> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hotglue.com/llms.txt
> Use this file to discover all available pages before exploring further.
# PostgreSQL
# Connector Details
| Name | Value |
| :------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Platform | [PostgreSQL](https://postgresql.org) |
| Auth Type | API Keys |
| Direction | Bidirectional |
| Tap Repo | [https://gitlab.com/hotglue/tap-postgres](https://gitlab.com/hotglue/tap-postgres) |
| Target Repo | [https://github.com/hotgluexyz/pipelinewise-target-postgres](https://github.com/hotgluexyz/pipelinewise-target-postgres) |
| Tap Metrics | Usage:
|
| Target Metrics | Usage:
|
# Target Postgres
## Config
`target-postgres` requires the standard 6 connection parameters to be specified in the config:
```json theme={null}
{
"host": "https://...",
"port": "5432",
"user": "...",
"password": "...",
"dbname": "...",
"default_target_schema": "public"
}
```
## Example ETL Script
```python theme={null}
import gluestick as gs
import os
# Define standard Hotglue directories
ROOT_DIR = os.environ.get("ROOT_DIR", ".")
INPUT_DIR = f"{ROOT_DIR}/sync-output"
OUTPUT_DIR = f"{ROOT_DIR}/etl-output"
# Read sync output
input = gs.Reader()
# Get tenant id
tenant_id = os.environ.get('USER_ID', os.environ.get('TENANT', 'default'))
# Iterate through the different streams in the sync output
for key in eval(str(input)):
input_df = input.get(key)
"""
Here we get the metadata for extracting the key properties, such as primary and replication keys.
The database export targets will utilize these primary keys when upserting data.
If you wish to hardcode your choice of primary keys, you can do so here.
"""
metadata = input.get_metadata(key)
if metadata.get("key_properties"):
# Use the key_properties (e.g. primary and replication keys) from the catalog
key_properties = eval(metadata["key_properties"])
else:
key_properties = []
# Include tenant_id as a field if desired
input_df["tenant"] = tenant_id
# Write this stream to the OUTPUT directory with the specified key_properties
gs.to_singer(input_df, key, OUTPUT_DIR, keys=key_properties)
```
## Optional config flags
| Property | Type | Description |
| ------------------------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ssl | Boolean | (Default: False) Enable SSL for the connection to Postgres. |
| insertion\_method | String | (Default: 'incremental') Currently only handles 'truncate' (drop and re-create table before adding rows) and 'incremental' (upsert new rows) |
| batch\_size\_rows | Integer | (Default: 100000) Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Postgres. |
| flush\_all\_streams | Boolean | (Default: False) Flush and load every stream into Postgres when one batch is full. Warning: This may trigger the COPY command to use files with low number of records. |
| parallelism | Integer | (Default: 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. |
| max\_parallelism | Integer | (Default: 16) Max number of parallel threads to use when flushing tables. |
| default\_target\_schema | String | 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. |
| default\_target\_schema\_select\_permission | String | Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created |
| schema\_mapping | Object | Useful if you want to load multiple streams from one tap to multiple Postgres schemas. If the tap sends the `stream_id` in `-` format then this option overwrites the `default_target_schema` value. Note, that using `schema_mapping` you can overwrite the `default_target_schema_select_permission` value to grant SELECT permissions to different groups per schemas or optionally you can create indices automatically for the replicated tables. **Note**: This is an experimental feature and recommended to use via PipelineWise YAML files that will generate the object mapping in the right JSON format. For further info check a [PipelineWise YAML Example](https://transferwise.github.io/pipelinewise/connectors/taps/mysql.html#configuring-what-to-replicate). |
| add\_metadata\_columns | Boolean | (Default: 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 postgres etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix `_SDC_`. The column names are following the stitch naming conventions documented at [https://www.stitchdata.com/docs/data-structure/integration-schemas#sdc-columns](https://www.stitchdata.com/docs/data-structure/integration-schemas#sdc-columns). 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 recognisable in Postgres. |
| hard\_delete | Boolean | (Default: False) When `hard_delete` option is true then DELETE SQL commands will be performed in Postgres 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. |
| data\_flattening\_max\_level | Integer | (Default: 0) Object type RECORD items from taps can be transformed to flattened columns by creating columns automatically. When value is 0 (default) then flattening functionality is turned off. |
| primary\_key\_required | Boolean | (Default: 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. |
| validate\_records | Boolean | (Default: 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 Postgres. Enabling this option will detect invalid records earlier but could cause performance degradation. |
| temp\_dir | String | (Default: platform-dependent) Directory of temporary CSV files with RECORD messages. |
| insertion\_method\_tables | Array(String) | (Default: \[]) Tables to apply "insertion method" to. Has no effect if insertion method not specified. |
# Target Changelog
| Version | Notes |
| :-------------------------------------------------------------------------- | :------------------------------- |
| [v0.3.0](https://github.com/hotgluexyz/target-postgres/releases/tag/v0.3.0) | |
| [v0.2.6](https://github.com/hotgluexyz/target-postgres/releases/tag/v0.2.6) | |
| [v0.2.5](https://github.com/hotgluexyz/target-postgres/releases/tag/v0.2.5) | - Switch to using psycog2 binary |