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Tools for working with Redshift Spectrum.

Project description

Spectrify Documentation Status

A simple yet powerful tool to move your data from Redshift to Redshift Spectrum.


One-liners to:

  • Export a Redshift table to S3 (CSV)

  • Convert exported CSVs to Parquet files in parallel

  • Create the Spectrum table on your Redshift cluster

  • Perform all 3 steps in sequence, essentially “copying” a Redshift table Spectrum in one command.

S3 credentials are specified using boto3. See

Redshift credentials are supplied via environment variables, command-line parameters, or interactive prompt.


$ pip install psycopg2  # or psycopg2-binary
$ pip install spectrify

Command-line Usage

Export Redshift table my_table to a folder of CSV files on S3:

$ spectrify --user=myuser --db=mydb export my_table \

Convert exported CSVs to Parquet:

$ spectrify --user=myuser --db=mydb convert my_table \

Create Spectrum table from S3 folder:

$ spectrify --user=myuser --db=mydb create_table \
    's3://example-bucket/my_table' my_table my_spectrum_table

Transform Redshift table by performing all 3 steps in sequence:

$ spectrify --user=myuser --db=mydb transform my_table \

Python Usage

Export to S3:

from spectrify.export import RedshiftDataExporter
RedshiftDataExporter(sa_engine, s3_config).export_to_csv('my_table')

Convert exported CSVs to Parquet:

from spectrify.convert import ConcurrentManifestConverter
from spectrify.utils.schema import SqlAlchemySchemaReader
sa_table = SqlAlchemySchemaReader(engine).get_table_schema('my_table')
ConcurrentManifestConverter(sa_table, s3_config).convert_manifest()

Create Spectrum table from S3 parquet folder:

from spectrify.create import SpectrumTableCreator
from spectrify.utils.schema import SqlAlchemySchemaReader
sa_table = SqlAlchemySchemaReader(engine).get_table_schema('my_table')
SpectrumTableCreator(sa_engine, dest_schema, dest_table_name, sa_table, s3_config).create()

Transform Redshift table by performing all 3 steps in sequence:

from spectrify.transform import TableTransformer
transformer = TableTransformer(engine, 'my_table', s3_config, dest_schema, dest_table_name)


Contributions always welcome! Read our guide on contributing here:


MIT License. Copyright (c) 2017, The Narrativ Company, Inc.


3.1.0 (2020-01-18)

  • Remove psycopg2 requirement (allows use of either psycopg2 or psycopg2-binary)

3.0.1 (2019-11-26)

  • Fix changelog

3.0.0 (2019-11-26)

Backwards incompatible changes:

  • Add REGION parameter to UNLOAD operations

  • Bugfix: Correctly construct path for S3 bucket in “create-table” command

Other Changes:

  • Support for obtaining credentials with AWS session token

  • Upgrade to pytest v4.6.6

  • Fix Flake8 errors

2.0.0 (2019-03-09)

  • Default to 256MB files

  • Flag for unicode support on Python 2.7 (performance implications)

  • Drop support for Python 3.4

  • Support for additional CSV format parameters

  • Support for REAL data type

1.0.1 (2018-07-12)

  • Loosen version requirement for PyArrow

  • Add example script

  • Update documentation

1.0.0 (2018-04-20)

  • Move functionality into classes to make customizing behavior easier

  • Add support for DATE columns

  • Add support for DECIMAL/NUMERIC columns

  • Upgrade to pyarrow v0.9.0

0.4.1 (2018-03-25)

  • Fix exception when source table is not in schema public

0.4.0 (2018-02-25)

  • Upgrade to pyarrow v0.8.0

  • Verify Redshift column types are supported before attempting conversion

  • Bugfix: Properly clean up multiprocessing.pool resource

0.3.0 (2017-10-30)

  • Support 16- and 32-bit integers

  • Packaging updates

0.2.1 (2017-09-27)

  • Fix Readme

0.2.0 (2017-09-27)

  • First release on PyPI.

0.1.0 (2017-09-13)

  • Didn’t even make it to PyPI.

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