Getting Started¶
Installation¶
pip install arbalest
This will install Arbalest and any dependencies. However for Windows it may be necessary to install psycopg2, a PostgreSQL database driver manually.
64 bit Python installation:
pip install -e git+https://github.com/nwcell/psycopg2-windows.git@win64-py27#egg=psycopg2
32 bit Python installation:
pip install -e git+https://github.com/nwcell/psycopg2-windows.git@win32-py27#egg=psycopg2
Concepts¶
Pipelines¶
Arbalest orchestrates data loading using pipelines. Each Pipeline can have one or many steps that are made up of three parts:
metadata: Path in an S3 bucket to store information needed for the copy process.
source: Path in an S3 bucket where data to be copied from is located consisting of JSON object files:
{ "id": "66bc8153-d6d9-4351-bada-803330f22db7", "someNumber": 1 }
schema: Definition of JSON objects to map into Redshift rows.
Schemas¶
A schema is defined using a JsonObject mapper which consists of one or many Property declarations. By default the name of the JSON property is used as the column, but can be set to a custom column name. Column names have a maximum length of 127 characters. Column names longer than 127 characters will be truncated. Nested properties will create a default column name delimited by an underscore.
Example JSON Object (whitespace for clarity):
{
"id": "66bc8153-d6d9-4351-bada-803330f22db7",
"someNumber": 1,
"child" : {
"someBoolean": true
}
}
Example Schema:
JsonObject('destination_table_name',
Property('id', 'VARCHAR(36)'),
Property('someNumber', 'INTEGER', 'custom_column_name'),
Property('child', Property('someBoolean', 'BOOLEAN')))
Copy Strategies¶
The S3CopyPipeline supports different strategies for copying data from S3 to Redshift.
Bulk copy
Bulk copy imports all keys in an S3 path into a Redshift table using a staging table. By dropping and reimporting all data, duplication is eliminated. This type of copy is useful for data that does not change very often or will only be ingested once (e.g. immutable time series).
Manifest copy
A manifest copy imports all keys in an S3 path into a Redshift table using a manifest. In addition, a journal of successfully imported objects is persisted to the metadata path. Subsequent runs of this copy step will only copy S3 keys that do not exist in the journal. This type of copy is useful for data in a path that changes often.
Example data copies:
#!/usr/bin/env python
import psycopg2
from arbalest.configuration import env
from arbalest.redshift import S3CopyPipeline
from arbalest.redshift.schema import JsonObject, Property
if __name__ == '__main__':
pipeline = S3CopyPipeline(
aws_access_key_id=env('AWS_ACCESS_KEY_ID'),
aws_secret_access_key=env('AWS_SECRET_ACCESS_KEY'),
bucket=env('BUCKET_NAME'),
db_connection=psycopg2.connect(env('REDSHIFT_CONNECTION')))
pipeline.bulk_copy(metadata='path_to_save_pipeline_metadata',
source='path_of_source_data',
schema=JsonObject('destination_table_name',
Property('id', 'VARCHAR(36)'),
Property('someNumber', 'INTEGER',
'custom_column_name')))
pipeline.manifest_copy(metadata='path_to_save_pipeline_metadata',
source='path_of_incremental_source_data',
schema=JsonObject('incremental_destination_table_name',
Property('id', 'VARCHAR(36)'),
Property('someNumber', 'INTEGER',
'custom_column_name')))
pipeline.run()
SQL¶
Pipelines can also have arbitrary SQL steps. Each SQL step can have one or many statements which are executed in a transaction, for example, orchestrating additional ETL (extract, transform, and load). Expanding on the previous example:
#!/usr/bin/env python
import psycopg2
from arbalest.configuration import env
from arbalest.redshift import S3CopyPipeline
from arbalest.redshift.schema import JsonObject, Property
if __name__ == '__main__':
pipeline = S3CopyPipeline(
aws_access_key_id=env('AWS_ACCESS_KEY_ID'),
aws_secret_access_key=env('AWS_SECRET_ACCESS_KEY'),
bucket=env('BUCKET_NAME'),
db_connection=psycopg2.connect(env('REDSHIFT_CONNECTION')))
pipeline.bulk_copy(metadata='path_to_save_pipeline_metadata',
source='path_of_source_data',
schema=JsonObject('destination_table_name',
Property('id', 'VARCHAR(36)'),
Property('someNumber', 'INTEGER',
'custom_column_name')))
pipeline.manifest_copy(metadata='path_to_save_pipeline_metadata',
source='path_of_incremental_source_data',
schema=JsonObject('incremental_destination_table_name',
Property('id', 'VARCHAR(36)'),
Property('someNumber', 'INTEGER',
'custom_column_name')))
pipeline.sql(('SELECT someNumber + %s '
'INTO some_olap_table FROM destination_table_name', 1),
('SELECT * INTO destination_table_name_copy '
'FROM destination_table_name'))
pipeline.run()
Orchestration Helpers¶
Included in this project are a variety of orchestration helpers to assist with the creation of pipelines. These classes are defined in the arbalest.pipeline and arbalest.contrib modules.
Sorted Data Sources¶
Assuming source data is stored in a sortable series of directories, S3SortedDataSources facilitates the retrieval of S3 paths in a sequence for import, given a start and/or end. In addition, it has methods to mark a cursor in an S3 persisted journal.
Examples of data stored as a sorted series
Sequential integers:
s3://bucket/child/1/*
s3://bucket/child/2/*
s3://bucket/child/3/*
Time series:
s3://bucket/child/2015-01-01/*
s3://bucket/child/2015-01-02/*
s3://bucket/child/2015-01-03/*
s3://bucket/child/2015-01-04/00/*
Example of sorted data source class
S3SortedDataSources(
metadata='',
source='child',
bucket=bucket,
start=env('START'),
end=env('END'))
Time Series¶
SqlTimeSeriesImport implements a bulk copy and update strategy of data from a list of time series sources from S3SortedDataSources into an existing target table.
Example time series import from an S3 time series topology, ingesting a day of objects
Time series path topology:
s3://bucket/child/2015-01-01/*
s3://bucket/child/2015-01-02/*
ExamplePipeline(S3CopyPipeline):
def __init__(self,
aws_access_key_id,
aws_secret_access_key,
bucket,
db_connection):
super(ExamplePipeline, self).__init__(
aws_access_key_id,
aws_secret_access_key,
bucket,
db_connection)
# Create table to ingest data into if it does not exist
self.sql('CREATE target_table IF NOT EXISTS target_table(id VARCHAR(36), someNumber INTEGER, timestamp TIMESTAMP);')
time_series = SqlTimeSeriesImport(
destination_table='target_table',
update_date='2015-01-01', # Replace existing events, if any, after this timestamp
sources=S3SortedDataSources(
metadata='',
source='child',
bucket=bucket,
start='2015-01-01',
end='2015-01-02'),
Property('id', 'VARCHAR(36)'),
Property('someNumber', 'INTEGER'),
Property('timestamp', 'TIMESTAMP'))
# Populate target_table using a bulk copy per day
time_series.bulk_copy(
pipeline=self,
metadata='',
max_error=1000, # Maximum errors tolerated by Redshift COPY
order_by_column='timestamp') # Use column named timestamp to sort by and replace existing events, if any
Luigi¶
PipelineTask wraps any arbalest.core.Pipeline into a Luigi Task. This allows for the composition of workflows with dependency graphs, for example, data pipelines that are dependent on multiple steps or other pipelines. Luigi then takes care of the heavy lifting of scheduling and executing multistep pipelines.