github dbt-labs/dbt v0.5.1
dbt version 0.5.1

0. tl;dr

  1. Raiders of the Lost Archive -- version your raw data to make historical queries more accurate
  2. Column type resolution for incremental models (no more Value too long for character type errors)
  3. Postgres support
  4. Top-level configs applied to your project + all dependencies
  5. --threads CLI option + better multithreaded output

1. Source table archival https://github.com/analyst-collective/dbt/pull/183

Commonly, analysts need to "look back in time" at some previous state of data in their mutable tables. Imagine a users table which is synced to your data warehouse from a production database. This users table is a representation of what your users look like now. Consider what happens if you need to look at revenue by city for each of your users trended over time. Specifically, what happens if a user moved from, say, Philadelphia to New York? To do this correctly, you need to archive snapshots of the users table on a recurring basis. With this release, dbt now provides an easy mechanism to store such snapshots.

To use this new feature, declare the tables you want to archive in your dbt_project.yml file:

archive:
    - source_schema: synced_production_data  # schema to look for tables in (declared below)
      target_schema: dbt_archive             # where to archive the data to
      tables:                                # list of tables to archive
        - source_table: users                # table to archive
          target_table: users_archived       # table to insert archived data into
          updated_at: updated_at             # used to determine when data has changed
          unique_key: id                     # used to generate archival query

        - source_table: some_other_table
           target_table: some_other_table_archive
           updated_at: "updatedAt"
           unique_key: "expressions || work || LOWER(too)"

    - source_schema: some_other_schema
      ....

The archived tables will mirror the schema of the source tables they're generated from. In addition, three fields are added to the archive table:

  1. valid_from: The timestamp when this archived row was inserted (and first considered valid)
  2. valid_to: The timestamp when this archived row became invalidated. The first archived record for a given unique_key has valid_to = NULL. When newer data is archived for that unique_key, the valid_to field of the old record is set to the valid_from field of the new record!
  3. scd_id: A unique key generated for each archive record. Scd = Slowly Changing Dimension.

dbt models can be built on top of these archived tables. The most recent record for a given unique_key is the one where valid_to is null.

To run this archive process, use the command dbt archive. After testing and confirming that the archival works, you should schedule this process through cron (or similar).

2. Incremental column expansion https://github.com/analyst-collective/dbt/issues/175

Incremental tables are a powerful dbt feature, but there was at least one edge case which makes working with them difficult. During the first run of an incremental model, Redshift will infer a type for every column in the table. Subsequent runs can insert new data which does not conform to the expected type. One example is a varchar(16) field which is inserted into a varchar(8) field.
In practice, this error looks like:

Value too long for character type
DETAIL:
  -----------------------------------------------
  error:  Value too long for character type
  code:      8001
  context:   Value too long for type character varying(8)
  query:     3743263
  location:  funcs_string.hpp:392
  process:   query4_35 [pid=18194]
  -----------------------------------------------

With this release, dbt will detect when column types are incongruent and will attempt to reconcile these different types if possible. Specifically, dbt will alter the incremental model table schema from character varying(x) to character varying(y) for some y > x. This should drastically reduce the occurrence of this class of error.

3. First-class Postgres support https://github.com/analyst-collective/dbt/pull/183

With this release, Postgres became a first-class dbt target. You can configure a postgres database target in your ~/.dbt/profiles.yml file:

warehouse:
  outputs:
    dev:
      type: postgres    # configure a target for Postgres
      host: localhost
      user: Drew
      ....
  run-target: dev

While Redshift is built on top of Postgres, the two are subtly different. For instance, Redshift supports sort and dist keys, while Postgres does not! dbt will use the database target type parameter to generate the appropriate SQL for the target database.

4. Root-level configs https://github.com/analyst-collective/dbt/issues/161

Configurations in dbt_project.yml can now be declared at the models: level. These configurations will apply to the primary project, as well as any dependency projects. This feature is particularly useful for setting pre- or post- hooks that run for every model. In practice, this looks like:

name: 'My DBT Project'

models:
    post-hook:
        - "grant select on {{this}} to looker_user"     # Applied to 'My DBT Project' and 'Snowplow' dependency
    'My DBT Project':
        enabled: true
    'Snowplow':
        enabled: true

5. --threads CLI option https://github.com/analyst-collective/dbt/issues/143

The number of threads that DBT uses can now be overridden with a CLI argument. The number of threads used must be between 1 and 8.

dbt run --threads 1    # fine
# or
dbt run --threads 4    # great
# or
dbt run --threads 42    # too many!

In addition to this new CLI argument, the output from multi-threaded dbt runs should be a little more orderly now. Models won't show as STARTed until they're actually queued to run. Previously, the output here was a little confusing. Happy threading!

Upgrading

To upgrade to version 0.5.1 of dbt, run:

pip install --upgrade dbt

And another thing

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