Getting Started
We’re glad to see you here. Today we’re going to install SDF and create a hello world project. Let’s get started.
Installing SDF
Installing SDF is easy as running the following command:
curl -LSfs https://cdn.sdf.com/releases/download/install.sh | sh -s
For more information on installation, see our installation guide.
Verify Installation
To verify that SDF is installed correctly, run the following command:
sdf --help
SDF: A fast SQL compiler, local development framework, and in-memory analytical database
Usage: sdf [OPTIONS] <COMMAND>
Commands:
new Create a new sdf workspace
clean Remove artifacts that sdf has generated in the past
compile Compile models
lint Lint models
fmt format models
run Run models
test Test your models
stats Statistics for your data
report Report code quality
check Check code quality
lineage Display lineage for a given table and/or column
push Push a local workspace to the SDF Service
system System maintenance, install and update
auth Authenticate CLI to services like SDF, AWS, OpenAI, etc
man Display reference material, like the CLI, dialect specific functions, schemas for authoring and interchange
dbt Initialize an sdf workspace from an existing dbt project
exec Execute custom scripts
help Print this message or the help of the given subcommand(s)
Options:
—log-level <LOG_LEVEL> Set log level [possible values: trace, debug, debug-pretty, info, warn, error]
—log-file <LOG_FILE> Creates or replaces the log file
-h, —help Print help
-V, —version Print version
Creating a Workspace
To create a new SDF Workspace, run the following command:
sdf new --sample hello && cd hello
After running the command, you will see the following output:
Created hello/.gitignore
Created hello/models/main.sql
Created hello/workspace.sdf.yml
Finished new in 0.102 secs
You can view and modify the source code for all our examples directly in our GitHub repository.
This will create a new directory with the name of your workspace. In this case, that’s hello. The workspace will contain the following files and folder structure:
.
├── models
│ └── main.sql
└── workspace.sdf.yml
1 directory, 2 files
The directory will contain a workspace.sdf.yml file, which is the primary configuration file for your project. It contains the following YML:
workspace:
name: hello
edition: “1.3”
description: “A minimal workspace”
includes:
- path: models
For more on this, see our workspaces guide and for a full reference to our YML schema and more see the reference section.
Lastly, our workspace contains a single model file, main.sql, which contains the following SQL:
select ‘Hello World!’ as message;
Next, let’s take a deeper look at your project.
SDF uses a cache to fingerprint outputs and accelerate recomputation. This cache is by default located in the sdftarget/ directory.
The cache is machine specific and should not be checked in to git. An appropriate .gitignore file is created as part of the sdf new command.
We refer to SQL statements in SDF as models. Models are SQL statements that will materialized in your data warehouse, or locally with the SDF DB. They differ from tables
as they can be materialized as tables, views, and more based on the configuration. Furthermore, they can be templatized with jinja and SDF SQL variables. SDF recommends specifying one model per file, as each model receives a fully qualified name (database.schema.table) that can correspond nicely to a directory structure.
See our indexing documentation for more.
Exploring Your Project
Let’s see just how easy it is to set up SDF and run your first query.
Static Analysis with `sdf compile`
First, we’ll run the core command of SDF: sdf compile.
The --show flag allows you to modulate SDF’s output and the all option indicates that we would like to see all schemas from all models referenced in the workspace.
sdf compile --show all
Your output should look like:
Working set 1 model file, 1 .sdf file
Compiling hello.pub.main (./models/main.sql)
Finished 1 model [1 succeeded] in 0.585 secs
Schema hello.pub.main
┌─────────────┬──────────────────┬────────────┬─────────────┐
│ column_name ┆ data_type ┆ classifier ┆ description │
╞═════════════╪══════════════════╪════════════╪═════════════╡
│ message ┆ varchar not null ┆ ┆ │
└─────────────┴──────────────────┴────────────┴─────────────┘
As you can see from the output, SDF has statically analyzed the query and determined there’s a single non-nullable column named column and it’s of type varchar.
You’ll also see an empty classifier block in the output. This is for metadata we’ll attach to columns, but we’ll get to that later.
Create a New Model
Now, create a new file in the source directory called main2.sql with the query:
SELECT * FROM main;
Lineage with `sdf lineage`
SDF guarantees rich column level lineage. The command below specifies a particular column, in a particular table that we would like to inspect.
sdf lineage main2 --column message
Working set 2 model files, 1 .sdf file
Compiling hello.pub.main (./models/main.sql)
Compiling hello.pub.main2 (./models/main2.sql)
Finished 2 models [2 succeeded] in 0.592 secs
hello.pub.main2.message
│
│ copy
└──────┐
hello.pub.main.message
Execution with `sdf run`
Next, let’s execute the query, using SDF as the database. We’ll execute the query with SDF’s integrated execution runtime, right on your machine.
sdf run --show all
Your output should look like:
Working set 2 model files, 1 .sdf file
Running hello.pub.main (./models/main.sql)
Running hello.pub.main2 (./models/main2.sql)
Finished 2 models [2 succeeded] in 0.591 secs
Table hello.pub.main
┌──────────────┐
│ message │
╞══════════════╡
│ Hello World! │
└──────────────┘
1 rows.
Table hello.pub.main2
┌──────────────┐
│ message │
╞══════════════╡
│ Hello World! │
└──────────────┘
1 rows.
In this guide we showed you just how easy it is to install SDF and run your first query.
If using VSCode, SDF’s YML schema is available for type and syntax checking via the Red HAT YAML. This will
add auto-fill, type checking, and YML validation directly inline while editing sdf.yml files.