Skip to content

MMS_DATA_MODEL_AUDIT

MMS_DATA_MODEL_AUDIT shows the audit trail of scripts applied to this installation of MMS Data Model. Participants should ensure that if a database is cloned the content of this table is copied to the target database.

Source

Delivered within scripts comprising install or updates to the MMS Data Model schema.

Volume

1 record is inserted per release of MMS Data Model managed product

Tip

This table contains metadata about the overall dataset. You probably do not need to look at this unless you are configuring PDR Loader.

Column Schema

INSTALL_TYPE

The type of the patch applied. Valid entries are: FULL, UPGRADE, DML

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

INSTALLATION_DATE

The date in which the changes to the MMS Data Model were installed

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

MMSDM_VERSION

The version of MMS Data Model after the script has been applied

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

NEM_CHANGE_NOTICE

The NEM Change notice for which this MMS Data Model applies

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

PROJECT_TITLE

The name of the business project for which these changes to the MMS Data Model apply

SQL Type: VARCHAR2(200) Polars Type: str

This column is not mandatory.

SCRIPT_VERSION

The version of the patch set to the MMS Data Model

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

STATUS

The status of the upgrade. Valid entries are STARTED, FAILED, SUCCESS

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

USERNAME

The USER applying this script

SQL Type: VARCHAR2(40) Polars Type: str

This column is not mandatory.

New Data Timing

When scripts are used to install or update the MMS Data Model

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("INSTALLATION_DATE").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "INSTALL_TYPE": pl.Categorical,
            "MMSDM_VERSION": str,
            "NEM_CHANGE_NOTICE": str,
            "PROJECT_TITLE": str,
            "SCRIPT_VERSION": str,
            "STATUS": str,
            "USERNAME": str,
        }
    )
    # Deduplicate
    # We want one row per ['INSTALL_TYPE', 'INSTALLATION_DATE', 'MMSDM_VERSION']
    .unique(subset=["INSTALL_TYPE", "INSTALLATION_DATE", "MMSDM_VERSION"], keep="any")
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

Look at the data and use your best judgement.

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: CONFIGURATION