Any reason not use PostgreSQL's built-in full text search on Heroku?

Question

I\'m preparing to deploy a Rails app on Heroku that requires full text search. Up to now I\'ve been running it on a VPS using MySQL with Sphinx.

However, if I want to us

Answer 1

Since I just went through the effort of comparing elastic search (1.9) against postgres FTS, I figured I should share my results since they're somewhat more current than the ones @gustavodiazjaimes cites.

My main concern with postgres was that it did not have faceting built in, but that's trivial to build yourself, here's my example (in django):

results = YourModel.objects.filter(vector_search=query)
facets = (results
    .values('book')
    .annotate(total=Count('book'))
    .order_by('book'))

I'm using postgres 9.6 and elastic-search 1.9 (through haystack on django). Here's a comparison between elasticsearch and postgres across 16 various types of queries.

    es_times  pg_times  es_times_faceted  pg_times_faceted
0   0.065972  0.000543          0.015538          0.037876
1   0.000292  0.000233          0.005865          0.007130
2   0.000257  0.000229          0.005203          0.002168
3   0.000247  0.000161          0.003052          0.001299
4   0.000276  0.000150          0.002647          0.001167
5   0.000245  0.000151          0.005098          0.001512
6   0.000251  0.000155          0.005317          0.002550
7   0.000331  0.000163          0.005635          0.002202
8   0.000268  0.000168          0.006469          0.002408
9   0.000290  0.000236          0.006167          0.002398
10  0.000364  0.000224          0.005755          0.001846
11  0.000264  0.000182          0.005153          0.001667
12  0.000287  0.000153          0.010218          0.001769
13  0.000264  0.000231          0.005309          0.001586
14  0.000257  0.000195          0.004813          0.001562
15  0.000248  0.000174          0.032146          0.002246
                  count      mean       std       min       25%       50%       75%       max
es_times           16.0  0.004382  0.016424  0.000245  0.000255  0.000266  0.000291  0.065972
pg_times           16.0  0.000209  0.000095  0.000150  0.000160  0.000178  0.000229  0.000543
es_times_faceted   16.0  0.007774  0.007150  0.002647  0.005139  0.005476  0.006242  0.032146
pg_times_faceted   16.0  0.004462  0.009015  0.001167  0.001580  0.002007  0.002400  0.037876

In order to get postgres to these speeds for faceted searches I had to use an GIN index on the field with a SearchVectorField, which is django specific but I'm sure other frameworks have a similar vector type.

One other consideration is that pg 9.6 now supports phrase matching, which is huge.

My take away is that postgres is for most cases going to be preferrable as it offers:

1. simpler stack
2. no search backend api wrapper dependencies to contend with (thinking-sphinx, django-sphinx, haystack etc.). These can be a drag since they might not support the features your search back-end does (e.g. haystack faceting/aggregates).
3. has similar performance and features (for my needs)