![]() Note that when accessing json hashes, you should always use strings for keys. If you plan to use the json or jsonb types, load the pg_array extension before the pg_json extension: DB. This extension integrates with the pg_array extension. See the schema modification guide for details on using json columns in CREATE/ALTER TABLE statements. To use this extension, load it into the Database instance: DB. class # => Sequel::Postgres::JSONNull obj. If you want to set a JSON null value when using a model, you must wrap it explicitly: obj. Note that nil values are never automatically wrapped: obj. So if you want to insert an array or hash into an json database column: DB. pg_json_wrap( object) # json type Sequel. To wrap an existing Ruby array, hash, string, integer, float, nil, true, or false, use Sequel.pg_json_wrap or Sequel.pg_jsonb_wrap: Sequel. To extract the Ruby primitive object from the wrapper object, you can use _getobj_ (this comes from Ruby’s delegate library). # called if the value of json_column is null/false # if you are wrapping primitives end Because only false and nil in Ruby are considered falsey, wrapping these objects results in unexpected behavior if you use the values directly in conditionals: if DB. Note that wrapping JSON primitives changes the behavior for JSON false and null values. If you would like to wrap JSON primitives (numbers, strings, null, true, and false), you need to use the wrap_json_primitives setter: DB. By default, it wraps JSON arrays and JSON objects with ruby array-like and hash-like objects. ![]() Pass it directly to INSERT.The pg_json extension adds support for Sequel to handle PostgreSQL’s json and jsonb types. ![]() What we can do instead is transform the data via a SELECT statement, and then Pretty inefficient, both in terms of memory usage and speed. This is a common way people copy data, but it’s actually Table, transforming it in Ruby, then writing the result in batches into theĭestination table. Notice how in the last example we were fetching data from the temporary drop_table temp_table Inserting from SELECT Product_data #=> # [ # transform into desired format end DB. So our schema migration contained the following table definition: Our app, we wanted to have monthly partitions of product data for each client, ( :partition_by), as well as the type of partitioning ( :partition_type). a table we canĬreate partitions of), we need to specify the column(s) we want to partition by In order to create a partitioned table (i.e. Sequel supports Postgres’ table partitioning Performance for queries where most partitions have been filtered out during Postgres’ query planner then determines which partitions it needs to read from These conditions most commonly specify a range or list ofĬolumn values, though you can also partition based on hash values. In a single table into multiple tables (“partitions”) based on certainĬonditions. What this feature does is allow you to split data that you would otherwise have Number of new records every day, so in order to keep query performance atĪcceptable levels, I’ve decided to try out Postgres’ table partitioning Storing snapshots of our product data for each day. I mentioned that our analytics data is time-series, which means that we’re Them in this article, and at the same time demonstrate what Sequel is capable Since not all of these features are common, I wanted to showcase Utilize many cool Postgres features that helped me implement this taskĮfficiently. Thanks to Sequel’s advanced Postgres support, I was able to ![]() (which could be performance-sensitive), I decided it would be a good That we’d probably need to be retrieving large quantities of time-series data However, given that weĮxpected the queries to our analytics database would be fairly complex, and Gained support for multiple databases in version 6.0. We’re using Active Record for interaction with our primary database, which Opportunity to use Postgres which we’re most comfortable with anyway. Our RailsĪpp’s primary database is currently MariaDB, but we wanted to have ourĪnalytics data in a separate database either way, so this was a good At work I was tasked to migrate our time-series analytics data from CSV fileĭumps that we’ve been feeding into Power BI to a dedicated database.
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