Skip to content

PMS_GROUP

Entity table for group

Column Schema

GROUPID

Abstract identifier for the group

SQL Type: NUMBER(20,0) Polars Type: pl.Int128

This column is mandatory, and a primary key.

Examples: 3, 283

CREATEDDATE

Date record was created

SQL Type: TIMESTAMP(3) Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2021/09/21 13:38:51.717

Unlike most datetime columns, this one includes a millisecond component. e.g. 2029/03/15 00:00:00.123.

LASTCHANGED

Date record was last changed

For more information, see the dedicated documentation page.

SQL Type: TIMESTAMP(3) Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2021/09/21 13:38:51.717

Unlike most datetime columns, this one includes a millisecond component. e.g. 2029/03/15 00:00:00.123.

New Data Timing

When the record is created or updated

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

# for "TIMESTAMP" columns
# Note that Polars uses slightly different format strings for fractional seconds to `dt.strptime`
# e.g. "2029/03/15 00:00:00.123".
dt_ms_fmt = "%Y/%m/%d %H:%M:%S%.3f"

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("CREATEDDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_ms_fmt),
    )
    .cast({"GROUPID": int})  # or pl.Int128
    # Deduplicate
    # We want one row per ['GROUPID']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["GROUPID"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PMS_GROUP_FILE01_202609010000.zip

GROUPIDCREATEDDATELASTCHANGED
1042023/03/15 08:56:34.6572023/03/15 08:56:34.657
1112023/07/28 08:05:42.6732023/07/28 08:05:42.673
1212024/01/03 13:23:39.8802024/01/03 13:23:39.880
2122025/06/10 14:29:37.3602025/06/10 14:29:37.360
2502025/11/13 08:13:11.1072025/11/13 08:13:11.107
62021/10/29 12:23:17.2032021/10/29 12:23:17.203
72021/10/29 16:32:28.7832021/10/29 16:32:28.783
82021/10/29 16:41:20.4332021/10/29 16:41:20.433
782022/11/16 00:05:57.4902022/11/16 00:05:57.490
792022/11/16 00:06:26.8872022/11/16 00:06:26.887
802022/11/16 00:07:20.7672022/11/16 00:07:20.767
...

CSETP.WORLDDVD_PMS_GROUPAEMOPUBLIC2026/10/0916:26:45001791527205388MONTHLY_ARCHIVE001791527205388
IPARTICIPANT_REGISTRATIONPMS_GROUP1GROUPIDCREATEDDATELASTCHANGED
DPARTICIPANT_REGISTRATIONPMS_GROUP11042023/03/15 08:56:34.6572023/03/15 08:56:34.657
DPARTICIPANT_REGISTRATIONPMS_GROUP11112023/07/28 08:05:42.6732023/07/28 08:05:42.673
DPARTICIPANT_REGISTRATIONPMS_GROUP11212024/01/03 13:23:39.8802024/01/03 13:23:39.880
DPARTICIPANT_REGISTRATIONPMS_GROUP12122025/06/10 14:29:37.3602025/06/10 14:29:37.360
DPARTICIPANT_REGISTRATIONPMS_GROUP12502025/11/13 08:13:11.1072025/11/13 08:13:11.107
DPARTICIPANT_REGISTRATIONPMS_GROUP162021/10/29 12:23:17.2032021/10/29 12:23:17.203
DPARTICIPANT_REGISTRATIONPMS_GROUP172021/10/29 16:32:28.7832021/10/29 16:32:28.783
DPARTICIPANT_REGISTRATIONPMS_GROUP182021/10/29 16:41:20.4332021/10/29 16:41:20.433
DPARTICIPANT_REGISTRATIONPMS_GROUP1782022/11/16 00:05:57.4902022/11/16 00:05:57.490
DPARTICIPANT_REGISTRATIONPMS_GROUP1792022/11/16 00:06:26.8872022/11/16 00:06:26.887
DPARTICIPANT_REGISTRATIONPMS_GROUP1802022/11/16 00:07:20.7672022/11/16 00:07:20.767
...
CEND OF REPORT280

GROUPID,CREATEDDATE,LASTCHANGED
104,"2023/03/15 08:56:34.657","2023/03/15 08:56:34.657"
111,"2023/07/28 08:05:42.673","2023/07/28 08:05:42.673"
121,"2024/01/03 13:23:39.880","2024/01/03 13:23:39.880"
212,"2025/06/10 14:29:37.360","2025/06/10 14:29:37.360"
250,"2025/11/13 08:13:11.107","2025/11/13 08:13:11.107"
6,"2021/10/29 12:23:17.203","2021/10/29 12:23:17.203"
7,"2021/10/29 16:32:28.783","2021/10/29 16:32:28.783"
8,"2021/10/29 16:41:20.433","2021/10/29 16:41:20.433"
78,"2022/11/16 00:05:57.490","2022/11/16 00:05:57.490"
79,"2022/11/16 00:06:26.887","2022/11/16 00:06:26.887"
80,"2022/11/16 00:07:20.767","2022/11/16 00:07:20.767"
...

C,SETP.WORLD,DVD_PMS_GROUP,AEMO,PUBLIC,2026/10/09,16:26:45,001791527205388,MONTHLY_ARCHIVE,001791527205388
I,PARTICIPANT_REGISTRATION,PMS_GROUP,1,GROUPID,CREATEDDATE,LASTCHANGED
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,104,"2023/03/15 08:56:34.657","2023/03/15 08:56:34.657"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,111,"2023/07/28 08:05:42.673","2023/07/28 08:05:42.673"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,121,"2024/01/03 13:23:39.880","2024/01/03 13:23:39.880"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,212,"2025/06/10 14:29:37.360","2025/06/10 14:29:37.360"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,250,"2025/11/13 08:13:11.107","2025/11/13 08:13:11.107"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,6,"2021/10/29 12:23:17.203","2021/10/29 12:23:17.203"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,7,"2021/10/29 16:32:28.783","2021/10/29 16:32:28.783"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,8,"2021/10/29 16:41:20.433","2021/10/29 16:41:20.433"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,78,"2022/11/16 00:05:57.490","2022/11/16 00:05:57.490"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,79,"2022/11/16 00:06:26.887","2022/11/16 00:06:26.887"
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,80,"2022/11/16 00:07:20.767","2022/11/16 00:07:20.767"
...
C,"END OF REPORT",280

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 8 of 2024. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 54 KB when zipped, 543 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "UPDATE-INSERT", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name Report Subtype Version
PARTICIPANT_REGISTRATION PMS_GROUP 1

This means that the CSV files start with metadata columns:

I,PARTICIPANT_REGISTRATION,PMS_GROUP,1,...
D,PARTICIPANT_REGISTRATION,PMS_GROUP,1,...

ROW_FILTER_TYPE is LASTCHANGED

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: PARTICIPANT_REGISTRATION