mozilla-bigquery-etl

Bigquery ETL

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BigQuery ETL

This repository contains Mozilla Data Team's

  • Derived ETL jobs that do not require a custom container
  • User-defined functions (UDFs)
  • Airflow DAGs for scheduled bigquery-etl queries
  • Tools for query & UDF deployment, management and scheduling

Quick Start

Ensure Python 3.8+ is available on your machine (see this guide for instructions if you're on a mac and haven't installed anything other than the default system Python.)

For some functionality Java JDK 11+ is also required (e.g. AdoptOpenJDK) with $JAVA_HOME set, and maven is needed for downloading jar dependencies. Maven is available from your package manager in most Linux distributions and from homebrew on mac, or you can install yourself by downloading a binary and following maven's install instructions. The Rosetta 2 terminal is also suggested for installing the above requirements on Apple Silicon (M1).

Install and set up the GCP command line tools:

  • (For Mozilla Employees or Contributors not in Data Engineering) Set up GCP command line tools, as described on docs.telemetry.mozilla.org. Note that some functionality (e.g. writing UDFs or backfilling queries) may not be allowed.
  • (For Data Engineering) In addition to setting up the command line tools, you will want to log in to shared-prod if making changes to production systems. Run gcloud auth login --update-adc --project=moz-fx-data-shared-prod (if you have not run it previously).

Clone the repository

git clone git@github.com:mozilla/bigquery-etl.git
cd bigquery-etl

Install the bqetl command line tool

./bqetl bootstrap

Install standard pre-commit hooks

venv/bin/pre-commit install

Optionally, download java dependencies

mvn dependency:copy-dependencies
venv/bin/pip-sync requirements.txt java-requirements.txt

Finally, if you are using Visual Studio Code, you may also wish to use our recommended defaults:

cp .vscode/settings.json.default .vscode/settings.json

And you should now be set up to start working in the repo! The easiest way to do this is for many tasks is to use bqetl, which is described below.

The bqetl CLI

The bqetl command-line tool aims to simplify working with the bigquery-etl repository by supporting common workflows, such as creating, validating and scheduling queries or adding new UDFs.

Usage

The CLI groups commands into different groups:

$ ./bqetl --help
Commands:
  dag     Commands for managing DAGs.
  dryrun  Dry run SQL.
  format  Format SQL.
  mozfun  Commands for managing mozfun UDFs.
  query   Commands for managing queries.
  udf     Commands for managing UDFs.
  ...

To get information about commands and available options, simply append the --help flag:

$ ./bqetl query create --help
Usage: bqetl query create [OPTIONS] NAME

  Create a new query with name <dataset>.<query_name>, for example:
  telemetry_derived.asn_aggregates

Options:
  -p, --path DIRECTORY  Path to directory in which query should be created
  -o, --owner TEXT      Owner of the query (email address)
  -i, --init            Create an init.sql file to initialize the table
  --help                Show this message and exit.

Documentation of all bqetl commands including usage examples can be found in the bigquery-etl docs.

Running some commands, for example to create or query tables, will require access to Mozilla's GCP Account.

Formatting SQL

We enforce consistent SQL formatting as part of CI. After adding or changing a query, use ./bqetl format to apply formatting rules.

Directories and files passed as arguments to ./bqetl format will be formatted in place, with directories recursively searched for files with a .sql extension, e.g.:

$ echo 'SELECT 1,2,3' > test.sql
$ ./bqetl format test.sql
modified test.sql
1 file(s) modified
$ cat test.sql
SELECT
  1,
  2,
  3

If no arguments are specified the script will read from stdin and write to stdout, e.g.:

$ echo 'SELECT 1,2,3' | ./bqetl format
SELECT
  1,
  2,
  3

To turn off sql formatting for a block of SQL, wrap it in format:off and format:on comments, like this:

SELECT
  -- format:off
  submission_date, sample_id, client_id
  -- format:on

Queries

  • Should be defined in files named as sql/<project>/<dataset>/<table>_<version>/query.sql e.g.
    • <project> defines both where the destination table resides and in which project the query job runs sql/moz-fx-data-shared-prod/telemetry_derived/clients_daily_v7/query.sql
    • Queries that populate tables should always be named with a version suffix; we assume that future optimizations to the data representation may require schema-incompatible changes such as dropping columns
  • May be generated using a python script that prints the query to stdout
    • Should save output as sql/<project>/<dataset>/<table>_<version>/query.sql as above
    • Should be named as sql/<project>/query_type.sql.py e.g. sql/moz-fx-data-shared-prod/clients_daily.sql.py
    • May use options to generate queries for different destination tables e.g. using --source telemetry_core_parquet_v3 to generate sql/moz-fx-data-shared-prod/telemetry/core_clients_daily_v1/query.sql and using --source main_summary_v4 to generate sql/moz-fx-data-shared-prod/telemetry/clients_daily_v7/query.sql
    • Should output a header indicating options used e.g.
      -- Query generated by: sql/moz-fx-data-shared-prod/clients_daily.sql.py --source telemetry_core_parquet
      
  • Should not specify a project or dataset in table names to simplify testing
  • Should be incremental
  • Should filter input tables on partition and clustering columns
  • Should use _ prefix in generated column names not meant for output
  • Should use _bits suffix for any integer column that represents a bit pattern
  • Should not use DATETIME type, due to incompatibility with spark-bigquery-connector
  • Should read from *_stable tables instead of including custom deduplication
    • Should use the earliest row for each document_id by submission_timestamp where filtering duplicates is necessary
  • Should escape identifiers that match keywords, even if they aren't reserved keywords

Views

  • Should be defined in files named as sql/<project>/<dataset>/<table>/view.sql e.g. sql/moz-fx-data-shared-prod/telemetry/core/view.sql
    • Views should generally not be named with a version suffix; a view represents a stable interface for users and whenever possible should maintain compatibility with existing queries; if the view logic cannot be adapted to changes in underlying tables, breaking changes must be communicated to fx-data-dev@mozilla.org
  • Must specify project and dataset in all table names
    • Should default to using the moz-fx-data-shared-prod project; the scripts/publish_views tooling can handle parsing the definitions to publish to other projects such as derived-datasets

UDFs

  • Should limit the number of expression subqueries to avoid: BigQuery error in query operation: Resources exceeded during query execution: Not enough resources for query planning - too many subqueries or query is too complex.
  • Should be used to avoid code duplication
  • Must be named in files with lower snake case names ending in .sql e.g. mode_last.sql
    • Each file must only define effectively private helper functions and one public function which must be defined last
      • Helper functions must not conflict with function names in other files
    • SQL UDFs must be defined in the udf/ directory and JS UDFs must be defined in the udf_js directory
      • The udf_legacy/ directory is an exception which must only contain compatibility functions for queries migrated from Athena/Presto.
  • Functions must be defined as persistent UDFs using CREATE OR REPLACE FUNCTION syntax
    • Function names must be prefixed with a dataset of <dir_name>. so, for example, all functions in udf/*.sql are part of the udf dataset
      • The final syntax for creating a function in a file will look like CREATE OR REPLACE FUNCTION <dir_name>.<file_name>
    • We provide tooling in scripts/publish_persistent_udfs for publishing these UDFs to BigQuery
      • Changes made to UDFs need to be published manually in order for the dry run CI task to pass
  • Should use SQL over js for performance

Backfills

  • Should be avoided on large tables
    • Backfills may double storage cost for a table for 90 days by moving data from long-term storage to short-term storage
      • For example regenerating clients_last_seen_v1 from scratch would cost about $1600 for the query and about $6800 for data moved to short-term storage
    • Should combine multiple backfills happening around the same time
    • Should delay column deletes until the next other backfill
      • Should use NULL for new data and EXCEPT to exclude from views until dropped
  • Should use copy operations in append mode to change column order
    • Copy operations do not allow changing partitioning, changing clustering, or column deletes
  • Should split backfilling into queries that finish in minutes not hours
  • May use script/generate_incremental_table to automate backfilling incremental queries
  • May be performed in a single query for smaller tables that do not depend on history
    • A useful pattern is to have the only reference to @submission_date be a clause WHERE (@submission_date IS NULL OR @submission_date = submission_date) which allows recreating all dates by passing --parameter=submission_date:DATE:NULL

Incremental Queries

Benefits

Properties

  • Must accept a date via @submission_date query parameter
    • Must output a column named submission_date matching the query parameter
  • Must produce similar results when run multiple times
    • Should produce identical results when run multiple times
  • May depend on the previous partition
    • If using previous partition, must include an init.sql query to initialize the table, e.g. sql/moz-fx-data-shared-prod/telemetry_derived/clients_last_seen_v1/init.sql
    • Should be impacted by values from a finite number of preceding partitions
      • This allows for backfilling in chunks instead of serially for all time and limiting backfills to a certain number of days following updated data
      • For example sql/moz-fx-data-shared-prod/clients_last_seen_v1.sql can be run serially on any 28 day period and the last day will be the same whether or not the partition preceding the first day was missing because values are only impacted by 27 preceding days

Query Metadata

  • For each query, a metadata.yaml file should be created in the same directory
  • This file contains a description, owners and labels. As an example:
friendly_name: SSL Ratios
description: >
  Percentages of page loads Firefox users have performed that were
  conducted over SSL broken down by country.
owners:
  - example@mozilla.com
labels:
  application: firefox
  incremental: true     # incremental queries add data to existing tables
  schedule: daily       # scheduled in Airflow to run daily
  public_json: true
  public_bigquery: true
  review_bugs:
   - 1414839   # Bugzilla bug ID of data review
  incremental_export: false  # non-incremental JSON export writes all data to a single location

Publishing a Table Publicly

For background, see Accessing Public Data on docs.telemetry.mozilla.org.

Dataset Metadata

To provision a new BigQuery dataset for holding tables, you'll need to create a dataset_metadata.yaml which will cause the dataset to be automatically deployed a few hours after merging. Changes to existing datasets may trigger manual operator approval (such as changing access policies).

The bqetl query create command will automatically generate a skeleton dataset_metadata.yaml file if the query name contains a dataset that is not yet defined.

See example with commentary for telemetry_derived:

friendly_name: Telemetry Derived
description: |-
  Derived data based on pings from legacy Firefox telemetry, plus many other
  general-purpose derived tables
labels: {}

# Base ACL should can be:
#   "derived" for `_derived` datasets that contain concrete tables
#   "view" for user-facing datasets containing virtual views
dataset_base_acl: derived

# Datasets with user-facing set to true will be created both in shared-prod
# and in mozdata; this should be false for all `_derived` datasets
user_facing: false

# Most datasets can have mozilla-confidential access like below,
# but some datasets will be defined with more restricted access
# or with additional access for services.
workgroup_access:
- role: roles/bigquery.dataViewer
  members:
  - workgroup:mozilla-confidential

Scheduling Queries in Airflow

  • bigquery-etl has tooling to automatically generate Airflow DAGs for scheduling queries
  • To be scheduled, a query must be assigned to a DAG that is specified in dags.yaml
    • New DAGs can be configured in dags.yaml, e.g., by adding the following:
    bqetl_ssl_ratios:   # name of the DAG; must start with bqetl_
      schedule_interval: 0 2 * * *    # query schedule
      description: The DAG schedules SSL ratios queries.
      default_args:
        owner: example@mozilla.com
        start_date: '2020-04-05'  # YYYY-MM-DD
        email: ['example@mozilla.com']
        retries: 2    # number of retries if the query execution fails
        retry_delay: 30m
    
    • All DAG names need to have bqetl_ as prefix.
    • schedule_interval is either defined as a CRON expression or alternatively as one of the following CRON presets: once, hourly, daily, weekly, monthly
    • start_date defines the first date for which the query should be executed
      • Airflow will not automatically backfill older dates if start_date is set in the past, backfilling can be done via the Airflow web interface
    • email lists email addresses alerts should be sent to in case of failures when running the query
  • Alternatively, new DAGs can also be created via the bqetl CLI by running bqetl dag create bqetl_ssl_ratios --schedule_interval='0 2 * * *' --owner="example@mozilla.com" --start_date="2020-04-05" --description="This DAG generates SSL ratios."
  • To schedule a specific query, add a metadata.yaml file that includes a scheduling section, for example:
    friendly_name: SSL ratios
    # ... more metadata, see Query Metadata section above
    scheduling:
      dag_name: bqetl_ssl_ratios
    
    • Additional scheduling options:
      • depends_on_past keeps query from getting executed if the previous schedule for the query hasn't succeeded
      • date_partition_parameter - by default set to submission_date; can be set to null if query doesn't write to a partitioned table
      • parameters specifies a list of query parameters, e.g. ["n_clients:INT64:500"]
      • arguments - a list of arguments passed when running the query, for example: ["--append_table"]
      • referenced_tables - manually curated list of tables the query depends on; used to speed up the DAG generation process or to specify tables that the dry run doesn't have permissions to access, e. g. [['telemetry_stable', 'main_v4']]
      • multipart indicates whether a query is split over multiple files part1.sql, part2.sql, ...
      • depends_on defines external dependencies in telemetry-airflow that are not detected automatically:
        depends_on:
          - task_id: external_task
            dag_name: external_dag
            execution_delta: 1h
      
      • task_id: name of task query depends on
      • dag_name: name of the DAG the external task is part of
      • execution_delta: time difference between the schedule_intervals of the external DAG and the DAG the query is part of
      • destination_table: The table to write to. If unspecified, defaults to the query destination; if None, no destination table is used (the query is simply run as-is). Note that if no destination table is specified, you will need to specify the submission_date parameter manually
  • Queries can also be scheduled using the bqetl CLI: ./bqetl query schedule path/to/query_v1 --dag bqetl_ssl_ratios
  • To generate all Airflow DAGs run ./script/generate_airflow_dags or ./bqetl dag generate
    • Generated DAGs are located in the dags/ directory
    • Dependencies between queries scheduled in bigquery-etl and dependencies to stable tables are detected automatically
  • Specific DAGs can be generated by running ./bqetl dag generate bqetl_ssl_ratios
  • Generated DAGs will be automatically detected and scheduled by Airflow
    • It might take up to 10 minutes for new DAGs and updates to show up in the Airflow UI

Contributing

When adding or modifying a query in this repository, make your changes in the sql/ directory.

When adding a new library to the Python requirements, first add the library to the requirements and then add any meta-dependencies into constraints. Constraints are discovered by installing requirements into a fresh virtual environment. A dependency should be added to either requirements.txt or constraints.txt, but not both.

# Create a python virtual environment (not necessary if you have already
# run `./bqetl bootstrap`)
python3 -m venv venv/

# Activate the virtual environment
source venv/bin/activate

# If not installed:
pip install pip-tools

# Add the dependency to requirements.in e.g. Jinja2.
echo Jinja2==2.11.1 >> requirements.in

# Compile hashes for new dependencies.
pip-compile --generate-hashes requirements.in

# Deactivate the python virtual environment.
deactivate

When opening a pull-request to merge a fork, the manual-trigger-required-for-fork CI task will fail and some integration test tasks will be skipped. A user with repository write permissions will have to run the Push to upstream workflow and provide the <username>:<branch> of the fork as parameter. The parameter will also show up in the logs of the manual-trigger-required-for-fork CI task together with more detailed instructions. Once the workflow has been executed, the CI tasks, including the integration tests, of the PR will be executed.

Tests

See the documentation in tests/

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