Target Redshift

A Singer redshift target, for use with Singer streams generated by Singer taps.

Features

• Creates SQL tables for Singer streams
• Denests objects flattening them into the parent object's table
• Denests rows into separate tables
• Adds columns and sub-tables as new fields are added to the stream JSON Schema
• Full stream replication via record version and ACTIVATE_VERSION messages.

Install

pip install target-redshift

Usage

1. Follow the Singer.io Best Practices for setting up separate tap and target virtualenvs to avoid version conflicts.

2. Create a config file at ~/singer.io/target_redshift_config.json with redshift connection information and target redshift schema.

   {
     "redshift_host": "aws.something.or.other",
     "redshift_port": 5439,
     "redshift_database": "my_analytics",
     "redshift_username": "myuser",
     "redshift_password": "1234",
     "redshift_schema": "mytapname",
     "default_column_length": 1000,
     "target_s3": {
       "aws_access_key_id": "AKIA...",
       "aws_secret_access_key": "supersecret",
       "bucket": "target_redshift_staging",
       "key_prefix": "__tmp"
     }
   }

3. Run target-redshift against a Singer tap.

   ~/.virtualenvs/tap-something/bin/tap-something \
     | ~/.virtualenvs/target-redshift/bin/target-redshift \
       --config ~/singer.io/target_redshift_config.json

Config.json

The fields available to be specified in the config file are specified here.

Field	Type	Default	Details
redshift_host	["string"]	N/A
redshift_port	["integer", "null"]	5432
redshift_database	["string"]	N/A
redshift_username	["string"]	N/A
redshift_password	["string"]	N/A
redshift_schema	["string", "null"]	"public"
invalid_records_detect	["boolean", "null"]	true	Include false in your config to disable target-redshift from crashing on invalid records
invalid_records_threshold	["integer", "null"]	0	Include a positive value n in your config to allow for target-redshift to encounter at most n invalid records per stream before giving up.
disable_collection	["string", "null"]	false	Include true in your config to disable Singer Usage Logging.
logging_level	["string", "null"]	"INFO"	The level for logging. Set to DEBUG to get things like queries executed, timing of those queries, etc. See Python's Logger Levels for information about valid values.
max_batch_rows	["integer", "null"]	200000	The maximum number of rows to buffer in memory before writing to the destination table in Redshift
max_buffer_size	["integer", "null"]	104857600 (100MB in bytes)	The maximum number of bytes to buffer in memory before writing to the destination table in Redshift
batch_detection_threshold	["integer", "null"]	5000, or 1/40th max_batch_rows	How often, in rows received, to count the buffered rows and bytes to check if a flush is necessary. There's a slight performance penalty to checking the buffered records count or bytesize, so this controls how often this is polled in order to mitigate the penalty. This value is usually not necessary to set as the default is dynamically adjusted to check reasonably often.
persist_empty_tables	["boolean", "null"]	False	Whether the Target should create tables which have no records present in Remote.
redshift_copy_options	["list"]	[]	Allows adding additional options to the COPY statement sent to Redshift. A list of available parameters can be found here. For example, this could be set to ["TRUNCATECOLUMNS"] to enable the TRUNCATECOLUMNS data conversion parameter.
default_column_length	["integer", "null"]	1000	All columns with the VARCHAR(CHARACTER VARYING) type will be have this length.Range: 1-65535.
state_support	["boolean", "null"]	True	Whether the Target should emit STATE messages to stdout for further consumption. In this mode, which is on by default, STATE messages are buffered in memory until all the records that occurred before them are flushed according to the batch flushing schedule the target is configured with.
target_s3	["object"]	N/A	See S3 below

S3 Config.json

Field	Type	Default	Details
aws_access_key_id	["string"]	N/A
aws_secret_access_key	["string"]	N/A
aws_session_token	["string"]	N/A	STS session token if using temporary credentials
bucket	["string"]	N/A	Bucket where staging files should be uploaded to.
key_prefix	["string", "null"]	""	Prefix for staging file uploads to allow for better delineation of tmp files

Known Limitations

• Ignores STATE Singer messages.
• Requires a JSON Schema for every stream.
• Only string, string with date-time format, integer, number, boolean, object, and array types with or without null are supported. Arrays can have any of the other types listed, including objects as types within items.
  • Example of JSON Schema types that work
    • ['number']
    • ['string']
    • ['string', 'null']
  • Exmaple of JSON Schema types that DO NOT work
    • ['string', 'integer']
    • ['integer', 'number']
    • ['any']
    • ['null']
• JSON Schema combinations such as anyOf and allOf are not supported.
• JSON Schema $ref is partially supported:
  • NOTE: The following limitations are known to NOT fail gracefully
  • Presently you cannot have any circular or recursive $refs
  • $refs must be present within the schema:
    • URI's do not work
    • if the $ref is broken, the behaviour is considered unexpected
• Any values which are the string NULL will be streamed to Redshift as the literal null
• Table names are restricted to:
  • 127 characters in length
  • can only be composed of _, lowercase letters, numbers, $
  • cannot start with $
  • ASCII characters
• Field/Column names are restricted to:
  • 127 characters in length
  • ASCII characters
• Fields/Columns are ALL nullable
• Fields/Columns use a default of 1000 characters (in order to be able to work with a large number of columns).

Usage Logging

Singer.io requires official taps and targets to collect anonymous usage data. This data is only used in aggregate to report on individual tap/targets, as well as the Singer community at-large. IP addresses are recorded to detect unique tap/targets users but not shared with third-parties.

To disable anonymous data collection set disable_collection to true in the configuration JSON file.

Developing

target-redshift utilizes setup.py for package management, and PyTest for testing.

Docker

If you have Docker and Docker Compose installed, you can easily run the following to get a local env setup quickly.

First, make sure to create a .env file in the root of this repo (it has been .gitignored so don't worry about accidentally staging it).

Therein, fill out the following information:

REDSHIFT_HOST='<your-host-name>' # Most likely 'localhost'
REDSHIFT_DATABASE='<your-db-name>' # Most likely 'dev'
REDSHIFT_SCHEMA='<your-schema-name>' # Probably 'public'
REDSHIFT_PORT='<your-port>' # Probably 5439
REDSHIFT_USERNAME='<your-user-name'
REDSHIFT_PASSWORD='<your-password>'
DEFAULT_COLUMN_LENGTH='<your-default-column-length>'
TARGET_S3_AWS_ACCESS_KEY_ID='<AKIA...>'
TARGET_S3_AWS_SECRET_ACCESS_KEY='<secret>'
TARGET_S3_BUCKET='<bucket-string>'
TARGET_S3_KEY_PREFIX='<some-string>' # We use 'target_redshift_test'

$ docker-compose up -d --build
$ docker logs -tf target-redshift_target-redshift_1 # You container names might differ

As soon as you see INFO: Dev environment ready. you can shell into the container and start running test commands:

$ docker exec -it target-redshift_target-redshift_1 bash # Your container names might differ
root@... $ source /code/venv--target-redshift/bin/activate
root@... $ pytest

See the PyTest commands below!

DB

To run the tests, you will need an actual Redshift cluster running, and a user that either:

• Has the ability to create schemas therein

  • This is required if you wish to run multiple versions of the tests, similar to how we run our CI tests by varying the REDSHIFT_SCHEMA envvar

• has access to the public schema

  • If the REDSHIFT_SCHEMA is seen to be the string "public", the tests will ignore creating and dropping schemas
  • This setup is often preferred for situations in which GRANT CREATE ON DATABASE db TO user; is viewed as too risky

Make sure to set the following env vars for PyTest:

$ EXPORT REDSHIFT_HOST='<your-host-name>' # Most likely 'localhost'
$ EXPORT REDSHIFT_DATABASE='<your-db-name>' # Most likely 'dev'
$ EXPORT REDSHIFT_SCHEMA='<your-schema-name>' # Probably 'public'
$ EXPORT REDSHIFT_PORT='<your-port>' # Probably 5439
$ EXPORT REDSHIFT_USERNAME='<your-user-name'
$ EXPORT REDSHIFT_PASSWORD='<your-password>' # Redshift requires passwords
$ EXPORT DEFAULT_COLUMN_LENGTH='<your-default-column-length>'

S3

To run the tests, you will need an actual S3 bucket available.

Make sure to set the following env vars for PyTest:

$ EXPORT TARGET_S3_AWS_ACCESS_KEY_ID='<AKIA...>'
$ EXPORT TARGET_S3_AWS_SECRET_ACCESS_KEY='<secret>'
$ EXPORT TARGET_S3_BUCKET='<bucket-string>'
$ EXPORT TARGET_S3_KEY_PREFIX='<some-string>' # We use 'target_redshift_test'

PyTest

To run tests, try:

$ python setup.py pytest

If you've bash shelled into the Docker Compose container (see above), you should be able to simply use:

$ pytest

Sponsorship

Target Redshift is sponsored by Data Mill (Data Mill Services, LLC) datamill.co.

Data Mill helps organizations utilize modern data infrastructure and data science to power analytics, products, and services.

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Copyright Data Mill Services, LLC 2018