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Project description

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dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

dbt-glue

The dbt-glue package implements the dbt adapter protocol for AWS Glue's Spark engine. It supports running dbt against Spark, through the new Glue Interactive Sessions API.

Installation

The package can be installed from PyPI with:

$ pip install dbt-glue

For further (and more likely up-to-date) info, see the README

Connection Methods

Configuring your AWS profile for Glue Interactive Session

There are two IAM principals used with interactive sessions.

  • Client principal: The princpal (either user or role) calling the AWS APIs (Glue, Lake Formation, Interactive Sessions) from the local client. This is the principal configured in the AWS CLI and likely the same.
  • Service role: The IAM role that AWS Glue uses to execute your session. This is the same as AWS Glue ETL.

Read this documentation to configure these principals.

To enjoy all features of dbt-glue adapter, you will need to attach to the Service role the 3 AWS managed policies below:

Service managed policy required
Amazon S3 AmazonS3FullAccess
AWS Glue AWSGlueConsoleFullAccess
AWS Lake formation AWSLakeFormationDataAdmin

Configuration of the local environment

Because dbt and dbt-glue adapter are compatible with Python versions 3.7, 3.8, and 3.9, check the version of Python:

$ python3 --version

Configure a Python virtual environment to isolate package version and code dependencies:

$ sudo yum install git
$ python3 -m pip install --upgrade pip
$ python3 -m venv dbt_venv
$ source dbt_venv/bin/activate
$ python3 -m pip install --upgrade pip

Configure the last version of AWS CLI

$ curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
$ unzip awscliv2.zip
$ sudo ./aws/install

Configure the aws-glue-session package

$ sudo yum install gcc krb5-devel.x86_64 python3-devel.x86_64 -y
$ pip3 install —upgrade boto3
$ pip3 install —upgrade aws-glue-sessions

Example config

<File name='profiles.yml'>
type: glue
query-comment: This is a glue dbt example
role_arn: arn:aws:iam::1234567890:role/GlueInteractiveSessionRole
region: us-east-1
workers: 2
worker_type: G.1X
idle_timeout: 10
schema: "dbt_demo"
database: "dbt_demo"
session_provisioning_timeout_in_seconds: 120
location: "s3://dbt_demo_bucket/dbt_demo_data"

The table below describes all the options.

Option Description Mandatory
project_name The dbt project name. This must be the same as the one configured in the dbt project. yes
type The driver to use. yes
query-comment A string to inject as a comment in each query that dbt runs. no
role_arn The ARN of the interactive session role created as part of the CloudFormation template. yes
region The AWS Region were you run the data pipeline. yes
workers The number of workers of a defined workerType that are allocated when a job runs. yes
worker_type The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. yes
schema The schema used to organize data stored in Amazon S3. yes
database The database in Lake Formation. The database stores metadata tables in the Data Catalog. yes
session_provisioning_timeout_in_seconds The timeout in seconds for AWS Glue interactive session provisioning. yes
location The Amazon S3 location of your target data. yes
idle_timeout The AWS Glue session idle timeout in minutes. (The session stops after being idle for the specified amount of time.) no
glue_version The version of AWS Glue for this session to use. Currently, the only valid options are 2.0 and 3.0. The default value is 2.0. no
security_configuration The security configuration to use with this session. no
connections A comma-separated list of connections to use in the session. no

Configs

Configuring tables

When materializing a model as table, you may include several optional configs that are specific to the dbt-spark plugin, in addition to the standard model configs.

Option Description Required? Example
file_format The file format to use when creating tables (parquet, csv, json, text, jdbc or orc). Optional parquet
partition_by Partition the created table by the specified columns. A directory is created for each partition. Optional date_day
clustered_by Each partition in the created table will be split into a fixed number of buckets by the specified columns. Optional country_code
buckets The number of buckets to create while clustering Required if clustered_by is specified 8

Incremental models

dbt seeks to offer useful, intuitive modeling abstractions by means of its built-in configurations and materializations.

For that reason, the dbt-glue plugin leans heavily on the incremental_strategy config. This config tells the incremental materialization how to build models in runs beyond their first. It can be set to one of three values:

  • append (default): Insert new records without updating or overwriting any existing data.
  • insert_overwrite: If partition_by is specified, overwrite partitions in the table with new data. If no partition_by is specified, overwrite the entire table with new data.
  • merge (Apache Hudi only): Match records based on a unique_key; update old records, insert new ones. (If no unique_key is specified, all new data is inserted, similar to append.)

Each of these strategies has its pros and cons, which we'll discuss below. As with any model config, incremental_strategy may be specified in dbt_project.yml or within a model file's config() block.

Notes: The default strategie is insert_overwrite

The append strategy

Following the append strategy, dbt will perform an insert into statement with all new data. The appeal of this strategy is that it is straightforward and functional across all platforms, file types, connection methods, and Apache Spark versions. However, this strategy cannot update, overwrite, or delete existing data, so it is likely to insert duplicate records for many data sources.

Source code

{{ config(
    materialized='incremental',
    incremental_strategy='append',
) }}

--  All rows returned by this query will be appended to the existing table

select * from {{ ref('events') }}
{% if is_incremental() %}
  where event_ts > (select max(event_ts) from {{ this }})
{% endif %}

Run Code

create temporary view spark_incremental__dbt_tmp as

    select * from analytics.events

    where event_ts >= (select max(event_ts) from {{ this }})

;

insert into table analytics.spark_incremental
    select `date_day`, `users` from spark_incremental__dbt_tmp

The insert_overwrite strategy

This strategy is most effective when specified alongside a partition_by clause in your model config. dbt will run an atomic insert overwrite statement that dynamically replaces all partitions included in your query. Be sure to re-select all of the relevant data for a partition when using this incremental strategy.

If no partition_by is specified, then the insert_overwrite strategy will atomically replace all contents of the table, overriding all existing data with only the new records. The column schema of the table remains the same, however. This can be desirable in some limited circumstances, since it minimizes downtime while the table contents are overwritten. The operation is comparable to running truncate + insert on other databases. For atomic replacement of Delta-formatted tables, use the table materialization (which runs create or replace) instead.

Source Code

{{ config(
    materialized='incremental',
    partition_by=['date_day'],
    file_format='parquet'
) }}

/*
  Every partition returned by this query will be overwritten
  when this model runs
*/

with new_events as (

    select * from {{ ref('events') }}

    {% if is_incremental() %}
    where date_day >= date_add(current_date, -1)
    {% endif %}

)

select
    date_day,
    count(*) as users

from events
group by 1

Run Code

create temporary view spark_incremental__dbt_tmp as

    with new_events as (

        select * from analytics.events


        where date_day >= date_add(current_date, -1)


    )

    select
        date_day,
        count(*) as users

    from events
    group by 1

;

insert overwrite table analytics.spark_incremental
    partition (date_day)
    select `date_day`, `users` from spark_incremental__dbt_tmp

Specifying insert_overwrite as the incremental strategy is optional, since it's the default strategy used when none is specified.

The merge strategy

Usage notes: The merge incremental strategy requires:

  • file_format: hudi
  • AWS Glue runtime 2 with hudi libraries as extra jars

You can add hudi libraries as extra jars in the classpath using extra_jars options in your profiles.yml. Here is an example:

extra_jars: "s3://dbt-glue-hudi/Dependencies/hudi-spark.jar,s3://dbt-glue-hudi/Dependencies/spark-avro_2.11-2.4.4.jar"

dbt will run an atomic merge statement which looks nearly identical to the default merge behavior on Snowflake and BigQuery. If a unique_key is specified (recommended), dbt will update old records with values from new records that match on the key column. If a unique_key is not specified, dbt will forgo match criteria and simply insert all new records (similar to append strategy).

Source Code

{{ config(
    materialized='incremental',
    incremental_strategy='merge',
    unique_key='user_id',
    file_format='hudi'
) }}

with new_events as (

    select * from {{ ref('events') }}

    {% if is_incremental() %}
    where date_day >= date_add(current_date, -1)
    {% endif %}

)

select
    user_id,
    max(date_day) as last_seen

from events
group by 1
</File> </TabItem> </Tabs>

Persisting model descriptions

Relation-level docs persistence is supported since dbt v0.17.0. For more information on configuring docs persistence, see the docs.

When the persist_docs option is configured appropriately, you'll be able to see model descriptions in the Comment field of describe [table] extended or show table extended in [database] like '*'.

Always schema, never database

Apache Spark uses the terms "schema" and "database" interchangeably. dbt understands database to exist at a higher level than schema. As such, you should never use or set database as a node config or in the target profile when running dbt-glue.

If you want to control the schema/database in which dbt will materialize models, use the schema config and generate_schema_name macro only.

Caveats

Supported Functionality

Most dbt Core functionality is supported, but some features are only available with Apache Hudi.

Apache Hudi-only features:

  1. Incremental model updates by unique_key instead of partition_by (see merge strategy)

Some dbt features, available on the core adapters, are not yet supported on Glue:

  1. Persisting column-level descriptions as database comments
  2. Snapshots

For more information on dbt:


Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.

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