Postgresql – Saving images as base64 encoded strings, why is it bad

Some thoughts....

Typically one does not want to store pieces of tightly interrelated information in different systems. The chances of things getting out of sync is significant and now instead of one problem on your hands you have two. One thing you can do with Mongo though is use it to pipeline your data in or data out. My preference is to keep everything in PostgreSQL to the extent this is possible. However, I would note that doing so really requires expert knowledge of PostgreSQL programming and is not for shops unwilling to dedicate to using advanced features. I see a somewhat different set of options than you do. Since my preference is not something I see listed I will give it to you.

You can probably separate your metadata into common data, data required for classes, and document data. In this regard you would have a general catalog table with the basic common information plus one table per class. In this table you would have an hstore, json, or xml field which would store the rest of the data along with columns where you are storing data that must be constrained significantly. This would reduce what you need to put in these tables per class, but would allow you to leverage constraints however you like. The three options have different issues and are worth considering separately:

hstore is relatively limited but also used by a lot of people. It isn't extremely new but it only is a key/value store, and is incapable of nested data structures, unlike json and xml.

json is quite new and doesn't really do a lot right now. This doesn't mean you can't do a lot with it, but you aren't going to do a lot out of the box. If you do you can expect to do a significant amount of programming, probably in plv8js or, if you want to stick with older environments, plperlu or plpython. json is better supported in 9.3 though at least in current development snapshots, so when that version is released things will get better.

xml is the best supported of the three, with the most features, and the longest support history. Then again, it is XML.....

However if you do decide to go with Mongo and PostgreSQL together, note that PostgreSQL supports 2 phase commit meaning you can run the write operations, then issue PREPARE TRANSACTION and if this succeeds do your atomic writes in Mongo. If that succeeds you can then COMMIT in PostgreSQL.

In a GiST index, the order of columns has a different significance than in a B-tree index. Per documentation:

A multicolumn GiST index can be used with query conditions that involve any subset of the index's columns. Conditions on additional columns restrict the entries returned by the index, but the condition on the first column is the most important one for determining how much of the index needs to be scanned. A GiST index will be relatively ineffective if its first column has only a few distinct values, even if there are many distinct values in additional columns.

In short: put the most selective columns first.

Your EXPLAIN output shows that the condition on pid is more selective (rows=7836) than the one on outline (rows=63112). If that can be generalized (a single example may be misleading) I suggest this alternative:

CREATE INDEX inventory_compound_idx ON portal.inventory USING gist (pid, outline);

If most of your (important) queries include conditions on both columns, a multicolumn index may serve you well. Else, single columns may be better overall.

Table layout

This is an educated guess since I don't have complete information.

• Don't use oid as column name. It's easy to confuse with the OID.

• Don't use the name date for a timestamp column. Or rather: don't use the name date for any column, don't use names of base-types for identifiers at all. Can lead to confusing mistakes and error messages.

• Create a lookup table for types and only put a small integer type_id into the big table. Pack fixed-length types tightly so not to waste space to padding. Details.

• I prefer the type text (or varchar without length limit) over varchar(n). Details.

For example:

CREATE TABLE portal.inventory (
   inventory_id bigint PRIMARY KEY
  ,type_id      integer NOT NULL REFERENCES inv_type(type_id)
  ,pid          integer NOT NULL
  ,size         bigint NOT NULL
  ,ts           timestamp NOT NULL
  ,outline      geography(Polygon,4326)
  ,product_name text
  ,path         text
);