This source needs to access system tables that require `superuser` permission; otherwise, it won't be able to see all schemas/tables.
To add a superuser or grant superuser permission, please refer to the [Superusers page](https://docs.aws.amazon.com/redshift/latest/dg/r_superusers.html).
If you are unable to add superuser permissions, please ensure the user has SELECT privilege on [`SVV_TABLE_INFO`](https://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html) table.
include_views: True # whether to include views, defaults to True
include_tables: True # whether to include views, defaults to True
sink:
# sink configs
```
<details>
<summary>Extra options when running Redshift behind a proxy</summary>
This requires you to have already installed the Microsoft ODBC Driver for SQL Server.
See https://docs.microsoft.com/en-us/sql/connect/python/pyodbc/step-1-configure-development-environment-for-pyodbc-python-development?view=sql-server-ver15
```yml
source:
type: redshift
config:
host_port: my-proxy-hostname:5439
options:
connect_args:
sslmode: "prefer" # or "require" or "verify-ca"
sslrootcert: ~ # needed to unpin the AWS Redshift certificate
| `database_alias` | | | Alias to apply to database when ingesting. |
| `env` | | `"PROD"` | Environment to use in namespace when constructing URNs. |
| `options.<option>` | | | Any options specified here will be passed to SQLAlchemy's `create_engine` as kwargs.<br/>See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine for details. |
| `table_pattern.allow` | | | List of regex patterns for tables to include in ingestion. |
| `table_pattern.deny` | | | List of regex patterns for tables to exclude from ingestion. |
| `table_pattern.ignoreCase` | | `True` | Whether to ignore case sensitivity during pattern matching. |
| `schema_pattern.allow` | | | List of regex patterns for schemas to include in ingestion. |
| `schema_pattern.deny` | | | List of regex patterns for schemas to exclude from ingestion. |
| `schema_pattern.ignoreCase` | | `True` | Whether to ignore case sensitivity during pattern matching. |
| `view_pattern.allow` | | | List of regex patterns for views to include in ingestion. |
| `view_pattern.deny` | | | List of regex patterns for views to exclude from ingestion. |
| `view_pattern.ignoreCase` | | `True` | Whether to ignore case sensitivity during pattern matching. |
| `include_tables` | | `True` | Whether tables should be ingested. |
| `include_views` | | `True` | Whether views should be ingested. |
| `include_table_lineage` | | `True` | Whether table lineage should be ingested. |
| `table_lineage_mode` | | `"stl_scan_based"` | Which table lineage collector mode to use |
| `include_copy_lineage` | | `True` | Whether lineage should be collected from copy commands |
| `default_schema` | | `"public"` | The default schema to use if the sql parser fails to parse the schema with `sql_based` lineage collector |
There are multiple lineage collector implementations as Redshift does not support table lineage out of the box.
### stl_scan_based
The stl_scan based collector uses Redshift's [stl_insert](https://docs.aws.amazon.com/redshift/latest/dg/r_STL_INSERT.html) and [stl_scan](https://docs.aws.amazon.com/redshift/latest/dg/r_STL_SCAN.html) system tables to
discover lineage between tables.
Pros:
- Fast
- Reliable
Cons:
- Does not work with Spectrum/external tables because those scans do not show up in stl_scan table.
- If a table is depending on a view then the view won't be listed as dependency. Instead the table will be connected with the view's dependencies.
### sql_based
The sql_based based collector uses Redshift's [stl_insert](https://docs.aws.amazon.com/redshift/latest/dg/r_STL_INSERT.html) to discover all the insert queries
and uses sql parsing to discover the dependecies.
Pros:
- Works with Spectrum tables
- Views are connected properly if a table depends on it
Cons:
- Slow.
- Less reliable as the query parser can fail on certain queries
### mixed
Using both collector above and first applying the sql based and then the stl_scan based one.
Pros:
- Works with Spectrum tables
- Views are connected properly if a table depends on it
- A bit more reliable than the sql_based one only
Cons:
- Slow
- May be incorrect at times as the query parser can fail on certain queries
# Note
- The redshift stl redshift tables which are used for getting data lineage only retain approximately two to five days of log history. This means you cannot extract lineage from queries issued outside that window.
This plugin extracts usage statistics for datasets in Amazon Redshift. For context on getting started with ingestion, check out our [metadata ingestion guide](../README.md).
Note: Usage information is computed by querying the following system tables -
| `env` | | `"PROD"` | Environment to use in namespace when constructing URNs. |
| `options.<option>` | | | Any options specified here will be passed to SQLAlchemy's `create_engine` as kwargs.<br/>See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine for details. |
| `email_domain` | ✅ | | Email domain of your organisation so users can be displayed on UI appropriately. |
| `start_time` | | Last full day in UTC (or hour, depending on `bucket_duration`) | Earliest date of usage to consider. |
| `end_time` | | Last full day in UTC (or hour, depending on `bucket_duration`) | Latest date of usage to consider. |
| `top_n_queries` | | `10` | Number of top queries to save to each table. |
| `bucket_duration` | | `"DAY"` | Size of the time window to aggregate usage stats. |
