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Datastore Selection Guidance

We’re fortunate to have dozens of battle-tested datastores available to us, filling many different niches and general use cases. Each has its own strengths, weaknesses, configuration, backup system, security profile, and cognitive overhead. In an effort to make this selection simpler, TTS engineering defaults to using Postgres for the majority of our applications. This also allows us to collectively learn best practices around security configurations, indexing strategies, and so forth, particularly between large, open data projects.

Our use case

Though we build many different types of systems, we have enough commonality across projects that we can note some generalities.

  1. We have significantly more reads than writes.
  2. We almost always have structured data when designing schemas. Very few projects need flexible/unstructured models.
  3. Maintainability is more important than “interesting” or “cutting-edge”. We’re building software to hand over to agency partners; choosing tools that are easy to develop with and easy for long-term maintenance is critical to that success.
  4. Similarly, stability is more important than performance. Our projects often waffle between active development and maintenance mode; we need to prepare for the latter. We don’t know who will be available to fix a downed database.

Of course, performance, writes, etc. are still important factors to a well-rounded application, but the above generalities give us a direction to lean (think: the agile manifesto).

This general use case negates a large number of the benefits of NoSQL and other niche solutions. On the other hand, we frequently need more flexibility than RedShift, VoltDB, and other highly scalable datastores allow.

Postgres serves our needs

We’ve used Postgres in some form on the majority of our projects and found that it covers almost all (if not all) of our needs. When starting a new project (that requires a datastore), we use Postgres; as the project grows, we first attempt to build solutions within Postgres, pushing it and researching features we need. Only if we hit a significant wall do we reach for another datastore.

A frequent reason to integrate Elastic, Solr, etc. is a need for text search (specifics, often, undefined). Luckily, Postgres has this functionality built in (either directly or by enabling additional modules). Let’s cover four of the more interesting types of text search:

  1. Full text search, search over large blobs of text, is provided by the tsearch module. This is capable of word stemming (e.g. ensuring “book” will be found if searching for “books”), skipping stop words (e.g. “am”, “is”, “the”), weighting fields, returning results with highlighted matches, creating a dictionary of synonyms, and more. The necessary vectorization can also be precomputed and indexed. Collectively, this account for 90+% of our need for a separate search index.
  2. Trigrams break the search term and potential results into three-letter, overlapping strings and count the number of matches. This type of search works really well for smaller text fields, like searching over city names. This is provided in Postgres by the pg_trgm module, and can also be precomputed and indexed.
  3. Edit distance calculates the number of “edits” (character inserts/deletes/shifts) between a search term and text corpus, making it very useful for finding misspelled words. This is provided by the fuzzystrmatch module.
  4. Soundex convert characters into phonemes (sounds), allowing for search over homophones like “Kris” and “Chris”. This is excellent option for certain types of word search such as name matching, and also provided by the fuzzystrmatch module. The necessary transforms can be precomputed and indexed.

Better still, these features are either already integrated into our existing ORMs (as with Django) or a quick module install away (including ActiveRecord, Sequelize, and Sqlalchemy).

Unstructured data

Another common argument for using Mongo, Elastic, or Couch is the need to store data without a predefined structure (notably, arbitrary JSON). While this use case doesn’t come up frequently for us, Postgres has us covered when it does. JSON is a first class data type in Postgres, meaning we can store arbitrary structures, query arbitrarily nested values, index the results, add constraints, etc. Postgres doesn’t have map-reduce-style functions, but can perform all of the SQL aggregations we’re familiar with over those JSON structures, which satisfies the majority of this need.

Additionally, Postgres has an older data type called HStore, which maps arbitrary string keys to string values. This provides the same query, indexing, etc. options as JSON, but can’t be nested and only allows string values. HStore’s been around much longer however, so may have more support in your ORMs (though JSON is supported by most, at this point). Postgres also supports XML as a native data type for the few eXist-db hold outs.

And more

If you reach for Mongo for GIS support, take a look at PostGIS, which has many more features. If you’re using a document store to store hierarchical data, consider using the nested set model in Postgres. Heck, it’s pretty easy to replace GridFS, though storing files in the database is generally a bad idea. The Foreign Data Wrapper also gives a very powerful avenue for including data from arbitrary sources.

As the pattern indicates, for almost all of the situations we might want to reach for another datastore, Postgres has a viable (often superior) solution.

Not a requirement

Postgres is our default datastore, not a requirement. There are certainly use cases where it isn’t the right choice, and we don’t take a dogmatic stance. If you have a Wordpress project, you should probably use MySQL; their support for other databases is cursory, at best. Few task queue libraries work well with Postgres; using Redis makes a lot of sense here. Memcached is still a workhorse for page caching. Be pragmatic. Push Postgres first, but be prepared to support alternatives if the need arises.