by standard time frames.

Filtering by date

Counting events from the previous month:

Event.previous_month.count # => 10000

Similar scopes filtering by time can be:

As the scopes are smart, you can combine them with more complex queries. For example, let’s count records from the previous month grouped by identifier:


Using the Timescale time_bucket function

You can combine it with the time_bucket function. For now, I couldn’t find an exciting way to build a DSL for the time bucket, but you can make it with SQL through the query.

  .select("time_bucket('1 day', created_at) as time, identifier, count(*)")
[{"time"=>2022-08-06 00:00:00 UTC, "identifier"=>"click", "count"=>224},
 {"time"=>2022-08-06 00:00:00 UTC, "identifier"=>"login", "count"=>244},
 {"time"=>2022-08-06 00:00:00 UTC, "identifier"=>"logout", "count"=>239},
 {"time"=>2022-08-06 00:00:00 UTC, "identifier"=>"scroll", "count"=>230},
 {"time"=>2022-08-06 00:00:00 UTC, "identifier"=>"sign_up", "count"=>227},
 {"time"=>2022-08-06 00:00:00 UTC, "identifier"=>"view", "count"=>245},
 {"time"=>2022-08-07 00:00:00 UTC, "identifier"=>"click", "count"=>516},
 {"time"=>2022-08-07 00:00:00 UTC, "identifier"=>"login", "count"=>471},
 {"time"=>2022-08-07 00:00:00 UTC, "identifier"=>"logout", "count"=>484},
 {"time"=>2022-08-07 00:00:00 UTC, "identifier"=>"scroll", "count"=>451},
 # ... more records here

If you’re interested in materializing the time_bucket grouped results to faster access to the processed data, take a look in the Creating continous aggregates with Ruby and Timescale post.

Querying metadata

The gem also contains several methods to inspect the Timescale metadata. So let’s start diving into the methods and how they can be helpful.

The hypertable method is available directly from the model and can give you the details in the timescaledb_information.hypertables view.

# => #<Timescaledb::Hypertable:0x00007fefc7b0ea78
 hypertable_schema: "public",
 hypertable_name: "events",
 owner: "jonatasdp",
 num_dimensions: 1,
 num_chunks: 5,
 compression_enabled: true,
 is_distributed: false,
 replication_factor: nil,
 data_nodes: nil,
 tablespaces: nil>

Behind the scenes, it’s executing the following query:

FROM "timescaledb_information"."hypertables"
WHERE "hypertable_name" = 'events';

Remember that a model named Timescaledb::Hypertable is available, and you can build the same query directly in this model.

Timescaledb::Hypertable.find_by hypertable_name: Event.table_name

The same can be done for chunks. So, a hypertable has many chunks, and you can query them in an ActiveRecord relation style.

For example, the statement:

Event.hypertable.chunks.count # => 5

Will execute the following query:

FROM "timescaledb_information"."chunks"
WHERE "hypertable_name" =  'events';

As the previous example, a model Timescaledb::Chunk is also available, and you can build the query directly on that too:

Timescaledb::Chunk.where(hypertable_name: Event.table_name).count

Let’s dive into some chunk objects:

chunk = Event.hypertable.chunks.first
# => #<Timescaledb::Chunk:0x00007fefc77096d0
 hypertable_schema: "public",
 hypertable_name: "events",
 chunk_schema: "_timescaledb_internal",
 chunk_name: "_hyper_1415_11429_chunk",
 primary_dimension: "created_at",
 primary_dimension_type: "timestamp without time zone",
 range_start: 2022-09-07 00:00:00 UTC,
 range_end: 2022-09-08 00:00:00 UTC,
 range_start_integer: nil,
 range_end_integer: nil,
 is_compressed: false,
 chunk_tablespace: nil,
 data_nodes: nil>

The TimescaleDB Compression

Chunk objects can also be compressed or decompressed. So, reusing the last variable:


That will basically execute the following query:

SELECT compress_chunk(('_timescaledb_internal._hyper_1415_11429_chunk')::regclass)

You can also check details about the detailed size of the hypertable. After compressing, you can see how much space you’re saving.

size = Event.hypertable.detailed_size
#<2, total_bytes=1933312, node_name=nil>`sql
 SELECT * from hypertable_compression_stats('events')

As you can see in the previous data, the compression was ineffective. So now, let’s compress all uncompressed chunks, as this was probably the chunk with fewer data.


Now, rechecking the status:

stats = Event.hypertable.compression_stats
# => #<OpenStruct total_chunks=5
#  number_compressed_chunks=5
#  before_compression_table_bytes=1400832
#  before_compression_index_bytes=491520
#  before_compression_toast_bytes=40960
#  before_compression_total_bytes=1933312
#  after_compression_table_bytes=40960
#  after_compression_index_bytes=81920
#  after_compression_toast_bytes=688128
#  after_compression_total_bytes=811008
#  node_name=nil>

Now, let’s do some math to calculate the compression ratio and understand how much space we’re saving in the table:

100 - (stats.after_compression_table_bytes / stats.before_compression_table_bytes.to_f) * 100
=> 97.07602339181287

Not bad! 97% of the space was saved 🚀

I hope you enjoyed this first post about the gem. If you have any feedback, don’t hesitate to reach out! Looking forward to seeing more Rubyists adopting TimescaleDB!

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Hello there, my name is Jônatas Davi Paganini and this is my personal blog.
I'm developer advocate at Timescale and I also have a few open source projects on github.

Check my talks or connect with me via linkedin / twitter / github / instagram / facebook / strava / meetup.