Amazon Redshift

target-redshift (transferwise variant)🥈

Amazon Redshift loader

The target-redshift loader sends data into Amazon Redshift after it was pulled from a source using an extractor

Alternate Implementations

Getting Started

Prerequisites

If you haven't already, follow the initial steps of the Getting Started guide:

  1. Install Meltano
  2. Create your Meltano project

Installation and configuration

  1. Add the target-redshift loader to your project using 
    meltano add
     : 
    2. meltano add loader target-redshift --variant transferwise
  2. Configure the target-redshift settings using 
    meltano config
     : 
    3. meltano config target-redshift set --interactive

Next steps

If you run into any issues, learn how to get help.

Capabilities

The current capabilities for target-redshift may have been automatically set when originally added to the Hub. Please review the capabilities when using this loader. If you find they are out of date, please consider updating them by making a pull request to the YAML file that defines the capabilities for this loader.

This plugin has the following capabilities:

  • activate-version
  • datatype-failsafe
  • hard-delete
  • schema-flattening
  • soft-delete

You can override these capabilities or specify additional ones in your meltano.yml by adding the capabilities key.

Settings

The target-redshift settings that are known to Meltano are documented below. To quickly find the setting you're looking for, click on any setting name from the list:

You can also list these settings using 

meltano config
with the list subcommand: 

meltano config target-redshift list

You can override these settings or specify additional ones in your meltano.yml by adding the settings key.

Please consider adding any settings you have defined locally to this definition on MeltanoHub by making a pull request to the YAML file that defines the settings for this plugin.

Add Metdata Columns (add_metadata_columns)

  • Environment variable: TARGET_REDSHIFT_ADD_METADATA_COLUMNS
  • Default Value: false

Metadata columns add extra row level information about data ingestions, (i.e. when was the row read in source, when was inserted or deleted in redshift etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix _SDC_.

The metadata columns are documented at https://transferwise.github.io/pipelinewise/data_structure/sdc-columns.html. Enabling metadata columns will flag the deleted rows by setting the _SDC_DELETED_AT metadata column.

Without the add_metadata_columns option the deleted rows from singer taps will not be recongisable in Redshift.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set add_metadata_columns [value]

AWS S3 Access Key ID (aws_access_key_id)

  • Environment variable: TARGET_REDSHIFT_AWS_ACCESS_KEY_ID

S3 Access Key Id. Used for S3 and Redshift copy operations. If not provided, AWS_ACCESS_KEY_ID environment variable will be used.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set aws_access_key_id [value]

AWS Profile Name (aws_profile)

  • Environment variable: TARGET_REDSHIFT_AWS_PROFILE

AWS profile name for profile based authentication. If not provided, AWS_PROFILE environment variable will be used.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set aws_profile [value]

AWS Redshift COPY role ARN (aws_redshift_copy_role_arn)

  • Environment variable: TARGET_REDSHIFT_AWS_REDSHIFT_COPY_ROLE_ARN

AWS Role ARN to be used for the Redshift COPY operation. Used instead of the given AWS keys for the COPY operation if provided - the keys are still used for other S3 operations


Configure this setting directly using the following Meltano command:

meltano config target-redshift set aws_redshift_copy_role_arn [value]

AWS S3 Secret Access Key (aws_secret_access_key)

  • Environment variable: TARGET_REDSHIFT_AWS_SECRET_ACCESS_KEY

S3 Secret Access Key. Used for S3 and Redshift copy operations. If not provided, AWS_SECRET_ACCESS_KEY environment variable will be used.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set aws_secret_access_key [value]

AWS S3 Session Token (aws_session_token)

  • Environment variable: TARGET_REDSHIFT_AWS_SESSION_TOKEN

S3 AWS STS token for temporary credentials. If not provided, AWS_SESSION_TOKEN environment variable will be used.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set aws_session_token [value]

Batch Size Rows (batch_size_rows)

  • Environment variable: TARGET_REDSHIFT_BATCH_SIZE_ROWS
  • Default Value: 100000

Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Redshift.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set batch_size_rows [value]

Compression (compression)

  • Environment variable: TARGET_REDSHIFT_COMPRESSION

The compression method to use when writing files to S3 and running Redshift COPY.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set compression [value]

COPY options (copy_options)

  • Environment variable: TARGET_REDSHIFT_COPY_OPTIONS
  • Default Value: EMPTYASNULL BLANKSASNULL TRIMBLANKS TRUNCATECOLUMNS TIMEFORMAT 'auto' COMPUPDATE OFF STATUPDATE OFF

Parameters to use in the COPY command when loading data to Redshift.

Some basic file formatting parameters are fixed values and not recommended overriding them by custom values. They are like: CSV GZIP DELIMITER ',' REMOVEQUOTES ESCAPE.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set copy_options [value]

Data Flattening Max Level (data_flattening_max_level)

  • Environment variable: TARGET_REDSHIFT_DATA_FLATTENING_MAX_LEVEL
  • Default Value: 0

Object type RECORD items from taps can be loaded into VARIANT columns as JSON (default) or we can flatten the schema by creating columns automatically. When value is 0 (default) then flattening functionality is turned off.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set data_flattening_max_level [value]

Database Name (dbname)

  • Environment variable: TARGET_REDSHIFT_DBNAME

Redshift database name


Configure this setting directly using the following Meltano command:

meltano config target-redshift set dbname [value]

Default Target Schema (default_target_schema)

  • Environment variable: TARGET_REDSHIFT_DEFAULT_TARGET_SCHEMA
  • Default Value: $MELTANO_EXTRACT__LOAD_SCHEMA

Note $MELTANO_EXTRACT__LOAD_SCHEMA will expand to the value of the load_schema extra for the extractor used in the pipeline, which defaults to the extractor's namespace, e.g. tap_gitlab for tap-gitlab.

Name of the schema where the tables will be created. If schema_mapping is not defined then every stream sent by the tap is loaded into this schema.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set default_target_schema [value]

Default Target Schema Select Permission (default_target_schema_select_permissions)

  • Environment variable: TARGET_REDSHIFT_DEFAULT_TARGET_SCHEMA_SELECT_PERMISSIONS

Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created tables to a specific list of users or groups. If schema_mapping is not defined then every stream sent by the tap is granted accordingly.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set default_target_schema_select_permissions [value]

Disable Table Cache (disable_table_cache)

  • Environment variable: TARGET_REDSHIFT_DISABLE_TABLE_CACHE
  • Default Value: false

By default the connector caches the available table structures in Redshift at startup. In this way it doesn't need to run additional queries when ingesting data to check if altering the target tables is required. With disable_table_cache option you can turn off this caching. You will always see the most recent table structures but will cause an extra query runtime.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set disable_table_cache [value]

Flush All Streams (flush_all_streams)

  • Environment variable: TARGET_REDSHIFT_FLUSH_ALL_STREAMS
  • Default Value: false

Flush and load every stream into Redshift when one batch is full. Warning - This may trigger the COPY command to use files with low number of records, and may cause performance problems.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set flush_all_streams [value]

Hard Delete (hard_delete)

  • Environment variable: TARGET_REDSHIFT_HARD_DELETE
  • Default Value: false

When hard_delete option is true then DELETE SQL commands will be performed in Redshift to delete rows in tables. It's achieved by continuously checking the _SDC_DELETED_AT metadata column sent by the singer tap. Due to deleting rows requires metadata columns, hard_delete option automatically enables the add_metadata_columns option as well.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set hard_delete [value]

Host (host)

  • Environment variable: TARGET_REDSHIFT_HOST

Redshift host


Configure this setting directly using the following Meltano command:

meltano config target-redshift set host [value]

Max Parallelism (max_parallelism)

  • Environment variable: TARGET_REDSHIFT_MAX_PARALLELISM
  • Default Value: 16

Max number of parallel threads to use when flushing tables.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set max_parallelism [value]

Parallelism (parallelism)

  • Environment variable: TARGET_REDSHIFT_PARALLELISM
  • Default Value: 0

The number of threads used to flush tables. 0 will create a thread for each stream, up to parallelism_max. -1 will create a thread for each CPU core. Any other positive number will create that number of threads, up to parallelism_max.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set parallelism [value]

Password (password)

  • Environment variable: TARGET_REDSHIFT_PASSWORD

Redshift password


Configure this setting directly using the following Meltano command:

meltano config target-redshift set password [value]

Port (port)

  • Environment variable: TARGET_REDSHIFT_PORT
  • Default Value: 5439

Redshift port


Configure this setting directly using the following Meltano command:

meltano config target-redshift set port [value]

Primary Key Required (primary_key_required)

  • Environment variable: TARGET_REDSHIFT_PRIMARY_KEY_REQUIRED
  • Default Value: true

Log based and Incremental replications on tables with no Primary Key cause duplicates when merging UPDATE events. When set to true, stop loading data if no Primary Key is defined.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set primary_key_required [value]

AWS S3 ACL (s3_acl)

  • Environment variable: TARGET_REDSHIFT_S3_ACL

S3 Object ACL


Configure this setting directly using the following Meltano command:

meltano config target-redshift set s3_acl [value]

S3 Bucket name (s3_bucket)

  • Environment variable: TARGET_REDSHIFT_S3_BUCKET

AWS S3 bucket name


Configure this setting directly using the following Meltano command:

meltano config target-redshift set s3_bucket [value]

S3 Key Prefix (s3_key_prefix)

  • Environment variable: TARGET_REDSHIFT_S3_KEY_PREFIX

A static prefix before the generated S3 key names. Using prefixes you can upload files into specific directories in the S3 bucket. Default(None)


Configure this setting directly using the following Meltano command:

meltano config target-redshift set s3_key_prefix [value]

Scema Mapping (schema_mapping)

  • Environment variable: TARGET_REDSHIFT_SCHEMA_MAPPING

Useful if you want to load multiple streams from one tap to multiple Redshift schemas.

If the tap sends the stream_id in <schema_name>-<table_name> format then this option overwrites the default_target_schema value.

Note, that using schema_mapping you can overwrite the default_target_schema_select_permissions value to grant SELECT permissions to different groups per schemas or optionally you can create indices automatically for the replicated tables.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set schema_mapping [value]

Skip Updates (skip_updates)

  • Environment variable: TARGET_REDSHIFT_SKIP_UPDATES
  • Default Value: false

Do not update existing records when Primary Key is defined. Useful to improve performance when records are immutable, e.g. events


Configure this setting directly using the following Meltano command:

meltano config target-redshift set skip_updates [value]

Slices (slices)

  • Environment variable: TARGET_REDSHIFT_SLICES
  • Default Value: 1

The number of slices to split files into prior to running COPY on Redshift. This should be set to the number of Redshift slices. The number of slices per node depends on the node size of the cluster - run SELECT COUNT(DISTINCT slice) slices FROM stv_slices to calculate this. Defaults to 1.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set slices [value]

Temp Directory (temp_dir)

  • Environment variable: TARGET_REDSHIFT_TEMP_DIR

(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set temp_dir [value]

User name (user)

  • Environment variable: TARGET_REDSHIFT_USER

Redshift user name


Configure this setting directly using the following Meltano command:

meltano config target-redshift set user [value]

Validate Records (validate_records)

  • Environment variable: TARGET_REDSHIFT_VALIDATE_RECORDS
  • Default Value: false

Validate every single record message to the corresponding JSON schema. This option is disabled by default and invalid RECORD messages will fail only at load time by Redshift. Enabling this option will detect invalid records earlier but could cause performance degradation.


Configure this setting directly using the following Meltano command:

meltano config target-redshift set validate_records [value]

Something missing?

This page is generated from a YAML file that you can contribute changes to.

Edit it on GitHub!

Looking for help?

If you're having trouble getting the target-redshift loader to work, look for an existing issue in its repository, file a new issue, or join the Meltano Slack community and ask for help in the 
#plugins-general
channel.

Install

meltano add loader target-redshift --variant transferwise

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Repo

https://github.com/transferwise/pipelinewise-target-redshift
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  • Wise

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Keywords

  • database