Regardless of whether you hate SQL or merely tolerate it, you're going to use it in a project at some point. Relational database structures don't always map well to application data structures but SQL's ubiquity means that it's the standard tool developers reach for when adding data persistence.
While Go has object-relational mapping libraries available such as GORM, we'll eschew that abstraction and integrate directly with the
database/sql standard library package. ORM tools add a layer of complexity that can help with simple data access patterns but make advanced queries & debugging more difficult.
In this post, we'll be looking at how to physically structure data access code, where to place transactional boundaries, and a few useful SQL tricks. I'll be referencing code from the WTF Dial project. You can read about it with this introductory blog post to get some context.
Implementing our service interface
In the previous blog post about CRUD design, I described an interface for managing
Dial entities. A dial is a way to measure the level of frustration of a group and is owned by a user but can also have multiple inputs from other users which are called memberships in the dial.
We'll be implementing this
wtf.DialService for SQLite so it will be in the
sqlite subpackage and it will be called
sqlite.DialService. The concepts we'll talk about here will work for any SQL database though.
Abstracting the database
It may seem odd to name our package something generic like
sqlite but our package will be wrapping the underlying
mattn/go-sqlite3 packages and will not expose the internals of those packages. The caller of our package will only interact with SQL & SQLite through our package. You could name it
wtfsql but I personally find that ugly.
Since we are not exposing the underlying SQL packages, we'll have our own
DB type in our
This abstraction lets us handle the setup & teardown of the database in our
Close() methods without requiring the caller package to worry about the details.
Separating interface implementation from SQL helpers
sqlite package, I've found it useful to make a distinction between the service interface implementation and the functions that actually execute SQL. The service implementation exists for a two reasons:
- Provide an implementation of the
- Provide a transactional boundary.
As such, the service methods are typically small and in this format:
In this example, we start a transaction, execute some lower level helper functions, and then commit. If any error occurs in our helper functions then the deferred rollback will abort the changes. If our function reaches the end and successfully calls
tx.Commit() then the deferred rollback will be a no-op.
A helper function typically takes this form:
The helper functions are where the functionality lives and they are not attached to a particular service. They can be reused by different services or even called by other helper functions. It's an easy way to abstract your higher level code from the low-level SQL calls.
Implementing entity search
wtf.DialService interface defines a function for searching for dials called
The implementation of service method in our
sqlite package look like this:
From a high level, it performs three steps:
- Establish the transactional boundary.
- Find a list of matching dials.
- Find associated data for each dial (such as the dial owner).
Working with associated data (aka joins) is a complicated topic and I'll be covering that in the next post.
We have a helper method for
Dial search because we want to reuse it for other services. For example, when looking up a user we may want to also return a list of dials for that user.
Our helper method looks like this from a high level:
Essentially, we're trying to convert SQL fields into our application domain object,
wtf.Dial. There are a couple interesting details though.
SQL builders vs hand-written SQL
Even if you're not using an ORM, there are options such as Squirrel for building SQL queries. I haven't personally used Squirrel but it looks like it could be a good option. However, for most of my queries I find that hand-rolling SQL works pretty well.
WHERE clause is typically the most dynamic part of the query so we'll focus on
WHERE clause generation. Our application domain specifies a
wtf.DialFilter that is a set of filter fields that are
AND-ed together. We perform this by building a slice of conditions:
Then when we build our query we can use
If we don't have any filters then our clause becomes
WHERE 1 = 1 which matches all rows. If we add our ID filter then it becomes
WHERE 1 = 1 AND id = ?. The SQL query optimizer is smart enough to ignore an always true condition like
1 = 1.
Obtaining total row count
Our service interface requires that
FindDials() returns the total number of matching dials to use for pagination. For example, if a
20 is set but there are a hundred matching dials then the caller wants to know that so they can show that there are 5 pages of results.
The naive approach is to execute your query and then re-execute it but with only a
COUNT(1) and no
LIMIT clause. However, there's some SQL trickery that lets you combine these into one query.
Many databases such as SQLite or Postgres provide windowing functions. We can specify a window as one of our fields that will utilize the
WHERE clause but ignore the
COUNT(*) OVER () field specifies that it should return the total count of the query over an empty window. It will return the same value for every row of our query so we can simply
Scan() it into a count variable on every iteration.
Implementing entity lookup
We could implement entity look up with
FindDials() but it's such a common task that I find it useful to create a separate function called
Our implementation in our service looks similar to
FindDials() except that we are now looking up a single dial:
findDialByID() actually wraps the
findDials() function but it changes the semantics:
findDials() will not return an error if no data is found, the
findDialByID() function expects that the given dial exists. Here we translate an empty result set into a "not found" error.
You can find details about using an application-specific
Error type in the post, Failure is Your Domain. I'll be writing an updated version of that soon that includes the Go 1.13 error wrapping changes but most of the concepts still hold true.
Optimizing primary key lookups
It might be tempting to create a separate optimized function for looking up dials by ID instead of reusing code from
findDials(). It would allow you to skip a slice allocation and optimize the query field list to include related object data such as the owner information from the
However, duplicating your query code makes it more difficult to maintain. It's best to wait until you find hot paths in your code that require optimization and only optimize them.
In this post we've looked at implementing the read side of our domain's service interfaces and we've separated out our high-level service methods from lower-level SQL helper functions. We also took a look at optimizing our pagination queries by integrating with SQL windowing functions. In the next post, we'll look at implementing the write side of our domain service.
It's easy to have SQL permeate throughout your application if you're not careful. By isolating our database code to a single package, we ensure that the rest of our application code can grow cleanly & independently.
Have comments or suggestions about this post or future content you'd like to see? I've set up a GitHub Discussion board instead of using a comments system so that folks can have deeper conversations.
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