Velzy's Postgres Document API

Postgres has an amazing JSON document storage capability, the only problem is that working with it is a bit clunky. Thus, I'm creating a set of extensions that, hopefully, will offer a basic API.

Quick Example

Let's say you have a JSON document called customer:

{
  name: "Jill",
  email: "jill@test.com",
  company: "Red:4"
}

You want to save this to Postgres using document storage as you know things will change. With this API you can do that by calling a simple function:

select * from velzy.save(collection => 'customers', doc => '[wad of json]');

This will do a few things:

• A table named customers will be created with a single JSONB field, dates, an ID and a tsvector search field.
• The id that's created will be appended to the new document, and returned from this call
• A search index is automatically created using conventional key names, which you can configure. In this case it will recognize email and name as something that needs indexing.
• The entire document will be indexed using GIN indexing, which again, is configurable.
• The search index will be indexed using GIN as well, for speed.

Now, you can query your document thus:

select * from velzy.search(collection => 'customers', term => 'jill'); -- full text search on a single term
select * from velzy.find_one(collection => 'customers', term => '{"name": "Jill"}'); -- simple query
select * from velzy.find(collection => 'customers', term => '{"company": "Red:4"}'); -- find all Red:4 people

These queries will be performant as they will be able to flex indexing, but there's a lot more you can do.

Fuzzy Queries, Starts and Ends With

One of the downsides of using JSONB with Postgres is finding things. If you do any kind of loose querying on text, you end up doing a query like this:

select json from json_table
where json ->> 'email' ilike '.com%';

This query blows because it can't use an index. What's worse is that Postgres has to materialize the JSON to check the condition. The good news? It's still faster than MongoDB :).

There are ways to get around this, such as creating a new column simply for lookups on common keys. That way you could:

select json from json_table
where lookup_email ilike '.com%';

This is OK as there's an index on lookup_email that you added. Nice and fast! Doing this for every table is a pain, and how do you manage changes to the underlying data? A trigger! OH HEAVENS!

If you use velzy.starts_with or velzy.ends_with all of that is done for you. I should note that this is not something you run in production. This is something that you run locally as you're developing, and then have your change management script move the updates live. The problem is that if you use this on a very large table the update will take a while and the index creation will lock everything as you can't run concurrently from a function.

Anyway, it's there if you want it.

You can also do things the sequential table scan way (aka "bad") if you have a small table. For that you can use velzy.fuzzy:

select * from velzy.fuzzy(collection => 'customers', key => 'company', term => 'Red');
select * from velzy.starts_with(collection => 'customers', key => 'company', term => 'Red');
select * from velzy.ends_with(collection => 'customers', key => 'company', term => '4);

Modification

Partial updates are also a pain with Postgres and JSONB although, yes, there is a way to do it better in 9.6+. All of that is wrapped up velzy.modify:

select * into res from velzy.modify(
  id => 1,
  collection => 'customers', 
  set => '{"name": "harold"}'
);

You can also just save things directly using velzy.save.

Installation

The simplest thing to do is to run make and you'll see a build.sql file in your home directory. You can run that against your database and off you go. It's just a set of functions placed within a schema to keep things clean.

You can also run make install if you change the name of the DB at the top of the file.